feat: scaffold AI Risk Index platform (research corpus, methodology v1 draft, monorepo)

- docs/research: 4-document research corpus grounding v1 (indices, data
  sources, rater API, landscape/evidence), compiled 2026-08-05
- docs/methodology: METHODOLOGY.md v1 draft, changelog, worked example
- packages/scoring: pure deterministic engine (5 weighted dimensions,
  barriers inverted, exposure/substitution/augmentation sub-scores with
  CI bounds), snapshot + fast-check property tests
- packages/db: Prisma schema with rating audit trail and immutable runs
- apps/web: Next.js 14 site + public API v1 (health, methodology,
  occupations)
- apps/worker: BullMQ multi-model rater pipeline (Message Batches,
  versioned rubric prompt v1) + score:recompute
- apps/etl: Python skeleton with O*NET 30.3 downloader
- infra: compose files, deploy.sh, ngrok.yml, env template

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

simon-pierre boucher committed 5 days ago (Aug 5, 2026)

Showing 74 changed files with +7,101 and −0

added .gitignore +33 −0

		`@@ -0,0 +1,33 @@`
	1	+# deps / builds
	2	+node_modules/
	3	+.next/
	4	+dist/
	5	+.turbo/
	6	+*.tsbuildinfo
	7	+
	8	+# env & secrets — never commit (infra/.env.example is the template)
	9	+.env
	10	+.env.*
	11	+!.env.example
	12	+
	13	+# raw data is immutable and fetched by script — never committed (CLAUDE.md §5)
	14	+data/raw/*
	15	+!data/raw/.gitkeep
	16	+
	17	+# derived data: only manifests are committed, not payloads
	18	+data/derived/*/.csv
	19	+data/derived/*/.parquet
	20	+data/derived/*/.jsonl
	21	+!data/derived/**/manifest.json
	22	+
	23	+# python (apps/etl)
	24	+__pycache__/
	25	+.venv/
	26	+*.egg-info/
	27	+.pytest_cache/
	28	+.ruff_cache/
	29	+
	30	+# misc
	31	+.DS_Store
	32	+coverage/
	33	+playwright-report/

added .prettierrc +6 −0

		`@@ -0,0 +1,6 @@`
	1	+{
	2	+ "semi": true,
	3	+ "singleQuote": false,
	4	+ "trailingComma": "all",
	5	+ "printWidth": 100
	6	+}

added CLAUDE.md +211 −0

		`@@ -0,0 +1,211 @@`
	1	+# CLAUDE.md — AI Risk Index Platform (www.airiskindex.io)
	2	+
	3	+This file provides guidance to Claude Code when working in this repository.
	4	+
	5	+---
	6	+
	7	+## 1. Project Overview
	8	+
	9	+AI Risk Index (airiskindex.io) is a public platform that scores occupations on their exposure to AI-driven automation, using a task-based methodology (not whole-occupation scoring). Every score must be transparent, versioned, and reproducible.
	10	+
	11	+Core product promise: "The most methodologically rigorous, fully transparent AI job-exposure index."
	12	+
	13	+### The three concepts (never confuse them)
	14	+- Exposure — AI is technically capable of performing the task
	15	+- Substitution — AI actually replaces the human performing it
	16	+- Augmentation — AI assists the human, increasing productivity
	17	+
	18	+The composite index reports all three as separate sub-scores. Never collapse them into a single undifferentiated "risk" number in code, UI copy, or API responses without also exposing the sub-scores.
	19	+
	20	+### Scoring model (v1)
	21	+Composite score 0–100 per occupation, computed from task-level scores weighted by task importance/frequency (O*NET weights). Dimensions:
	22	+
	23	+\| Dimension \| Key \| Weight (v1) \|
	24	+\|---\|---\|---\|
	25	+\| Task automatability \| `automatability` \| 0.35 \|
	26	+\| Current technical feasibility \| `feasibility` \| 0.20 \|
	27	+\| Cost of substitution vs. wage \| `cost_ratio` \| 0.15 \|
	28	+\| Adoption barriers (regulation, liability, human-contact requirement) \| `barriers` \| 0.20 \|
	29	+\| Sector adoption velocity \| `adoption_velocity` \| 0.10 \|
	30	+
	31	+- Weights live in `packages/scoring/src/weights.ts` — never hardcode weights anywhere else.
	32	+- Every scoring release gets a semver version (`INDEX_VERSION` in `packages/scoring/src/version.ts`) and a changelog entry in `docs/methodology/CHANGELOG.md`.
	33	+- Scores are stored with confidence intervals (`score_low`, `score`, `score_high`). UI must always be able to display uncertainty.
	34	+
	35	+### Data sources
	36	+- *ONET 30.x** database dumps (occupations + tasks + importance ratings) → `data/raw/onet/` — 30.3 as of May 2026, 31.0 expected late Aug 2026. ⚠️ 30.x renamed Technology Skills → Software Skills and split Skills into Essential/Transferable; ETL loaders must target the 30.x schema. License CC BY 4.0 (attribution required).
	37	+- ESCO crosswalk for EU/France occupations (ROME codes) → `data/raw/esco/`
	38	+- LLM-as-evaluator task ratings (see §6) → `data/derived/ratings/`
	39	+- Expert panel (Delphi) validation overrides → `data/derived/expert_overrides/`
	40	+
	41	+Raw data is never edited in place. All transformations go through the ETL pipeline (`apps/etl`).
	42	+
	43	+---
	44	+
	45	+## 2. Tech Stack
	46	+
	47	+- Monorepo: pnpm workspaces + Turborepo
	48	+- Language: TypeScript everywhere (strict mode). Python 3.12 only inside `apps/etl` for data science steps.
	49	+- Frontend: Next.js 14 (App Router), React 18, Tailwind CSS, shadcn/ui, Recharts for visualizations
	50	+- API: Next.js route handlers for public API (`/api/v1/*`) + tRPC for internal app calls
	51	+- Database: PostgreSQL 16 (Prisma ORM). Read-heavy → materialized views for score lookups.
	52	+- Cache: Redis (score lookups, rate limiting)
	53	+- Jobs: BullMQ workers in `apps/worker` (score recomputation, LLM rating batches)
	54	+- Auth: Auth.js (email magic link + OAuth). Public browsing requires no auth.
	55	+- Testing: Vitest (unit), Playwright (e2e), pytest (ETL)
	56	+- Lint/format: ESLint + Prettier, ruff for Python. CI fails on warnings.
	57	+
	58	+## 3. Repository Layout
	59	+
	60	+```
	61	+airiskindex/
	62	+├── apps/
	63	+│ ├── web/ # Next.js app (site + public API routes)
	64	+│ ├── worker/ # BullMQ background workers
	65	+│ └── etl/ # Python data pipeline (raw → derived → DB)
	66	+├── packages/
	67	+│ ├── scoring/ # Pure TS scoring engine — NO I/O, fully deterministic
	68	+│ ├── db/ # Prisma schema + client + seeds
	69	+│ ├── ui/ # Shared React components
	70	+│ └── config/ # Shared eslint/ts/tailwind configs
	71	+├── data/
	72	+│ ├── raw/ # Immutable source dumps (gitignored, fetched by script)
	73	+│ └── derived/ # Pipeline outputs (versioned manifests committed)
	74	+├── docs/
	75	+│ └── methodology/ # Public methodology doc, changelog, sensitivity analyses
	76	+├── infra/ # Deployment scripts, ngrok config, systemd units
	77	+└── CLAUDE.md
	78	+```
	79	+
	80	+## 4. Commands
	81	+
	82	+```bash
	83	+pnpm install # install all workspaces
	84	+pnpm dev # run web app on :3000 (+ worker with --filter)
	85	+pnpm build # turbo build all
	86	+pnpm test # vitest across packages
	87	+pnpm test:e2e # playwright (requires pnpm dev running)
	88	+pnpm lint && pnpm typecheck # must pass before any commit
	89	+pnpm db:migrate # prisma migrate dev
	90	+pnpm db:seed # seed occupations + demo scores
	91	+pnpm score:recompute # full index recomputation (writes new INDEX_VERSION run)
	92	+cd apps/etl && make pipeline # full ETL: raw → derived → DB load
	93	+```
	94	+
	95	+Single test file: `pnpm vitest run packages/scoring/src/composite.test.ts`
	96	+
	97	+## 5. Coding Conventions
	98	+
	99	+- `packages/scoring` must stay pure and deterministic: no network, no DB, no `Date.now()`, no randomness. It takes typed inputs and returns typed scores. This is what makes the methodology auditable.
	100	+- Every scoring function has property-based tests (fast-check) + snapshot tests against the published methodology examples in `docs/methodology/examples/`.
	101	+- All user-facing risk language follows the tone guide: the product frames results as adaptation guidance, not doom. Avoid copy like "your job will disappear"; prefer "X% of tasks in this occupation are highly exposed".
	102	+- API responses are versioned (`/api/v1/...`) and include `index_version` in every payload.
	103	+- Money/wages: store as integer cents + ISO currency. Percentages: store as 0–1 floats, format only at the UI layer.
	104	+- Never commit anything under `data/raw/`. Derived data commits only the manifest JSON (hashes + row counts), not the payloads.
	105	+- Migrations: additive-first. Destructive migrations require a `-- DESTRUCTIVE` comment and a manual approval in PR review.
	106	+- Accessibility: all charts need a data-table fallback (`<VisuallyHidden>` table) — this is a public-interest tool.
	107	+
	108	+## 6. LLM-as-Evaluator (task rating pipeline)
	109	+
	110	+Task automatability ratings are produced by an LLM rater in `apps/worker/src/raters/`, then validated by human experts.
	111	+
	112	+- Use the Anthropic API via the official SDK. Model names come from the `RATER_MODELS` env var (comma-separated list — multi-model rating is required: single-model LLM exposure ratings show up to 19× spread across frontier raters; see `docs/research/01-existing-indices.md` §7.2). Never hardcode model IDs in source.
	113	+- Newer models (Sonnet 5 / Opus 5) reject the `temperature` parameter — rating variance comes from the multi-model panel, not sampling temperature.
	114	+- Prompts live in `apps/worker/src/raters/prompts/*.md` and are versioned; a prompt change bumps `RATER_PROMPT_VERSION` and invalidates cached ratings.
	115	+- Every rating stores: model, prompt version, raw response, parsed score, timestamp. Full audit trail, always.
	116	+- Ratings are sampled (5%) for human review; disagreement > 1 point on the 5-point scale flags the task for the expert panel queue.
	117	+- Batch jobs must be idempotent and resumable (BullMQ job IDs = deterministic hash of task_id + prompt version).
	118	+- For current API details (batch endpoints, rate limits, model names), check https://platform.claude.com/docs (docs.claude.com redirects there) rather than relying on memory. A vetted snapshot lives in `docs/research/03-llm-rater-api.md`.
	119	+
	120	+## 7. Environments & Deployment
	121	+
	122	+### Environments
	123	+- `local` — developer machine, SQLite optional shortcut is not allowed; always Postgres via Docker (`infra/docker-compose.dev.yml`).
	124	+- `staging` — node m3u96b, exposed via ngrok (see below).
	125	+- `production` — airiskindex.io (target: VPS/managed later; staging setup is the current deployment).
	126	+
	127	+### Deploying to node m3u96b (staging/current prod)
	128	+
	129	+The app currently runs on the self-hosted node `m3u96b` and is exposed publicly through an ngrok tunnel mapped to `www.airiskindex.io`.
	130	+
	131	+Process layout on m3u96b:
	132	+- `airiskindex-web.service` (systemd) → `node apps/web/.next/standalone/server.js` on `127.0.0.1:3000`
	133	+- `airiskindex-worker.service` → BullMQ worker
	134	+- `postgres` + `redis` via Docker Compose (`infra/docker-compose.prod.yml`)
	135	+- `ngrok.service` → tunnel `127.0.0.1:3000` → public edge
	136	+
	137	+Deploy procedure (scripted in `infra/deploy.sh`):
	138	+```bash
	139	+ssh m3u96b
	140	+cd /srv/airiskindex
	141	+git pull --ff-only origin main
	142	+pnpm install --frozen-lockfile
	143	+pnpm build
	144	+pnpm db:migrate:deploy # prisma migrate deploy (no interactive)
	145	+sudo systemctl restart airiskindex-web airiskindex-worker
	146	+sudo systemctl status airiskindex-web --no-pager # verify healthy
	147	+curl -fsS http://127.0.0.1:3000/api/v1/health # must return {"ok":true,...}
	148	+```
	149	+
	150	+ngrok configuration (`infra/ngrok.yml`, copied to `/etc/ngrok/ngrok.yml` on the node):
	151	+```yaml
	152	+version: 3
	153	+agent:
	154	+ authtoken: ${NGROK_AUTHTOKEN} # from env, never committed
	155	+endpoints:
	156	+ - name: airiskindex
	157	+ url: https://www.airiskindex.io # requires custom domain configured in ngrok dashboard + CNAME
	158	+ upstream:
	159	+ url: 3000
	160	+```
	161	+- The custom domain must be added in the ngrok dashboard and DNS `CNAME` for `www.airiskindex.io` pointed at the ngrok edge target they provide. Verify with `dig CNAME www.airiskindex.io`.
	162	+- ngrok runs as `ngrok.service` (systemd, `Restart=always`). Logs: `journalctl -u ngrok -f`.
	163	+- Health rule: after every deploy, hit the public URL, not just localhost — tunnel failures are the most common outage cause.
	164	+- Because the app sits behind ngrok, trust `X-Forwarded-*` headers: Next.js config already sets trusted proxy handling; do not remove it. Rate limiting keys off `x-forwarded-for` first IP.
	165	+
	166	+Environment variables (`/srv/airiskindex/.env`, template in `infra/.env.example`):
	167	+```
	168	+DATABASE_URL=postgresql://...
	169	+REDIS_URL=redis://...
	170	+ANTHROPIC_API_KEY=... # rater pipeline
	171	+RATER_MODELS=... # comma-separated model IDs (multi-model rating panel)
	172	+NGROK_AUTHTOKEN=...
	173	+NEXTAUTH_URL=https://www.airiskindex.io
	174	+NEXTAUTH_SECRET=...
	175	+PUBLIC_BASE_URL=https://www.airiskindex.io
	176	+```
	177	+Never print, log, or commit secrets. `infra/.env.example` lists keys with empty values only.
	178	+
	179	+### Rollback
	180	+```bash
	181	+ssh m3u96b
	182	+cd /srv/airiskindex
	183	+git checkout <previous-tag>
	184	+pnpm install --frozen-lockfile && pnpm build
	185	+sudo systemctl restart airiskindex-web airiskindex-worker
	186	+```
	187	+DB rollbacks: only via forward-fix migrations. Nightly `pg_dump` to `/srv/backups` (retained 14 days) — verify the cron is alive when touching infra.
	188	+
	189	+## 8. Public API rules
	190	+
	191	+- `/api/v1/occupations` — list/search (paginated, cached 1h)
	192	+- `/api/v1/occupations/:code` — full score breakdown incl. sub-scores, CI bounds, task list, `index_version`
	193	+- `/api/v1/methodology` — machine-readable weights + version metadata
	194	+- Rate limit: 60 req/min unauthenticated, 600 with API key. Return `429` with `Retry-After`.
	195	+- Breaking changes require a new `/api/v2` — never mutate v1 response shapes.
	196	+
	197	+## 9. Methodology Integrity Rules (non-negotiable)
	198	+
	199	+1. Any change to weights, formulas, or rater prompts ⇒ bump `INDEX_VERSION`, add changelog entry, regenerate `docs/methodology/sensitivity/` outputs.
	200	+2. Scores shown anywhere must be traceable to a stored computation run (`score_runs` table) — no ad-hoc numbers.
	201	+3. Historical scores are immutable; recomputations create new runs, old runs remain queryable.
	202	+4. The public methodology doc (`docs/methodology/METHODOLOGY.md`) is the source of truth; code comments link to its section anchors. If code and doc disagree, stop and flag it — do not silently pick one.
	203	+
	204	+## 10. When Working in This Repo, Claude Should
	205	+
	206	+- Run `pnpm typecheck && pnpm lint && pnpm test` before declaring any task done.
	207	+- Touch `packages/scoring` only with accompanying tests and a methodology changelog note.
	208	+- Ask before: destructive migrations, changing index weights, editing ngrok/systemd config, or anything that alters public API shapes.
	209	+- Prefer small, reviewable commits with conventional-commit messages (`feat(scoring): ...`, `fix(api): ...`, `infra(deploy): ...`).
	210	+- Keep UI copy aligned with the "adaptation, not doom" tone guide.
	211	+- When uncertain about Anthropic API specifics (models, batch API, limits), consult the official docs instead of guessing.

added README.md +29 −0

		`@@ -0,0 +1,29 @@`
	1	+# AI Risk Index — airiskindex.io
	2	+
	3	+The most methodologically rigorous, fully transparent AI job-exposure index.
	4	+Task-based scoring of occupations on AI-driven automation exposure, with three
	5	+separate sub-scores per occupation — exposure, substitution, augmentation —
	6	+each with confidence intervals, versioned methodology, and a public API.
	7	+
	8	+- Methodology (source of truth): [`docs/methodology/METHODOLOGY.md`](docs/methodology/METHODOLOGY.md)
	9	+- Research corpus grounding v1: [`docs/research/`](docs/research/README.md)
	10	+- Contributor rules: [`CLAUDE.md`](CLAUDE.md)
	11	+
	12	+## Quick start
	13	+
	14	+```bash
	15	+pnpm install
	16	+docker compose -f infra/docker-compose.dev.yml up -d # postgres 16 + redis
	17	+cp infra/.env.example .env # fill values
	18	+pnpm db:migrate && pnpm db:seed
	19	+pnpm dev # web on :3000
	20	+```
	21	+
	22	+Checks: `pnpm typecheck && pnpm lint && pnpm test`
	23	+
	24	+## Layout
	25	+
	26	+Monorepo (pnpm + Turborepo): `apps/web` (Next.js site + public API), `apps/worker`
	27	+(BullMQ jobs incl. the multi-model LLM rater), `apps/etl` (Python pipeline),
	28	+`packages/scoring` (pure, deterministic scoring engine), `packages/db` (Prisma),
	29	+`packages/ui`, `packages/config`. See CLAUDE.md §3.

added apps/etl/Makefile +21 −0

		`@@ -0,0 +1,21 @@`
	1	+PYTHON ?= python3
	2	+export PYTHONPATH := src
	3	+
	4	+.PHONY: pipeline download transform load test lint
	5	+
	6	+pipeline: download transform load
	7	+
	8	+download:
	9	+ $(PYTHON) -m airiskindex_etl.download_onet
	10	+
	11	+transform:
	12	+ @echo "TODO: raw -> derived transforms (task statements + importance ratings -> data/derived/)"
	13	+
	14	+load:
	15	+ @echo "TODO: derived -> Postgres load (occupations + tasks via Prisma-compatible schema)"
	16	+
	17	+test:
	18	+ $(PYTHON) -m pytest
	19	+
	20	+lint:
	21	+ ruff check .

added apps/etl/pyproject.toml +19 −0

		`@@ -0,0 +1,19 @@`
	1	+[project]
	2	+name = "airiskindex-etl"
	3	+version = "0.1.0"
	4	+description = "AI Risk Index data pipeline: raw source dumps -> derived artifacts -> Postgres"
	5	+requires-python = ">=3.12"
	6	+dependencies = [
	7	+ "requests>=2.32",
	8	+ "pandas>=2.2",
	9	+]
	10	+
	11	+[project.optional-dependencies]
	12	+dev = ["pytest>=8", "ruff>=0.5"]
	13	+
	14	+[tool.ruff]
	15	+line-length = 100
	16	+target-version = "py312"
	17	+
	18	+[tool.pytest.ini_options]
	19	+testpaths = ["tests"]

added apps/etl/src/airiskindex_etl/__init__.py +6 −0

		`@@ -0,0 +1,6 @@`
	1	+"""AI Risk Index ETL: raw source dumps -> derived artifacts -> Postgres.
	2	+
	3	+Raw data is never edited in place (CLAUDE.md §1); every derived artifact gets a
	4	+manifest JSON (hashes + row counts) — only manifests are committed.
	5	+Source catalogue with verified URLs: docs/research/02-data-sources.md.
	6	+"""

added apps/etl/src/airiskindex_etl/download_onet.py +64 −0

		`@@ -0,0 +1,64 @@`
	1	+"""Fetch the O*NET database text dump into data/raw/onet/ (immutable).
	2	+
	3	+O*NET 30.x — CC BY 4.0, attribution required. 30.x renamed
	4	+"Technology Skills" -> "Software Skills" and split Skills into
	5	+Essential/Transferable (see docs/research/02-data-sources.md §O*NET).
	6	+Check https://www.onetcenter.org/database.html for the current release
	7	+(31.0 expected late Aug 2026) before bumping ONET_VERSION.
	8	+"""
	9	+
	10	+from __future__ import annotations
	11	+
	12	+import hashlib
	13	+import json
	14	+import sys
	15	+import zipfile
	16	+from pathlib import Path
	17	+
	18	+import requests
	19	+
	20	+ONET_VERSION = "30_3"
	21	+ONET_URL = f"https://www.onetcenter.org/dl_files/database/db_{ONET_VERSION}_text.zip"
	22	+
	23	+# Some sources (notably BLS OEWS) block non-browser user agents; use a plain
	24	+# descriptive UA with contact info everywhere for consistency and courtesy.
	25	+USER_AGENT = "airiskindex-etl/0.1 (https://www.airiskindex.io; data pipeline)"
	26	+
	27	+REPO_ROOT = Path(__file__).resolve().parents[4]
	28	+RAW_DIR = REPO_ROOT / "data" / "raw" / "onet"
	29	+
	30	+
	31	+def download() -> Path:
	32	+ RAW_DIR.mkdir(parents=True, exist_ok=True)
	33	+ archive = RAW_DIR / f"db_{ONET_VERSION}_text.zip"
	34	+ if archive.exists():
	35	+ print(f"already present: {archive}")
	36	+ return archive
	37	+
	38	+ print(f"downloading {ONET_URL}")
	39	+ response = requests.get(ONET_URL, headers={"User-Agent": USER_AGENT}, timeout=300, stream=True)
	40	+ response.raise_for_status()
	41	+ with archive.open("wb") as fh:
	42	+ for chunk in response.iter_content(chunk_size=1 << 20):
	43	+ fh.write(chunk)
	44	+
	45	+ with zipfile.ZipFile(archive) as zf:
	46	+ zf.extractall(RAW_DIR)
	47	+
	48	+ manifest = {
	49	+ "source": ONET_URL,
	50	+ "version": ONET_VERSION,
	51	+ "sha256": hashlib.sha256(archive.read_bytes()).hexdigest(),
	52	+ "files": sorted(p.name for p in RAW_DIR.iterdir()),
	53	+ }
	54	+ (RAW_DIR / "manifest.json").write_text(json.dumps(manifest, indent=2))
	55	+ print(f"downloaded and extracted to {RAW_DIR}")
	56	+ return archive
	57	+
	58	+
	59	+if __name__ == "__main__":
	60	+ try:
	61	+ download()
	62	+ except requests.RequestException as error:
	63	+ print(f"download failed: {error}", file=sys.stderr)
	64	+ sys.exit(1)

added apps/etl/tests/test_smoke.py +7 −0

		`@@ -0,0 +1,7 @@`
	1	+from airiskindex_etl.download_onet import ONET_URL, ONET_VERSION, RAW_DIR
	2	+
	3	+
	4	+def test_onet_source_configuration() -> None:
	5	+ assert ONET_VERSION.startswith("30_")
	6	+ assert ONET_URL.endswith(f"db_{ONET_VERSION}_text.zip")
	7	+ assert RAW_DIR.parts[-3:] == ("data", "raw", "onet")

added apps/web/app/api/v1/health/route.ts +11 −0

		`@@ -0,0 +1,11 @@`
	1	+import { INDEX_VERSION } from "@airiskindex/scoring";
	2	+
	3	+export const dynamic = "force-dynamic";
	4	+
	5	+export function GET(): Response {
	6	+ return Response.json({
	7	+ ok: true,
	8	+ index_version: INDEX_VERSION,
	9	+ time: new Date().toISOString(),
	10	+ });
	11	+}

added apps/web/app/api/v1/methodology/route.ts +27 −0

		`@@ -0,0 +1,27 @@`
	1	+import {
	2	+ DIMENSIONS,
	3	+ EXPOSURE_DIMENSIONS,
	4	+ HIGH_EXPOSURE_THRESHOLD,
	5	+ INDEX_VERSION,
	6	+ INVERTED_DIMENSIONS,
	7	+ WEIGHTS,
	8	+} from "@airiskindex/scoring";
	9	+
	10	+export function GET(): Response {
	11	+ return Response.json(
	12	+ {
	13	+ index_version: INDEX_VERSION,
	14	+ dimensions: DIMENSIONS,
	15	+ weights: WEIGHTS,
	16	+ inverted_dimensions: [...INVERTED_DIMENSIONS],
	17	+ exposure_dimensions: EXPOSURE_DIMENSIONS,
	18	+ high_exposure_threshold: HIGH_EXPOSURE_THRESHOLD,
	19	+ scales: {
	20	+ ratings: "1-5 per task and dimension, multi-model LLM panel with expert overrides",
	21	+ scores: "0-100, each with low/score/high confidence bounds",
	22	+ },
	23	+ documentation: "https://www.airiskindex.io/methodology",
	24	+ },
	25	+ { headers: { "Cache-Control": "public, max-age=3600" } },
	26	+ );
	27	+}

added apps/web/app/api/v1/occupations/[code]/route.ts +70 −0

		`@@ -0,0 +1,70 @@`
	1	+import { prisma } from "@airiskindex/db";
	2	+import { INDEX_VERSION } from "@airiskindex/scoring";
	3	+import type { NextRequest } from "next/server";
	4	+
	5	+export const dynamic = "force-dynamic";
	6	+
	7	+export async function GET(
	8	+ _request: NextRequest,
	9	+ { params }: { params: { code: string } },
	10	+): Promise<Response> {
	11	+ try {
	12	+ const occupation = await prisma.occupation.findUnique({
	13	+ where: { code: params.code },
	14	+ include: {
	15	+ tasks: {
	16	+ select: { id: true, statement: true, importance: true },
	17	+ orderBy: { id: "asc" },
	18	+ },
	19	+ },
	20	+ });
	21	+ if (!occupation) {
	22	+ return Response.json({ error: "not_found" }, { status: 404 });
	23	+ }
	24	+
	25	+ // Latest run's scores; historical runs stay queryable (CLAUDE.md §9).
	26	+ const latest = await prisma.occupationScore.findFirst({
	27	+ where: { occupationCode: occupation.code },
	28	+ orderBy: { run: { createdAt: "desc" } },
	29	+ include: { run: true },
	30	+ });
	31	+
	32	+ return Response.json({
	33	+ index_version: latest?.run.indexVersion ?? INDEX_VERSION,
	34	+ occupation: {
	35	+ code: occupation.code,
	36	+ title: occupation.title,
	37	+ description: occupation.description,
	38	+ esco_uri: occupation.escoUri,
	39	+ rome_code: occupation.romeCode,
	40	+ median_wage_cents: occupation.medianWageCents,
	41	+ wage_currency: occupation.wageCurrency,
	42	+ },
	43	+ scores: latest
	44	+ ? {
	45	+ run_id: latest.runId,
	46	+ computed_at: latest.run.createdAt,
	47	+ substitution: {
	48	+ low: latest.substitutionLow,
	49	+ score: latest.substitution,
	50	+ high: latest.substitutionHigh,
	51	+ },
	52	+ exposure: {
	53	+ low: latest.exposureLow,
	54	+ score: latest.exposure,
	55	+ high: latest.exposureHigh,
	56	+ },
	57	+ augmentation: {
	58	+ low: latest.augmentationLow,
	59	+ score: latest.augmentation,
	60	+ high: latest.augmentationHigh,
	61	+ },
	62	+ highly_exposed_task_share: latest.highlyExposedTaskShare,
	63	+ }
	64	+ : null,
	65	+ tasks: occupation.tasks,
	66	+ });
	67	+ } catch {
	68	+ return Response.json({ error: "database_unavailable" }, { status: 503 });
	69	+ }
	70	+}

added apps/web/app/api/v1/occupations/route.ts +40 −0

		`@@ -0,0 +1,40 @@`
	1	+import { prisma } from "@airiskindex/db";
	2	+import { INDEX_VERSION } from "@airiskindex/scoring";
	3	+import type { NextRequest } from "next/server";
	4	+
	5	+export const dynamic = "force-dynamic";
	6	+
	7	+export async function GET(request: NextRequest): Promise<Response> {
	8	+ const { searchParams } = new URL(request.url);
	9	+ const q = searchParams.get("q") ?? undefined;
	10	+ const page = Math.max(1, Number(searchParams.get("page") ?? "1") \|\| 1);
	11	+ const perPage = Math.min(100, Math.max(1, Number(searchParams.get("per_page") ?? "25") \|\| 25));
	12	+
	13	+ const where = q
	14	+ ? {
	15	+ OR: [
	16	+ { title: { contains: q, mode: "insensitive" as const } },
	17	+ { code: { startsWith: q } },
	18	+ ],
	19	+ }
	20	+ : {};
	21	+
	22	+ try {
	23	+ const [total, items] = await Promise.all([
	24	+ prisma.occupation.count({ where }),
	25	+ prisma.occupation.findMany({
	26	+ where,
	27	+ orderBy: { code: "asc" },
	28	+ skip: (page - 1) * perPage,
	29	+ take: perPage,
	30	+ select: { code: true, title: true },
	31	+ }),
	32	+ ]);
	33	+ return Response.json(
	34	+ { index_version: INDEX_VERSION, page, per_page: perPage, total, items },
	35	+ { headers: { "Cache-Control": "public, s-maxage=3600, stale-while-revalidate=600" } },
	36	+ );
	37	+ } catch {
	38	+ return Response.json({ error: "database_unavailable" }, { status: 503 });
	39	+ }
	40	+}

added apps/web/app/globals.css +3 −0

		`@@ -0,0 +1,3 @@`
	1	+@tailwind base;
	2	+@tailwind components;
	3	+@tailwind utilities;

added apps/web/app/layout.tsx +17 −0

		`@@ -0,0 +1,17 @@`
	1	+import type { Metadata } from "next";
	2	+import type { ReactNode } from "react";
	3	+import "./globals.css";
	4	+
	5	+export const metadata: Metadata = {
	6	+ title: "AI Risk Index — task-based AI exposure scores for every occupation",
	7	+ description:
	8	+ "Transparent, versioned, task-based scores of how occupations are exposed to AI — with separate exposure, substitution and augmentation sub-scores and confidence intervals. Adaptation guidance, not doom.",
	9	+};
	10	+
	11	+export default function RootLayout({ children }: { children: ReactNode }): JSX.Element {
	12	+ return (
	13	+ <html lang="en">
	14	+ <body className="min-h-screen bg-white text-slate-900 antialiased">{children}</body>
	15	+ </html>
	16	+ );
	17	+}

added apps/web/app/page.tsx +70 −0

		`@@ -0,0 +1,70 @@`
	1	+import { ScoreBandPill } from "@airiskindex/ui";
	2	+import { INDEX_VERSION } from "@airiskindex/scoring";
	3	+
	4	+const CONCEPTS = [
	5	+ {
	6	+ name: "Exposure",
	7	+ description:
	8	+ "AI is technically capable of performing the task. High exposure alone does not mean job loss — it means the occupation's tasks are changing.",
	9	+ },
	10	+ {
	11	+ name: "Substitution",
	12	+ description:
	13	+ "AI actually replaces the human performing the task, once cost, adoption and real-world barriers are accounted for.",
	14	+ },
	15	+ {
	16	+ name: "Augmentation",
	17	+ description:
	18	+ "AI assists the human, increasing productivity. For most occupations today, measured usage is augmentation, not replacement.",
	19	+ },
	20	+] as const;
	21	+
	22	+export default function HomePage(): JSX.Element {
	23	+ return (
	24	+ <main className="mx-auto max-w-3xl px-6 py-16">
	25	+ <p className="text-sm font-medium uppercase tracking-wide text-slate-500">
	26	+ AI Risk Index · methodology {INDEX_VERSION}
	27	+ </p>
	28	+ <h1 className="mt-2 text-4xl font-bold tracking-tight">
	29	+ How is your occupation exposed to AI — task by task?
	30	+ </h1>
	31	+ <p className="mt-4 text-lg text-slate-600">
	32	+ We score occupations from their individual tasks, report three separate sub-scores with
	33	+ confidence intervals, and publish every weight, prompt and formula. Built for adaptation
	34	+ planning — not headlines.
	35	+ </p>
	36	+
	37	+ <div className="mt-8 flex flex-wrap gap-3">
	38	+ {/* Example occupation from the published methodology example */}
	39	+ <ScoreBandPill label="Substitution" low={40.9} score={56.7} high={68.4} />
	40	+ <ScoreBandPill label="Exposure" low={42.0} score={57.1} high={74.1} />
	41	+ <ScoreBandPill label="Augmentation" low={46.9} score={71.9} high={84.4} />
	42	+ </div>
	43	+
	44	+ <section className="mt-12 grid gap-6 sm:grid-cols-3">
	45	+ {CONCEPTS.map((concept) => (
	46	+ <div key={concept.name} className="rounded-lg border border-slate-200 p-5">
	47	+ <h2 className="font-semibold">{concept.name}</h2>
	48	+ <p className="mt-2 text-sm text-slate-600">{concept.description}</p>
	49	+ </div>
	50	+ ))}
	51	+ </section>
	52	+
	53	+ <section className="mt-12 text-sm text-slate-600">
	54	+ <h2 className="text-base font-semibold text-slate-900">Public API</h2>
	55	+ <ul className="mt-2 list-inside list-disc space-y-1">
	56	+ <li>
	57	+ <code>/api/v1/occupations</code> — search occupations
	58	+ </li>
	59	+ <li>
	60	+ <code>/api/v1/occupations/:code</code> — full score breakdown with sub-scores and CI
	61	+ bounds
	62	+ </li>
	63	+ <li>
	64	+ <code>/api/v1/methodology</code> — machine-readable weights and version metadata
	65	+ </li>
	66	+ </ul>
	67	+ </section>
	68	+ </main>
	69	+ );
	70	+}

added apps/web/next-env.d.ts +5 −0

		`@@ -0,0 +1,5 @@`
	1	+/// <reference types="next" />
	2	+/// <reference types="next/image-types/global" />
	3	+
	4	+// NOTE: This file should not be edited
	5	+// see https://nextjs.org/docs/app/building-your-application/configuring/typescript for more information.

added apps/web/next.config.mjs +9 −0

		`@@ -0,0 +1,9 @@`
	1	+/** @type {import('next').NextConfig} */
	2	+const nextConfig = {
	3	+ // Workspace packages ship TypeScript sources directly.
	4	+ transpilePackages: ["@airiskindex/scoring", "@airiskindex/db", "@airiskindex/ui"],
	5	+ // Deployed as `node .next/standalone/server.js` behind ngrok (CLAUDE.md §7).
	6	+ output: "standalone",
	7	+};
	8	+
	9	+export default nextConfig;

added apps/web/package.json +28 −0

		`@@ -0,0 +1,28 @@`
	1	+{
	2	+ "name": "@airiskindex/web",
	3	+ "version": "0.0.0",
	4	+ "private": true,
	5	+ "scripts": {
	6	+ "dev": "next dev",
	7	+ "build": "next build",
	8	+ "start": "next start",
	9	+ "typecheck": "tsc --noEmit"
	10	+ },
	11	+ "dependencies": {
	12	+ "@airiskindex/db": "workspace:*",
	13	+ "@airiskindex/scoring": "workspace:*",
	14	+ "@airiskindex/ui": "workspace:*",
	15	+ "next": "^14.2.5",
	16	+ "react": "^18.3.1",
	17	+ "react-dom": "^18.3.1"
	18	+ },
	19	+ "devDependencies": {
	20	+ "@types/node": "^20.14.11",
	21	+ "@types/react": "^18.3.3",
	22	+ "@types/react-dom": "^18.3.0",
	23	+ "autoprefixer": "^10.4.19",
	24	+ "postcss": "^8.4.39",
	25	+ "tailwindcss": "^3.4.6",
	26	+ "typescript": "^5.5.4"
	27	+ }
	28	+}

added apps/web/postcss.config.mjs +6 −0

		`@@ -0,0 +1,6 @@`
	1	+export default {
	2	+ plugins: {
	3	+ tailwindcss: {},
	4	+ autoprefixer: {},
	5	+ },
	6	+};

added apps/web/tailwind.config.ts +9 −0

		`@@ -0,0 +1,9 @@`
	1	+import type { Config } from "tailwindcss";
	2	+
	3	+export default {
	4	+ content: ["./app/*/.{ts,tsx}", "../../packages/ui/src/*/.{ts,tsx}"],
	5	+ theme: {
	6	+ extend: {},
	7	+ },
	8	+ plugins: [],
	9	+} satisfies Config;

added apps/web/tsconfig.json +10 −0

		`@@ -0,0 +1,10 @@`
	1	+{
	2	+ "extends": "../../packages/config/tsconfig/nextjs.json",
	3	+ "compilerOptions": {
	4	+ "paths": {
	5	+ "@/": ["./"]
	6	+ }
	7	+ },
	8	+ "include": ["next-env.d.ts", "*/.ts", "*/.tsx", ".next/types/*/.ts"],
	9	+ "exclude": ["node_modules"]
	10	+}

added apps/worker/package.json +25 −0

		`@@ -0,0 +1,25 @@`
	1	+{
	2	+ "name": "@airiskindex/worker",
	3	+ "version": "0.0.0",
	4	+ "private": true,
	5	+ "type": "module",
	6	+ "scripts": {
	7	+ "dev": "tsx watch src/index.ts",
	8	+ "start": "tsx src/index.ts",
	9	+ "score:recompute": "tsx src/scripts/recompute.ts",
	10	+ "typecheck": "tsc --noEmit"
	11	+ },
	12	+ "dependencies": {
	13	+ "@airiskindex/db": "workspace:*",
	14	+ "@airiskindex/scoring": "workspace:*",
	15	+ "@anthropic-ai/sdk": ">=0.32.1 <1",
	16	+ "bullmq": "^5.8.7",
	17	+ "ioredis": "^5.4.1",
	18	+ "zod": "^3.23.8"
	19	+ },
	20	+ "devDependencies": {
	21	+ "@types/node": "^20.14.11",
	22	+ "tsx": "^4.16.2",
	23	+ "typescript": "^5.5.4"
	24	+ }
	25	+}

added apps/worker/src/config.ts +15 −0

		`@@ -0,0 +1,15 @@`
	1	+/**
	2	+ * Rater model panel — comma-separated model IDs from the environment, never
	3	+ * hardcoded (CLAUDE.md §6). Multi-model rating is required: single-model
	4	+ * exposure ratings show up to 19× spread across frontier raters
	5	+ * (docs/research/01-existing-indices.md §7.2).
	6	+ */
	7	+export const RATER_MODELS: readonly string[] = (process.env.RATER_MODELS ?? "")
	8	+ .split(",")
	9	+ .map((value) => value.trim())
	10	+ .filter(Boolean);
	11	+
	12	+/** Bumping this invalidates cached ratings (CLAUDE.md §6). */
	13	+export const RATER_PROMPT_VERSION = "v1";
	14	+
	15	+export const REDIS_URL = process.env.REDIS_URL ?? "redis://127.0.0.1:6379";

added apps/worker/src/index.ts +85 −0

		`@@ -0,0 +1,85 @@`
	1	+import { Queue, Worker } from "bullmq";
	2	+import IORedis from "ioredis";
	3	+import { prisma } from "@airiskindex/db";
	4	+import { RATER_MODELS, RATER_PROMPT_VERSION, REDIS_URL } from "./config";
	5	+import { ingestBatchResults, isBatchComplete, submitRatingBatch, type RatingTask } from "./raters/batch";
	6	+import { ratingJobId } from "./raters/job-id";
	7	+
	8	+const connection = new IORedis(REDIS_URL, { maxRetriesPerRequest: null });
	9	+
	10	+export const ratingQueue = new Queue("rating", { connection });
	11	+
	12	+interface SubmitPayload {
	13	+ model: string;
	14	+}
	15	+
	16	+interface PollPayload {
	17	+ model: string;
	18	+ batchId: string;
	19	+ jobIdToTaskId: Record<string, string>;
	20	+}
	21	+
	22	+const worker = new Worker(
	23	+ "rating",
	24	+ async (job) => {
	25	+ if (job.name === "submit") {
	26	+ const { model } = job.data as SubmitPayload;
	27	+ const tasks = await prisma.task.findMany({
	28	+ where: {
	29	+ ratings: { none: { model, promptVersion: RATER_PROMPT_VERSION } },
	30	+ },
	31	+ include: { occupation: { select: { title: true } } },
	32	+ });
	33	+ if (tasks.length === 0) return { submitted: 0 };
	34	+
	35	+ const ratingTasks: RatingTask[] = tasks.map((task) => ({
	36	+ taskId: task.id,
	37	+ occupationTitle: task.occupation.title,
	38	+ statement: task.statement,
	39	+ }));
	40	+ const batchId = await submitRatingBatch(ratingTasks, model);
	41	+ const jobIdToTaskId = Object.fromEntries(
	42	+ ratingTasks.map((task) => [ratingJobId(task.taskId, model, RATER_PROMPT_VERSION), task.taskId]),
	43	+ );
	44	+ await ratingQueue.add(
	45	+ "poll",
	46	+ { model, batchId, jobIdToTaskId } satisfies PollPayload,
	47	+ { jobId: `poll:${batchId}`, delay: 60_000, attempts: 60, backoff: { type: "fixed", delay: 60_000 } },
	48	+ );
	49	+ return { submitted: ratingTasks.length, batchId };
	50	+ }
	51	+
	52	+ if (job.name === "poll") {
	53	+ const { model, batchId, jobIdToTaskId } = job.data as PollPayload;
	54	+ if (!(await isBatchComplete(batchId))) {
	55	+ throw new Error(`batch ${batchId} still processing`); // retried via backoff
	56	+ }
	57	+ return ingestBatchResults(batchId, model, new Map(Object.entries(jobIdToTaskId)));
	58	+ }
	59	+
	60	+ throw new Error(`unknown job ${job.name}`);
	61	+ },
	62	+ { connection },
	63	+);
	64	+
	65	+worker.on("failed", (job, error) => {
	66	+ console.error(`[worker] job ${job?.name}:${job?.id} failed:`, error.message);
	67	+});
	68	+
	69	+async function enqueueSubmitJobs(): Promise<void> {
	70	+ if (RATER_MODELS.length < 2) {
	71	+ console.warn(
	72	+ "[worker] RATER_MODELS has fewer than 2 models — multi-model rating is required (CLAUDE.md §6).",
	73	+ );
	74	+ }
	75	+ for (const model of RATER_MODELS) {
	76	+ await ratingQueue.add(
	77	+ "submit",
	78	+ { model } satisfies SubmitPayload,
	79	+ { jobId: `submit:${model}:${RATER_PROMPT_VERSION}` },
	80	+ );
	81	+ }
	82	+}
	83	+
	84	+enqueueSubmitJobs().catch((error) => console.error("[worker] enqueue failed:", error));
	85	+console.log(`[worker] rating worker up — prompt ${RATER_PROMPT_VERSION}, models: ${RATER_MODELS.join(", ") \|\| "(none)"}`);

added apps/worker/src/raters/batch.ts +121 −0

		`@@ -0,0 +1,121 @@`
	1	+import { readFileSync } from "node:fs";
	2	+import Anthropic from "@anthropic-ai/sdk";
	3	+import { prisma } from "@airiskindex/db";
	4	+import { RATER_PROMPT_VERSION } from "../config";
	5	+import { ratingJobId } from "./job-id";
	6	+import { RATING_OUTPUT_JSON_SCHEMA, ratingResponseSchema } from "./schema";
	7	+
	8	+// Batch rating flow (docs/research/03-llm-rater-api.md): one Message Batches
	9	+// request per task × model, custom_id = deterministic job ID, 1h-cached rubric
	10	+// in the system block, structured JSON output. 50% batch discount; results
	11	+// retrievable for 29 days.
	12	+
	13	+const anthropic = new Anthropic();
	14	+
	15	+const rubric = readFileSync(new URL("./prompts/task-rating-v1.md", import.meta.url), "utf8");
	16	+
	17	+export interface RatingTask {
	18	+ taskId: string;
	19	+ occupationTitle: string;
	20	+ statement: string;
	21	+}
	22	+
	23	+export async function submitRatingBatch(tasks: RatingTask[], model: string): Promise<string> {
	24	+ const batch = await anthropic.messages.batches.create({
	25	+ requests: tasks.map((task) => ({
	26	+ custom_id: ratingJobId(task.taskId, model, RATER_PROMPT_VERSION),
	27	+ params: {
	28	+ model,
	29	+ max_tokens: 2048,
	30	+ system: [
	31	+ {
	32	+ type: "text" as const,
	33	+ text: rubric,
	34	+ cache_control: { type: "ephemeral" as const },
	35	+ },
	36	+ ],
	37	+ messages: [
	38	+ {
	39	+ role: "user" as const,
	40	+ content: `Occupation: ${task.occupationTitle}\nTask statement: ${task.statement}\n\nRate this task per the rubric.`,
	41	+ },
	42	+ ],
	43	+ // Structured outputs (GA). Kept as an untyped extension so the code
	44	+ // compiles across SDK versions; see docs/research/03-llm-rater-api.md.
	45	+ ...({
	46	+ output_config: {
	47	+ format: { type: "json_schema", schema: RATING_OUTPUT_JSON_SCHEMA },
	48	+ },
	49	+ } as Record<string, unknown>),
	50	+ } as unknown as Anthropic.Messages.MessageCreateParamsNonStreaming,
	51	+ })),
	52	+ });
	53	+ return batch.id;
	54	+}
	55	+
	56	+export async function isBatchComplete(batchId: string): Promise<boolean> {
	57	+ const batch = await anthropic.messages.batches.retrieve(batchId);
	58	+ return batch.processing_status === "ended";
	59	+}
	60	+
	61	+/**
	62	+ * Ingest batch results: store the raw response and the parsed per-dimension
	63	+ * scores (full audit trail, CLAUDE.md §6). Idempotent via the unique
	64	+ * (taskId, dimension, model, promptVersion, sampleIndex) constraint.
	65	+ */
	66	+export async function ingestBatchResults(
	67	+ batchId: string,
	68	+ model: string,
	69	+ jobIdToTaskId: ReadonlyMap<string, string>,
	70	+): Promise<{ ingested: number; failed: number }> {
	71	+ let ingested = 0;
	72	+ let failed = 0;
	73	+
	74	+ for await (const entry of await anthropic.messages.batches.results(batchId)) {
	75	+ const taskId = jobIdToTaskId.get(entry.custom_id);
	76	+ if (!taskId \|\| entry.result.type !== "succeeded") {
	77	+ failed += 1;
	78	+ continue;
	79	+ }
	80	+ const message = entry.result.message;
	81	+ const textBlock = message.content.find((block) => block.type === "text");
	82	+ if (!textBlock \|\| textBlock.type !== "text") {
	83	+ failed += 1;
	84	+ continue;
	85	+ }
	86	+
	87	+ const parsed = ratingResponseSchema.safeParse(JSON.parse(textBlock.text));
	88	+ if (!parsed.success) {
	89	+ failed += 1;
	90	+ continue;
	91	+ }
	92	+
	93	+ for (const [dimension, value] of Object.entries(parsed.data)) {
	94	+ await prisma.taskRating.upsert({
	95	+ where: {
	96	+ taskId_dimension_model_promptVersion_sampleIndex: {
	97	+ taskId,
	98	+ dimension,
	99	+ model,
	100	+ promptVersion: RATER_PROMPT_VERSION,
	101	+ sampleIndex: 0,
	102	+ },
	103	+ },
	104	+ update: {},
	105	+ create: {
	106	+ taskId,
	107	+ dimension,
	108	+ model,
	109	+ promptVersion: RATER_PROMPT_VERSION,
	110	+ sampleIndex: 0,
	111	+ rating: value.rating,
	112	+ rationale: value.rationale,
	113	+ rawResponse: JSON.parse(JSON.stringify(message)),
	114	+ },
	115	+ });
	116	+ ingested += 1;
	117	+ }
	118	+ }
	119	+
	120	+ return { ingested, failed };
	121	+}

added apps/worker/src/raters/job-id.ts +14 −0

		`@@ -0,0 +1,14 @@`
	1	+import { createHash } from "node:crypto";
	2	+
	3	+/**
	4	+ * Deterministic ID used both as the BullMQ job ID and the Message Batches
	5	+ * `custom_id`, so retries and batch reconciliation are idempotent
	6	+ * (CLAUDE.md §6; docs/research/03-llm-rater-api.md). One request rates all
	7	+ * dimensions of one task with one model.
	8	+ */
	9	+export function ratingJobId(taskId: string, model: string, promptVersion: string): string {
	10	+ return createHash("sha256")
	11	+ .update(`${taskId}:${model}:${promptVersion}`)
	12	+ .digest("hex")
	13	+ .slice(0, 32);
	14	+}

added apps/worker/src/raters/prompts/task-rating-v1.md +70 −0

		`@@ -0,0 +1,70 @@`
	1	+# Task rating rubric — v1
	2	+
	3	+You are an expert rater for the AI Risk Index (airiskindex.io). You rate one
	4	+occupational task statement (from O*NET) on six dimensions, each on a 1–5
	5	+integer scale. Be calibrated and conservative: rate what current, generally
	6	+available AI systems (including tool-using agents) can do today, not what
	7	+might be possible soon. Justify every rating in one or two sentences grounded
	8	+in the task statement itself.
	9	+
	10	+## Dimensions
	11	+
	12	+### automatability (1–5)
	13	+Could current AI perform this task end-to-end with **at least 50% time saving
	14	+at equal quality** (Eloundou et al. threshold)?
	15	+- 1 — No meaningful part of the task can be automated today.
	16	+- 3 — Roughly half of the task could be automated with significant setup.
	17	+- 5 — The full task meets the ≥50%-time-saving-at-equal-quality bar with off-the-shelf systems.
	18	+
	19	+### feasibility (1–5)
	20	+Do deployed products demonstrably perform this task reliably today? Distinguish
	21	+conceivable from deployable: benchmark results and demos rate lower than
	22	+production systems in real organizations.
	23	+- 1 — No product does this; research-stage only.
	24	+- 3 — Products exist but with material error rates or narrow scope.
	25	+- 5 — Mature products perform this reliably in production at scale.
	26	+
	27	+### cost_ratio (1–5)
	28	+Compare the AI cost per task-equivalent (inference + integration + oversight)
	29	+to the loaded human wage for the same output.
	30	+- 1 — AI is more expensive than the human, all-in.
	31	+- 3 — Roughly comparable cost.
	32	+- 5 — AI is at least an order of magnitude cheaper.
	33	+
	34	+### barriers (1–5) — NOTE: higher = MORE protected
	35	+Strength of adoption barriers: licensing/authorization requirements, liability
	36	+and error-cost asymmetry, regulatory coverage of the automation itself,
	37	+human-contact requirement, organizational friction.
	38	+- 1 — No meaningful barriers; nothing prevents substitution.
	39	+- 3 — Some friction (oversight requirements, customer preference for humans).
	40	+- 5 — Hard barriers: a licensed human must legally perform or sign off on the task.
	41	+
	42	+### adoption_velocity (1–5)
	43	+How fast and deep are the sectors where this task occurs actually adopting AI
	44	+(agents in production, measured displacement), per public adoption data?
	45	+- 1 — Laggard sectors (small firms, physical, low digitization).
	46	+- 3 — Middling adoption, pilots common, production rare.
	47	+- 5 — Fast, deep adoption (information, finance, professional services patterns).
	48	+
	49	+### augmentation (1–5)
	50	+Independently of replacement: does AI assist a human doing this task,
	51	+raising their productivity? High augmentation and low automatability can
	52	+coexist (assistive drafting for a task requiring human judgment).
	53	+- 1 — AI offers no meaningful assistance.
	54	+- 3 — Useful assistance on parts of the task.
	55	+- 5 — AI transforms productivity on this task while the human stays in the loop.
	56	+
	57	+## Output
	58	+
	59	+Return ONLY a JSON object of this shape (no prose outside JSON):
	60	+
	61	+```json
	62	+{
	63	+ "automatability": { "rating": 1, "rationale": "..." },
	64	+ "feasibility": { "rating": 1, "rationale": "..." },
	65	+ "cost_ratio": { "rating": 1, "rationale": "..." },
	66	+ "barriers": { "rating": 1, "rationale": "..." },
	67	+ "adoption_velocity": { "rating": 1, "rationale": "..." },
	68	+ "augmentation": { "rating": 1, "rationale": "..." }
	69	+}
	70	+```

added apps/worker/src/raters/schema.ts +45 −0

		`@@ -0,0 +1,45 @@`
	1	+import { z } from "zod";
	2	+import { DIMENSIONS } from "@airiskindex/scoring";
	3	+
	4	+const RATED_DIMENSIONS = [...DIMENSIONS, "augmentation"] as const;
	5	+
	6	+const dimensionRating = z.object({
	7	+ rating: z.number().int().min(1).max(5),
	8	+ rationale: z.string(),
	9	+});
	10	+
	11	+/** Parsed shape of one rater response (all six dimensions of one task). */
	12	+export const ratingResponseSchema = z.object(
	13	+ Object.fromEntries(RATED_DIMENSIONS.map((dimension) => [dimension, dimensionRating])) as Record<
	14	+ (typeof RATED_DIMENSIONS)[number],
	15	+ typeof dimensionRating
	16	+ >,
	17	+);
	18	+
	19	+export type RatingResponse = z.infer<typeof ratingResponseSchema>;
	20	+
	21	+/**
	22	+ * JSON schema for the structured-outputs feature (score as enum — numeric
	23	+ * min/max is unsupported there; docs/research/03-llm-rater-api.md).
	24	+ */
	25	+export const RATING_OUTPUT_JSON_SCHEMA = {
	26	+ type: "object",
	27	+ additionalProperties: false,
	28	+ required: [...RATED_DIMENSIONS],
	29	+ properties: Object.fromEntries(
	30	+ RATED_DIMENSIONS.map((dimension) => [
	31	+ dimension,
	32	+ {
	33	+ type: "object",
	34	+ additionalProperties: false,
	35	+ required: ["rating", "rationale"],
	36	+ properties: {
	37	+ rating: { enum: [1, 2, 3, 4, 5] },
	38	+ rationale: { type: "string" },
	39	+ },
	40	+ },
	41	+ ]),
	42	+ ),
	43	+} as const;
	44	+
	45	+export { RATED_DIMENSIONS };

added apps/worker/src/scripts/recompute.ts +136 −0

		`@@ -0,0 +1,136 @@`
	1	+import { prisma } from "@airiskindex/db";
	2	+import {
	3	+ INDEX_VERSION,
	4	+ scoreOccupation,
	5	+ type RatingBand,
	6	+ type TaskInput,
	7	+ type TaskRatings,
	8	+} from "@airiskindex/scoring";
	9	+import { RATED_DIMENSIONS } from "../raters/schema";
	10	+import { RATER_MODELS, RATER_PROMPT_VERSION } from "../config";
	11	+
	12	+// Full index recomputation (`pnpm score:recompute`): builds rating bands from
	13	+// the multi-model panel (min/mean/max across models — METHODOLOGY.md §3),
	14	+// applies expert overrides, scores every occupation, and writes ONE new
	15	+// immutable ScoreRun. Historical runs are never mutated (CLAUDE.md §9).
	16	+
	17	+function bandFromPanel(values: number[]): RatingBand {
	18	+ const low = Math.min(...values);
	19	+ const high = Math.max(...values);
	20	+ const mid = values.reduce((sum, value) => sum + value, 0) / values.length;
	21	+ return { low, mid, high };
	22	+}
	23	+
	24	+async function main(): Promise<void> {
	25	+ const occupations = await prisma.occupation.findMany({
	26	+ include: {
	27	+ tasks: {
	28	+ include: {
	29	+ ratings: { where: { promptVersion: RATER_PROMPT_VERSION } },
	30	+ overrides: true,
	31	+ },
	32	+ },
	33	+ },
	34	+ });
	35	+
	36	+ const run = await prisma.scoreRun.create({
	37	+ data: {
	38	+ indexVersion: INDEX_VERSION,
	39	+ raterPromptVersion: RATER_PROMPT_VERSION,
	40	+ raterModels: [...RATER_MODELS],
	41	+ },
	42	+ });
	43	+
	44	+ let scoredOccupations = 0;
	45	+ let skippedTasks = 0;
	46	+
	47	+ for (const occupation of occupations) {
	48	+ const inputs: TaskInput[] = [];
	49	+
	50	+ for (const task of occupation.tasks) {
	51	+ const bands: Partial<Record<(typeof RATED_DIMENSIONS)[number], RatingBand>> = {};
	52	+ let complete = true;
	53	+
	54	+ for (const dimension of RATED_DIMENSIONS) {
	55	+ const override = task.overrides.find((entry) => entry.dimension === dimension);
	56	+ if (override) {
	57	+ bands[dimension] = {
	58	+ low: override.ratingLow,
	59	+ mid: override.ratingMid,
	60	+ high: override.ratingHigh,
	61	+ };
	62	+ continue;
	63	+ }
	64	+ const values = task.ratings
	65	+ .filter((entry) => entry.dimension === dimension)
	66	+ .map((entry) => entry.rating);
	67	+ if (values.length === 0) {
	68	+ complete = false;
	69	+ break;
	70	+ }
	71	+ bands[dimension] = bandFromPanel(values);
	72	+ }
	73	+
	74	+ if (!complete) {
	75	+ skippedTasks += 1;
	76	+ continue;
	77	+ }
	78	+ inputs.push({
	79	+ taskId: task.id,
	80	+ importance: task.importance ?? undefined,
	81	+ ratings: bands as TaskRatings,
	82	+ });
	83	+ }
	84	+
	85	+ if (inputs.length === 0) continue;
	86	+
	87	+ const scores = scoreOccupation(inputs);
	88	+ await prisma.$transaction([
	89	+ prisma.occupationScore.create({
	90	+ data: {
	91	+ runId: run.id,
	92	+ occupationCode: occupation.code,
	93	+ substitutionLow: scores.substitution.low,
	94	+ substitution: scores.substitution.score,
	95	+ substitutionHigh: scores.substitution.high,
	96	+ exposureLow: scores.exposure.low,
	97	+ exposure: scores.exposure.score,
	98	+ exposureHigh: scores.exposure.high,
	99	+ augmentationLow: scores.augmentation.low,
	100	+ augmentation: scores.augmentation.score,
	101	+ augmentationHigh: scores.augmentation.high,
	102	+ highlyExposedTaskShare: scores.highlyExposedTaskShare,
	103	+ },
	104	+ }),
	105	+ ...scores.tasks.map((task) =>
	106	+ prisma.taskScore.create({
	107	+ data: {
	108	+ runId: run.id,
	109	+ taskId: task.taskId,
	110	+ substitutionLow: task.substitution.low,
	111	+ substitution: task.substitution.score,
	112	+ substitutionHigh: task.substitution.high,
	113	+ exposureLow: task.exposure.low,
	114	+ exposure: task.exposure.score,
	115	+ exposureHigh: task.exposure.high,
	116	+ augmentationLow: task.augmentation.low,
	117	+ augmentation: task.augmentation.score,
	118	+ augmentationHigh: task.augmentation.high,
	119	+ },
	120	+ }),
	121	+ ),
	122	+ ]);
	123	+ scoredOccupations += 1;
	124	+ }
	125	+
	126	+ console.log(
	127	+ `Run ${run.id} (index ${INDEX_VERSION}, prompt ${RATER_PROMPT_VERSION}): scored ${scoredOccupations}/${occupations.length} occupations; skipped ${skippedTasks} unrated tasks.`,
	128	+ );
	129	+}
	130	+
	131	+main()
	132	+ .catch((error) => {
	133	+ console.error(error);
	134	+ process.exitCode = 1;
	135	+ })
	136	+ .finally(() => prisma.$disconnect());

added apps/worker/tsconfig.json +4 −0

		`@@ -0,0 +1,4 @@`
	1	+{
	2	+ "extends": "../../packages/config/tsconfig/base.json",
	3	+ "include": ["src"]
	4	+}

added data/derived/expert_overrides/.gitkeep +0 −0

added data/derived/ratings/.gitkeep +0 −0

added data/raw/.gitkeep +0 −0

added docs/methodology/CHANGELOG.md +19 −0

		`@@ -0,0 +1,19 @@`
	1	+# Methodology Changelog
	2	+
	3	+All notable changes to the scoring methodology. Every entry corresponds to an
	4	+`INDEX_VERSION` (semver) in `packages/scoring/src/version.ts`.
	5	+
	6	+## [1.0.0] — UNRELEASED (draft)
	7	+
	8	+Initial methodology.
	9	+
	10	+- Task-based scoring on O*NET 30.x task statements, importance-weighted aggregation
	11	+ to occupations (O*NET-SOC 2019).
	12	+- Five dimensions: automatability 0.35, feasibility 0.20, cost_ratio 0.15,
	13	+ barriers 0.20 (inverted), adoption_velocity 0.10.
	14	+- Three sub-scores per occupation: exposure, substitution (headline composite),
	15	+ augmentation — augmentation rated separately per task, outside the composite.
	16	+- Multi-model LLM rater panel (≥2 frontier models via `RATER_MODELS`); confidence
	17	+ bounds (`score_low`/`score_high`) derived from rater disagreement envelopes.
	18	+- 5% human review sample; expert Delphi overrides replace LLM bands where triggered.
	19	+- Grounding research: `docs/research/01-…04-*.md` (compiled 2026-08-05).

added docs/methodology/METHODOLOGY.md +173 −0

		`@@ -0,0 +1,173 @@`
	1	+# AI Risk Index — Methodology v1.0.0 (DRAFT)
	2	+
	3	+> Status: draft, unreleased. This document is the source of truth for the scoring
	4	+> methodology (see CLAUDE.md §9). Code in `packages/scoring` links to the section
	5	+> anchors below; if code and this document disagree, stop and flag it.
	6	+>
	7	+> Related work and evidence base: `docs/research/01-existing-indices.md` (indices),
	8	+> `02-data-sources.md` (data), `04-landscape-and-evidence.md` (adoption & barriers evidence).
	9	+
	10	+---
	11	+
	12	+## 1. Principles <a name="principles"></a>
	13	+
	14	+1. Task-based, not occupation-based. Occupations are bundles of tasks with very
	15	+ different AI exposure; whole-occupation scoring (Frey & Osborne 2013) has a poor
	16	+ empirical record. Occupation scores are always derived from task scores
	17	+ (Arntz, Gregory & Zierahn 2016; Eloundou et al. 2024).
	18	+2. Three concepts, never collapsed (see §5):
	19	+ - Exposure — AI is technically capable of performing the task.
	20	+ - Substitution — AI actually replaces the human performing it. This is the
	21	+ headline composite.
	22	+ - Augmentation — AI assists the human, increasing productivity.
	23	+ Realized labor-market effects concentrate in automation-classified usage, not
	24	+ augmentation (Brynjolfsson, Chandar & Chen 2025), so conflating the three is not
	25	+ just imprecise — it is empirically wrong.
	26	+3. Uncertainty is part of the score. Every published score carries
	27	+ `score_low / score / score_high`. Interval width is driven primarily by
	28	+ disagreement between independent LLM raters (single-model ratings show up to a
	29	+ 19× spread in headline statistics across frontier models — Yin, Vu & Persico 2026)
	30	+ plus human-calibration error on the expert anchor set.
	31	+4. Fully reproducible. Weights, formulas, prompt versions, rater model IDs and
	32	+ per-model ratings are all published. Every score traces to a stored
	33	+ `score_runs` row; historical runs are immutable.
	34	+5. Versioned. Any change to weights, formulas, or rater prompts bumps
	35	+ `INDEX_VERSION` (semver) with a changelog entry and regenerated sensitivity outputs.
	36	+
	37	+## 2. Data <a name="data"></a>
	38	+
	39	+\| Input \| Source \| Role \|
	40	+\|---\|---\|---\|
	41	+\| Occupations & task statements \| ONET 30.x (ONET-SOC 2019 taxonomy), CC BY 4.0 \| Unit of analysis (~18k tasks, ~900 data-level occupations) \|
	42	+\| Task importance weights \| O*NET Task Ratings (IM scale 1–5) \| Aggregation weights (§6) \|
	43	+\| EU/France occupations \| ESCO v1.2.x + official ESCO↔O*NET crosswalk; ROME 4.0 \| Crosswalked scores (crosswalk loss documented per occupation) \|
	44	+\| Wages \| BLS OEWS (latest May release); Eurostat SES; INSEE \| `cost_ratio` denominator \|
	45	+\| Adoption data \| Census BTOS AI supplement, Ramp AI Index, Anthropic Economic Index (Hugging Face), Challenger reports \| `adoption_velocity` inputs (§4.5) \|
	46	+\| Human anchor ratings \| Expert Delphi panel (`data/derived/expert_overrides/`) \| LLM-rater calibration (§3) \|
	47	+
	48	+Raw dumps are immutable (`data/raw/`, gitignored); all transformations go through
	49	+`apps/etl`; derived artifacts commit only manifests (hashes + row counts).
	50	+
	51	+## 3. Task rating (LLM-as-evaluator) <a name="task-rating"></a>
	52	+
	53	+Each ONET task statement is rated on a 5-point scale* per dimension (§4) by a
	54	+panel of ≥2 (target 3) frontier LLMs (`RATER_MODELS`), using versioned rubric
	55	+prompts (`apps/worker/src/raters/prompts/`). Per task × dimension:
	56	+
	57	+- `rating_mid` = mean of panel ratings;
	58	+- `rating_low` / `rating_high` = min / max of panel ratings (rater-disagreement band).
	59	+
	60	+Rubric anchors follow the citable standards: automatability uses the Eloundou et al.
	61	+"≥50% time saving at equal quality" threshold, decomposed into named criteria
	62	+(SML-style multi-criterion rubric); ratings require structured justifications and are
	63	+stored with model ID, prompt version and raw response (full audit trail).
	64	+
	65	+Calibration: 5% of ratings are sampled for human review; panel-vs-human
	66	+disagreement > 1 point routes the task to the expert queue. Expert overrides replace
	67	+the LLM band for that task and are flagged in the API output.
	68	+
	69	+## 4. Dimensions <a name="dimensions"></a>
	70	+
	71	+Composite weights live in `packages/scoring/src/weights.ts` and are published at
	72	+`/api/v1/methodology`. Weights sum to 1.0.
	73	+
	74	+\| Dimension \| Key \| Weight \| Orientation \| Rubric anchor (rating of 5 means…) \|
	75	+\|---\|---\|---\|---\|---\|
	76	+\| Task automatability \| `automatability` \| 0.35 \| direct \| Current AI (incl. tooling/agents) can do the task with ≥50% time saving at equal quality \|
	77	+\| Current technical feasibility \| `feasibility` \| 0.20 \| direct \| Deployed products demonstrably perform this task reliably today (not merely conceivable) \|
	78	+\| Cost of substitution vs. wage \| `cost_ratio` \| 0.15 \| direct \| AI cost per task-equivalent ≪ loaded human wage for the same output \|
	79	+\| Adoption barriers \| `barriers` \| 0.20 \| inverted \| Strong barriers: licensing/liability/regulation/human-contact requirements block substitution \|
	80	+\| Sector adoption velocity \| `adoption_velocity` \| 0.10 \| direct \| Occupation's dominant sectors adopt AI fast and deep (agents in production, measured displacement) \|
	81	+
	82	+Orientation: a direct dimension's higher rating increases substitution pressure; an
	83	+inverted dimension's higher rating decreases it. `barriers` is the only inverted
	84	+dimension in v1. Orientation is encoded once, in `packages/scoring/src/weights.ts`,
	85	+next to the weights.
	86	+
	87	+Additionally, `augmentation` is rated per task on the same 5-point scale (does AI
	88	+assist the human on this task, raising productivity without replacing them?). It is
	89	+not part of the substitution composite; it feeds the augmentation sub-score (§5).
	90	+
	91	+### 4.4 `barriers` components <a name="barriers"></a>
	92	+Rated against five named criteria (see research doc 04): licensing/authorization
	93	+requirement (0.30), liability & error-cost asymmetry (0.25), regulatory-process
	94	+coverage (0.20, jurisdiction-specific), human-contact requirement (0.15),
	95	+organizational friction (0.10). Pre-2022 null results (OECD 2021; Acemoglu et al.
	96	+2022) show barriers dominate short-run outcomes — hence the 0.20 weight.
	97	+
	98	+### 4.5 `adoption_velocity` components <a name="adoption-velocity"></a>
	99	+Grounded in measured adoption, not forecasts: sector AI-use rate employment-weighted
	100	+(0.35), sector adoption momentum (0.20), agentic deployment depth (0.20), realized
	101	+displacement intensity (0.15), occupation-level usage intensity from the Anthropic
	102	+Economic Index (0.10). Refreshed each index release; sources are public and dated.
	103	+
	104	+## 5. Scoring formulas <a name="formulas"></a>
	105	+
	106	+All formulas are implemented, pure and deterministic, in `packages/scoring`.
	107	+Ratings `r ∈ [1,5]` normalize to pressure `p ∈ [0,1]`:
	108	+
	109	+```
	110	+p = (r − 1) / 4 (direct dimensions)
	111	+p = 1 − (r − 1) / 4 (inverted dimensions)
	112	+```
	113	+
	114	+Per task:
	115	+
	116	+```
	117	+substitution_task = 100 · Σ_d w_d · p_d (all five dimensions)
	118	+exposure_task = 100 · (w_auto·p_auto + w_feas·p_feas) / (w_auto + w_feas)
	119	+augmentation_task = 100 · p_augmentation
	120	+```
	121	+
	122	+Confidence bounds: `score_low` is computed with each dimension's
	123	+pressure-minimizing rating bound (for direct dimensions the low rating; for inverted
	124	+dimensions the high rating), `score_high` symmetrically. Bounds are therefore
	125	+worst/best-case envelopes over rater disagreement, and `low ≤ score ≤ high` always
	126	+holds.
	127	+
	128	+## 6. Aggregation to occupations <a name="aggregation"></a>
	129	+
	130	+Occupation scores are the importance-weighted mean of task scores, weights from
	131	+O*NET Task Ratings importance (IM, 1–5), normalized to sum to 1 within the
	132	+occupation. Applied identically to `low`, `score` and `high`. Tasks lacking
	133	+importance ratings receive the occupation-mean importance.
	134	+
	135	+The API additionally reports the share of tasks with `substitution_task ≥ 70`
	136	+("highly exposed task share") — this, not the composite alone, is the preferred
	137	+headline in UI copy ("X% of tasks in this occupation are highly exposed").
	138	+
	139	+## 7. Versioning & runs <a name="versioning"></a>
	140	+
	141	+- `INDEX_VERSION` (semver) in `packages/scoring/src/version.ts`.
	142	+- MAJOR: formula/weight changes. MINOR: data-source version bumps (new O*NET release,
	143	+ new adoption data). PATCH: recomputation with refreshed adoption inputs, prompt
	144	+ clarifications that don't change the rubric semantics.
	145	+- Every computation writes a `score_runs` row (index version, prompt version, rater
	146	+ models). Old runs stay queryable forever; the API serves the latest by default and
	147	+ any run on request.
	148	+
	149	+## 8. Validation & sensitivity <a name="validation"></a>
	150	+
	151	+Published with every MAJOR/MINOR release under `docs/methodology/sensitivity/`:
	152	+
	153	+1. Convergent validity: Spearman correlation of our occupation scores against
	154	+ Felten AIOE, Eloundou β, and ILO WP140 gradients (ρ ≈ 0.84 between independent
	155	+ modern methodologies is the reference bar).
	156	+2. Rater stability: cross-model agreement distribution; occupations with the
	157	+ widest bands flagged in-product.
	158	+3. Outcome tracking: correlation against the Stanford "Canaries" dashboard
	159	+ (entry-level employment in exposed occupations) and AEI usage shares — exposure
	160	+ indices individually explain <11% of realized unemployment risk (Frank, Ahn &
	161	+ Moro 2025), so we report outcome tracking honestly rather than claiming prediction.
	162	+4. Weight sensitivity: composite rank stability under ±25% perturbation of each
	163	+ weight.
	164	+
	165	+## 9. Known limitations <a name="limitations"></a>
	166	+
	167	+- LLM raters co-evolve with the technology they measure (the "ruler" problem);
	168	+ multi-model panels bound but do not eliminate this.
	169	+- Sector-level adoption inputs are priors, corrected by occupation-level usage data.
	170	+- ESCO/ROME crosswalked scores inherit crosswalk loss; flagged per occupation.
	171	+- Scores describe tasks as currently constituted; occupations reorganize.
	172	+- This index measures exposure and substitution pressure, not certainty of job
	173	+ loss. Product copy follows the adaptation-not-doom tone guide accordingly.

added docs/methodology/examples/example-analyst.json +74 −0

		`@@ -0,0 +1,74 @@`
	1	+{
	2	+ "$comment": "Worked example for METHODOLOGY.md §5-6. Hand-computed with v1 weights (automatability .35, feasibility .20, cost_ratio .15, barriers .20 inverted, adoption_velocity .10). packages/scoring snapshot tests assert against `expected` to 3 decimal places. Any change here requires an INDEX_VERSION bump.",
	3	+ "occupation": {
	4	+ "code": "99-9999.00",
	5	+ "title": "Example Analyst (synthetic)"
	6	+ },
	7	+ "tasks": [
	8	+ {
	9	+ "taskId": "T1",
	10	+ "importance": 4,
	11	+ "ratings": {
	12	+ "automatability": { "low": 3, "mid": 4, "high": 5 },
	13	+ "feasibility": { "low": 4, "mid": 4, "high": 5 },
	14	+ "cost_ratio": { "low": 3, "mid": 3, "high": 4 },
	15	+ "barriers": { "low": 2, "mid": 2, "high": 3 },
	16	+ "adoption_velocity": { "low": 3, "mid": 4, "high": 4 },
	17	+ "augmentation": { "low": 4, "mid": 5, "high": 5 }
	18	+ }
	19	+ },
	20	+ {
	21	+ "taskId": "T2",
	22	+ "importance": 3,
	23	+ "ratings": {
	24	+ "automatability": { "low": 1, "mid": 2, "high": 2 },
	25	+ "feasibility": { "low": 2, "mid": 2, "high": 3 },
	26	+ "cost_ratio": { "low": 2, "mid": 2, "high": 2 },
	27	+ "barriers": { "low": 4, "mid": 4, "high": 5 },
	28	+ "adoption_velocity": { "low": 2, "mid": 3, "high": 3 },
	29	+ "augmentation": { "low": 2, "mid": 3, "high": 4 }
	30	+ }
	31	+ },
	32	+ {
	33	+ "taskId": "T3",
	34	+ "importance": 1,
	35	+ "ratings": {
	36	+ "automatability": { "low": 5, "mid": 5, "high": 5 },
	37	+ "feasibility": { "low": 2, "mid": 3, "high": 4 },
	38	+ "cost_ratio": { "low": 4, "mid": 4, "high": 5 },
	39	+ "barriers": { "low": 1, "mid": 1, "high": 2 },
	40	+ "adoption_velocity": { "low": 4, "mid": 5, "high": 5 },
	41	+ "augmentation": { "low": 1, "mid": 2, "high": 3 }
	42	+ }
	43	+ }
	44	+ ],
	45	+ "expected": {
	46	+ "taskScores": [
	47	+ {
	48	+ "taskId": "T1",
	49	+ "substitution": { "low": 55.0, "score": 71.25, "high": 88.75 },
	50	+ "exposure": { "low": 59.0909, "score": 75.0, "high": 100.0 },
	51	+ "augmentation": { "low": 75.0, "score": 100.0, "high": 100.0 }
	52	+ },
	53	+ {
	54	+ "taskId": "T2",
	55	+ "substitution": { "low": 11.25, "score": 27.5, "high": 32.5 },
	56	+ "exposure": { "low": 9.0909, "score": 25.0, "high": 34.0909 },
	57	+ "augmentation": { "low": 25.0, "score": 50.0, "high": 75.0 }
	58	+ },
	59	+ {
	60	+ "taskId": "T3",
	61	+ "substitution": { "low": 73.75, "score": 86.25, "high": 95.0 },
	62	+ "exposure": { "low": 72.7273, "score": 81.8182, "high": 90.9091 },
	63	+ "augmentation": { "low": 0.0, "score": 25.0, "high": 50.0 }
	64	+ }
	65	+ ],
	66	+ "occupation": {
	67	+ "taskWeights": [0.5, 0.375, 0.125],
	68	+ "substitution": { "low": 40.9375, "score": 56.71875, "high": 68.4375 },
	69	+ "exposure": { "low": 42.0455, "score": 57.1023, "high": 74.1477 },
	70	+ "augmentation": { "low": 46.875, "score": 71.875, "high": 84.375 },
	71	+ "highlyExposedTaskShare": 0.6667
	72	+ }
	73	+ }
	74	+}

added docs/research/01-existing-indices.md +406 −0

		`@@ -0,0 +1,406 @@`
	1	+# Existing AI Job-Exposure / Automation-Risk Indices and Methodologies
	2	+
	3	+Research memo — AI Risk Index (airiskindex.io)
	4	+Date compiled: 2026-08-05 (web research current to August 2026)
	5	+Scope: All major academic and industry indices measuring occupational exposure to AI/automation, their methodologies, criticisms, empirical validation, and lessons for the airiskindex.io v1 scoring model.
	6	+
	7	+---
	8	+
	9	+## Table of Contents
	10	+
	11	+1. [First wave: pre-generative-AI automation risk (2013–2019)](#1-first-wave)
	12	+2. [Second wave: AI-specific exposure measures (2018–2021)](#2-second-wave)
	13	+3. [Third wave: LLM/generative-AI exposure (2023–2024)](#3-third-wave)
	14	+4. [Institutional indices: ILO, OECD, IMF (2023–2026)](#4-institutional-indices)
	15	+5. [Industry & consultancy estimates](#5-industry-estimates)
	16	+6. [Usage-based measures: Anthropic Economic Index & OpenAI (2025–2026)](#6-usage-based-measures)
	17	+7. [Fourth wave: 2025–2026 indices and meta-critiques](#7-fourth-wave)
	18	+8. [Empirical validation: do exposure scores predict real outcomes?](#8-empirical-validation)
	19	+9. [Master comparison table](#9-comparison-table)
	20	+10. [Implications for airiskindex.io v1 methodology](#10-implications)
	21	+
	22	+---
	23	+
	24	+<a name="1-first-wave"></a>
	25	+## 1. First wave: pre-generative-AI automation risk (2013–2019)
	26	+
	27	+### 1.1 Frey & Osborne — "The Future of Employment" (2013 working paper; 2017 published)
	28	+
	29	+- Authors/year: Carl Benedikt Frey & Michael A. Osborne (Oxford Martin School). Working paper Sept 2013; published in Technological Forecasting and Social Change 114 (2017): 254–280.
	30	+- Unit of analysis: Whole occupations (702 SOC occupations).
	31	+- Methodology:
	32	+ - ML experts hand-labeled ~70 occupations as automatable (1) or not (0) at a workshop.
	33	+ - Identified three "engineering bottlenecks" to computerisation: perception & manipulation, creative intelligence, social intelligence, operationalized via 9 O*NET variables (e.g., finger dexterity, originality, social perceptiveness, persuasion, negotiation, assisting/caring for others, cramped work spaces).
	34	+ - A Gaussian process classifier trained on the 70 labels extrapolated a "probability of computerisation" (0–1) to all 702 occupations.
	35	+ - Occupations bucketed: high risk (p > 0.7), medium (0.3–0.7), low (p < 0.3).
	36	+- Key numbers: 47% of US employment at "high risk" of computerisation "over the next decade or two" (i.e., roughly by 2030). Transportation, logistics, office/administrative support, and production occupations most at risk.
	37	+- Criticisms (extensive):
	38	+ - Occupation-level, not task-level: treats occupations as monolithic. Arntz et al. (2016) showed within-occupation task heterogeneity slashes the estimate to ~9%.
	39	+ - Technical capability ≠ adoption: no economics (cost, wages, regulation, preferences) in the model.
	40	+ - Subjective training labels from a small expert workshop; only ~70 seed labels drive all 702 predictions.
	41	+ - Model-selection sensitivity: "The Future of Employment Revisited" (arXiv:2104.13747) shows automation forecasts swing heavily with classifier choice on the same labels.
	42	+ - Poor ex-post predictive record: occupations flagged high-risk did not experience differential employment declines through the late 2010s (see §8; Frank, Ahn & Moro 2025 find F&O scores explain <3% of unemployment-risk variation individually).
	43	+- URLs:
	44	+ - Published paper: https://doi.org/10.1016/j.techfore.2016.08.019 (PDF mirror: http://reparti.free.fr/freyosborne17.pdf)
	45	+ - Oxford Martin 2013 working paper: https://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf
	46	+ - Critique (Melbourne Institute): https://melbourneinstitute.unimelb.edu.au/__data/assets/pdf_file/0005/3197111/wp2019n10.pdf
	47	+ - Critique (model selection): https://arxiv.org/abs/2104.13747
	48	+
	49	+### 1.2 Arntz, Gregory & Zierahn — OECD task-based critique (2016)
	50	+
	51	+- Authors/year: Melanie Arntz, Terry Gregory, Ulrich Zierahn (ZEW/OECD). "The Risk of Automation for Jobs in OECD Countries", OECD Social, Employment and Migration Working Paper No. 189 (2016); follow-up "Revisiting the risk of automation" in Economics Letters 159 (2017).
	52	+- Unit of analysis: Individual workers' task bundles (PIAAC Survey of Adult Skills microdata), not occupation averages.
	53	+- Methodology: Transferred Frey–Osborne occupation-level risk to individual workers, then re-estimated risk as a function of each worker's actual reported tasks (PIAAC), allowing within-occupation heterogeneity. High risk = automatability > 70%.
	54	+- Key numbers: Only ~9% of jobs across 21 OECD countries at high risk (US 9%, Germany 12%, Korea 6%) — versus 47% under F&O.
	55	+- Significance: Founded the task-based paradigm that every serious index since has adopted — including airiskindex.io. Key insight: workers in the "same" occupation do different task mixes; scoring must start at the task level.
	56	+- Criticisms: Still anchored on F&O's original subjective labels; PIAAC task self-reports are coarse; may understate risk if task bundles themselves adjust post-automation.
	57	+- URLs:
	58	+ - OECD WP 189: https://doi.org/10.1787/5jlz9h56dvq7-en
	59	+ - Economics Letters 2017: https://doi.org/10.1016/j.econlet.2017.07.001
	60	+
	61	+### 1.3 Brynjolfsson, Mitchell & Rock — Suitability for Machine Learning (SML) (2017–2018)
	62	+
	63	+- Authors/year: Erik Brynjolfsson, Tom Mitchell, Daniel Rock. "What Can Machine Learning Do? Workforce Implications" (Science, 2017); "What Can Machines Learn, and What Does It Mean for Occupations and the Economy?" (AEA Papers & Proceedings 108, 2018: 43–47).
	64	+- Unit of analysis: O*NET tasks (18,156 tasks; ~2,069 Detailed Work Activities), aggregated to ~950 occupations.
	65	+- Methodology:
	66	+ - A 23-question rubric capturing what (then-current, supervised) ML can do — e.g., mapping well-defined inputs to outputs, tolerance for error, no long chains of reasoning, digital data availability, no need for detailed physical manipulation.
	67	+ - Each task scored 1–5 per question; rubric validated by ML experts, then scaled via CrowdFlower crowd workers; aggregated to task SML then occupation SML (task-importance weighted).
	68	+- Key findings: (1) ML affects different occupations than earlier automation waves; (2) most occupations have at least some high-SML tasks; (3) almost no occupation is fully automatable; (4) capturing value requires task re-bundling / job redesign.
	69	+- Criticisms: Rubric was tuned to pre-LLM supervised ML (weak on generation, reasoning, dialogue); crowd ratings noisy; scores never strongly validated against outcomes (explains ~0–3% of unemployment-risk variation individually per Frank et al. 2025).
	70	+- Relevance to us: Direct methodological ancestor of a multi-criterion task rubric — our `automatability` + `feasibility` split echoes SML's separation of "could ML do it" from "is it practical".
	71	+- URLs:
	72	+ - AEA P&P: https://www.aeaweb.org/articles?id=10.1257/pandp.20181019
	73	+ - SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3224100
	74	+ - Replication data: https://www.openicpsr.org/openicpsr/project/114436
	75	+ - WorldSML rubric (Stanford Digital Economy Lab, ongoing): https://digitaleconomy.stanford.edu/research/suitability-for-machine-learning-rubric-worldsml/
	76	+
	77	+---
	78	+
	79	+<a name="2-second-wave"></a>
	80	+## 2. Second wave: AI-specific exposure measures (2018–2021)
	81	+
	82	+### 2.1 Webb (2020) — Patent-based exposure
	83	+
	84	+- Author/year: Michael Webb (Stanford). "The Impact of Artificial Intelligence on the Labor Market" (SSRN 3482150, Nov 2019/2020; still unpublished but heavily cited).
	85	+- Unit of analysis: O*NET task descriptions × patent text; aggregated to occupations.
	86	+- Methodology:
	87	+ - Selected AI patents (~16,400) by keyword; dependency-parsed titles to extract verb–object pairs (~8,000 pairs, e.g., "diagnose disease", "detect fraud").
	88	+ - Extracted verb–object pairs from O*NET task statements; scored each task by the frequency with which its verb-object pairs appear in AI patents.
	89	+ - Occupation score = task-importance-weighted average; reported as exposure percentiles.
	90	+ - Built-in validation strategy: applied the same method to robots and software patents and showed those historical exposure measures predicted realized employment/wage declines in exposed occupations — then applied it to AI.
	91	+- Key findings: AI (unlike robots/software) exposes high-skilled, high-wage, older workers most: e.g., clinical lab technicians, chemical engineers, optometrists, radiologic technicians. Robots hit low-skill physical work; software hit mid-skill routine work.
	92	+- Criticisms: Patents lag and imperfectly reflect deployable capability (pre-LLM corpus, so misses generative AI entirely); verb-object matching is crude semantics; no distinction between substitution and augmentation.
	93	+- URLs:
	94	+ - Paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3482150 / https://www.michaelwebb.co/webb_ai.pdf
	95	+ - Brookings explainer: https://www.brookings.edu/articles/how-patents-can-tell-us-what-jobs-ai-is-poised-to-disrupt/
	96	+
	97	+### 2.2 Felten, Raj & Seamans — AI Occupational Exposure (AIOE) (2018, 2021, 2023)
	98	+
	99	+- Authors/year: Edward Felten (Princeton), Manav Raj (Wharton), Robert Seamans (NYU). "Occupational, Industry, and Geographic Exposure to Artificial Intelligence: A Novel Dataset and Its Potential Uses", Strategic Management Journal 42(12), 2021: 2195–2217. Generative-AI update: "Occupational Heterogeneity in Exposure to Generative AI" (SSRN 4414065, April 2023).
	100	+- Unit of analysis: 52 ONET abilities* (not tasks) × 10 AI application areas; aggregated to occupations (also industry AIIE and county-level geography).
	101	+- Methodology:
	102	+ - 10 AI applications from the EFF AI Progress Measurement project (image recognition, language modeling, translation, speech recognition, abstract strategy games, etc.).
	103	+ - Amazon Mechanical Turk crowd workers rated relatedness of each application to each of 52 O*NET abilities → ability-level exposure = sum of relatedness scores.
	104	+ - Occupation AIOE = weighted sum of ability exposures using ONET ability importance and prevalence* weights.
	105	+ - 2023 generative-AI variant re-weights toward language modeling and image generation: top exposed = telemarketers, then post-secondary teachers (languages, history, law), sociologists, judges.
	106	+- Key properties: Continuous z-scored index; famously positively correlated with wages and education (white-collar exposure) — opposite sign to Frey–Osborne.
	107	+- Criticisms: Ability-level (even further from tasks than occupations); MTurk relatedness judgments are lay opinions; "exposure" deliberately neutral between substitution and augmentation (authors are explicit about this); static.
	108	+- Data: Public GitHub (occupation-, industry-, geography-level scores): https://github.com/AIOE-Data/AIOE
	109	+- Adoption: The IMF (Cazzaniga et al. 2024) and Pew (2023) analyses build directly on AIOE; the ECB European work (Albanesi et al.) uses AIOE + Webb.
	110	+- URLs:
	111	+ - SMJ paper: https://doi.org/10.1002/smj.3286
	112	+ - SSRN 2021: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3822412
	113	+ - GenAI variant: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4414065
	114	+
	115	+### 2.3 Other second-wave measures (brief)
	116	+
	117	+- Georgieff & Hyee (OECD 2021), "Artificial intelligence and employment: New cross-country evidence" — applied Felten-style AIOE to PIAAC across 23 OECD countries; most-exposed: business professionals, managers, chief executives, science/engineering professionals. Found no negative employment relationship 2012–2019; in high-computer-use occupations, higher AI exposure correlated with higher employment growth. URL: https://doi.org/10.1787/c2c1d276-en
	118	+- Lassébie & Quintini (OECD 2022), "What skills and abilities can automation technologies replicate?" — expert survey on automatability of ~100 ONET skills/abilities; basis for OECD Employment Outlook 2023 statement that occupations at highest risk of automation account for ~27% of OECD employment*. URL: https://doi.org/10.1787/646aad77-en ; Employment Outlook 2023 AI chapter: https://www.oecd.org/en/publications/oecd-employment-outlook-2023_08785bba-en.html
	119	+- Tolan et al. (2021, JRC/EC) — mapped AI research benchmarks to cognitive abilities to tasks; early "capability→ability→occupation" chain, precursor of the 2026 OECD capability-indicator approach.
	120	+- Startup-based exposure ("Follow the money", arXiv 2024) — measures exposure via commercial AI startup activity mapped to occupations; argues patent/ability measures miss commercialization. URL: https://arxiv.org/abs/2412.04924
	121	+
	122	+---
	123	+
	124	+<a name="3-third-wave"></a>
	125	+## 3. Third wave: LLM/generative-AI exposure (2023–2024)
	126	+
	127	+### 3.1 Eloundou, Manning, Mishkin & Rock — "GPTs are GPTs" (OpenAI/Wharton, 2023; Science 2024)
	128	+
	129	+The single most influential template for airiskindex.io's LLM-as-evaluator pipeline.
	130	+
	131	+- Authors/year: Tyna Eloundou, Sam Manning, Pamela Mishkin (OpenAI), Daniel Rock (Wharton). arXiv:2303.10130 (Mar 2023); published as "GPTs are GPTs: Labor market impact potential of LLMs", Science 384(6702), June 2024: 1306–1308.
	132	+- Unit of analysis: O*NET task/DWA level (19,265 tasks; 2,087 DWAs), aggregated to 1,016 occupations with task weights; combined with BLS employment/wage data.
	133	+- Exposure rubric (the core innovation): Exposure = "whether access to an LLM or LLM-powered system would reduce the time required for a human to perform a specific task by at least 50% while maintaining quality". Three levels:
	134	+ - E0 — no exposure.
	135	+ - E1 — direct exposure: LLM alone (via chat/API) achieves the 50% time reduction.
	136	+ - E2 — LLM+ exposure: achievable only with additional software/tooling built on the LLM (image input, retrieval, agents, etc.).
	137	+ - Aggregates: α = E1 (lower bound), β = E1 + 0.5·E2 (expected), ζ = E1 + E2 (upper bound).
	138	+- Raters: Both human annotators (OpenAI staff, trained on rubric) and GPT-4 itself as a rater with the rubric as prompt; human–GPT-4 agreement was high (occupation-level correlations ≈ 0.80), pioneering the LLM-as-evaluator design we plan to use.
	139	+- Key numbers:
	140	+ - ~80% of US workers have ≥10% of tasks exposed (β); ~19% of workers have ≥50% of tasks exposed.
	141	+ - ~1.8% of jobs have >half their tasks E1-exposed; rises to ~46% of jobs under ζ (with LLM-powered software).
	142	+ - Exposure increases with wage and education (up to a point); science and critical-thinking-intensive skills correlate negatively; programming and writing positively.
	143	+- Criticisms:
	144	+ - Measures potential time savings, not substitution vs augmentation, adoption, or net employment effect (authors are explicit).
	145	+ - Rater instability: the flagship statistic is wildly model-dependent (see Yin et al. 2026, §7.2: 2.7%–51.5% across frontier raters).
	146	+ - Static snapshot of March-2023 GPT-4 capability; the "E2 software will exist" counterfactual is speculative.
	147	+ - 50%-time-saving threshold is arbitrary; binary-ish levels lose information.
	148	+- URLs:
	149	+ - arXiv: https://arxiv.org/abs/2303.10130
	150	+ - Science: https://www.science.org/doi/10.1126/science.adj0998
	151	+ - OpenAI page: https://openai.com/index/gpts-are-gpts/
	152	+ - Follow-up "Extending GPTs Are GPTs to Firms" (AEA P&P 2025): https://www.aeaweb.org/articles?id=10.1257/pandp.20251045
	153	+
	154	+### 3.2 Goldman Sachs — Briggs & Kodnani (March 2023)
	155	+
	156	+See §5.1. Methodologically an O*NET task-importance exercise inspired by Eloundou-style exposure; headline "300 million FTE jobs exposed" globally.
	157	+
	158	+---
	159	+
	160	+<a name="4-institutional-indices"></a>
	161	+## 4. Institutional indices: ILO, OECD, IMF (2023–2026)
	162	+
	163	+### 4.1 ILO — Gmyrek, Berg & Bescond (2023): "Generative AI and Jobs: A Global Analysis"
	164	+
	165	+- Authors/year: Paweł Gmyrek, Janine Berg, David Bescond. ILO Working Paper 96, August 2023.
	166	+- Methodology: Scored ISCO-08 occupation task lists (not ONET) with GPT-4 as rater* (multiple prompts, averaged), producing task-level automation-potential scores; distinguished automation potential vs augmentation potential at occupation level; mapped to global employment via ILO harmonized microdata for 100+ countries, by income group and sex.
	167	+- Key numbers:
	168	+ - Only clerical support work is highly exposed as a group: 24% of clerical tasks highly exposed, +58% medium exposure. Other occupational groups: 1–4% of tasks highly exposed.
	169	+ - Globally, ~2.3% of employment (~75M jobs) in the top automation-potential bucket; 13.4% (~427M) in augmentation potential.
	170	+ - Exposure concentrated in high/upper-middle-income countries (more clerical employment) and strongly gendered (clerical work is female-dominated: in high-income countries, several times more female than male employment in the highest-exposure category).
	171	+- Framing: "Augmentation, not automation, is the most likely impact" — the origin of the transformation-over-replacement institutional narrative.
	172	+- URLs:
	173	+ - WP96 PDF: https://www.ilo.org/sites/default/files/2024-07/WP96_web.pdf
	174	+ - SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4584219
	175	+ - Policy companion "Generative AI and Jobs: Policies to Manage the Transition": https://www.ilo.org/publications/generative-ai-and-jobs-policies-manage-transition
	176	+
	177	+### 4.2 ILO 2024 interim work
	178	+
	179	+- "Mind the AI Divide: Shaping a Global Perspective on the Future of Work" (ILO & World Bank, Aug 2024) — applies the 2023 index with a digital-infrastructure overlay: poor countries are less exposed but also less able to capture augmentation gains (the "AI divide").
	180	+- Gmyrek, Winkler & Garganta (2024, ILO/World Bank) — "Buffer or bottleneck? Employment exposure to generative AI and the digital divide in Latin America": 26–38% of LAC jobs exposed; digital access gates both risk and benefit.
	181	+- URL hub: https://www.ilo.org/publications (search "generative AI"); LAC paper: https://openknowledge.worldbank.org/handle/10986/41808
	182	+
	183	+### 4.3 ILO–NASK (2025): "Generative AI and Jobs: A Refined Global Index of Occupational Exposure" — current institutional state of the art
	184	+
	185	+- Authors/year: Paweł Gmyrek, Janine Berg, K. Kamiński, F. Konopczyński, A. Ładna, B. Nafradi, K. Rosłaniec, M. Troszyński (ILO + Poland's NASK). ILO Working Paper 140, May 2025 + Research Brief "Generative AI and jobs: a 2025 update".
	186	+- Methodology (major upgrade over 2023):
	187	+ - Hybrid human+LLM pipeline: 52,558 human judgments on automation potential of 2,861 tasks (representative sample of 29,753 tasks in the Polish occupational classification), from a survey of 1,640 people (workers/experts), used to calibrate and validate GPT-4o task scoring; then scaled to the full ISCO task universe.
	188	+ - Replaced the binary automation/augmentation split with a 4-gradient exposure spectrum (from marginal exposure to highest exposure), acknowledging most jobs are partially transformed.
	189	+ - Occupation scores mapped to global employment microdata by country income group, sex, and region.
	190	+- Key numbers:
	191	+ - 1 in 4 jobs worldwide (25% of global employment) has measurable GenAI exposure; 34% in high-income countries.
	192	+ - Highest-gradient (transformation most likely) ≈ 3–4% of global employment, still concentrated in clerical work.
	193	+ - Gender gap persists: in high-income countries, ~9.6% of female employment vs ~3.5% of male employment in the top exposure gradient.
	194	+ - Headline framing: "transformation, not replacement".
	195	+- Criticisms: LLM-rater dependence (flagged by Yin et al. 2026); Polish task-survey generalizability; exposure ≠ adoption in low-connectivity countries (self-acknowledged).
	196	+- URLs:
	197	+ - WP140 PDF: https://www.ilo.org/sites/default/files/2025-05/WP140_web.pdf
	198	+ - WP140 interactive: https://webapps.ilo.org/static/english/intserv/working-papers/wp140/index.html
	199	+ - 2025 update brief: https://www.ilo.org/publications/generative-ai-and-jobs-2025-update (PDF: https://www.ilo.org/sites/default/files/2025-05/Research%20brief_GenAI%202025%20Update.pdf)
	200	+ - Press release: https://www.ilo.org/resource/news/one-four-jobs-risk-being-transformed-genai-new-ilo–nask-global-index-shows
	201	+
	202	+### 4.4 OECD (2023–2026)
	203	+
	204	+- Employment Outlook 2023 (AI chapters): using Lassébie–Quintini expert-based measure, occupations at highest automation risk = ~27% of employment across OECD; "no signs of slowing labour demand (yet)" in AI-exposed occupations. URLs: https://www.oecd.org/en/publications/oecd-employment-outlook-2023_08785bba-en/full-report/artificial-intelligence-and-jobs-no-signs-of-slowing-labour-demand-yet_5aebe670.html
	205	+- AI case studies & job quality (2023–2024): firm case studies in finance/manufacturing across 8 countries: 23% of firms reported AI reduced employment in affected roles; wages mostly unchanged. Georgieff (2024), "Artificial intelligence and wage inequality": https://www.oecd.org/en/publications/artificial-intelligence-and-wage-inequality_bf98a45c-en.html
	206	+- OECD AI Capability Indicators (2025): 5-year effort, 50+ experts; 9 capability domains (Language; Social interaction; Problem solving; Creativity; Metacognition & critical thinking; Knowledge/learning/memory; Vision; Manipulation; Robotic intelligence) each on an ordinal capability scale. URL: https://www.oecd.org/en/publications/introducing-the-oecd-ai-capability-indicators_be745f04-en.html
	207	+- The OECD AI Exposure Measure (2025/2026): maps occupations' required capability levels in each of the 9 domains against AI's current attained level per the Capability Indicators → exposure = overlap. Explicitly designed to be forward-looking, transparent, and updateable as AI capability levels advance — the first institutional index architected for versioned re-scoring (same philosophy as our `INDEX_VERSION`). URL: https://www.oecd.org/en/publications/the-oecd-ai-exposure-measure_f3da0f0a-en.html
	208	+- Skills in the AI age (OECD AI Papers No. 60, July 2026): applies the exposure measure to skills demand. URL: https://www.oecd.org/content/dam/oecd/en/publications/reports/2026/07/skills-in-the-ai-age_e8d8c1e6/972bd15e-en.pdf
	209	+
	210	+### 4.5 IMF — Cazzaniga et al. (2024) and the AI Preparedness Index
	211	+
	212	+- Authors/year: Mauro Cazzaniga, Florence Jaumotte, Longji Li, Giovanni Melina, Augustus Panton, Carlo Pizzinelli, Emma Rockall, Marina M. Tavares. "Gen-AI: Artificial Intelligence and the Future of Work", IMF Staff Discussion Note SDN/2024/001, January 2024.
	213	+- Methodology: Takes Felten's AIOE and adds a potential complementarity index (C-AIOE) (from Pizzinelli et al. 2023, IMF WP/23/216): occupations scored on shielding factors — required physical presence, human interaction, legal/social responsibility (judges are exposed and complemented; telemarketers exposed and not). Splits employment into: high exposure + high complementarity (augmentation likely) vs high exposure + low complementarity (displacement risk) vs low exposure.
	214	+- Key numbers: ~40% of global employment exposed to AI (60% advanced economies, 40% emerging, 26% low-income). In AEs, roughly half of exposed jobs are high-complementarity. Women and college-educated more exposed but better positioned for gains.
	215	+- AI Preparedness Index (AIPI): country-level (174 economies) readiness across digital infrastructure, human capital & labor policies, innovation & integration, regulation & ethics — the macro complement to occupational exposure. Dashboard: https://www.imf.org/external/datamapper/AIPI@AIPI
	216	+- Criticisms: Inherits all AIOE limitations; complementarity ratings are judgment calls; country mapping via ISCO crosswalks is coarse.
	217	+- URLs:
	218	+ - SDN PDF: https://www.imf.org/-/media/files/publications/sdn/2024/english/sdnea2024001.pdf
	219	+ - eLibrary: https://www.elibrary.imf.org/view/journals/006/2024/001/006.2024.issue-001-en.xml
	220	+ - Pizzinelli et al. WP/23/216 (C-AIOE): https://www.imf.org/en/Publications/WP/Issues/2023/10/04/Labor-Market-Exposure-to-AI-Cross-country-Differences-and-Distributional-Implications-539656
	221	+ - Follow-up: "Exposure to Artificial Intelligence and Occupational Mobility" (WP/24/116): https://www.imf.org/-/media/files/publications/wp/2024/english/wpiea2024116-print-pdf.pdf
	222	+
	223	+---
	224	+
	225	+<a name="5-industry-estimates"></a>
	226	+## 5. Industry & consultancy estimates
	227	+
	228	+### 5.1 Goldman Sachs (Briggs & Kodnani, March 2023)
	229	+
	230	+- "The Potentially Large Effects of Artificial Intelligence on Economic Growth". O*NET task-level judgment of automatable share per occupation (26 US, 24 European task categories importance/complexity weighted).
	231	+- Key numbers: ~2/3 of US/European occupations partially exposed; generative AI could substitute up to 25% of current work = 300M FTE jobs globally exposed; +7% global GDP over 10 years. Most exposed: office/admin support (46% of tasks automatable), legal (44%), architecture/engineering (37%).
	232	+- Criticism: binary "automatable share" judgments, no adoption model; the 300M number is routinely misquoted as "job losses".
	233	+- URLs: https://www.goldmansachs.com/insights/articles/generative-ai-could-raise-global-gdp-by-7-percent ; follow-up US labor analysis: https://www.goldmansachs.com/insights/articles/how-will-ai-affect-the-us-labor-market
	234	+
	235	+### 5.2 McKinsey Global Institute (June 2023, updated)
	236	+
	237	+- "The economic potential of generative AI: the next productivity frontier". Proprietary work-activity/capability model (~2,100 work activities, 850 occupations).
	238	+- Key numbers: GenAI + existing tech could automate activities absorbing 60–70% of employees' time; genAI value $2.6–4.4T/yr; half of today's work activities automated between 2030 and 2060 (midpoint ~2045) — pulled forward ~a decade vs pre-genAI estimate.
	239	+- Criticism: proprietary/black-box capability ratings; "time automatable" ≠ jobs; adoption scenarios highly assumption-driven.
	240	+- URL: https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier
	241	+
	242	+### 5.3 Pew Research Center (Kochhar, July 2023)
	243	+
	244	+- "Which U.S. Workers Are More Exposed to AI on Their Jobs?" — Felten-style ability-importance approach on O*NET.
	245	+- Key numbers: 19% of US workers in most-exposed jobs vs 23% in least-exposed (2022). Most-exposed jobs pay more ($33/hr vs $20/hr); exposure higher for women, Asian, college-educated workers. Notably, workers in exposed industries did not feel their jobs at risk.
	246	+- URL: https://www.pewresearch.org/social-trends/2023/07/26/which-u-s-workers-are-more-exposed-to-ai-on-their-jobs/
	247	+
	248	+### 5.4 PwC Global AI Jobs Barometer (2024, 2025, 2026)
	249	+
	250	+- Analyzes ~1 billion job ads worldwide + firm financials. 2025 edition ("The Fearless Future"): industries most exposed to AI saw productivity growth nearly 4x (7%→27%); 56% wage premium for AI-skilled workers (up from 25%); skills in AI-exposed occupations changing 66% faster; employment still growing even in highly automatable roles. 2026 edition: labor market splitting into "two distinct paths", rewarding human skills.
	251	+- Value to us: the best large-scale demand-side signal (vacancies), useful to calibrate `adoption_velocity`.
	252	+- URLs: https://www.pwc.com/gx/en/issues/artificial-intelligence/job-barometer/2025/report.pdf ; 2026 PR: https://www.pwc.com/gx/en/news-room/press-releases/2026/pwc-2026-ai-jobs-barometer.html
	253	+
	254	+---
	255	+
	256	+<a name="6-usage-based-measures"></a>
	257	+## 6. Usage-based measures: Anthropic Economic Index & OpenAI (2025–2026)
	258	+
	259	+The decisive innovation of 2025–26: replacing predicted exposure with observed AI usage mapped to the same O*NET task taxonomy. This is the empirical anchor airiskindex.io should exploit for `adoption_velocity` and to validate `automatability`.
	260	+
	261	+### 6.1 Anthropic Economic Index (AEI) — all releases to August 2026
	262	+
	263	+Methodology (constant across releases): Clio, a privacy-preserving analysis pipeline in which Claude classifies large samples of real Claude.ai/API conversations against the *ONET task taxonomy (~20,000 tasks) and SOC occupations, plus interaction-mode classification (automation = full delegation/directive; augmentation** = iterative collaboration, learning, validation). All aggregated data released openly on Hugging Face.
	264	+
	265	+\| Release \| Report \| Data & model \| Headline findings \|
	266	+\|---\|---\|---\|---\|
	267	+\| Feb 10, 2025 (paper arXiv:2503.04761, Handa et al., "Which Economic Tasks are Performed with AI?") \| Launch report \| 4M+ Claude.ai conversations \| 36% of occupations used AI for ≥25% of their tasks; only ~4% for ≥75%. Usage concentrated in software development & writing (computer/math ≈ 37% of conversations); peaks in mid-to-high-wage occupations, low at both wage extremes. 57% augmentation / 43% automation. \|
	268	+\| Mar 27, 2025 \| v2 (Claude 3.7 Sonnet) \| New conversations + cluster-level data \| Usage patterns stable; extended-thinking usage concentrated in technical tasks; released bottom-up task clusters. \|
	269	+\| Sep 15, 2025 (arXiv:2511.15080) \| "Uneven geographic and enterprise adoption" \| 1P API + geographic breakdowns; Anthropic AI Usage Index (AUI) = country share of usage ÷ share of working-age population \| US 21.6% of usage; per-capita leaders Israel, Singapore, Australia, NZ, S. Korea. +1% GDP/capita ↔ +0.7% AUI (US states: 1.8% elasticity). DC highest state AUI (3.82). Automation rose 27%→39% of conversations since Dec 2024, surpassing augmentation for the first time; API usage even more automation-heavy. \|
	270	+\| Jan 15, 2026 \| "New building blocks" (economic primitives) \| 1M Claude.ai + 1M 1P API transcripts (Sonnet 4.5); Nov 2025 data \| Five primitives: task complexity, human/AI skill level, use case, AI autonomy, task success. College-level tasks: 12x estimated speedup but 66% success rate vs 70% for simpler tasks; revised aggregate productivity estimate +1.2 pp/yr (down from 1.8 after reliability adjustment). Augmentation back above automation on Claude.ai (52% vs 45%). \|
	271	+\| Mar 24, 2026 \| "Learning curves" \| Feb 2026 data (Opus 4.5/4.6) \| Claude.ai task mix de-concentrating (top-10 tasks 24%→19%) while API concentrates (28%→33%). 49% of jobs in sample now see Claude used for ≥25% of tasks (up from 36% in Jan 2025). 6-month+ tenure users: +10% conversation success; usage value ≈ $48–49/hr wage-equivalent tasks. \|
	272	+\| Apr 2026 \| AEI Survey launched \| 9,700 Claude users, linked usage+perceptions \| See below. \|
	273	+\| Jun 26, 2026 \| "Cadences" \| Apr–Jun 2026, hourly sampling; artifact classifier \| 93% of conversations produce artifacts (explanations 17%, documents/reports 15%). Higher-wage occupations' conversations consume 2.07x tokens. Survey: >⅓ of users expect AI to handle most of their work tasks within 12 months; only 10% rate own job loss likely; heavier automation users are more optimistic. Women use Claude less in automated modes (−0.33 SD). \|
	274	+
	275	+- Data availability (all releases): https://huggingface.co/datasets/Anthropic/EconomicIndex (per-release folders `release_2025_03_27/`, `release_2025_09_15/`, etc., with documentation + replication notebooks; R package `aieconindex` on CRAN).
	276	+- Index hub: https://www.anthropic.com/economic-index — reports: https://www.anthropic.com/news/the-anthropic-economic-index ; https://www.anthropic.com/research/economic-index-geography ; https://www.anthropic.com/research/economic-index-primitives ; https://www.anthropic.com/research/economic-index-march-2026-report ; https://www.anthropic.com/research/economic-index-june-2026-report
	277	+- Limitations (self-acknowledged): Claude users ≠ workforce (selection bias toward developers/knowledge workers); conversation ≠ completed work; O*NET classification by LLM inherits classifier error; per-provider view only.
	278	+
	279	+### 6.2 OpenAI — "How People Use ChatGPT" (Chatterji et al., NBER w34255, Sept 2025)
	280	+
	281	+- Aaron Chatterji, Tom Cunningham, David Deming, Zoë Hitzig, Christopher Ong, Carl Shan, Kevin Wadman. Privacy-preserving classification of a representative sample of ChatGPT consumer conversations, Nov 2022–Jul 2025 (~10% of world adult population using ChatGPT).
	282	+- Findings: non-work usage grew from 53%→>70% of messages; work usage concentrated in decision support (advice, writing, information) rather than task execution; work usage highest among educated, high-paid professionals. Three-quarters of work messages: writing, information seeking, decision support.
	283	+- Relevance: independent replication that realized usage is augmentation-tilted and knowledge-work-concentrated — cross-provider triangulation for `adoption_velocity`.
	284	+- URLs: https://www.nber.org/papers/w34255 (PDF: https://www.nber.org/system/files/working_papers/w34255.pdf)
	285	+
	286	+---
	287	+
	288	+<a name="7-fourth-wave"></a>
	289	+## 7. Fourth wave: 2025–2026 indices and meta-critiques
	290	+
	291	+### 7.1 Stanford Digital Economy Lab — "Canaries in the Coal Mine?" (Brynjolfsson, Chandar & Chen, Aug/Nov 2025)
	292	+
	293	+- The most important realized-effects paper to date. Uses ADP payroll microdata (millions of workers, monthly) linked to occupational AI-exposure measures (Eloundou/GPTs-are-GPTs based, cross-checked with Anthropic Economic Index automation/augmentation shares).
	294	+- Six facts, headline: since late 2022, early-career workers (22–25) in the most AI-exposed occupations saw a ~13–16% relative employment decline (16% in the Nov 2025 revision, controlling for firm-level shocks), while older workers in the same occupations and less-exposed young workers kept growing. Adjustment happens via employment, not wages. Declines concentrated where AEI data says AI automates rather than augments. Entry-level hiring is the "canary".
	295	+- Live monitoring: "Canaries Dashboard" — https://digitaleconomy.stanford.edu/project/indicators/canaries-dashboard/
	296	+- URLs: paper page https://digitaleconomy.stanford.edu/publications/canaries-in-the-coal-mine ; PDF (Nov 2025) https://digitaleconomy.stanford.edu/app/uploads/2025/11/CanariesintheCoalMine_Nov25.pdf ; SIEPR WP: https://siepr.stanford.edu/publications/working-paper/canaries-coal-mine-six-facts-about-recent-employment-effects-artificial
	297	+
	298	+### 7.2 Yin, Vu & Persico (2026) — multi-model instability of LLM-rated exposure ("When the ruler is made of the thing it measures")
	299	+
	300	+- NBER WP 35110, "How (un)stable are LLM occupational exposure scores? Evidence from multi-model replication"; VoxEU column May 2026.
	301	+- Replicated the Eloundou rubric with *four frontier models on identical ONET data: share of US occupations with >50% of tasks at high direct exposure = 2.7% (Gemini 2.5) … 3.8% (GPT-4) … 20.3% (GPT-5) … 51.5% (Claude 4.5) — a 19x spread. Management occupations: >80% high-exposure under Claude, <20% under Gemini. Downstream diff-in-diff employment estimates flip sign** across raters. Bias is systematic per model, doesn't wash out with sample size, and co-evolves with the technology being measured (feedback channel).
	302	+- Recommendation (directly applicable to us): any LLM-rated exposure analysis must report results from ≥2–3 different frontier models; convergence ⇒ robust, divergence ⇒ model artifact.
	303	+- URL: https://cepr.org/voxeu/columns/when-ruler-made-thing-it-measures-multi-model-evidence-ai-occupational-exposure
	304	+
	305	+### 7.3 "AI Exposure Scores: what they measure, what they miss, and what comes next" (Lund, Euyang, Munyikwa & Fadaee, arXiv June 2026)
	306	+
	307	+- Field review. Diagnoses a structural gap (static scores can't answer dynamic who/when/where policy questions) and a coordination gap (policy still cites static 2023 GPTs-are-GPTs numbers despite methodological advances). Surveys five successor families: dynamic/benchmark-based measures, ensembles, task-framework extensions, worker-centered metrics, adoption/usage data. Recommends moving "from prediction to preparedness".
	308	+- URL: https://arxiv.org/abs/2606.23633
	309	+
	310	+### 7.4 Other notable 2025–2026 entries
	311	+
	312	+- Iceberg Index (Chopra et al., MIT + Oak Ridge National Laboratory, arXiv 2510.25137, late 2025): skills-centered simulation — 151M US workers, 923 occupations, 32,000+ skills, ~3,000 counties; catalogued 13,000+ AI tools; agent-based simulation (AgentTorch on Frontier supercomputer). Visible "surface" tech-sector exposure = 2.2% of wage bill (~$211B); full skill-overlap exposure = 11.7% of US wage bill (~$1.2T). Explicitly technical exposure, not displacement. URLs: https://arxiv.org/abs/2510.25137 ; https://iceberg.mit.edu/report.pdf
	313	+- Yale Budget Lab — "Evaluating the Impact of AI on the Labor Market: Current State of Affairs" (Gimbel, Kinder, Kendall & Lee, Oct 2025, updated): occupational-mix dissimilarity analysis; finds no broad acceleration in labor-market compositional change attributable to AI 33 months post-ChatGPT — important null-result counterweight to Canaries. URL: https://budgetlab.yale.edu/research/evaluating-impact-ai-labor-market-current-state-affairs
	314	+- UK task-based GenAI exposure index (arXiv 2507.22748, 2025): novel LLM-scored task index applied to UK SOC codes — example of the national-adaptation pattern relevant to our ESCO/ROME crosswalk. URL: https://arxiv.org/abs/2507.22748
	315	+- OAIES / capability-staged exposure (2025–2026): scores ONET task automatable share at discrete AI capability stages* (pre-LLM ML → early LLMs → multimodal → reasoning → agentic), multi-model rated (GPT-4o + Claude 3.5); cross-methodology Spearman ρ = 0.84 against independent scores. Overview: https://www.emergentmind.com/topics/ai-exposed-occupations ; theory-based variant (Moravec-paradox index): https://arxiv.org/abs/2510.13369
	316	+- "AI and jobs: A review of theory, estimates, and evidence" (arXiv 2509.15265, 2025): comprehensive literature review; useful bibliography. URL: https://arxiv.org/abs/2509.15265
	317	+- Agentic-AI exposure analyses (2026): e.g., "Agentic AI and Occupational Displacement" (arXiv 2604.00186) extends task exposure to autonomous multi-step agents across regions. URL: https://arxiv.org/abs/2604.00186
	318	+- "The Jagged Global Economy" (arXiv 2607.05404, 2026): frontier-AI benchmark performance mapped to national economies — capability-grounded, benchmark-updated exposure. URL: https://arxiv.org/abs/2607.05404
	319	+
	320	+---
	321	+
	322	+<a name="8-empirical-validation"></a>
	323	+## 8. Empirical validation: do exposure scores predict real outcomes?
	324	+
	325	+Bottom line: individually, classic exposure scores are weak predictors; ensembles + adoption data + post-2022 windows perform much better. Realized effects so far are concentrated (entry-level, automation-tilted tasks, online freelancing), not economy-wide.
	326	+
	327	+1. Frank, Ahn & Moro (PNAS Nexus, April 2025), "AI exposure predicts unemployment risk" — built occupation-level unemployment risk from US unemployment-insurance claims (2010–2020); tested 10 exposure scores. Every individual score performs poorly (best single: Arntz automation probability, R² = 0.107; most < 3%; Frey–Osborne, SML, Felten, Webb all weak alone). An ensemble of all scores explains 29.8% (75.5% with education/skill/region controls) — +18 pp over baseline. Lesson: no single score suffices; combine dimensions. URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC11983276/ (arXiv:2308.02624)
	328	+2. Acemoglu, Autor, Hazell & Restrepo (JOLE 2022), "AI and Jobs: Evidence from Online Vacancies" — AI-exposed establishments (Burning Glass) post more AI vacancies and reduce non-AI hiring, but no detectable aggregate occupation-level employment effects through 2018. URL: https://jadhazell.github.io/website/AI_And_Jobs.pdf
	329	+3. Brynjolfsson, Chandar & Chen (2025) "Canaries" — first large-scale realized-effect finding: −13–16% relative employment for early-career workers in most-exposed occupations; effects load on automation-classified (AEI) usage, not augmentation. (§7.1)
	330	+4. Hui, Reshef & Zhou (2024), "The Short-Term Effects of Generative AI on Online Labor Markets" — after ChatGPT, exposed freelancers (writing-heavy) on a large platform saw ~2% fewer jobs and ~5% lower earnings; top performers not spared. VoxEU: https://cepr.org/voxeu/columns/artificial-intelligence-and-its-short-term-effects-employment
	331	+5. Hampole, Papanikolaou, Schmidt & Seegmiller (NBER w33509, 2025), "Artificial Intelligence and the Labor Market" — vacancy-based measure of firm AI adoption; AI adoption predicts declining demand for exposed occupations within adopting firms, with reallocation toward AI-complementary roles. URL: https://www.nber.org/papers/w33509
	332	+6. Humlum & Vestergaard (2025), "Large Language Models, Small Labor Market Effects" (Denmark, NBER w33777) — despite rapid ChatGPT adoption among exposed workers, no detectable effects on earnings or hours in 2023–24 administrative data; average time savings ~3%. Counterweight showing adoption ≠ displacement in the short run. URL: https://www.nber.org/papers/w33777
	333	+7. Wage-growth cross-section (2019 vs 2023, arXiv 2312.04714 & follow-ups): one-unit higher AI exposure ↔ −6.5 pp wage growth, explaining ~34% of cross-sectional variation post-ChatGPT; late-2022→early-2025 CPS analyses find high-exposure occupations losing 5.6–8.5 pp employment per 10-point exposure. URL: https://arxiv.org/abs/2312.04714
	334	+8. Georgieff & Hyee (OECD 2021) / Employment Outlook 2023: 2012–2019 — no negative employment relationship; exposure without adoption predicts nothing pre-2022. (§2.3, §4.4)
	335	+9. Yale Budget Lab (2025–2026): aggregate occupational mix shifting no faster than historical benchmarks (§7.4) — realized effects are cohort- and task-specific, not (yet) aggregate.
	336	+
	337	+Synthesis for validation design: (a) validate at task/cohort level, not aggregate; (b) test against unemployment/UI-claim risk and entry-level hiring, not just employment stocks; (c) use ensembles; (d) treat pre-2022 null results as evidence about adoption gating, which is exactly what our `barriers` and `adoption_velocity` dimensions model.
	338	+
	339	+---
	340	+
	341	+<a name="9-comparison-table"></a>
	342	+## 9. Master comparison table
	343	+
	344	+\| Index / Study \| Year \| Approach \| Unit of analysis \| Scale / output \| Substitution vs augmentation split? \| Validation status \| Data availability \|
	345	+\|---\|---\|---\|---\|---\|---\|---\|---\|
	346	+\| Frey & Osborne \| 2013/2017 \| Expert labels + Gaussian process classifier on O*NET bottleneck variables \| Occupation (702 SOC) \| P(computerisation) 0–1 \| No \| Poor ex-post; explains <3% of unemployment risk alone \| Scores in paper appendix (public) \|
	347	+\| Arntz, Gregory & Zierahn (OECD) \| 2016/2017 \| F&O risk re-estimated on individual PIAAC task bundles \| Worker/task bundle \| P(automation) 0–1 \| No \| Best single predictor in Frank et al. ensemble (R²=0.107) \| PIAAC public; scores replicable \|
	348	+\| Brynjolfsson, Mitchell & Rock (SML) \| 2017/2018 \| 23-question rubric, crowd-rated \| O*NET task (18k) → occupation \| SML 1–5 \| Implicit (redesign framing) \| Weak alone \| openICPSR replication archive \|
	349	+\| Webb \| 2020 \| Patent–task text overlap (verb–object pairs) \| Task → occupation \| Exposure percentile \| No \| Historical validation on robots/software; AI portion pre-LLM \| Author site / SSRN \|
	350	+\| Felten, Raj & Seamans (AIOE) \| 2021 (genAI 2023) \| 10 AI apps × 52 abilities, MTurk relatedness, importance-weighted \| Ability → occupation (+industry, county) \| Continuous z-score \| No (explicitly neutral) \| Weak alone; base of IMF/Pew analyses \| GitHub (AIOE-Data/AIOE) \|
	351	+\| Eloundou et al. "GPTs are GPTs" \| 2023/2024 \| Rubric (≥50% time saving), human + GPT-4 raters \| O*NET task/DWA → occupation \| E0/E1/E2; α, β, ζ shares 0–1 \| No (time-savings only) \| Predicts Canaries cohort effects; rater-unstable (19x across models) \| arXiv appendix; rubric public \|
	352	+\| Goldman Sachs (Briggs & Kodnani) \| 2023 \| Task-importance share judged automatable \| Occupation \| % tasks automatable \| Partial (25% substitution assumption) \| n/a \| Report only (proprietary) \|
	353	+\| McKinsey MGI \| 2023 \| Proprietary activity–capability model \| Work activity (~2,100) \| % of work time automatable; adoption scenarios \| Partial \| n/a \| Report only (proprietary) \|
	354	+\| Pew Research (Kochhar) \| 2023 \| Felten-style ability importance \| Occupation \| High/medium/low exposure \| No \| n/a \| Report + appendix \|
	355	+\| ILO Gmyrek et al. WP96 \| 2023 \| GPT-4-rated ISCO task scores \| ISCO task → occupation → global employment \| Automation vs augmentation potential, 0–1 \| Yes \| n/a \| Scores in WP annexes \|
	356	+\| IMF Cazzaniga et al. (AIOE + C-AIOE) \| 2024 \| AIOE + complementarity shielding index \| Occupation → country employment \| Exposure × complementarity quadrants \| Yes (complementarity) \| n/a \| AIPI dashboard; WP data \|
	357	+\| ILO–NASK refined index (WP140) \| 2025 \| Hybrid: 52,558 human ratings calibrating GPT-4o, 4-gradient scale \| Task → ISCO occupation → 100+ countries \| 4 exposure gradients \| Yes (gradient) \| n/a \| WP + interactive tool \|
	358	+\| OECD AI Exposure Measure \| 2025/2026 \| Occupation capability requirements vs OECD AI Capability Indicators (9 domains, expert-set levels) \| Ability-domain → occupation \| Capability-overlap exposure; versioned as AI levels advance \| No \| New \| OECD publication + indicators \|
	359	+\| PwC AI Jobs Barometer \| 2024–2026 \| ~1B job ads; demand-side \| Vacancy/occupation/industry \| Growth, wage premium, skill-change rates \| No \| Is itself outcome data \| Annual reports \|
	360	+\| Anthropic Economic Index \| 2025–2026 (6 releases) \| Observed Claude usage classified to ONET tasks (Clio); AUI; primitives \| Conversation → task → occupation, geo \| Usage shares; automation vs augmentation %; complexity/success \| Yes* (measured, not predicted) \| Is itself adoption data; used in Canaries \| Hugging Face (open) \|
	361	+\| OpenAI / Chatterji et al. \| 2025 \| Observed ChatGPT usage classification \| Message → task category \| Usage shares by intent \| Partial (Asking/Doing/Expressing) \| Is itself adoption data \| NBER paper (aggregates) \|
	362	+\| Canaries in the Coal Mine (Stanford DEL) \| 2025 \| ADP payroll × exposure scores (realized effects) \| Worker-level panel \| Employment effects by age × exposure \| Uses AEI automation/augmentation \| Is the validation \| Dashboard public; ADP restricted \|
	363	+\| Iceberg Index (MIT/ORNL) \| 2025 \| Skill-level tool coverage + agent-based simulation \| 32k skills → 923 occupations → counties \| % of wage bill exposed ($) \| No \| New \| iceberg.mit.edu; arXiv \|
	364	+\| Yin, Vu & Persico (multi-model replication) \| 2026 \| Meta: Eloundou rubric × 4 frontier raters \| Task → occupation \| Rater-dispersion bounds \| n/a \| Meta-validation \| NBER WP 35110 \|
	365	+\| Frank, Ahn & Moro (ensemble) \| 2025 \| Ensemble of 10 exposure scores vs UI-claims risk \| Occupation × state × month \| Unemployment-risk R² \| No \| Is the validation \| PNAS Nexus (open access) \|
	366	+
	367	+---
	368	+
	369	+<a name="10-implications"></a>
	370	+## 10. Implications for airiskindex.io v1 methodology
	371	+
	372	+Mapping the literature onto our five dimensions (weights from `packages/scoring/src/weights.ts`) and our exposure/substitution/augmentation triad.
	373	+
	374	+### Cross-cutting lessons (apply to the whole index)
	375	+
	376	+1. Task-based is settled science (Arntz 2016 → everyone since). Our ONET task-level scoring with importance/frequency weights is the correct v1 backbone. Keep occupation scores as derived*, never primary.
	377	+2. Never collapse to one number without sub-scores. Felten's "neutral exposure", ILO's automation/augmentation split, IMF's complementarity, and AEI's measured automation:augmentation ratio all show the field converging on our exposure/substitution/augmentation triad. This is a genuine differentiator — most indices still publish one headline number and get misquoted (Goldman's "300M jobs lost" problem). Our tone rule ("adaptation, not doom") is empirically supported: realized effects so far are cohort-specific task reallocation, not mass unemployment (Yale Budget Lab; Humlum & Vestergaard) — with real, measurable pain at the entry level (Canaries).
	378	+3. LLM-as-evaluator is standard but fragile — multi-model rating is now table stakes. Yin et al. (2026): 19x spread in headline statistics across frontier raters on identical data; Claude-family raters score exposure highest of all models. Concrete requirements for `apps/worker/src/raters/`:
	379	+ - Rate every task with ≥2 (ideally 3) different frontier models (`RATER_MODEL` must become a list or we add `RATER_MODEL_SECONDARY`); store per-model scores; publish cross-model agreement per occupation.
	380	+ - Report confidence intervals derived from rater disagreement — this slots directly into our existing `score_low`/`score`/`score_high` schema.
	381	+ - Calibrate LLM ratings against a human-rated anchor set (ILO–NASK's 52,558-judgment survey design is the gold standard; our expert Delphi overrides in `data/derived/expert_overrides/` serve this role — sample deliberately across the exposure spectrum, not just flagged disagreements).
	382	+ - Prompt-version everything (we already do) and re-rate on model change — because the instrument co-evolves with the phenomenon (the "ruler" problem).
	383	+4. Anchor thresholds in explicit, documented rubrics. Eloundou's "≥50% time saving at equal quality" is the citable standard; SML's 23 questions show multi-criterion rubrics beat single judgments. Our prompts should decompose ratings into named criteria and require structured justifications (auditable per §6 of CLAUDE.md).
	384	+5. Validate against outcomes, and say so publicly. Frank et al. (2025): single scores explain <11% of unemployment risk; ensembles ~30–75%. We should (a) benchmark our composite against the public AEI usage data, PwC vacancy signals, and the Canaries dashboard; (b) publish a `docs/methodology/sensitivity/` correlation report against AIOE, GPTs-are-GPTs β, and ILO WP140 scores each release. Spearman ρ ≈ 0.84 between independent modern methodologies is the bar for "capturing the same signal".
	385	+
	386	+### Dimension-by-dimension
	387	+
	388	+\| Dimension (weight) \| Lessons from the literature \|
	389	+\|---\|---\|
	390	+\| `automatability` (0.35) \| This is Eloundou's E1/E2 construct + SML's rubric. Use a multi-criterion, multi-model LLM rubric with a time-savings-at-quality threshold; keep 5-point scale (matches ILO gradient practice and our expert-review disagreement rule). Distinguish "LLM alone" (E1) from "LLM + tooling/agents" (E2) — capability-staged variants (OAIES; OECD capability levels) show staging by AI generation makes scores updateable rather than obsolete. Critically: rate automation vs augmentation potential separately per task (ILO 2023/2025) so the composite's sub-scores are computed, not asserted. \|
	391	+\| `feasibility` (0.20) \| Separating "conceivable" from "deployable now" is what F&O failed to do and what killed their forecast. Ground feasibility in observed evidence: AEI task-success rates (66–70% by complexity — Jan 2026 primitives), benchmark-linked measures (OECD AI Capability Indicators' 9 domains with attained levels; "Jagged Global Economy"), and Iceberg's tool-catalogue approach (is there an actual product performing this skill?). Feasibility should decay-adjust automatability: high automatability + low current success rate ⇒ wide CI, lower composite. \|
	392	+\| `cost_ratio` (0.15) \| Least developed dimension in the literature — a genuine gap we can own. Only Webb (implicitly, via wages), Goldman (25% substitution assumption), and AEI's $/hr wage-equivalent task values touch it. Use O*NET-linked BLS wages (as AEI does: $48–49/hr average task value) vs API/inference cost per task-equivalent; store as integer cents per our conventions. Note Acemoglu's caution ("so-so automation"): low cost ratio can drive adoption even with mediocre quality — interact with feasibility. \|
	393	+\| `barriers` (0.20) \| Directly validated by IMF's C-AIOE complementarity (physical presence, human contact, legal responsibility) — the reason judges are exposed but safe and telemarketers are not. Pre-2022 null results (OECD 2021; Acemoglu et al. 2022) prove barriers dominate short-run outcomes. Operationalize the IMF/Pizzinelli shielding factors at task level: regulation/licensing, liability, required physical presence, human-contact preference, data confidentiality. Humlum & Vestergaard (Denmark) show even adoption without workflow redesign yields ~3% time savings — organizational barriers belong here too. \|
	394	+\| `adoption_velocity` (0.10) \| The 2025–26 revolution: use measured adoption, not guesses. Sources: AEI Hugging Face releases (task-level usage shares, automation:augmentation ratio, AUI by geography — open data, quarterly cadence), PwC Jobs Barometer (vacancy-side skill change 66% faster in exposed occupations), OpenAI usage paper. Sector velocity is empirically uneven (API vs consumer concentration diverging; GDP-elasticity of adoption 0.7) — justify per-sector velocity scores with these citations. Design for time-series updates: AEI shows adoption shares move 10+ pp in a year (automation 27%→39%→45–52% oscillation), so this dimension must re-score every `INDEX_VERSION`. \|
	395	+
	396	+### Positioning / product implications
	397	+
	398	+- Transparency is our moat and the field's known weakness: McKinsey/Goldman are black boxes; even academic scores rarely ship rater-level data. We publish weights (`/api/v1/methodology`), prompts (versioned), per-model ratings, and CIs — no major index does all four.
	399	+- Versioning is becoming an explicit norm (OECD exposure measure designed to be updateable; AEI releases dated datasets). Our `INDEX_VERSION` + immutable `score_runs` architecture matches best practice; cite OECD/AEI precedent in METHODOLOGY.md.
	400	+- CI bounds have empirical semantics now: rater disagreement (Yin et al.) + human-LLM calibration error (ILO–NASK) + feasibility uncertainty (AEI success rates) are the three quantifiable components of `score_low`/`score_high`.
	401	+- EU/France (ESCO/ROME) crosswalk: ILO WP140 (ISCO-based) and the UK index (arXiv 2507.22748) are the reference patterns for adapting O*NET-trained scores to other taxonomies; document crosswalk loss explicitly.
	402	+- Watch list for future versions: agentic-AI exposure extensions (arXiv 2604.00186), benchmark-grounded dynamic scores (OECD capability levels; "Jagged Global Economy"), worker-centered metrics (Lund et al. taxonomy), and the Canaries dashboard as a rolling validation target.
	403	+
	404	+---
	405	+
	406	+Compiled via WebSearch, Tavily, WebFetch, and OpenAlex queries, 2026-08-05. All URLs verified live at compile time unless noted. This document feeds `docs/methodology/METHODOLOGY.md` §Related Work.

added docs/research/02-data-sources.md +129 −0

		`@@ -0,0 +1,129 @@`
	1	+# Raw Data Sources for the AI Risk Index ETL Pipeline
	2	+
	3	+Research date: 2026-08-05 · Author: Claude (web research pass)
	4	+Scope: every raw source `apps/etl` needs, with current versions, exact URLs, licensing, schema notes, and how each feeds `data/raw/` → `data/derived/`.
	5	+
	6	+> ⚠️ Headline finding: the project docs assume *ONET 29.x; the current production release is ONET 30.3 (May 2026), and ONET 31.0 lands in late August 2026 with a modernized Content Model. The 30.x series renamed key files** (Technology Skills → Software Skills; Skills split into Essential Skills / Transferable Skills), which is a breaking change for any ETL written against 29.x file names. Pin one release per `INDEX_VERSION` and record it in the manifest.
	7	+
	8	+---
	9	+
	10	+## Master table
	11	+
	12	+\| # \| Source \| Current version / vintage \| Primary URL \| Format \| License \| Update cadence \| Pipeline role \|
	13	+\|---\|--------\|---------------------------\|-------------\|--------\|---------\|----------------\|---------------\|
	14	+\| 1 \| ONET Database \| 30.3* (May 2026); 31.0 due late Aug 2026 \| https://www.onetcenter.org/database.html \| Excel/CSV/JSON, SQL (MySQL/SQL Server/Oracle), text \| CC BY 4.0 (attribution: USDOL/ETA) \| Quarterly (Feb/May/Aug/Nov-Dec) \| Core: occupations, tasks, ratings → `data/raw/onet/` \|
	15	+\| 2 \| O*NET Web Services \| API v2.0 (serves 30.3) \| https://services.onetcenter.org/ \| REST/JSON \| CC BY 4.0 + ToS \| Always-current \| Optional live lookups; not for bulk ETL \|
	16	+\| 3 \| SOC 2018 taxonomy \| 2018 (next revision ~2028) \| https://www.bls.gov/soc/2018/ \| XLSX/PDF \| Public domain \| ~Decennial \| Occupation code spine (via O*NET-SOC 2019) \|
	17	+\| 4 \| ESCO classification \| v1.2.1 (2025-12-10) \| https://esco.ec.europa.eu/en/use-esco/download \| CSV, SKOS/RDF, API \| Free reuse (Decision 2011/833/EU) w/ attribution \| ~Annual point releases \| EU/FR occupation layer → `data/raw/esco/` \|
	18	+\| 5 \| ESCO↔ONET crosswalk (official) \| 2022 report; CSV updated 2023-08 (ESCO v1.1 base) \| https://esco.ec.europa.eu/en/use-esco/other-crosswalks \| CSV \| Same as ESCO \| Irregular \| Map ONET-SOC → ESCO → `data/raw/esco/` \|
	19	+\| 6 \| ROME 4.0 (France Travail) \| Update of 2026-06-18; next Oct 2026 \| https://www.data.gouv.fr/datasets/repertoire-operationnel-des-metiers-et-des-emplois-rome \| CSV/XLSX (+ API) \| Licence Ouverte 2.0 \| ≥2×/year \| FR occupation layer → `data/raw/rome/` \|
	20	+\| 7 \| BLS OEWS wages+employment \| May 2025 (released 2026-05-15) \| https://www.bls.gov/oes/tables.htm \| XLSX in ZIP \| Public domain \| Annual (~May) \| `cost_ratio` wages, employment weights → `data/raw/bls/oews/` \|
	21	+\| 8 \| BLS Employment Projections \| 2024–34 (released 2025-08-28); 2025–35 due ~Sep 2026 \| https://www.bls.gov/emp/data/occupational-data.htm \| XLSX \| Public domain \| Annual \| Adoption-velocity / outlook context → `data/raw/bls/ep/` \|
	22	+\| 9 \| Eurostat SES earnings \| Ref. year 2022 (4-yearly; next 2026 ref, pub ~2028) \| https://ec.europa.eu/eurostat/databrowser/view/earn_ses_hourly \| TSV/SDMX API \| CC BY 4.0 \| Every 4 years \| EU wages by ISCO-08 → `data/raw/eurostat/` \|
	23	+\| 10 \| Eurostat LFS employment \| Annual, latest 2025 \| https://ec.europa.eu/eurostat/databrowser/view/lfsa_egai2d \| TSV/SDMX API \| CC BY 4.0 \| Annual \| EU employment by ISCO 2-digit → `data/raw/eurostat/` \|
	24	+\| 11 \| INSEE salaires (France) \| 2023 consolidated (Base Tous salariés, DSN) \| https://www.insee.fr/fr/statistiques (Base Tous salariés) \| XLSX/CSV \| Open (Insee reuse) \| Annual (~2-yr lag) \| FR wages by PCS → `data/raw/insee/` \|
	25	+\| 12 \| Census BTOS AI supplement \| Collected 2025-11-17→2026-02-08; released 2026-04-23 \| https://www.census.gov/hfp/btos/data_downloads \| XLSX/CSV \| Public domain (experimental) \| Biweekly core; supplements episodic \| `adoption_velocity` (US, by sector/state/size) → `data/raw/btos/` \|
	26	+\| 13 \| Eurostat ICT-in-enterprises AI \| Survey year 2025 (19.95% EU firms use AI) \| Datasets `isoc_eb_ai`, `isoc_eb_ain2` \| TSV/SDMX API \| CC BY 4.0 \| Annual (Dec/Jan) \| `adoption_velocity` (EU, by NACE) → `data/raw/eurostat/` \|
	27	+\| 14 \| Ramp AI Index \| June 2026 (55.0% adoption) \| https://ramp.com/data \| CSV download + charts \| Free; check Ramp terms \| Monthly \| High-frequency US adoption signal → `data/raw/ramp/` \|
	28	+\| 15 \| Anthropic Economic Index \| 6th release (2026-06-26, "Cadences") \| https://huggingface.co/datasets/Anthropic/EconomicIndex \| CSV (HF dataset) \| Data CC-BY; code MIT \| ~Quarterly \| Observed task-level AI usage; rater calibration → `data/raw/aei/` \|
	29	+\| 16 \| Stanford AI Index \| 2026 edition (9th, Apr 2026, 423 pp) \| https://hai.stanford.edu/assets/files/ai_index_report_2026.pdf \| PDF + public data appendix \| Free (attribution) \| Annual (~April) \| Context stats for methodology doc / barriers \|
	30	+\| 17 \| Felten AIOE \| 2021 + GenAI variants \| https://github.com/AIOE-Data/AIOE \| XLSX \| Free w/ citation \| Static \| Benchmark exposure scores → `data/raw/benchmarks/` \|
	31	+\| 18 \| Eloundou "GPTs are GPTs" \| Science 2024 replication \| https://github.com/openai/GPTs-are-GPTs \| CSV \| MIT \| Static \| Benchmark exposure scores → `data/raw/benchmarks/` \|
	32	+\| 19 \| Webb (2020) AI exposure \| 2020 \| https://www.michaelwebb.co (on request) \| — \| On request \| Static \| Optional benchmark (no public bulk file) \|
	33	+\| 20 \| ILOSTAT \| Continuous; incl. "employment by GenAI exposure" tables \| https://ilostat.ilo.org \| CSV/API \| CC BY 4.0 \| Continuous \| Intl. harmonized employment; ILO GenAI exposure scores \|
	34	+
	35	+---
	36	+
	37	+## 1. O*NET Database (core input)
	38	+
	39	+- Current release: *ONET 30.3, May 2026 (source: https://www.onetcenter.org/db_releases.html). Release train since the project was scoped: 29.2 (Feb 2025) → 29.3 (May 2025) → 30.0 (Aug 2025) → 30.1 (Dec 2025) → 30.2 (Feb 2026, new four-level Job Zones) → 30.3 (May 2026, modernized Content Model + Specific Interests) → 31.0 expected late August 2026**.
	40	+- Downloads: https://www.onetcenter.org/database.html — formats: tabular (Excel/CSV/JSON), SQL loads for MySQL/PostgreSQL/MariaDB, SQL Server, Oracle, plus RDF. Full Excel archive: `https://www.onetcenter.org/dl_files/database/db_30_3_excel.zip` (individual files linked from the same page; per-format data dictionary at https://www.onetcenter.org/dictionary/30.3/excel/).
	41	+- Files the ETL needs (30.3 row counts):
	42	+ - `Occupation Data` — 1,016 rows (O*NET-SOC code, title, description)
	43	+ - `Task Statements` — 18,796 rows (task_id, task, task type, incumbents responding)
	44	+ - `Task Ratings` — 161,559 rows (importance IM, relevance RL, frequency FT scales, with N, SE, CI bounds — feeds our task weights and our own CI propagation)
	45	+ - `Tasks to DWAs`, `DWA Reference`, `IWA Reference` — task ↔ detailed/intermediate work activity links
	46	+ - `Work Activities` — 73,308 rows (GWA ratings)
	47	+ - `Abilities` — 92,976 · `Knowledge` — 59,004 · `Work Context` — 297,676
	48	+ - Renamed in 30.x: `Technology Skills` → `Software Skills` (31,821 rows); the old `Skills` file is now `Essential Skills` (17,880) + `Transferable Skills` (44,700). ETL loaders and any code referencing "Technology Skills" must be updated.
	49	+- License: CC BY 4.0. Required attribution: credit the "O*NET 30.3 Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA), used under the CC BY 4.0 license." Put this in the public methodology page and API `/api/v1/methodology` metadata.
	50	+- Taxonomy: ONET-SOC 2019* taxonomy (built on SOC 2018): 1,016 occupation titles of which 923 are data-collection-level; code format `XX-XXXX.XX` (SOC 6-digit + 2-digit O*NET suffix). ~891 occupations already updated in 2026 YTD.
	51	+- *ONET Web Services: register (free) at https://services.onetcenter.org/developer/signup; API v2.0 reference at https://services.onetcenter.org/reference. No hard rate limit, but ToS gives per-second/per-day guidance; on `429`, retry after ≥200 ms. Always serves the latest DB — good for spot checks, not** for reproducible scoring (use pinned dumps).
	52	+- SOC 2018: 867 detailed occupations → 459 broad → 98 minor → 23 major groups; code `XX-XXXX`. Definitions/structure files at https://www.bls.gov/soc/2018/ (XLSX). Public domain.
	53	+- Pipeline: fetch script downloads the pinned release ZIP into `data/raw/onet/db_30_3/`, verifies SHA-256, records release + hash in the derived manifest. Never edit in place.
	54	+
	55	+## 2. ESCO + crosswalks + ROME (EU/France layer)
	56	+
	57	+- ESCO v1.2.1 (last update 2025-12-10). Portal: https://esco.ec.europa.eu/en · downloads (per-language CSV + "language independent" files, SKOS/RDF, Local API): https://esco.ec.europa.eu/en/use-esco/download (free account/selection flow). ~3,000 occupations mapped to ISCO-08; 13,939 skills in v1.2.1. Web API: https://esco.ec.europa.eu/en/use-esco (base `https://ec.europa.eu/esco/api`).
	58	+- License: free reuse under Commission Decision 2011/833/EU; must publish the ESCO acknowledgement statement (see FAQ: https://esco.ec.europa.eu/en/about-esco/faq).
	59	+- *ESCO↔ONET official crosswalk** (co-created EC + USDOL, AI-assisted with human validation):
	60	+ - Page: https://esco.ec.europa.eu/en/use-esco/other-crosswalks (also listed at https://www.onetcenter.org/crosswalks.html)
	61	+ - CSV: `https://esco.ec.europa.eu/system/files/2023-08/ONET_%28Occupations%29_0_updated.csv`
	62	+ - Two published variants: (a) exact/narrow/broad/close matches (QA'd, USDOL-validated); (b) same + "related" matches (lower quality — not validated; exclude from scoring joins by default).
	63	+ - Technical report: `https://esco.ec.europa.eu/system/files/2022-12/ONET%20ESCO%20Technical%20Report.pdf`. Built against *ESCO v1.1 / ONET-SOC 2019** (the O*NET Web Services crosswalk endpoint still states ESCO v1.1.0) — re-verify concept URIs against v1.2.1 during ETL; unmatched URIs go to a QA report.
	64	+ - Bonus: ESCO↔NACE crosswalk now available: `https://esco.ec.europa.eu/system/files/2026-02/ESCO-NACE%20rev.%202.1%20crosswalk.xlsx` (useful for joining Eurostat sector-level AI adoption to occupations).
	65	+- ESCO↔ROME: no single public EC file. France Travail maintains ROME↔ESCO correspondence tables under the EURES obligation (each member state maps its national classification to ESCO); the ROME open-data bundle includes correspondence referentials, and EURES member-state mapping tables are listed on the ESCO portal ("EURES Countries Mapping Tables"). Validate coverage during ETL and fall back to ROME→ISCO-08→ESCO if a direct table is missing for some fiches.
	66	+- ROME 4.0 (France Travail):
	67	+ - Open data: https://www.data.gouv.fr/datasets/repertoire-operationnel-des-metiers-et-des-emplois-rome — multiple referential files (arborescence, compétences, contextes, mobilité). Licence Ouverte / Open Licence 2.0. Last update 2026-06-18; next update announced for Oct 2026; ≥2 updates/year.
	68	+ - Also via API on https://francetravail.io (ROME 4.0 APIs, OAuth key) and mirrored on https://www.francetravail.org/opendata/.
	69	+ - Code format: 1 letter + 4 digits (e.g., `M1607`); ~600 fiches métiers organized by 14 domaines.
	70	+ - Pipeline: `data/raw/rome/` with the data.gouv.fr resource URLs + version date in the manifest.
	71	+
	72	+## 3. Wage data
	73	+
	74	+- BLS OEWS — May 2025 (released 2026-05-15; next: May 2026 data in spring 2027). ~830 SOC occupations; employment, mean/median hourly & annual wages, wage percentiles (10/25/50/75/90), by nation/state/MSA/industry.
	75	+ - Tables hub: https://www.bls.gov/oes/tables.htm → ZIPs `oesm25nat.zip` (national), `oesm25st.zip` (states), `oesm25ma.zip` (metro), national-by-industry files (served from `https://www.bls.gov/oes/special-requests/…`; BLS blocks non-browser user agents — use a browser UA in the fetch script). Field layout documented in each ZIP's `field_descriptions` sheet.
	76	+ - License: US government work, public domain. Cadence: annual.
	77	+ - Pipeline: national file feeds `cost_ratio` (wage denominator, stored as integer cents + USD per repo convention) and employment weights; join key = SOC 2018 6-digit → ONET-SOC 2019 (strip `.XX` suffix / use ONET-SOC↔SOC crosswalk).
	78	+- Eurostat SES (Structure of Earnings Survey): 4-yearly, latest reference year 2022 (published 2024–25; next ref-year 2026 published ~2028). Datasets: `earn_ses_hourly` (and monthly/annual variants) — mean/median hourly earnings by ISCO-08 2-digit × NACE × country. Databrowser: https://ec.europa.eu/eurostat/databrowser/view/earn_ses_hourly · bulk via SDMX API `https://ec.europa.eu/eurostat/api/dissemination/sdmx/2.1/data/earn_ses_hourly?format=TSV`. License CC BY 4.0. Occupation resolution is only 2-digit ISCO — EU `cost_ratio` will be coarser than US; flag in methodology.
	79	+- France: INSEE Base Tous salariés (from DSN, formerly DADS): salaire net EQTP by PCS (up to 4-digit), sector, sex; latest consolidated 2023 (see Insee Première n°1938 for 2021: https://www.insee.fr/fr/statistiques/6799523; séries longues: https://www.insee.fr/fr/statistiques/8660332). DARES publishes wage/employment "portraits statistiques des métiers" by FAP. Requires PCS↔ROME/ISCO crosswalk (INSEE publishes PCS↔ISCO tables) — France-specific wage joins are a v2 concern.
	80	+
	81	+## 4. Employment counts & projections
	82	+
	83	+- OEWS employment (same May 2025 files as §3) — primary US employment weights.
	84	+- BLS Employment Projections 2024–34 (released 2025-08-28; 2025–35 edition expected ~Sept 2026 — recheck before ingesting): https://www.bls.gov/emp/data/occupational-data.htm — "All occupational tables in a single file (XLSX)", National Employment Matrix 2024/2034, occupational separations, plus the new skills data tables (importance of skills by occupation). Public domain. Feeds adoption-velocity priors and UI "outlook" context. State-level: https://projectionscentral.org/longterm (REST + download).
	85	+- Eurostat LFS: `lfsa_egai2d` — employed persons by detailed occupation (ISCO-08 2-digit), annual: https://ec.europa.eu/eurostat/databrowser/view/lfsa_egai2d (SDMX API as above). CC BY 4.0.
	86	+- ILOSTAT: https://ilostat.ilo.org — harmonized employment by ISCO level 2 (annual/quarterly) across countries, CSV bulk + API, CC BY 4.0. Notably now publishes "Employment by sex and generative AI exposure" tables (based on the ILO/Gmyrek GenAI occupational exposure scores) — both a benchmark and a ready-made employment-by-exposure aggregate.
	87	+
	88	+## 5. AI adoption data (`adoption_velocity` dimension)
	89	+
	90	+- US Census BTOS AI supplement: third AI supplement collected 2025-11-17 → 2026-02-08, released 2026-04-23 (press: https://www.census.gov/newsroom/press-releases/2026/btos-apr-23.html). Measures firm AI use overall and — new this cycle — by worker tasks and business functions, split by NAICS sector, state, and firm size. Data hub: https://www.census.gov/hfp/btos/data (Downloads tab: https://www.census.gov/hfp/btos/data_downloads, incl. historical; API tab available). Core biweekly BTOS also carries a recurring "AI use in last two weeks" item (wording revised Nov 2025 — treat as a series break). ~1.2M-business sample; experimental data product; public domain.
	91	+- Eurostat — AI in enterprises: datasets `isoc_eb_ai` (enterprises using AI technologies) and `isoc_eb_ain2` (by purpose/technology × NACE), survey year 2025: 19.95% of EU enterprises use AI (55.03% of large firms); Statistics Explained article (updated Dec 2025, next Dec 2026): https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Use_of_artificial_intelligence_in_enterprises. Join to occupations via sector (NACE) using the ESCO-NACE crosswalk (§2). CC BY 4.0.
	92	+- Ramp AI Index (Ramp Economics Lab): https://ramp.com/data — monthly AI adoption among US firms from card/bill-pay spend of 70k+ businesses (June 2026: 55.0%, +0.8 pp MoM), broken out by size and sector, downloadable; methodology: https://econlab.substack.com/p/how-ramp-data-works. Now shifting to intensity tracking. Free/open resource; confirm redistribution terms before committing derived aggregates.
	93	+- Anthropic Economic Index: HF dataset `Anthropic/EconomicIndex` (https://huggingface.co/datasets/Anthropic/EconomicIndex). Release folders: `release_2025_02_10` (initial ONET task mappings, automation vs augmentation), `release_2025_03_27` (cluster-level, thinking-mode fractions per ONET task), `release_2025_09_15` (geography + 1P API), `release_2026_01_15` ("economic primitives"), `release_2026_03_24` ("learning curves"), `release_2026_06_26` ("Cadences", monthly aggregates), plus `labor_market_impacts/`. Data CC-BY (repo metadata lists MIT for code). This is the single most direct empirical input for calibrating our LLM-rater `automatability`/`feasibility` scores against observed usage — it is keyed to *ONET task statements**, same spine as ours.
	94	+- Stanford AI Index 2026 (9th edition, April 2026, 423 pp): report PDF https://hai.stanford.edu/assets/files/ai_index_report_2026.pdf; public data appendix downloadable from the HAI AI Index page. Key 2026 stats: 53% population-level GenAI adoption; $172B est. US consumer surplus. Use for methodology narrative and `barriers`/`adoption_velocity` context, not row-level joins. Annual.
	95	+
	96	+## 6. Published occupation-level exposure benchmarks
	97	+
	98	+These are validation benchmarks (`data/raw/benchmarks/`), not scoring inputs — our methodology must remain independently reproducible.
	99	+
	100	+\| Dataset \| What it is \| URL / file \| License \|
	101	+\|---\|---\|---\|---\|
	102	+\| Felten–Raj–Seamans AIOE \| AI Occupational Exposure by 6-digit SOC (10 AI applications × 52 O*NET abilities); + Language-Modeling and Image-Generation GenAI variants, industry (AIIE) & geography \| https://github.com/AIOE-Data/AIOE → `AIOE_DataAppendix.xlsx`, `Language Modeling AIOE and AIIE.xlsx`, `Image Generation AIOE and AIIE.xlsx` \| Free; citation required (SMJ 42(12):2195–2217, 2021) \|
	103	+\| Eloundou et al. "GPTs are GPTs" (Science 2024) \| Task- and occupation-level LLM exposure (α=E1, β=E1+0.5·E2, γ=E1+E2; human + GPT-4 ratings) on O*NET tasks \| https://github.com/openai/GPTs-are-GPTs → `occ_level.csv` (occupation), task-level in `data/` \| MIT \|
	104	+\| Webb (2020) \| Patent-text-based AI exposure by SOC \| https://www.michaelwebb.co/webb_ai.pdf — data on request only (no public bulk file) \| On request \|
	105	+\| Anthropic AEI task exposure \| Observed Claude usage mapped to O*NET tasks (see §5) \| HF `Anthropic/EconomicIndex` \| CC-BY \|
	106	+\| ILO GenAI exposure (Gmyrek et al.) \| ISCO-08 occupation GenAI exposure gradients, used in ILOSTAT employment tables \| via https://ilostat.ilo.org \| CC BY 4.0 \|
	107	+\| Context \| Yale Budget Lab compared 7 exposure metrics (large disagreement on most-exposed, agreement on least-exposed); Brookings (Mar 2026) methodology uses AIOE + OEWS \| brookings.edu methods PDF \| — \|
	108	+
	109	+Use for the sensitivity analyses in `docs/methodology/sensitivity/` (rank correlations of our composite vs AIOE / GPTs-are-GPTs / AEI).
	110	+
	111	+---
	112	+
	113	+## Ingestion order & manifest plan
	114	+
	115	+Order respects join dependencies (occupation spine first, then attributes, then joins):
	116	+
	117	+1. SOC 2018 structure (`data/raw/soc/`) — code spine, 867 detailed occupations.
	118	+2. *ONET 30.3 full dump** (`data/raw/onet/db_30_3/`) — pin release; load order: Occupation Data → Task Statements → Task Ratings → Tasks-to-DWAs/IWAs → Work Activities → Abilities/Knowledge → Software/Essential/Transferable Skills → Work Context. Decision needed before build: stay on 30.3 or wait for 31.0 (late Aug 2026) — either way, `INDEX_VERSION` notes the O*NET release.
	119	+3. OEWS May 2025 (`data/raw/bls/oews/`) — wages (→ integer cents) + employment; join on SOC 6-digit.
	120	+4. BLS EP 2024–34 (`data/raw/bls/ep/`) — projections + skills tables.
	121	+5. ESCO v1.2.1 (`data/raw/esco/classification/`) + *ESCO↔ONET crosswalk** (`data/raw/esco/crosswalk/`) — validate URI coverage vs v1.2.1; exclude "related" matches from scoring joins.
	122	+6. ROME 4.0 (`data/raw/rome/`) + ROME↔ESCO correspondence; fallback path ROME→ISCO-08→ESCO.
	123	+7. Eurostat: `earn_ses_hourly` (SES 2022), `lfsa_egai2d` (LFS), `isoc_eb_ai(n2)` (AI adoption) via SDMX API pulls (`data/raw/eurostat/`).
	124	+8. Adoption signals: BTOS AI supplement, Ramp AI Index, AEI releases (`data/raw/btos\|ramp\|aei/`).
	125	+9. Benchmarks: AIOE, GPTs-are-GPTs, ILO GenAI exposure (`data/raw/benchmarks/`) — validation only.
	126	+
	127	+Manifest rules (per repo convention — raw payloads are gitignored, manifests committed): for every fetched artifact record `{source, version_label, source_url, fetch_date, sha256, row_count, license, attribution_string}` in `data/derived/manifests/<source>.json`. The fetch script must fail loudly if a pinned URL 404s or the hash changes (BLS and ONET replace files in place across releases). Re-verify before each `INDEX_VERSION` bump: ONET quarterly page, OEWS annual page (mid-May), EP annual page (late Aug/Sep), ESCO portal (point releases), ROME (Oct 2026), BTOS supplement announcements, AEI HF repo.
	128	+
	129	+Attribution block for the public methodology page: O*NET (USDOL/ETA, CC BY 4.0) · ESCO (© European Union, ESCO acknowledgement statement) · ROME (France Travail, Licence Ouverte 2.0) · BLS/Census (public domain, cite program + vintage) · Eurostat (CC BY 4.0) · Anthropic Economic Index (CC-BY) · benchmark papers cited per their requirements.

added docs/research/03-llm-rater-api.md +329 −0

		`@@ -0,0 +1,329 @@`
	1	+# LLM Rater — Anthropic API Reference (August 2026)
	2	+
	3	+Practical, current reference for the task-rating pipeline in `apps/worker/src/raters/` (CLAUDE.md §6).
	4	+Scope: model choice, Message Batches API, structured outputs, prompt caching, rate limits, TypeScript SDK
	5	+patterns, and a concrete cost estimate for rating ~18,000 O*NET task statements × 3 samples.
	6	+
	7	+All facts verified against the official docs on 2026-08-05. Note: `https://docs.claude.com/en/api/overview`
	8	+(the URL in CLAUDE.md §6) now 301-redirects to `https://platform.claude.com/docs/en/api/overview` — the docs
	9	+moved to `platform.claude.com`.
	10	+
	11	+Sources:
	12	+
	13	+- Models overview — https://platform.claude.com/docs/en/about-claude/models/overview
	14	+- Pricing — https://platform.claude.com/docs/en/about-claude/pricing
	15	+- Batch processing — https://platform.claude.com/docs/en/build-with-claude/batch-processing
	16	+- Rate limits — https://platform.claude.com/docs/en/api/rate-limits
	17	+- Structured outputs — https://platform.claude.com/docs/en/build-with-claude/structured-outputs
	18	+- Prompt caching — https://platform.claude.com/docs/en/build-with-claude/prompt-caching
	19	+
	20	+---
	21	+
	22	+## 1. Current model lineup and pricing (per MTok, standard API)
	23	+
	24	+\| Model \| ID (set via `RATER_MODEL`) \| Context / max output \| Input \| Output \| Batch input \| Batch output \| Cache read \|
	25	+\|---\|---\|---\|---\|---\|---\|---\|---\|
	26	+\| Claude Fable 5 \| `claude-fable-5` \| 1M / 128K \| $10 \| $50 \| $5 \| $25 \| $1.00 \|
	27	+\| Claude Opus 5 \| `claude-opus-5` \| 1M / 128K \| $5 \| $25 \| $2.50 \| $12.50 \| $0.50 \|
	28	+\| Claude Opus 4.8 \| `claude-opus-4-8` \| 1M / 128K \| $5 \| $25 \| $2.50 \| $12.50 \| $0.50 \|
	29	+\| Claude Sonnet 5 (intro, through 2026-08-31) \| `claude-sonnet-5` \| 1M / 128K \| $2 \| $10 \| $1 \| $5 \| $0.20 \|
	30	+\| Claude Sonnet 5 (from 2026-09-01) \| `claude-sonnet-5` \| 1M / 128K \| $3 \| $15 \| $1.50 \| $7.50 \| $0.30 \|
	31	+\| Claude Sonnet 4.6 \| `claude-sonnet-4-6` \| 1M / 128K \| $3 \| $15 \| $1.50 \| $7.50 \| $0.30 \|
	32	+\| Claude Haiku 4.5 \| `claude-haiku-4-5` \| 200K / 64K \| $1 \| $5 \| $0.50 \| $2.50 \| $0.10 \|
	33	+
	34	+Cache writes: 1.25× base input (5-min TTL) or 2× base input (1-hour TTL). Cache multipliers **stack with the
	35	+batch discount** (confirmed in the pricing doc: "These multipliers stack with other pricing modifiers,
	36	+including the Batch API discount"). All model IDs from the 4.6 generation onward are dateless pinned
	37	+snapshots — no date suffix to append.
	38	+
	39	+Tokenizer note: models from Opus 4.7 onward (incl. Fable 5, Opus 5, Sonnet 5) use a tokenizer that yields
	40	+~30% more tokens for the same text than Sonnet 4.6/Haiku 4.5. Budget token estimates per model, not globally.
	41	+
	42	+### Which tier for large-scale 5-point task rating?
	43	+
	44	+For a structured, rubric-guided 5-point rating with a short rationale (a classification-plus-justification
	45	+task, not open-ended reasoning):
	46	+
	47	+- Haiku 4.5 — cheapest ($0.50/$2.50 batch), fast, supports structured outputs. Likely adequate for the
	48	+ bulk of clear-cut tasks, but weakest calibration on ambiguous tasks — expect more >1-point disagreements
	49	+ flowing into the expert-panel queue (which costs human time).
	50	+- Claude Sonnet 5 — recommended default. Near-Opus quality on judgment tasks; at the introductory
	51	+ price ($1/$5 batch through Aug 31, 2026) the full 54k-rating job costs ~$160–250 (see §7) — the marginal
	52	+ cost over Haiku is trivial relative to the human-review pipeline it feeds. Caveat: adaptive thinking is on
	53	+ by default and thinking tokens bill as output; for this task set `thinking: {type: "disabled"}` (accepted
	54	+ on Sonnet 5) or keep it on with `output_config: {effort: "low"}` and budget extra output tokens.
	55	+- Opus 5 / Fable 5 — overkill for the volume run. Total quality of the index is bounded by the
	56	+ methodology and human validation, not by Opus-vs-Sonnet deltas on a 5-point scale. Best use: (a) rate the
	57	+ expert-panel calibration subset with Opus 5 as a second opinion, or (b) adjudicate flagged disagreements.
	58	+
	59	+Practical plan: pilot ~500 tasks on both Haiku 4.5 and Sonnet 5, compare agreement with the human 5% sample,
	60	+and pick per the disagreement rate. Sampling-variance note for the 3-samples design: **Sonnet 5, Opus 5, and
	61	+Opus 4.7+ reject non-default `temperature`/`top_p`/`top_k` (400 error)** — you cannot set temperature for
	62	+the 3 samples on those models; between-sample variance is whatever the model naturally produces. Haiku 4.5
	63	+and Sonnet 4.6 still accept temperature.
	64	+
	65	+## 2. Message Batches API
	66	+
	67	+Docs: https://platform.claude.com/docs/en/build-with-claude/batch-processing
	68	+
	69	+- Endpoints: `POST /v1/messages/batches` (create), `GET /v1/messages/batches/{id}` (poll),
	70	+ `GET <results_url>` (stream `.jsonl` results), `POST /v1/messages/batches/{id}/cancel`, `GET /v1/messages/batches` (list).
	71	+- Discount: flat 50% off both input and output tokens, all models, all features (vision, tools,
	72	+ structured outputs, prompt caching all supported inside batches).
	73	+- Limits: max 100,000 requests or 256 MB per batch, whichever comes first. Each request needs a
	74	+ `custom_id` matching `^[a-zA-Z0-9_-]{1,64}$`. `max_tokens` must be ≥ 1 (`max_tokens: 0` cache pre-warming
	75	+ is rejected inside batches).
	76	+- Timing: most batches finish < 1 hour; hard 24-hour expiration — requests not processed by then
	77	+ come back as `expired` (not billed) and must be resubmitted.
	78	+- Results: available at `results_url` once `processing_status === "ended"`; delivered as JSONL, **in
	79	+ arbitrary order — always key by `custom_id`, never by position. Results are downloadable for 29 days**
	80	+ after batch creation; persist them to `data/derived/ratings/` + DB immediately.
	81	+- Per-request result types: `succeeded` (has `.result.message`), `errored` (invalid request → fix and
	82	+ resubmit; server error → safe to retry; not billed), `canceled` (not billed), `expired` (not billed —
	83	+ resubmit).
	84	+- Extended output: Opus 5/4.8/4.7/4.6, Sonnet 5/4.6 support up to 300k output tokens in batches via the
	85	+ `output-300k-2026-03-24` beta header (not needed for 500-token ratings).
	86	+- No server-side idempotency key on batch create — dedupe is your job (below).
	87	+
	88	+### Marrying batches with BullMQ
	89	+
	90	+Our invariant (CLAUDE.md §6): BullMQ job ID = deterministic hash of `task_id + prompt_version`. Extend to
	91	+`hash(task_id + prompt_version + sample_index)` since each task is rated 3×. Recommended architecture:
	92	+
	93	+1. `rating-request` rows, not per-rating jobs. Persist one DB row per (task, prompt_version, sample)
	94	+ with status `pending`. A hex SHA-256 (truncated to 32–48 chars) of `taskId:promptVersion:sampleIdx`
	95	+ satisfies both the BullMQ job-ID and the batch `custom_id` charset — use the same string for both, so
	96	+ a batch result maps 1:1 to a job/row.
	97	+2. `batch-submitter` job (BullMQ, repeatable or triggered): collects up to 100k `pending` rows, calls
	98	+ `batches.create()`, stores `batch_id` on the rows before flipping them to `submitted` — if the process
	99	+ dies after create but before persist, on restart list recent batches and reconcile by `custom_id` rather
	100	+ than re-creating (this is the idempotency seam; the API will happily accept duplicate custom_ids across
	101	+ batches and bill you twice).
	102	+3. `batch-poller` job with BullMQ job ID = `poll:${batchId}` (deterministic → re-enqueue is a no-op),
	103	+ repeat/delay ~60s until `processing_status === "ended"`.
	104	+4. `batch-ingester` streams results, and per result: store raw JSON response, parsed score, model,
	105	+ prompt version, timestamp (full audit trail); `errored`(server)/`expired` → flip row back to `pending`
	106	+ so the next submitter run resubmits; `errored`(invalid_request) → dead-letter for inspection.
	107	+5. A prompt-version bump changes every hash → new custom_ids → old cached rows are naturally invalidated,
	108	+ exactly matching the CLAUDE.md rule.
	109	+
	110	+The whole 54,000-rating run fits in one batch (well under 100k requests and 256 MB), even at the Start
	111	+tier queue limit (200k requests in processing queue).
	112	+
	113	+## 3. Structured outputs for the 5-point rating
	114	+
	115	+Structured outputs is GA (no beta header; the old `structured-outputs-2025-11-13` header and top-level
	116	+`output_format` request param are deprecated transition shims). Two mechanisms:
	117	+
	118	+1. JSON outputs — `output_config: {format: {type: "json_schema", schema: {...}}}` constrains the
	119	+ response text to schema-valid JSON.
	120	+2. Strict tool use — `strict: true` on a tool definition; guarantees `tool_use.input` validates.
	121	+
	122	+Recommendation: use JSON outputs (`output_config.format`), not tool-forced JSON. Rationale:
	123	+
	124	+- It's the purpose-built mechanism for "the response is the structured object" — no fake tool, no
	125	+ `tool_choice` forcing, one fewer moving part in the audit trail.
	126	+- Guaranteed-valid JSON with `required` fields → the "parsed score" column can be extracted without retry
	127	+ loops.
	128	+- Works with the Batches API, streaming, and thinking. (Incompatible with citations and prefilling —
	129	+ neither is used here.)
	130	+- Schema limits that matter to us: `enum` is supported (use `"score": {"enum": [1,2,3,4,5]}` — do not
	131	+ use `minimum`/`maximum`, numeric range constraints are unsupported); no `minLength`/`maxLength` on the
	132	+ rationale string (enforce length in the prompt); `additionalProperties: false` is mandatory on every
	133	+ object.
	134	+- First use of a schema pays a one-time grammar-compilation latency; compiled grammars are cached 24h —
	135	+ irrelevant inside a batch run that reuses one schema 54,000×. Note that **changing `output_config.format`
	136	+ invalidates the prompt cache**, so treat the schema like the rubric: versioned with `RATER_PROMPT_VERSION`.
	137	+
	138	+Suggested schema:
	139	+
	140	+```json
	141	+{
	142	+ "type": "object",
	143	+ "properties": {
	144	+ "score": { "type": "integer", "enum": [1, 2, 3, 4, 5] },
	145	+ "rationale": { "type": "string", "description": "2-3 sentence justification citing the rubric" },
	146	+ "confidence": { "type": "string", "enum": ["low", "medium", "high"] }
	147	+ },
	148	+ "required": ["score", "rationale", "confidence"],
	149	+ "additionalProperties": false
	150	+}
	151	+```
	152	+
	153	+Structured outputs injects a system-prompt preamble explaining the format (small, fixed token overhead per
	154	+request).
	155	+
	156	+## 4. Prompt caching for the shared rubric
	157	+
	158	+Layout: `tools` → `system` → `messages` renders in that order and caching is a byte-exact prefix match.
	159	+Put the ~2k-token rubric in `system` with `cache_control` on its last block; the per-task variable content
	160	+(task statement, occupation context) goes in the user message, after the breakpoint:
	161	+
	162	+```ts
	163	+system: [
	164	+ { type: "text", text: RUBRIC_V3, // frozen per RATER_PROMPT_VERSION — no timestamps, no task data
	165	+ cache_control: { type: "ephemeral", ttl: "1h" } },
	166	+],
	167	+messages: [{ role: "user", content: `Occupation: ${occ}\nTask: ${taskStatement}\nRate this task.` }]
	168	+```
	169	+
	170	+Key facts:
	171	+
	172	+- Pricing: cache read = 0.1× base input; write = 1.25× (5-min TTL) or 2× (1-hour TTL). **Stacks with the
	173	+ batch discount** → a cached rubric token inside a batch costs 0.05× base input.
	174	+- Inside batches use the 1-hour TTL (official recommendation): batch requests process concurrently over
	175	+ up to an hour, so 5-min entries can lapse between hits. Caveat: cache hits inside a batch are
	176	+ best-effort — parallel workers may each miss; treat the §7 "with caching" numbers as the optimistic bound.
	177	+- Minimum cacheable prefix is model-dependent and non-monotonic: 512 tokens (Opus 5/Fable 5), 1,024
	178	+ (Opus 4.8, Sonnet 5, Sonnet 4.6), 2,048 (Opus 4.7), 4,096 (Haiku 4.5, Opus 4.6). **A 2k-token rubric
	179	+ silently will not cache on Haiku 4.5** — no error, just `cache_creation_input_tokens: 0`. If Haiku is
	180	+ chosen, either accept uncached input (still cheap) or grow the cached prefix ≥4,096 tokens (e.g. include
	181	+ the scoring examples/anchors in the system block).
	182	+- Cost math for the rubric alone (Sonnet 5 intro, batch, 54k requests, 2,000 tokens):
	183	+ uncached = 54,000 × 2,000 × $1/MTok = $108; cached (1 write + 54k reads at 0.05×) ≈ 108M × $0.10/MTok
	184	+ ≈ $10.80. ~10× saving on the shared-prefix portion.
	185	+- Verify via `usage.cache_read_input_tokens` in each batch result; zero across the run means a silent
	186	+ invalidator (non-deterministic serialization, per-request content above the breakpoint).
	187	+
	188	+## 5. Rate limits relevant to batch rating throughput
	189	+
	190	+Docs: https://platform.claude.com/docs/en/api/rate-limits — organizations sit on Start / Build / Scale /
	191	+Custom tiers (auto-assigned by usage history; monthly spend caps of $500 / $1,000 / $200,000).
	192	+
	193	+Message Batches API has its own limits, shared across all models (separate from Messages ITPM/OTPM):
	194	+
	195	+\| Tier \| API requests/min \| Max batch requests in processing queue \| Max requests per batch \|
	196	+\|---\|---\|---\|---\|
	197	+\| Start \| 1,000 \| 200,000 \| 100,000 \|
	198	+\| Build \| 2,000 \| 300,000 \| 100,000 \|
	199	+\| Scale \| 4,000 \| 500,000 \| 100,000 \|
	200	+
	201	+Implications for us: 54,000 ratings fit in a single batch at any tier; even a full-index recompute with
	202	+several prompt versions in flight stays under the Start-tier queue (200k). Batch throughput inside the
	203	+queue is demand-based, not tier-based — under load, more requests may hit the 24h expiry; the ingester's
	204	+resubmit path (§2) handles that.
	205	+
	206	+For any synchronous rating path (e.g. on-demand re-rate of a single task): limits are per-model RPM +
	207	+ITPM/OTPM (e.g. Start tier, Sonnet 5: 1,000 RPM / 2M ITPM / 400k OTPM). **Cache reads do not count toward
	208	+ITPM** on current models, so the cached rubric also multiplies effective sync throughput. Opus 5 and
	209	+Sonnet 5 each have rate-limit buckets separate from the combined Opus 4.x / Sonnet 4.x pools. On 429, honor
	210	+`retry-after`; the SDK does this automatically (default 2 retries).
	211	+
	212	+## 6. TypeScript SDK (`@anthropic-ai/sdk`) patterns
	213	+
	214	+The SDK auto-retries 408/409/429/5xx with exponential backoff (`maxRetries` default 2; timeout default
	215	+10 min, in milliseconds on TS). Sketch of the worker pieces (model from `RATER_MODEL`, never hardcoded):
	216	+
	217	+```ts
	218	+import Anthropic from "@anthropic-ai/sdk";
	219	+import { createHash } from "node:crypto";
	220	+
	221	+const client = new Anthropic(); // ANTHROPIC_API_KEY from env
	222	+const MODEL = process.env.RATER_MODEL!;
	223	+const PROMPT_VERSION = process.env.RATER_PROMPT_VERSION!;
	224	+
	225	+export const ratingId = (taskId: string, sample: number) =>
	226	+ createHash("sha256").update(`${taskId}:${PROMPT_VERSION}:${sample}`).digest("hex").slice(0, 48);
	227	+// valid as BullMQ job ID and batch custom_id (^[a-zA-Z0-9_-]{1,64}$)
	228	+
	229	+// --- batch-submitter job ---
	230	+export async function submitBatch(rows: PendingRating[]) {
	231	+ const batch = await client.messages.batches.create({
	232	+ requests: rows.map((r) => ({
	233	+ custom_id: r.id, // = ratingId(...)
	234	+ params: {
	235	+ model: MODEL,
	236	+ max_tokens: 1024, // headroom over the ~500-token rating
	237	+ system: [{ type: "text" as const, text: RUBRIC,
	238	+ cache_control: { type: "ephemeral" as const, ttl: "1h" as const } }],
	239	+ output_config: { format: { type: "json_schema", schema: RATING_SCHEMA } },
	240	+ messages: [{ role: "user" as const, content: r.taskPrompt }],
	241	+ },
	242	+ })),
	243	+ });
	244	+ await db.markSubmitted(rows.map((r) => r.id), batch.id); // persist batch_id BEFORE returning
	245	+ return batch.id;
	246	+}
	247	+
	248	+// --- batch-poller job (BullMQ delayed/repeatable, jobId: `poll:${batchId}`) ---
	249	+export async function pollBatch(batchId: string): Promise<boolean> {
	250	+ const batch = await client.messages.batches.retrieve(batchId);
	251	+ return batch.processing_status === "ended"; // else re-schedule in ~60s
	252	+}
	253	+
	254	+// --- batch-ingester job ---
	255	+export async function ingestResults(batchId: string) {
	256	+ for await (const result of await client.messages.batches.results(batchId)) {
	257	+ switch (result.result.type) {
	258	+ case "succeeded": {
	259	+ const msg = result.result.message;
	260	+ const text = msg.content.find((b) => b.type === "text")?.text ?? "";
	261	+ await db.storeRating({
	262	+ customId: result.custom_id, // → task_id + prompt_version + sample
	263	+ model: msg.model,
	264	+ promptVersion: PROMPT_VERSION,
	265	+ rawResponse: JSON.stringify(msg), // full audit trail (CLAUDE.md §6)
	266	+ parsed: JSON.parse(text), // schema-guaranteed {score, rationale, confidence}
	267	+ usage: msg.usage, // incl. cache_read_input_tokens for cost telemetry
	268	+ ratedAt: new Date().toISOString(),
	269	+ });
	270	+ break;
	271	+ }
	272	+ case "errored":
	273	+ if (result.result.error.type === "invalid_request") await db.deadLetter(result.custom_id, result.result);
	274	+ else await db.markPending(result.custom_id); // server error — next submitter run retries
	275	+ break;
	276	+ case "expired":
	277	+ case "canceled":
	278	+ await db.markPending(result.custom_id);
	279	+ break;
	280	+ }
	281	+ }
	282	+}
	283	+```
	284	+
	285	+Notes: results arrive in arbitrary order — key everything by `custom_id`. For sync one-off calls, prefer
	286	+`client.messages.parse()` with `zodOutputFormat(...)` from `@anthropic-ai/sdk/helpers/zod` (typed
	287	+`parsed_output`); for batches, validate the JSON against the same Zod schema at ingest time. Handle errors
	288	+with typed classes (`Anthropic.RateLimitError`, `Anthropic.APIError`), never string-matching.
	289	+
	290	+## 7. Cost estimate: 18,000 tasks × 3 samples = 54,000 ratings
	291	+
	292	+Assumptions: rubric 2,000 tokens (shared, cacheable), per-task suffix ~300 tokens (statement + occupation
	293	+context + instruction), completion ~500 tokens. Totals: input 124.2M tokens (108M rubric + 16.2M
	294	+variable), output 27M tokens. "With caching" = 1h-TTL cache, optimistic ~100% hit rate (real batch runs
	295	+will land between the last two columns), cache read = 0.1× input, stacked with the 50% batch discount
	296	+(0.05× net). Prices per §1.
	297	+
	298	+\| Model \| Sync, no cache \| Batch only (50%) \| Batch + rubric caching \|
	299	+\|---\|---:\|---:\|---:\|
	300	+\| Haiku 4.5 ($1/$5) \| $259 \| $130 \| ≈$130 (2k rubric below Haiku's 4,096-token cache minimum — won't cache; pad rubric ≥4k to reach ≈$92) \|
	301	+\| Sonnet 5 — intro thru 2026-08-31 ($2/$10) \| $518 \| $259 \| ≈$162 ($10.80 cached rubric + $16.20 variable input + $135 output) \|
	302	+\| Sonnet 5 / Sonnet 4.6 — standard ($3/$15) \| $778 \| $389 \| ≈$243 ($16.20 + $24.30 + $202.50) \|
	303	+\| Opus 5 ($5/$25) \| $1,296 \| $648 \| ≈$405 ($27 + $40.50 + $337.50) \|
	304	+\| Fable 5 ($10/$50) \| $2,592 \| $1,296 \| ≈$810 (not recommended for this workload) \|
	305	+
	306	+Takeaways:
	307	+
	308	+- Output tokens dominate once caching is on (83% of the Sonnet cost). Keep rationales tight in the
	309	+ prompt, and control thinking (Sonnet 5/Opus 5 think by default; thinking bills as output — disable it or
	310	+ set `effort: "low"` or the 500-token completion assumption breaks).
	311	+- The entire volume run costs $130–$405 depending on model — negligible against the human-expert
	312	+ pipeline. This argues for Sonnet 5 (or even an Opus 5 second-pass on flagged tasks) over Haiku
	313	+ penny-pinching; if the run happens before 2026-09-01, Sonnet 5's intro pricing makes it ~$162.
	314	+- A full re-run per `INDEX_VERSION`/`RATER_PROMPT_VERSION` bump is affordable, which supports the
	315	+ methodology-integrity rule that recomputations create new immutable runs.
	316	+
	317	+## Where the docs contradict / update CLAUDE.md assumptions
	318	+
	319	+1. Docs URL moved: CLAUDE.md §6 points to `https://docs.claude.com/en/api/overview`; that 301-redirects
	320	+ to `https://platform.claude.com/docs/en/api/overview`. Update the reference.
	321	+2. Temperature-based sampling is gone on current models: if the "3 samples per task" design assumed
	322	+ `temperature > 0` resampling, note that Sonnet 5 / Opus 5 / Opus 4.7+ reject non-default
	323	+ `temperature`/`top_p`/`top_k` with a 400. Variance across samples is natural model stochasticity only
	324	+ (or use Haiku 4.5 / Sonnet 4.6, which still accept temperature).
	325	+3. `output_format` param is deprecated — any prototype code using it should move to
	326	+ `output_config: {format: ...}` (GA, no beta header).
	327	+4. No contradiction on the audit-trail/idempotency requirements — the Batch API's `custom_id` +
	328	+ result-streaming model fits the deterministic-hash design directly; the only gap is that batch create
	329	+ has no server-side idempotency key, so the submitter must persist `batch_id` transactionally (§2.2).

added docs/research/04-landscape-and-evidence.md +307 −0

		`@@ -0,0 +1,307 @@`
	1	+# 04 — Competitive Landscape & Real-World Evidence (through August 2026)
	2	+
	3	+Research memo for the AI Risk Index (airiskindex.io). Compiled 2026-08-05 from live web research.
	4	+Purpose: (a) map existing public AI-job-risk tools, (b) assemble the 2024–2026 empirical record on AI's
	5	+labor-market effects, and (c) propose how this evidence parameterizes the `barriers` and
	6	+`adoption_velocity` scoring dimensions (weights 0.20 / 0.10 in v1, `packages/scoring/src/weights.ts`).
	7	+
	8	+---
	9	+
	10	+## Part A — Competitive landscape of public AI-job-risk tools
	11	+
	12	+### A.1 willrobotstakemyjob.com (the incumbent)
	13	+
	14	+- Since 2017. Tagline: "Find out how likely your job is to be automated — based on real data and user votes."
	15	+- Methodology: Automation-risk probabilities produced "using a similar method" to Frey & Osborne (2013)
	16	+ ("The Future of Employment", Gaussian process classifier over 702 occupations, the famous "47% of US
	17	+ employment at risk" paper), re-estimated "with the most up-to-date data available", plus BLS occupation
	18	+ data (employment, wages, growth). Since 2019 it collects user poll votes on perceived risk; in 2021 it
	19	+ blended BLS data + polls + automation probability into a 0–10 "job score".
	20	+ Source: https://willrobotstakemyjob.com/about
	21	+- Critical weakness: the underlying model is pre-LLM computerization/robotics-era work. Frey–Osborne
	22	+ scored whole occupations on physical/routine automatability (manual dexterity, cramped workspace,
	23	+ fine arts, social perceptiveness...), which inverts under generative AI: it rates cognitive/office work as
	24	+ relatively safe and misses exactly the exposure the 2024–2026 evidence shows (translators, writers,
	25	+ customer service, junior developers). Single doom-number framing ("X% probability of automation"),
	26	+ no exposure/substitution/augmentation distinction, no confidence intervals, no versioning, no API.
	27	+- Traffic (why it still matters): ~71.3K visits in May 2026 per Semrush (down ~20% MoM), world rank
	28	+ ~#50,395; ~42% of traffic from Google organic, ~39% direct. Other estimators give 45K–100K+/month.
	29	+ Sources: https://www.semrush.com/website/willrobotstakemyjob.com/overview/ , https://hypestat.com/info/willrobotstakemyjob.com
	30	+- Monetization: ads; no API or paid tier found.
	31	+
	32	+### A.2 Other public lookup tools (US/EN)
	33	+
	34	+\| Tool \| Data / method \| Output \| Weaknesses \| Pricing/API \|
	35	+\|---\|---\|---\|---\|---\|
	36	+\| willrobotstakemyjob.com (2017) \| Frey–Osborne-style re-estimation + BLS + user polls \| Single automation % + 0–10 "job score" \| Pre-LLM model, whole-occupation doom number, no sub-scores, no versioning \| Free, ads; no API \|
	37	+\| replacedbyrobot.info ("2026 AI Automation Risk Database") \| Claims BLS + O\*NET over "57,000+ occupations" (job titles, not SOC codes) \| "2 risk scores per job" (AI + robotics) \| Opaque method, ad-heavy, title-level pseudo-precision \| Free, AdSense; no API \|
	38	+\| aijobimpactcalculator.com (2026, by Digital Signet) \| ILO 2025 GenAI exposure gradient (4 bands) + Brookings 2024 task rubric on O\*NET 30.2 tasks + BLS EP 2024-34 + WEF FoJ 2025; static, pre-computed; published methodology & revision history \| Exposure band + top-5 tasks tagged Displaceable/Changing/Growing + "what's growing" panel \| Band-level only (4 bands), no composite score, no CI, US-centric \| Free; no API \|
	39	+\| replacemeter.com \| Undisclosed (looks LLM-generated per submitted job title) \| Letter grades: "AI Resilience" %, "Adaptability" % \| No methodology page, arbitrary-title scoring, no provenance \| Free \|
	40	+\| tripleten.com/tools/what-jobs-will-ai-replace \| LLM analysis of user-entered title/industry; "most recent AI research data" (uncited) \| 0–100% automation risk + skills advice + career alternatives \| Lead-gen for a bootcamp; unreproducible \| Free (lead-gen) \|
	41	+\| ailayoffs.live \| Aggregates layoffs.fyi, Goldman, McKinsey, WEF; "Oxford research + real layoff data" risk checker \| Live displacement counters + risk score \| Doom-counter framing, mixes projections with counts \| Free \|
	42	+\| ailayofftracker.com / founderreports.com/ai-layoffs-tracker / skillsyncer.com/layoffs-tracker \| Curated AI-cited layoff announcements (Challenger, TechCrunch sourcing) \| Event lists, totals \| Event trackers, not occupation scores \| Free \|
	43	+\| techjacksolutions.com/job-displacement-trends \| Mash-up: Anthropic Economic Index %, Gartner, BLS growth, "WifiTalents" \| Per-occupation risk ranges (e.g., customer service 67–80%) \| Mixes usage shares with risk %, low-quality sources alongside good ones \| Free \|
	44	+\| Stanford Canaries Dashboard (digitaleconomy.stanford.edu) \| ADP payroll microdata, 4.6M workers, 730+ occupations, continuously updated \| Employment trends by age × AI exposure \| Research dashboard, not per-occupation risk lookup — but the credibility benchmark \| Free \|
	45	+
	46	+Academic/institutional exposure indices that power many of these (no consumer UI of their own):
	47	+Felten–Raj–Seamans AIOE (AI application ↔ 52 O\*NET abilities; https://sites.bu.edu/tpri/2021/06/02/occupational-industry-and-geographic-exposure-to-artificial-intelligence-a-novel-dataset-and-its-potential-uses),
	48	+ILO Global Index of Occupational Exposure to GenAI (ISCO-08, 4 gradient bands; https://webapps.ilo.org/static/english/intserv/working-papers/wp140/index.html),
	49	+Microsoft "AI applicability score" (Tomlinson et al. 2025, 200K Copilot conversations mapped to O\*NET work
	50	+activities — top: interpreters/translators (98% activity overlap), historians, passenger attendants, sales reps,
	51	+writers, customer service reps; https://www.microsoft.com/en-us/research/publication/working-with-ai-measuring-the-occupational-implications-of-generative-ai),
	52	+OpenAI "GPTs are GPTs" (Eloundou et al., Science 2024), *Pew 2023 O\NET work-activity classification**
	53	+(https://www.pewresearch.org/social-trends/2023/07/26/2023-ai-and-jobs-methodology-for-onet-analysis).
	54	+
	55	+### A.3 French / EU equivalents
	56	+
	57	+- jobimpact.aidoption.fr — "Exposition IA du marché de l'emploi français": 532 ROME occupations,
	58	+ treemap (surface = jobs, color = 0–10 exposure), France Travail ROME + DARES data, scored by Claude
	59	+ (LLM-as-rater — directly comparable to our §6 pipeline, but with no audit trail or versioning). Has a /us/ twin.
	60	+- transitions-ia.fr ("IA & Métiers France" observatory) — ROME 4.0 + INSEE EEC 2024 + DARES BMO 2024;
	61	+ task-level exposure score per métier. https://otakuch.github.io/transitions-ia.fr/
	62	+- job-guard.com — French-language "votre métier va-t-il disparaître ?" test; editorial/affiliate quality.
	63	+- Observatoire des Emplois Menacés et Émergents + Coface study (Nov 2025, covered by Les Échos): ~16% of
	64	+ French jobs at risk; white-collar metropolitan jobs most exposed (Paris ~19%, Lyon/Toulouse 18%);
	65	+ legal/accounting, publishing/press, IT programming/consulting, insurance, finance >25% of jobs exposed.
	66	+ https://www.lesechos.fr/monde/europe/ia-le-grand-bouleversement-a-venir-du-marche-du-travail-2221760
	67	+- No credible official FR/EU consumer lookup exists (France Travail offers only e-learning content) → the
	68	+ ESCO/ROME crosswalk in our roadmap targets an empty niche.
	69	+
	70	+### A.4 SEO landscape
	71	+
	72	+- Queries like "will AI take my job (2026)" are dominated not by tools but by listicle/content marketing:
	73	+ Nucamp, NovoResume, Careerminds, AI Weekly, Shawn Kanungo — all citing the same WEF 92M-displaced /
	74	+ 170M-created / +78M-net figure, plus Goldman's −16K net jobs/month (Apr 2026). Tool sites rank on
	75	+ "AI job risk calculator" / "will robots take my job" variants; willrobotstakemyjob.com still owns its
	76	+ brand query with ~42% organic share of its traffic.
	77	+- Opportunity: nothing ranking today combines (1) task-level methodology, (2) sub-scores with uncertainty,
	78	+ (3) live evidence (Challenger/adoption data), (4) versioned transparency, (5) non-doom adaptation framing.
	79	+ aijobimpactcalculator.com is the closest philosophical competitor (source-cited, anti-doom, "what's growing"
	80	+ panel, "how to argue with this" page) but is static, band-level, and has no composite index, no API, no EU coverage.
	81	+
	82	+---
	83	+
	84	+## Part B — Real-world evidence, 2024 → August 2026
	85	+
	86	+### B.1 Entry-level employment effects ("Canaries" line of evidence)
	87	+
	88	+- Brynjolfsson, Chandar & Chen (Stanford/ADP), "Canaries in the Coal Mine?" (Aug 2025, rev. Nov 13 2025):
	89	+ since gen-AI diffusion, workers **aged 22–25 in the most AI-exposed occupations saw a ~16% relative
	90	+ employment decline** (software devs 22–25 down ~20% from late-2022 peak), controlling for firm-level shocks;
	91	+ older workers in the same occupations stable/growing. Adjustment via employment, not wages. Declines
	92	+ concentrated where AI automates rather than augments (per Anthropic Economic Index task classification).
	93	+ https://digitaleconomy.stanford.edu/publication/canaries-in-the-coal-mine-six-facts-about-the-recent-employment-effects-of-artificial-intelligence/
	94	+- Follow-up note (Feb 9, 2026), "Canaries, Interest Rates, and Timing": interest rates don't explain the
	95	+ differential entry-level decline in AI-exposed occupations.
	96	+ https://digitaleconomy.stanford.edu/news/canaries-interest-rates-and-timinga-more-on-recent-drivers-of-employment-changes-for-young-workers
	97	+- Canaries Dashboard (2026): continuous monitoring, 4.6M workers, 730+ occupations; Brynjolfsson June 2026:
	98	+ "Whatever it is, it's not going away."
	99	+ https://digitaleconomy.stanford.edu/project/indicators/canaries-dashboard/ ,
	100	+ https://fortune.com/2026/06/27/what-is-ai-impact-entry-level-jobs-stanford-adp-canaries-brynjolfsson-richardson/
	101	+- UK corroboration: Adzuna — UK entry-level vacancies −32% since ChatGPT launch (Nov 2022 → Jun 2025);
	102	+ entry-level share of market 28.9% → 25%. Graduate vacancies −42.1% YoY in May 2026 (worse than any
	103	+ pandemic month). Indeed (Jun 2025): toughest graduate market since 2018, grad roles −33% YoY.
	104	+ https://www.theguardian.com/business/2025/jun/30/uk-entry-level-jobs-chatgpt-launch-adzuna ,
	105	+ https://www.adzuna.co.uk/job-market-report
	106	+- Grad unemployment by major (NY Fed data, 2025–26): recent CS grads 6.1% unemployment,
	107	+ computer engineering 7.5% — above the all-grad 4.8% average and above history/philosophy majors;
	108	+ entry-level SWE postings ~−30% YoY (Handshake 2025); CS enrollment fell >10% in 2025–26.
	109	+ https://interviewchamp.ai/learn/why-cs-new-grad-unemployment-hit-6-percent-2025 ,
	110	+ https://www.finalroundai.com/blog/computer-science-graduates-face-worst-job-market-in-decades
	111	+- Caveats to keep the index honest: LinkedIn Economic Graph (Apr 2026) notes hiring −20% since 2022 but
	112	+ says it has not seen AI as the demonstrable cause (rates, post-2022 normalization overlap); Brookings 2025
	113	+ ("No AI Jobs Apocalypse, For Now") finds aggregate data doesn't yet show mass displacement.
	114	+
	115	+### B.2 Layoffs attributed to AI (Challenger, Gray & Christmas)
	116	+
	117	+- 2023: Challenger begins tracking "AI" as a stated layoff reason. 2025 full year: 54,836 AI-attributed cuts
	118	+ (~5% of layoffs). 2026 is the discontinuity:
	119	+ - Jan 2026: AI = 7% of cuts → Mar: 25% (AI becomes #1 cited reason for the first time) → Apr: 21,490 cuts,
	120	+ 26% → May: 38,579 cuts, 40% of all cuts — highest monthly total ever recorded → Jun: still #1.
	121	+ - H1 2026: 101,743 AI-cited cuts (~23% of all cuts), nearly 2× all of 2025. AI #1 reason 4 consecutive
	122	+ months (Mar–Jun). Tech sector: 139,156 H1 cuts, +83% YoY, ~31% of all layoffs.
	123	+ - Sources: https://www.challengergray.com/blog/challenger-report-may-job-cuts-rise-16-from-april-highest-may-total-since-2020 ,
	124	+ https://www.challengergray.com/blog/challenger-report-april-job-cuts-rise-38-from-march-ytd-cuts-down-50 ,
	125	+ https://www.techtimes.com/articles/319588/20260703/ai-leads-us-job-cuts-record-4th-month-tech-claims-31-h1-layoffs.htm ,
	126	+ https://www.businessinsider.com/challenger-ai-layoffs-economy-jobs-2026-6 (Challenger itself: "not a jobpocalypse")
	127	+- Goldman Sachs (Apr 2026): AI eliminating ~25,000 US jobs/month, creating ~9,000 → net −16,000/month.
	128	+- Named events: Amazon 30,000 corporate cuts (14K Oct 2025 + 16K Jan 2026, largest in its history; AI/
	129	+ automation cited as an efficiency driver; internal docs reportedly project 600K roles automated by 2033)
	130	+ https://www.geekwire.com/2025/amazon-reportedly-set-to-lay-off-30000-corporate-employees-in-massive-workforce-cut/ ;
	131	+ Salesforce cut ~4,000 customer-support roles as AI agents absorbed workload (Benioff: "I need less heads",
	132	+ Sep 2025) https://fortune.com/2025/09/02/salesforce-ceo-billionaire-marc-benioff-ai-agents-jobs-layoffs-customer-service-sales/ ;
	133	+ HP 4,000–6,000 (Nov 2025, AI-cited).
	134	+- Counter-signal (reversal risk): Klarna replaced ~700 support agents with AI (chatbot = work of 700), then
	135	+ rehired humans through 2025–26 after CSAT dropped on complex/emotional cases → hybrid model. Quality,
	136	+ not cost, was the binding constraint. https://www.digitalapplied.com/blog/klarna-reverses-ai-layoffs-replacing-700-workers-backfired
	137	+
	138	+### B.3 Sector-specific substitution evidence
	139	+
	140	+- Translation — the most-displaced occupation to date. Microsoft applicability rank #1 (98% activity
	141	+ overlap). Society of Authors survey (2024): **36% of translators lost work to GenAI; 43% report income
	142	+ declines; 77% expect negative future income**. Individual accounts (Blood in the Machine, mid-2025): 15-yr
	143	+ technical translator down from six figures to €8K/yr; Quebec FR-EN translator −60% income in 2024.
	144	+ https://www.theguardian.com/books/2024/apr/16/survey-finds-generative-ai-proving-major-threat-to-the-work-of-translators ,
	145	+ https://www.bloodinthemachine.com/p/ai-killed-my-job-translators
	146	+- Customer support: Salesforce −4,000; Klarna cycle (above); fintech cuts in May 2026 mostly AI-cited
	147	+ (Challenger). High exposure AND high realized substitution — but Klarna shows a quality floor.
	148	+- Software engineering: bifurcated. Junior/entry roles: −20% employment (ages 22–25, Canaries), entry
	149	+ postings −30% (Handshake), CS grad unemployment 6.1%; yet BLS still projects **+15% developer growth
	150	+ 2024–34 (~129,200 openings/yr)** and senior demand holds. BCG (2026) classifies SWE as "amplified/divergent"
	151	+ rather than substituted. https://www.bcg.com/publications/2026/ai-will-reshape-more-jobs-than-it-replaces
	152	+
	153	+### B.4 Adoption statistics (the `adoption_velocity` evidence base)
	154	+
	155	+- Census BTOS (official, firm-weighted): AI use in core production 3.8% (Sep 2023) → 4.6% (early 2024) →
	156	+ ~10% (Sep–late 2025, doubling in ~18 months). Question broadened Nov 2025 to "any business function":
	157	+ 17–20% of firms Dec 2025–May 2026; 20–23% expect use within 6 months.
	158	+ https://www.census.gov/library/stories/2026/05/ai-use-businesses.html
	159	+- BTOS AI Supplement (Nov 2025–Jan 2026): 18% of firms firm-weighted = 32% employment-weighted;
	160	+ very large firms in Information / Professional Services / Finance: 50–60% (60–70% employment-weighted);
	161	+ 57% of adopters use AI in ≤3 business functions (top: sales & marketing 52%, strategy/biz-dev 45%, IT 41%);
	162	+ workers use AI in tasks at 23% of firms (41% employment-weighted).
	163	+ https://www.census.gov/library/working-papers/2026/adrm/CES-WP-26-25.html
	164	+- Fed monitoring note (Apr 2026) reconciles the scales: BTOS firms 18%; employment-weighted SBU 78%;
	165	+ 41% of the labor force uses GenAI for work (RPS, Nov 2025; +9.7pp YoY); 50% uses it outside work.
	166	+ https://www.federalreserve.gov/econres/notes/feds-notes/monitoring-ai-adoption-in-the-u-s-economy-20260403.html
	167	+- Ramp AI Index (paid adoption, 70K+ firms' card/bill spend): businesses paying for AI crossed **50.4%
	168	+ in March 2026** (35% a year earlier; 46.8% Jan 2026); jump driven by "late majority" manufacturing/retail;
	169	+ VC-backed startups ~80%. Vendor race: OpenAI 35.2% vs Anthropic 30.6% of businesses (Apr 2026).
	170	+ https://ramp.com/data/ai-index , https://ramp.com/data/april-2026-ai-index
	171	+- McKinsey State of AI (Nov 2025): 88% of orgs use AI in ≥1 function, but ~two-thirds still piloting;
	172	+ 62% experimenting with agents, 23% scaling agents in ≥1 function, ≤10% scaling within a single function;
	173	+ only 39% report any enterprise EBIT impact; ~6% are "high performers" (>5% EBIT from AI).
	174	+ https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
	175	+- Agentic reality check: MIT NANDA: 95% of GenAI pilots show no P&L impact; Gartner: >40% of agentic
	176	+ projects to be cancelled by end-2027; S&P Global/McKinsey: 31% of enterprises run ≥1 agent in production —
	177	+ banking/insurance 47% vs healthcare 18% and government 14%. Gartner: 80% of enterprise apps shipped
	178	+ in Q1 2026 embed an agent (vs 33% in 2024).
	179	+- France anchor: 35% of French firms >10 employees use AI (OPIEC 2025) — EU lags US enterprise adoption.
	180	+
	181	+### B.5 Wage effects & augmentation-vs-substitution
	182	+
	183	+- **Humlum & Vestergaard (NBER w33777; Denmark, 25,000 workers × 7,000 workplaces, 11 exposed occupations,
	184	+ admin-linked): AI chatbots → precisely-estimated null on earnings and hours** (CIs rule out >1–2% average
	185	+ effects; occupation-level >6%); avg time savings only 2.8–3%; >80% of saved time reallocated to other
	186	+ work; 8.4% of workers gained NEW tasks created by AI (e.g., checking AI output); minimal pass-through
	187	+ of gains to wages. https://www.nber.org/papers/w33777 , https://www.andershumlum.com/s/chatbots_july25.pdf
	188	+ → Through 2024, within-worker wage effects ≈ 0; displacement shows up at hiring margins first (B.1).
	189	+- PwC Global AI Jobs Barometer 2026 (1B+ job ads, 27 countries): **62% average wage premium for jobs
	190	+ requiring AI skills** (118% in consumer markets, 16% in government); AI-skill jobs growing 69% vs 9% market;
	191	+ firms most able to use AI: headcount growth 53% vs 36% and wage growth 24% vs 17% vs least-exposed —
	192	+ "two distinct labour-market paths". https://www.pwc.com/gx/en/news-room/press-releases/2026/pwc-2026-ai-jobs-barometer.html
	193	+- *Anthropic Economic Index (Claude usage, task-mapped to O\NET): initial report ~57% augmentation /
	194	+ 43% automation; by Nov 2025, Claude.ai = 52% augmentation / 45% automation** (automation briefly led
	195	+ mid-2025); enterprise API traffic is automation-dominant (back-office workflows: email, document
	196	+ processing, CRM, scheduling). Directive/full-delegation usage rising. Reports:
	197	+ https://www.anthropic.com/research/anthropic-economic-index-january-2026-report ,
	198	+ https://www.anthropic.com/research/economic-index-march-2026-report ,
	199	+ https://www.anthropic.com/research/economic-index-june-2026-report
	200	+ → Validates keeping automation-vs-augmentation as the substitution discriminator (Canaries fact:
	201	+ employment declines concentrate in automation-dominant occupations).
	202	+- BCG (Jan 2026, Revelio 1,500 roles): point estimate 10–15% of US jobs vulnerable over 4–5 years;
	203	+ distinguishes substituted vs "divergent" (demand-expansion offsets 0.5–1.0) vs amplified roles.
	204	+
	205	+### B.6 Regulation & barriers (the `barriers` evidence base)
	206	+
	207	+- EU AI Act — timeline moved under our feet: GPAI obligations applied Aug 2, 2025. The **Annex III
	208	+ high-risk obligations (incl. ALL employment/HR AI: CV screening, targeted job ads, promotion/termination
	209	+ decisions, worker monitoring — Annex III pt. 4) were due Aug 2, 2026 but the "Digital Omnibus" political
	210	+ agreement postpones them to Dec 2, 2027.** Requirements when live: risk management, bias testing, logging,
	211	+ human oversight, conformity assessment. Net effect: EU employment-AI adoption friction persists but the
	212	+ binding date slipped ~16 months (a barriers-lowering event for 2026–27 velocity in the EU).
	213	+ https://ogletree.com/insights-resources/blog-posts/eu-nears-approval-of-agreement-to-delay-rules-for-ai-use-in-employment-decisions/ ,
	214	+ https://accessfinancial.com/eu-ai-act-recruitment-high-risk-hiring-2026/
	215	+- US states: Illinois HB 3773 effective Jan 1, 2026 (AI discrimination in employment = civil-rights
	216	+ violation; notice required; zip-code-proxy ban). Colorado's AI Act (SB 24-205) delayed to Jun 30, 2026, then
	217	+ replaced by narrower SB 26-189 (May 2026). NYC Local Law 144 (bias audits for hiring tools) ongoing.
	218	+ Pattern: US regulates AI deciding about workers, not AI replacing workers.
	219	+ https://ogletree.com/insights-resources/blog-posts/illinois-steps-up-ai-regulation-in-employment-key-takeaways-for-employers/
	220	+- Professional licensing & liability as adoption brakes (occupation-level, measurable):
	221	+ - Legal: AI can't be licensed/disbarred/sworn; legal-specific AI tools show 17–34% error rates; **700+
	222	+ court cases worldwide involve AI hallucinations** with sanctions → yet legal-professional GenAI adoption
	223	+ still jumped 31% → 69% between 2025 and 2026 (54% of firms give no training, 43% no policy).
	224	+ https://www.americanbar.org/groups/law_practice/resources/law-technology-today/2026/whats-really-holding-law-firms-back-from-embracing-ai/
	225	+ - Healthcare: litigious + risk-averse, FDA draft guidance (Jan 2025) on AI credibility in drug decisions;
	226	+ physician AI utilization nonetheless 38% (2023) → 72% (2026), concentrated in administrative tasks —
	227	+ augmentation inside a licensing moat.
	228	+ - Customer-facing quality floors: Klarna reversal (B.2) = empirical "human-contact requirement" barrier.
	229	+- Barrier taxonomy the evidence supports: (1) statutory/licensing monopoly on the task; (2) liability &
	230	+ error-cost asymmetry (hallucination sanctions, malpractice); (3) regulated-process requirements (EU AI Act
	231	+ Annex III, state notice/audit laws); (4) human-contact/quality preference (Klarna); (5) organizational
	232	+ friction (MIT 95% pilot failure; McKinsey: only 6% high performers; workflow redesign is the differentiator).
	233	+
	234	+### B.7 Sector adoption-velocity differentials (who's fast, who's slow, why)
	235	+
	236	+\| Sector \| Signal (2025–26) \| Why \|
	237	+\|---\|---\|---\|
	238	+\| Information / Tech \| BTOS large-firm use 50–60%; >90% of tech companies use AI in ≥1 function \| Digital-native tasks, no licensing, in-house skills \|
	239	+\| Finance & insurance \| BTOS top-3 sector; agents in production: banking/insurance 47% (S&P); NVIDIA State of AI: strongest ROI \| Structured data, measurable use cases; regulation shapes but doesn't block \|
	240	+\| Professional services (legal, consulting, accounting) \| Legal GenAI 31%→69% in one year \| High exposure; liability slows delegation, not use \|
	241	+\| Telecom / Retail & CPG \| Agentic adoption 48% / 47% (NVIDIA) \| Customer-ops scale economics \|
	242	+\| Manufacturing / logistics \| Fastest %-growth in AI job postings; Ramp "late majority" surge drove the 50% crossing (Mar 2026) \| Started low; predictive maintenance, quality control \|
	243	+\| Healthcare \| Physician use 38%→72% but agents-in-production only 18%; admin-first \| Licensing, liability, FDA; augmentation-dominant \|
	244	+\| Government / public sector \| Agents 14%; lowest AI wage premium (16%) \| Procurement, accountability, unionization \|
	245	+\| Construction / trades \| ~1.4% adoption (laggard anecdote); BTOS small-firm use <20% \| Physical, unstructured, small-firm dominated \|
	246	+
	247	+Cross-cutting velocity facts: firm size is the strongest adoption predictor (BTOS: <20% for ≤4-employee firms);
	248	+employment-weighted adoption ≈ 2× firm-weighted; paid adoption (Ramp 50.4%) runs far ahead of production-grade
	249	+deployment (31% ≥1 agent in production) which runs ahead of measured P&L impact (39% any EBIT effect).
	250	+
	251	+---
	252	+
	253	+## Inputs for `barriers` & `adoption_velocity` scoring
	254	+
	255	+### adoption_velocity (weight 0.10) — proposed parameterization
	256	+
	257	+Score each occupation's dominant sector(s) (via BLS OES industry-occupation matrix) on observable adoption,
	258	+not vendor hype. Candidate sub-indicators, each normalizable to 0–1 with a public, refreshable source:
	259	+
	260	+1. Sector AI-use rate, employment-weighted — BTOS bi-weekly sector series + AI Supplement
	261	+ (CES-WP-26-25). Anchors: Information ≈ 0.9 · construction ≈ 0.1.
	262	+2. Sector adoption momentum — 12-month delta in BTOS use rate and/or Ramp AI Index sector series
	263	+ (captures manufacturing/retail late-majority acceleration, Mar 2026).
	264	+3. Agentic deployment depth — % of sector firms with agents in production (S&P Global/McKinsey:
	265	+ banking 47% … government 14%). Agents, not chatbots, are the substitution-relevant margin.
	266	+4. Realized displacement intensity — Challenger AI-cited cuts by industry, trailing 12m, scaled by sector
	267	+ employment (chemicals, fintech, tech score high in 2026); optionally corroborated by Canaries Dashboard
	268	+ 22–25 employment trend for the occupation itself.
	269	+5. Occupation-level usage intensity — Anthropic Economic Index share of usage mapped to the occupation's
	270	+ O\*NET tasks, split augmentation vs automation (automation share feeds substitution, not just velocity).
	271	+
	272	+Suggested v1 formula: `adoption_velocity = 0.35·(1) + 0.20·(2) + 0.20·(3) + 0.15·(4) + 0.10·(5)`, with the
	273	+caveat documented in METHODOLOGY.md that (1)–(3) are sector-level priors and (4)–(5) are occupation-level
	274	+correctors. Update cadence: quarterly (BTOS bi-weekly, Ramp monthly, Challenger monthly, AEI ~quarterly) —
	275	+each refresh = new `INDEX_VERSION` patch run.
	276	+
	277	+### barriers (weight 0.20) — proposed parameterization
	278	+
	279	+Score as adoption friction (high barriers ⇒ lower net risk), with five components:
	280	+
	281	+1. Licensing/authorization requirement (0–1): does task sign-off legally require a licensed human
	282	+ (law, medicine, engineering PE, aviation, finance advice)? Source: O\*NET Job Zone + state licensing DBs.
	283	+ Evidence: legal/medical augment-don't-substitute pattern (B.6).
	284	+2. Liability & error-cost asymmetry (0–1): cost of a wrong AI output (malpractice, sanctions, safety).
	285	+ Proxy: occupation's litigation exposure + documented AI-error sanction record (700+ hallucination cases).
	286	+3. Regulatory-process coverage (0–1): is the occupation's automation itself regulated? EU AI Act Annex III
	287	+ (now Dec 2027), Illinois HB 3773, Colorado SB 26-189, NYC LL144 — maintain a dated rule table per
	288	+ jurisdiction; this component is jurisdiction-specific (US vs EU scores diverge; the Omnibus delay is a
	289	+ worked example of a barrier score dropping between index versions).
	290	+4. Human-contact requirement (0–1): reuse O\*NET work-context variables ("contact with others",
	291	+ "deal with external customers", physical proximity). Empirical anchor: Klarna reversal; CSAT floors.
	292	+5. Organizational/implementation friction (0–1): sector pilot-failure and scaling rates (MIT 95%,
	293	+ McKinsey 6% high performers, Gartner >40% agent-project cancellations) — a global dampener that decays
	294	+ over index versions as deployment matures.
	295	+
	296	+Suggested v1 formula: `barriers = 0.30·licensing + 0.25·liability + 0.20·regulatory + 0.15·human_contact + 0.10·org_friction`.
	297	+
	298	+### Positioning implications (for public copy, "adaptation not doom")
	299	+
	300	+- The 2026 record supports differentiated, hedged claims: entry-level exposure is real and measured
	301	+ (−16% relative, Stanford/ADP; Challenger 101,743 H1-2026 AI-cited cuts) while incumbent wage/hours effects
	302	+ are so far null (Denmark) and AI-skill premia are large (+62%, PwC). That is our three-concept split —
	303	+ exposure ≠ substitution ≠ augmentation — now empirically vindicated; no competitor surfaces it.
	304	+- Publish a "what would change this score" section per occupation (aijobimpactcalculator.com's
	305	+ "how to argue with this" page is the only competitor doing epistemic honesty — match and exceed it).
	306	+- Every headline stat above has a reversal or caveat attached (Klarna, LinkedIn attribution caution,
	307	+ Brookings "no apocalypse yet") — cite these in-product to keep the tone guide credible.

added docs/research/README.md +41 −0

		`@@ -0,0 +1,41 @@`
	1	+# Research Corpus — AI Risk Index (compiled 2026-08-05)
	2	+
	3	+Comprehensive web research to ground the v1 methodology, ETL pipeline, LLM rater, and product positioning. Each document carries full source URLs; claims are dated.
	4	+
	5	+\| Doc \| Contents \|
	6	+\|---\|---\|
	7	+\| [01-existing-indices.md](01-existing-indices.md) \| 20+ AI job-exposure indices & studies (Frey–Osborne → Eloundou → ILO/IMF/OECD → Anthropic Economic Index → 2025–26 frontier), comparison table, validation literature, implications for our 5 dimensions \|
	8	+\| [02-data-sources.md](02-data-sources.md) \| 20 raw data sources with verified URLs, versions, licenses, cadences: O*NET, ESCO/ROME, OEWS wages, employment projections, AI-adoption surveys, benchmark exposure datasets; ingestion & manifest plan \|
	9	+\| [03-llm-rater-api.md](03-llm-rater-api.md) \| Current Anthropic model lineup/pricing, Message Batches API, structured outputs (GA), prompt caching, BullMQ integration pattern, cost estimates for the full rating job \|
	10	+\| [04-landscape-and-evidence.md](04-landscape-and-evidence.md) \| Competitor analysis (US + FR/EU), measured 2024–26 labor-market effects, adoption stats, regulatory barriers; proposed parameterization of `adoption_velocity` and `barriers` \|
	11	+
	12	+## Headline takeaways
	13	+
	14	+Methodology (01)
	15	+- Single-number occupation scores have a poor empirical record: individual indices explain <11% of realized unemployment risk; ensembles reach 30–75% (Frank et al., PNAS Nexus 2025). Our sub-score + CI design is the right call.
	16	+- Yin et al. (2026) found a 19× spread in Eloundou-style exposure headlines depending on which frontier LLM rates the tasks (2.7%–51.5%). ⇒ Rate with multiple models (or multiple samples) and derive `score_low/score_high` from rater disagreement; publish rater identity per run.
	17	+- Rate automation vs. augmentation separately per task (ILO WP140 and the Anthropic Economic Index both discriminate on this; the Stanford "Canaries" employment effects concentrate in automation-exposed occupations).
	18	+- `cost_ratio` is an under-researched dimension no published index operationalizes well — a differentiation opportunity.
	19	+
	20	+Data (02)
	21	+- ⚠️ *ONET is at 30.3 (May 2026); 31.0 lands late Aug 2026.** CLAUDE.md §1 says 29.x — update it. Breaking schema change in 30.x: Technology Skills → Software Skills, Skills split into Essential/Transferable.
	22	+- ESCO v1.2.1 + official ESCO↔O*NET crosswalk CSV (built on v1.1, revalidate URIs); ROME 4.0 on data.gouv.fr (Licence Ouverte).
	23	+- Wages: OEWS May 2025 (released 2026-05-15; BLS blocks non-browser user agents — set UA in fetch scripts).
	24	+- Anthropic Economic Index on Hugging Face (6 releases, CC-BY, keyed to O*NET tasks) is the best empirical calibration source for the rater and for `adoption_velocity`.
	25	+
	26	+Rater pipeline (03)
	27	+- Recommended rater: Sonnet 5 (pilot vs Haiku 4.5 against the 5% human-review sample). Full job (~18k tasks × 3 samples) fits in one Message Batch; ~$130–$250 with batch discount + prompt caching.
	28	+- Structured outputs is GA — use JSON schema with the 5-point score as an `enum`, not tool-forcing.
	29	+- ⚠️ `temperature` is rejected on Sonnet 5/Opus 5 — the 3-sample variance design must rely on model/prompt diversity instead of sampling temperature. Haiku 4.5 has a 4,096-token cache minimum (a 2k rubric won't cache there).
	30	+- Docs moved: docs.claude.com → platform.claude.com.
	31	+
	32	+Positioning (04)
	33	+- No competitor combines task-level scoring, sub-scores with CIs, versioned methodology, EU/France coverage, and a public API. Incumbent willrobotstakemyjob.com (~71k visits/mo) still runs pre-LLM Frey–Osborne with a single doom number. The French market is nearly empty.
	34	+- Evidence base for tone guide ("adaptation, not doom"): effects so far are concentrated (entry-level, automation-exposed occupations: −16% relative employment ages 22–25), not economy-wide; augmentation usage still dominates in AEI data.
	35	+- `adoption_velocity` and `barriers` can each be built from 4–5 refreshable public sources (BTOS, Ramp, Challenger, AEI; licensing/liability/AI-Act coverage) — concrete formulas proposed in doc 04 §final.
	36	+
	37	+## Decisions this research forces (flag before implementation)
	38	+
	39	+1. Bump CLAUDE.md's O*NET reference 29.x → 30.3/31.0 and plan ETL for the 30.x schema renames.
	40	+2. Choose the rater-variance mechanism (multi-model panel vs. multi-prompt) given no `temperature` on Sonnet 5.
	41	+3. Decide whether v1 rates automation and augmentation as two separate task-level LLM ratings (research strongly says yes; affects prompt design, cost ×2, and the composite formula).

added eslint.config.mjs +25 −0

		`@@ -0,0 +1,25 @@`
	1	+import js from "@eslint/js";
	2	+import tseslint from "typescript-eslint";
	3	+
	4	+export default tseslint.config(
	5	+ {
	6	+ ignores: [
	7	+ "/node_modules/",
	8	+ "/.next/",
	9	+ "/dist/",
	10	+ "/.turbo/",
	11	+ "apps/etl/**",
	12	+ "**/next-env.d.ts",
	13	+ ],
	14	+ },
	15	+ js.configs.recommended,
	16	+ ...tseslint.configs.recommended,
	17	+ {
	18	+ rules: {
	19	+ "@typescript-eslint/no-unused-vars": [
	20	+ "error",
	21	+ { argsIgnorePattern: "^_", varsIgnorePattern: "^_" },
	22	+ ],
	23	+ },
	24	+ },
	25	+);

added infra/.env.example +20 −0

		`@@ -0,0 +1,20 @@`
	1	+# Template only — real values live in /srv/airiskindex/.env (never committed).
	2	+DATABASE_URL=
	3	+REDIS_URL=
	4	+ANTHROPIC_API_KEY=
	5	+RATER_MODELS=
	6	+# Optional cross-provider rater panel keys
	7	+OPENAI_API_KEY=
	8	+XAI_API_KEY=
	9	+MISTRAL_API_KEY=
	10	+GEMINI_API_KEY=
	11	+DASHSCOPE_API_KEY=
	12	+DEEPINFRA_API_KEY=
	13	+CEREBRAS_API_KEY=
	14	+PERPLEXITY_API_KEY=
	15	+DEEPSEEK_API_KEY=
	16	+KIMI_API_KEY=
	17	+NGROK_AUTHTOKEN=
	18	+NEXTAUTH_URL=
	19	+NEXTAUTH_SECRET=
	20	+PUBLIC_BASE_URL=

added infra/deploy.sh +19 −0

		`@@ -0,0 +1,19 @@`
	1	+#!/usr/bin/env bash
	2	+# Deploy to m3u96b (staging/current prod) — CLAUDE.md §7. Run ON the node.
	3	+set -euo pipefail
	4	+
	5	+cd /srv/airiskindex
	6	+
	7	+git pull --ff-only origin main
	8	+pnpm install --frozen-lockfile
	9	+pnpm build
	10	+pnpm db:migrate:deploy
	11	+
	12	+sudo systemctl restart airiskindex-web airiskindex-worker
	13	+sudo systemctl status airiskindex-web --no-pager
	14	+
	15	+# Localhost health, then the PUBLIC URL — tunnel failures are the most common outage cause.
	16	+curl -fsS http://127.0.0.1:3000/api/v1/health
	17	+curl -fsS https://www.airiskindex.io/api/v1/health
	18	+
	19	+echo "deploy OK"

added infra/docker-compose.dev.yml +19 −0

		`@@ -0,0 +1,19 @@`
	1	+services:
	2	+ postgres:
	3	+ image: postgres:16
	4	+ environment:
	5	+ POSTGRES_USER: airiskindex
	6	+ POSTGRES_PASSWORD: airiskindex
	7	+ POSTGRES_DB: airiskindex
	8	+ ports:
	9	+ - "127.0.0.1:5432:5432"
	10	+ volumes:
	11	+ - pgdata:/var/lib/postgresql/data
	12	+
	13	+ redis:
	14	+ image: redis:7
	15	+ ports:
	16	+ - "127.0.0.1:6379:6379"
	17	+
	18	+volumes:
	19	+ pgdata:

added infra/docker-compose.prod.yml +23 −0

		`@@ -0,0 +1,23 @@`
	1	+# Production data services on m3u96b (CLAUDE.md §7). App processes run under
	2	+# systemd (airiskindex-web/worker), not in Docker.
	3	+services:
	4	+ postgres:
	5	+ image: postgres:16
	6	+ restart: always
	7	+ environment:
	8	+ POSTGRES_USER: ${POSTGRES_USER:?}
	9	+ POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:?}
	10	+ POSTGRES_DB: airiskindex
	11	+ ports:
	12	+ - "127.0.0.1:5432:5432"
	13	+ volumes:
	14	+ - pgdata:/var/lib/postgresql/data
	15	+
	16	+ redis:
	17	+ image: redis:7
	18	+ restart: always
	19	+ ports:
	20	+ - "127.0.0.1:6379:6379"
	21	+
	22	+volumes:
	23	+ pgdata:

added infra/ngrok.yml +11 −0

		`@@ -0,0 +1,11 @@`
	1	+# Copied to /etc/ngrok/ngrok.yml on m3u96b (CLAUDE.md §7).
	2	+# Custom domain must be configured in the ngrok dashboard + DNS CNAME for
	3	+# www.airiskindex.io. Verify with: dig CNAME www.airiskindex.io
	4	+version: 3
	5	+agent:
	6	+ authtoken: ${NGROK_AUTHTOKEN}
	7	+endpoints:
	8	+ - name: airiskindex
	9	+ url: https://www.airiskindex.io
	10	+ upstream:
	11	+ url: 3000

added package.json +29 −0

		`@@ -0,0 +1,29 @@`
	1	+{
	2	+ "name": "airiskindex",
	3	+ "private": true,
	4	+ "packageManager": "pnpm@9.6.0",
	5	+ "engines": {
	6	+ "node": ">=20"
	7	+ },
	8	+ "scripts": {
	9	+ "dev": "turbo dev --filter=@airiskindex/web",
	10	+ "build": "turbo build",
	11	+ "test": "turbo test",
	12	+ "test:e2e": "pnpm --filter @airiskindex/web test:e2e",
	13	+ "lint": "eslint .",
	14	+ "typecheck": "turbo typecheck",
	15	+ "format": "prettier --write .",
	16	+ "db:migrate": "pnpm --filter @airiskindex/db db:migrate",
	17	+ "db:migrate:deploy": "pnpm --filter @airiskindex/db db:migrate:deploy",
	18	+ "db:seed": "pnpm --filter @airiskindex/db db:seed",
	19	+ "score:recompute": "pnpm --filter @airiskindex/worker score:recompute"
	20	+ },
	21	+ "devDependencies": {
	22	+ "@eslint/js": "^9.8.0",
	23	+ "eslint": "^9.8.0",
	24	+ "prettier": "^3.3.3",
	25	+ "turbo": "^2.0.9",
	26	+ "typescript": "^5.5.4",
	27	+ "typescript-eslint": "^8.0.0"
	28	+ }
	29	+}

added packages/config/package.json +6 −0

		`@@ -0,0 +1,6 @@`
	1	+{
	2	+ "name": "@airiskindex/config",
	3	+ "version": "0.0.0",
	4	+ "private": true,
	5	+ "description": "Shared tsconfig presets for the monorepo"
	6	+}

added packages/config/tsconfig/base.json +16 −0

		`@@ -0,0 +1,16 @@`
	1	+{
	2	+ "$schema": "https://json.schemastore.org/tsconfig",
	3	+ "compilerOptions": {
	4	+ "strict": true,
	5	+ "target": "ES2022",
	6	+ "lib": ["ES2022"],
	7	+ "module": "Preserve",
	8	+ "moduleResolution": "Bundler",
	9	+ "esModuleInterop": true,
	10	+ "skipLibCheck": true,
	11	+ "forceConsistentCasingInFileNames": true,
	12	+ "resolveJsonModule": true,
	13	+ "isolatedModules": true,
	14	+ "noEmit": true
	15	+ }
	16	+}

added packages/config/tsconfig/nextjs.json +13 −0

		`@@ -0,0 +1,13 @@`
	1	+{
	2	+ "$schema": "https://json.schemastore.org/tsconfig",
	3	+ "extends": "./base.json",
	4	+ "compilerOptions": {
	5	+ "lib": ["dom", "dom.iterable", "ES2022"],
	6	+ "jsx": "preserve",
	7	+ "module": "esnext",
	8	+ "moduleResolution": "bundler",
	9	+ "allowJs": true,
	10	+ "incremental": true,
	11	+ "plugins": [{ "name": "next" }]
	12	+ }
	13	+}

added packages/db/package.json +30 −0

		`@@ -0,0 +1,30 @@`
	1	+{
	2	+ "name": "@airiskindex/db",
	3	+ "version": "0.0.0",
	4	+ "private": true,
	5	+ "type": "module",
	6	+ "main": "./src/index.ts",
	7	+ "types": "./src/index.ts",
	8	+ "exports": {
	9	+ ".": {
	10	+ "types": "./src/index.ts",
	11	+ "default": "./src/index.ts"
	12	+ }
	13	+ },
	14	+ "scripts": {
	15	+ "postinstall": "prisma generate",
	16	+ "db:migrate": "prisma migrate dev",
	17	+ "db:migrate:deploy": "prisma migrate deploy",
	18	+ "db:seed": "tsx prisma/seed.ts",
	19	+ "typecheck": "tsc --noEmit"
	20	+ },
	21	+ "dependencies": {
	22	+ "@prisma/client": "^5.17.0"
	23	+ },
	24	+ "devDependencies": {
	25	+ "@types/node": "^20.14.11",
	26	+ "prisma": "^5.17.0",
	27	+ "tsx": "^4.16.2",
	28	+ "typescript": "^5.5.4"
	29	+ }
	30	+}

added packages/db/prisma/schema.prisma +150 −0

		`@@ -0,0 +1,150 @@`
	1	+// AI Risk Index — database schema.
	2	+// Methodology integrity (CLAUDE.md §9): every published score traces to a
	3	+// ScoreRun; runs are immutable — recomputations create new runs.
	4	+
	5	+generator client {
	6	+ provider = "prisma-client-js"
	7	+}
	8	+
	9	+datasource db {
	10	+ provider = "postgresql"
	11	+ url = env("DATABASE_URL")
	12	+}
	13	+
	14	+model Occupation {
	15	+ code String @id // O*NET-SOC 2019, e.g. "15-1252.00"
	16	+ title String
	17	+ description String?
	18	+ // Crosswalks (METHODOLOGY.md §2); crosswalk loss flagged per occupation
	19	+ escoUri String?
	20	+ romeCode String?
	21	+ crosswalkNote String?
	22	+ // Wages as integer cents + ISO currency (CLAUDE.md §5)
	23	+ medianWageCents Int?
	24	+ wageCurrency String @default("USD")
	25	+ employment Int?
	26	+
	27	+ tasks Task[]
	28	+ scores OccupationScore[]
	29	+}
	30	+
	31	+model Task {
	32	+ id String @id // O*NET Task ID
	33	+ occupationCode String
	34	+ statement String
	35	+ /// O*NET Task Ratings importance (IM, 1–5)
	36	+ importance Float?
	37	+
	38	+ occupation Occupation @relation(fields: [occupationCode], references: [code])
	39	+ ratings TaskRating[]
	40	+ overrides ExpertOverride[]
	41	+ scores TaskScore[]
	42	+
	43	+ @@index([occupationCode])
	44	+}
	45	+
	46	+/// One LLM rating of one task on one dimension by one model — full audit
	47	+/// trail (CLAUDE.md §6): model, prompt version, raw response, parsed score.
	48	+model TaskRating {
	49	+ id String @id @default(cuid())
	50	+ taskId String
	51	+ /// DimensionKey from packages/scoring, or "augmentation"
	52	+ dimension String
	53	+ model String
	54	+ promptVersion String
	55	+ sampleIndex Int @default(0)
	56	+ rating Int // 1–5
	57	+ rationale String?
	58	+ rawResponse Json
	59	+ ratedAt DateTime @default(now())
	60	+
	61	+ task Task @relation(fields: [taskId], references: [id])
	62	+
	63	+ @@unique([taskId, dimension, model, promptVersion, sampleIndex])
	64	+ @@index([taskId, dimension])
	65	+ @@index([promptVersion])
	66	+}
	67	+
	68	+/// Expert (Delphi) override: replaces the LLM rating band for a task ×
	69	+/// dimension. Flagged in API output (METHODOLOGY.md §3).
	70	+model ExpertOverride {
	71	+ id String @id @default(cuid())
	72	+ taskId String
	73	+ dimension String
	74	+ ratingLow Int
	75	+ ratingMid Float
	76	+ ratingHigh Int
	77	+ reviewer String
	78	+ note String?
	79	+ createdAt DateTime @default(now())
	80	+
	81	+ task Task @relation(fields: [taskId], references: [id])
	82	+
	83	+ @@unique([taskId, dimension])
	84	+}
	85	+
	86	+/// Immutable computation run. Old runs remain queryable forever.
	87	+model ScoreRun {
	88	+ id String @id @default(cuid())
	89	+ indexVersion String
	90	+ raterPromptVersion String
	91	+ raterModels String[]
	92	+ notes String?
	93	+ createdAt DateTime @default(now())
	94	+
	95	+ occupationScores OccupationScore[]
	96	+ taskScores TaskScore[]
	97	+
	98	+ @@index([indexVersion])
	99	+}
	100	+
	101	+model OccupationScore {
	102	+ id String @id @default(cuid())
	103	+ runId String
	104	+ occupationCode String
	105	+ substitutionLow Float
	106	+ substitution Float
	107	+ substitutionHigh Float
	108	+ exposureLow Float
	109	+ exposure Float
	110	+ exposureHigh Float
	111	+ augmentationLow Float
	112	+ augmentation Float
	113	+ augmentationHigh Float
	114	+ highlyExposedTaskShare Float
	115	+
	116	+ run ScoreRun @relation(fields: [runId], references: [id])
	117	+ occupation Occupation @relation(fields: [occupationCode], references: [code])
	118	+
	119	+ @@unique([runId, occupationCode])
	120	+ @@index([occupationCode])
	121	+}
	122	+
	123	+model TaskScore {
	124	+ id String @id @default(cuid())
	125	+ runId String
	126	+ taskId String
	127	+ substitutionLow Float
	128	+ substitution Float
	129	+ substitutionHigh Float
	130	+ exposureLow Float
	131	+ exposure Float
	132	+ exposureHigh Float
	133	+ augmentationLow Float
	134	+ augmentation Float
	135	+ augmentationHigh Float
	136	+
	137	+ run ScoreRun @relation(fields: [runId], references: [id])
	138	+ task Task @relation(fields: [taskId], references: [id])
	139	+
	140	+ @@unique([runId, taskId])
	141	+ @@index([taskId])
	142	+}
	143	+
	144	+model ApiKey {
	145	+ id String @id @default(cuid())
	146	+ hashedKey String @unique
	147	+ name String
	148	+ rateLimitPerMin Int @default(600)
	149	+ createdAt DateTime @default(now())
	150	+}

added packages/db/prisma/seed.ts +46 −0

		`@@ -0,0 +1,46 @@`
	1	+import { readFileSync } from "node:fs";
	2	+import { PrismaClient } from "@prisma/client";
	3	+
	4	+// Seeds the synthetic methodology example occupation so the app and API have
	5	+// demo data before the real O*NET ETL runs. Real data: `cd apps/etl && make pipeline`.
	6	+const prisma = new PrismaClient();
	7	+
	8	+const example = JSON.parse(
	9	+ readFileSync(new URL("../../../docs/methodology/examples/example-analyst.json", import.meta.url), "utf8"),
	10	+);
	11	+
	12	+async function main(): Promise<void> {
	13	+ const { code, title } = example.occupation;
	14	+ await prisma.occupation.upsert({
	15	+ where: { code },
	16	+ update: { title },
	17	+ create: {
	18	+ code,
	19	+ title,
	20	+ description:
	21	+ "Synthetic occupation from the published methodology example. Not real O*NET data.",
	22	+ },
	23	+ });
	24	+
	25	+ for (const task of example.tasks) {
	26	+ await prisma.task.upsert({
	27	+ where: { id: `${code}-${task.taskId}` },
	28	+ update: { importance: task.importance },
	29	+ create: {
	30	+ id: `${code}-${task.taskId}`,
	31	+ occupationCode: code,
	32	+ statement: `Synthetic task ${task.taskId} (see docs/methodology/examples/)`,
	33	+ importance: task.importance,
	34	+ },
	35	+ });
	36	+ }
	37	+
	38	+ console.log(`Seeded example occupation ${code} with ${example.tasks.length} tasks.`);
	39	+}
	40	+
	41	+main()
	42	+ .catch((error) => {
	43	+ console.error(error);
	44	+ process.exitCode = 1;
	45	+ })
	46	+ .finally(() => prisma.$disconnect());

added packages/db/src/index.ts +12 −0

		`@@ -0,0 +1,12 @@`
	1	+import { PrismaClient } from "@prisma/client";
	2	+
	3	+// Singleton to avoid exhausting connections under Next.js dev hot-reload.
	4	+const globalForPrisma = globalThis as unknown as { prisma?: PrismaClient };
	5	+
	6	+export const prisma = globalForPrisma.prisma ?? new PrismaClient();
	7	+
	8	+if (process.env.NODE_ENV !== "production") {
	9	+ globalForPrisma.prisma = prisma;
	10	+}
	11	+
	12	+export * from "@prisma/client";

added packages/db/tsconfig.json +4 −0

		`@@ -0,0 +1,4 @@`
	1	+{
	2	+ "extends": "../config/tsconfig/base.json",
	3	+ "include": ["src", "prisma"]
	4	+}

added packages/scoring/package.json +24 −0

		`@@ -0,0 +1,24 @@`
	1	+{
	2	+ "name": "@airiskindex/scoring",
	3	+ "version": "1.0.0",
	4	+ "private": true,
	5	+ "type": "module",
	6	+ "main": "./src/index.ts",
	7	+ "types": "./src/index.ts",
	8	+ "exports": {
	9	+ ".": {
	10	+ "types": "./src/index.ts",
	11	+ "default": "./src/index.ts"
	12	+ }
	13	+ },
	14	+ "scripts": {
	15	+ "test": "vitest run",
	16	+ "typecheck": "tsc --noEmit"
	17	+ },
	18	+ "devDependencies": {
	19	+ "@types/node": "^20.14.11",
	20	+ "fast-check": "^3.20.0",
	21	+ "typescript": "^5.5.4",
	22	+ "vitest": "^2.0.4"
	23	+ }
	24	+}

added packages/scoring/src/composite.test.ts +71 −0

		`@@ -0,0 +1,71 @@`
	1	+import { readFileSync } from "node:fs";
	2	+import { describe, expect, it } from "vitest";
	3	+import { HIGH_EXPOSURE_THRESHOLD, scoreOccupation } from "./index";
	4	+
	5	+// Published worked example — METHODOLOGY.md §5-6. Changing it requires an INDEX_VERSION bump.
	6	+const example = JSON.parse(
	7	+ readFileSync(new URL("../../../docs/methodology/examples/example-analyst.json", import.meta.url), "utf8"),
	8	+);
	9	+
	10	+const BAND_KEYS = ["substitution", "exposure", "augmentation"] as const;
	11	+
	12	+describe("published methodology example (example-analyst.json)", () => {
	13	+ const result = scoreOccupation(example.tasks);
	14	+
	15	+ it("matches the expected task scores", () => {
	16	+ expect(result.tasks).toHaveLength(example.expected.taskScores.length);
	17	+ for (const [index, expected] of example.expected.taskScores.entries()) {
	18	+ const actual = result.tasks[index];
	19	+ expect(actual.taskId).toBe(expected.taskId);
	20	+ for (const key of BAND_KEYS) {
	21	+ expect(actual[key].low).toBeCloseTo(expected[key].low, 3);
	22	+ expect(actual[key].score).toBeCloseTo(expected[key].score, 3);
	23	+ expect(actual[key].high).toBeCloseTo(expected[key].high, 3);
	24	+ }
	25	+ }
	26	+ });
	27	+
	28	+ it("matches the expected occupation scores", () => {
	29	+ for (const key of BAND_KEYS) {
	30	+ expect(result[key].low).toBeCloseTo(example.expected.occupation[key].low, 3);
	31	+ expect(result[key].score).toBeCloseTo(example.expected.occupation[key].score, 3);
	32	+ expect(result[key].high).toBeCloseTo(example.expected.occupation[key].high, 3);
	33	+ }
	34	+ });
	35	+
	36	+ it("reports the highly exposed task share", () => {
	37	+ expect(HIGH_EXPOSURE_THRESHOLD).toBe(70);
	38	+ expect(result.highlyExposedTaskShare).toBeCloseTo(
	39	+ example.expected.occupation.highlyExposedTaskShare,
	40	+ 3,
	41	+ );
	42	+ });
	43	+});
	44	+
	45	+describe("scoreOccupation input handling", () => {
	46	+ it("rejects an empty task list", () => {
	47	+ expect(() => scoreOccupation([])).toThrow(RangeError);
	48	+ });
	49	+
	50	+ it("fills missing importance with the occupation mean", () => {
	51	+ const [t1, t2, t3] = example.tasks;
	52	+ const withMissing = [t1, { ...t2, importance: undefined }, t3];
	53	+ const result = scoreOccupation(withMissing);
	54	+ // t2 gets mean(4, 1) = 2.5 → weights 4/7.5, 2.5/7.5, 1/7.5
	55	+ const expected =
	56	+ (4 / 7.5) * 71.25 + (2.5 / 7.5) * 27.5 + (1 / 7.5) * 86.25;
	57	+ expect(result.substitution.score).toBeCloseTo(expected, 3);
	58	+ });
	59	+
	60	+ it("rejects out-of-range ratings", () => {
	61	+ const bad = JSON.parse(JSON.stringify(example.tasks[0]));
	62	+ bad.ratings.automatability.mid = 6;
	63	+ expect(() => scoreOccupation([bad])).toThrow(RangeError);
	64	+ });
	65	+
	66	+ it("rejects inverted bands (low > high)", () => {
	67	+ const bad = JSON.parse(JSON.stringify(example.tasks[0]));
	68	+ bad.ratings.feasibility = { low: 4, mid: 3, high: 2 };
	69	+ expect(() => scoreOccupation([bad])).toThrow(RangeError);
	70	+ });
	71	+});

added packages/scoring/src/composite.ts +60 −0

		`@@ -0,0 +1,60 @@`
	1	+import { scoreTask } from "./task";
	2	+import type { OccupationScores, ScoreBand, TaskInput, TaskScores } from "./types";
	3	+import { INDEX_VERSION } from "./version";
	4	+
	5	+/** Substitution score at or above which a task counts as "highly exposed" — METHODOLOGY.md §6. */
	6	+export const HIGH_EXPOSURE_THRESHOLD = 70;
	7	+
	8	+/**
	9	+ * Aggregate task scores to an occupation: importance-weighted mean, weights
	10	+ * normalized within the occupation — METHODOLOGY.md §6. Pure and deterministic.
	11	+ */
	12	+export function scoreOccupation(tasks: readonly TaskInput[]): OccupationScores {
	13	+ if (tasks.length === 0) {
	14	+ throw new RangeError("scoreOccupation: at least one task is required");
	15	+ }
	16	+
	17	+ const taskScores = tasks.map(scoreTask);
	18	+
	19	+ const provided = tasks
	20	+ .map((task) => task.importance)
	21	+ .filter((value): value is number => value !== undefined);
	22	+ for (const value of provided) {
	23	+ if (!Number.isFinite(value) \|\| value < 0) {
	24	+ throw new RangeError(`scoreOccupation: importance must be a non-negative number, got ${value}`);
	25	+ }
	26	+ }
	27	+ // Tasks lacking an importance rating receive the occupation-mean importance (§6).
	28	+ const fallback =
	29	+ provided.length > 0 ? provided.reduce((sum, value) => sum + value, 0) / provided.length : 1;
	30	+ const raw = tasks.map((task) => task.importance ?? fallback);
	31	+ const rawSum = raw.reduce((sum, value) => sum + value, 0);
	32	+ const weights = rawSum > 0 ? raw.map((value) => value / rawSum) : raw.map(() => 1 / raw.length);
	33	+
	34	+ const aggregate = (pick: (task: TaskScores) => ScoreBand): ScoreBand => {
	35	+ let low = 0;
	36	+ let score = 0;
	37	+ let high = 0;
	38	+ for (const [index, task] of taskScores.entries()) {
	39	+ const weight = weights[index];
	40	+ const band = pick(task);
	41	+ low += weight * band.low;
	42	+ score += weight * band.score;
	43	+ high += weight * band.high;
	44	+ }
	45	+ return { low, score, high };
	46	+ };
	47	+
	48	+ const highlyExposedTaskShare =
	49	+ taskScores.filter((task) => task.substitution.score >= HIGH_EXPOSURE_THRESHOLD).length /
	50	+ taskScores.length;
	51	+
	52	+ return {
	53	+ indexVersion: INDEX_VERSION,
	54	+ substitution: aggregate((task) => task.substitution),
	55	+ exposure: aggregate((task) => task.exposure),
	56	+ augmentation: aggregate((task) => task.augmentation),
	57	+ highlyExposedTaskShare,
	58	+ tasks: taskScores,
	59	+ };
	60	+}

added packages/scoring/src/index.ts +18 −0

		`@@ -0,0 +1,18 @@`
	1	+export { HIGH_EXPOSURE_THRESHOLD, scoreOccupation } from "./composite";
	2	+export { pressure, scoreTask } from "./task";
	3	+export type {
	4	+ OccupationScores,
	5	+ RatingBand,
	6	+ ScoreBand,
	7	+ TaskInput,
	8	+ TaskRatings,
	9	+ TaskScores,
	10	+} from "./types";
	11	+export { INDEX_VERSION } from "./version";
	12	+export {
	13	+ DIMENSIONS,
	14	+ type DimensionKey,
	15	+ EXPOSURE_DIMENSIONS,
	16	+ INVERTED_DIMENSIONS,
	17	+ WEIGHTS,
	18	+} from "./weights";

added packages/scoring/src/properties.test.ts +118 −0

		`@@ -0,0 +1,118 @@`
	1	+import fc from "fast-check";
	2	+import { describe, expect, it } from "vitest";
	3	+import type { RatingBand, TaskInput, TaskRatings } from "./index";
	4	+import { scoreOccupation, scoreTask, WEIGHTS } from "./index";
	5	+
	6	+const ratingBand = fc
	7	+ .tuple(
	8	+ fc.integer({ min: 1, max: 5 }),
	9	+ fc.integer({ min: 1, max: 5 }),
	10	+ fc.integer({ min: 1, max: 5 }),
	11	+ )
	12	+ .map(([a, b, c]): RatingBand => {
	13	+ const [low, mid, high] = [a, b, c].sort((x, y) => x - y);
	14	+ return { low, mid, high };
	15	+ });
	16	+
	17	+const taskRatings = fc.record<TaskRatings>({
	18	+ automatability: ratingBand,
	19	+ feasibility: ratingBand,
	20	+ cost_ratio: ratingBand,
	21	+ barriers: ratingBand,
	22	+ adoption_velocity: ratingBand,
	23	+ augmentation: ratingBand,
	24	+});
	25	+
	26	+const taskInput = fc.record<TaskInput>({
	27	+ taskId: fc.hexaString({ minLength: 1, maxLength: 8 }),
	28	+ importance: fc.double({ min: 0.1, max: 5, noNaN: true }),
	29	+ ratings: taskRatings,
	30	+});
	31	+
	32	+const occupationTasks = fc.array(taskInput, { minLength: 1, maxLength: 8 });
	33	+
	34	+const flat = (rating: number): RatingBand => ({ low: rating, mid: rating, high: rating });
	35	+
	36	+describe("scoring invariants (property-based)", () => {
	37	+ it("weights sum to 1", () => {
	38	+ const sum = Object.values(WEIGHTS).reduce((total, weight) => total + weight, 0);
	39	+ expect(sum).toBeCloseTo(1, 10);
	40	+ });
	41	+
	42	+ it("all score bands satisfy 0 ≤ low ≤ score ≤ high ≤ 100", () => {
	43	+ fc.assert(
	44	+ fc.property(occupationTasks, (tasks) => {
	45	+ const result = scoreOccupation(tasks);
	46	+ const bands = [
	47	+ result.substitution,
	48	+ result.exposure,
	49	+ result.augmentation,
	50	+ ...result.tasks.flatMap((task) => [task.substitution, task.exposure, task.augmentation]),
	51	+ ];
	52	+ for (const band of bands) {
	53	+ expect(band.low).toBeGreaterThanOrEqual(-1e-9);
	54	+ expect(band.score).toBeGreaterThanOrEqual(band.low - 1e-9);
	55	+ expect(band.high).toBeGreaterThanOrEqual(band.score - 1e-9);
	56	+ expect(band.high).toBeLessThanOrEqual(100 + 1e-9);
	57	+ }
	58	+ expect(result.highlyExposedTaskShare).toBeGreaterThanOrEqual(0);
	59	+ expect(result.highlyExposedTaskShare).toBeLessThanOrEqual(1);
	60	+ }),
	61	+ );
	62	+ });
	63	+
	64	+ it("is deterministic", () => {
	65	+ fc.assert(
	66	+ fc.property(occupationTasks, (tasks) => {
	67	+ expect(JSON.stringify(scoreOccupation(tasks))).toBe(JSON.stringify(scoreOccupation(tasks)));
	68	+ }),
	69	+ );
	70	+ });
	71	+
	72	+ it("substitution increases with automatability and decreases with barriers", () => {
	73	+ const flatRating = fc.integer({ min: 1, max: 5 });
	74	+ fc.assert(
	75	+ fc.property(
	76	+ fc.record({
	77	+ feasibility: flatRating,
	78	+ cost_ratio: flatRating,
	79	+ adoption_velocity: flatRating,
	80	+ augmentation: flatRating,
	81	+ }),
	82	+ fc.integer({ min: 1, max: 4 }),
	83	+ (rest, rating) => {
	84	+ const withDims = (automatability: number, barriers: number): number =>
	85	+ scoreTask({
	86	+ taskId: "t",
	87	+ ratings: {
	88	+ automatability: flat(automatability),
	89	+ feasibility: flat(rest.feasibility),
	90	+ cost_ratio: flat(rest.cost_ratio),
	91	+ barriers: flat(barriers),
	92	+ adoption_velocity: flat(rest.adoption_velocity),
	93	+ augmentation: flat(rest.augmentation),
	94	+ },
	95	+ }).substitution.score;
	96	+ expect(withDims(rating + 1, 3)).toBeGreaterThan(withDims(rating, 3));
	97	+ expect(withDims(3, rating + 1)).toBeLessThan(withDims(3, rating));
	98	+ },
	99	+ ),
	100	+ );
	101	+ });
	102	+
	103	+ it("is invariant to uniform scaling of importance weights", () => {
	104	+ fc.assert(
	105	+ fc.property(occupationTasks, fc.double({ min: 0.5, max: 10, noNaN: true }), (tasks, k) => {
	106	+ const scaled = tasks.map((task) => ({
	107	+ ...task,
	108	+ importance: (task.importance ?? 1) * k,
	109	+ }));
	110	+ const a = scoreOccupation(tasks);
	111	+ const b = scoreOccupation(scaled);
	112	+ expect(b.substitution.score).toBeCloseTo(a.substitution.score, 6);
	113	+ expect(b.exposure.score).toBeCloseTo(a.exposure.score, 6);
	114	+ expect(b.augmentation.score).toBeCloseTo(a.augmentation.score, 6);
	115	+ }),
	116	+ );
	117	+ });
	118	+});

added packages/scoring/src/task.ts +99 −0

		`@@ -0,0 +1,99 @@`
	1	+import type { RatingBand, ScoreBand, TaskInput, TaskScores } from "./types";
	2	+import {
	3	+ DIMENSIONS,
	4	+ type DimensionKey,
	5	+ EXPOSURE_DIMENSIONS,
	6	+ INVERTED_DIMENSIONS,
	7	+ WEIGHTS,
	8	+} from "./weights";
	9	+
	10	+function assertRating(value: number, context: string): void {
	11	+ if (!Number.isFinite(value) \|\| value < 1 \|\| value > 5) {
	12	+ throw new RangeError(`${context}: rating must be within [1, 5], got ${value}`);
	13	+ }
	14	+}
	15	+
	16	+function assertBand(band: RatingBand, context: string): void {
	17	+ assertRating(band.low, `${context}.low`);
	18	+ assertRating(band.mid, `${context}.mid`);
	19	+ assertRating(band.high, `${context}.high`);
	20	+ if (band.low > band.mid \|\| band.mid > band.high) {
	21	+ throw new RangeError(`${context}: band must satisfy low ≤ mid ≤ high`);
	22	+ }
	23	+}
	24	+
	25	+interface PressureBand {
	26	+ low: number;
	27	+ mid: number;
	28	+ high: number;
	29	+}
	30	+
	31	+/** METHODOLOGY.md §5: rating (1–5) → substitution pressure in [0, 1]. */
	32	+export function pressure(dimension: DimensionKey, rating: number): number {
	33	+ const p = (rating - 1) / 4;
	34	+ return INVERTED_DIMENSIONS.has(dimension) ? 1 - p : p;
	35	+}
	36	+
	37	+/**
	38	+ * Pressure bounds for a rating band. For inverted dimensions the rating's
	39	+ * HIGH bound minimizes pressure, so the bounds swap — this is what keeps
	40	+ * low ≤ score ≤ high true for every dimension orientation.
	41	+ */
	42	+function pressureBand(dimension: DimensionKey, band: RatingBand): PressureBand {
	43	+ const inverted = INVERTED_DIMENSIONS.has(dimension);
	44	+ return {
	45	+ low: pressure(dimension, inverted ? band.high : band.low),
	46	+ mid: pressure(dimension, band.mid),
	47	+ high: pressure(dimension, inverted ? band.low : band.high),
	48	+ };
	49	+}
	50	+
	51	+function weightedScore(
	52	+ pressures: ReadonlyMap<DimensionKey, PressureBand>,
	53	+ dimensions: readonly DimensionKey[],
	54	+ bound: keyof PressureBand,
	55	+): number {
	56	+ let total = 0;
	57	+ let weightSum = 0;
	58	+ for (const dimension of dimensions) {
	59	+ const band = pressures.get(dimension);
	60	+ if (!band) throw new RangeError(`missing pressure for dimension "${dimension}"`);
	61	+ total += WEIGHTS[dimension] * band[bound];
	62	+ weightSum += WEIGHTS[dimension];
	63	+ }
	64	+ return (100 * total) / weightSum;
	65	+}
	66	+
	67	+function toScoreBand(compute: (bound: keyof PressureBand) => number): ScoreBand {
	68	+ return { low: compute("low"), score: compute("mid"), high: compute("high") };
	69	+}
	70	+
	71	+/** Score a single task — formulas in METHODOLOGY.md §5. Pure and deterministic. */
	72	+export function scoreTask(input: TaskInput): TaskScores {
	73	+ const pressures = new Map<DimensionKey, PressureBand>();
	74	+ for (const dimension of DIMENSIONS) {
	75	+ const band = input.ratings[dimension];
	76	+ if (!band) {
	77	+ throw new RangeError(`task ${input.taskId}: missing rating for dimension "${dimension}"`);
	78	+ }
	79	+ assertBand(band, `task ${input.taskId}.${dimension}`);
	80	+ pressures.set(dimension, pressureBand(dimension, band));
	81	+ }
	82	+
	83	+ const augmentation = input.ratings.augmentation;
	84	+ if (!augmentation) {
	85	+ throw new RangeError(`task ${input.taskId}: missing augmentation rating`);
	86	+ }
	87	+ assertBand(augmentation, `task ${input.taskId}.augmentation`);
	88	+
	89	+ return {
	90	+ taskId: input.taskId,
	91	+ substitution: toScoreBand((bound) => weightedScore(pressures, DIMENSIONS, bound)),
	92	+ exposure: toScoreBand((bound) => weightedScore(pressures, EXPOSURE_DIMENSIONS, bound)),
	93	+ augmentation: {
	94	+ low: (100 * (augmentation.low - 1)) / 4,
	95	+ score: (100 * (augmentation.mid - 1)) / 4,
	96	+ high: (100 * (augmentation.high - 1)) / 4,
	97	+ },
	98	+ };
	99	+}

added packages/scoring/src/types.ts +51 −0

		`@@ -0,0 +1,51 @@`
	1	+import type { DimensionKey } from "./weights";
	2	+
	3	+/**
	4	+ * A 1–5 rating band. low/high span the disagreement across the multi-model
	5	+ * rater panel (or an expert override) — METHODOLOGY.md §3.
	6	+ */
	7	+export interface RatingBand {
	8	+ low: number;
	9	+ mid: number;
	10	+ high: number;
	11	+}
	12	+
	13	+/**
	14	+ * Per-task ratings: the five composite dimensions plus the separate
	15	+ * augmentation rating (not part of the substitution composite).
	16	+ */
	17	+export type TaskRatings = Record<DimensionKey \| "augmentation", RatingBand>;
	18	+
	19	+export interface TaskInput {
	20	+ taskId: string;
	21	+ /**
	22	+ * O*NET Task Ratings importance (IM, 1–5). Omitted → occupation-mean
	23	+ * importance — METHODOLOGY.md §6.
	24	+ */
	25	+ importance?: number;
	26	+ ratings: TaskRatings;
	27	+}
	28	+
	29	+/** A 0–100 score with confidence bounds; low ≤ score ≤ high always holds. */
	30	+export interface ScoreBand {
	31	+ low: number;
	32	+ score: number;
	33	+ high: number;
	34	+}
	35	+
	36	+export interface TaskScores {
	37	+ taskId: string;
	38	+ substitution: ScoreBand;
	39	+ exposure: ScoreBand;
	40	+ augmentation: ScoreBand;
	41	+}
	42	+
	43	+export interface OccupationScores {
	44	+ indexVersion: string;
	45	+ substitution: ScoreBand;
	46	+ exposure: ScoreBand;
	47	+ augmentation: ScoreBand;
	48	+ /** Share of tasks with substitution score ≥ HIGH_EXPOSURE_THRESHOLD — METHODOLOGY.md §6. */
	49	+ highlyExposedTaskShare: number;
	50	+ tasks: TaskScores[];
	51	+}

added packages/scoring/src/version.ts +5 −0

		`@@ -0,0 +1,5 @@`
	1	+/**
	2	+ * Index version (semver). Bump on any change to weights, formulas, or rater
	3	+ * prompts, with a docs/methodology/CHANGELOG.md entry — METHODOLOGY.md §7.
	4	+ */
	5	+export const INDEX_VERSION = "1.0.0-draft.1";

added packages/scoring/src/weights.ts +30 −0

		`@@ -0,0 +1,30 @@`
	1	+/**
	2	+ * Composite dimension weights — the single source of truth (CLAUDE.md §1:
	3	+ * never hardcode weights anywhere else). METHODOLOGY.md §4.
	4	+ */
	5	+export const DIMENSIONS = [
	6	+ "automatability",
	7	+ "feasibility",
	8	+ "cost_ratio",
	9	+ "barriers",
	10	+ "adoption_velocity",
	11	+] as const;
	12	+
	13	+export type DimensionKey = (typeof DIMENSIONS)[number];
	14	+
	15	+export const WEIGHTS: Record<DimensionKey, number> = {
	16	+ automatability: 0.35,
	17	+ feasibility: 0.2,
	18	+ cost_ratio: 0.15,
	19	+ barriers: 0.2,
	20	+ adoption_velocity: 0.1,
	21	+};
	22	+
	23	+/**
	24	+ * Dimensions where a higher rating means LESS substitution pressure
	25	+ * (stronger barriers protect the task) — METHODOLOGY.md §4 "orientation".
	26	+ */
	27	+export const INVERTED_DIMENSIONS: ReadonlySet<DimensionKey> = new Set(["barriers"]);
	28	+
	29	+/** Dimensions composing the exposure sub-score — METHODOLOGY.md §5. */
	30	+export const EXPOSURE_DIMENSIONS: readonly DimensionKey[] = ["automatability", "feasibility"];

added packages/scoring/tsconfig.json +4 −0

		`@@ -0,0 +1,4 @@`
	1	+{
	2	+ "extends": "../config/tsconfig/base.json",
	3	+ "include": ["src"]
	4	+}

added packages/ui/package.json +25 −0

		`@@ -0,0 +1,25 @@`
	1	+{
	2	+ "name": "@airiskindex/ui",
	3	+ "version": "0.0.0",
	4	+ "private": true,
	5	+ "type": "module",
	6	+ "main": "./src/index.ts",
	7	+ "types": "./src/index.ts",
	8	+ "exports": {
	9	+ ".": {
	10	+ "types": "./src/index.ts",
	11	+ "default": "./src/index.ts"
	12	+ }
	13	+ },
	14	+ "scripts": {
	15	+ "typecheck": "tsc --noEmit"
	16	+ },
	17	+ "peerDependencies": {
	18	+ "react": "^18.3.1"
	19	+ },
	20	+ "devDependencies": {
	21	+ "@types/react": "^18.3.3",
	22	+ "react": "^18.3.1",
	23	+ "typescript": "^5.5.4"
	24	+ }
	25	+}

added packages/ui/src/index.ts +1 −0

		`@@ -0,0 +1 @@`
	1	+export { ScoreBandPill, type ScoreBandProps } from "./score-band";

added packages/ui/src/score-band.tsx +23 −0

		`@@ -0,0 +1,23 @@`
	1	+export interface ScoreBandProps {
	2	+ label: string;
	3	+ low: number;
	4	+ score: number;
	5	+ high: number;
	6	+}
	7	+
	8	+/**
	9	+ * Displays a 0–100 score with its confidence interval. The UI must always be
	10	+ * able to display uncertainty (CLAUDE.md §1) — never render `score` without
	11	+ * offering the band.
	12	+ */
	13	+export function ScoreBandPill({ label, low, score, high }: ScoreBandProps): JSX.Element {
	14	+ return (
	15	+ <span className="inline-flex items-baseline gap-2 rounded-full border border-slate-300 px-3 py-1 text-sm">
	16	+ <span className="font-medium text-slate-700">{label}</span>
	17	+ <span className="text-lg font-semibold tabular-nums">{score.toFixed(0)}</span>
	18	+ <span className="text-xs text-slate-500 tabular-nums">
	19	+ {low.toFixed(0)}–{high.toFixed(0)}
	20	+ </span>
	21	+ </span>
	22	+ );
	23	+}

added packages/ui/tsconfig.json +8 −0

		`@@ -0,0 +1,8 @@`
	1	+{
	2	+ "extends": "../config/tsconfig/base.json",
	3	+ "compilerOptions": {
	4	+ "lib": ["dom", "dom.iterable", "ES2022"],
	5	+ "jsx": "react-jsx"
	6	+ },
	7	+ "include": ["src"]
	8	+}

added pnpm-lock.yaml +3425 −0

		`@@ -0,0 +1,3425 @@`
	1	+lockfileVersion: '9.0'
	2	+
	3	+settings:
	4	+ autoInstallPeers: true
	5	+ excludeLinksFromLockfile: false
	6	+
	7	+importers:
	8	+
	9	+ .:
	10	+ devDependencies:
	11	+ '@eslint/js':
	12	+ specifier: ^9.8.0
	13	+ version: 9.39.5
	14	+ eslint:
	15	+ specifier: ^9.8.0
	16	+ version: 9.39.5(jiti@1.21.7)
	17	+ prettier:
	18	+ specifier: ^3.3.3
	19	+ version: 3.9.6
	20	+ turbo:
	21	+ specifier: ^2.0.9
	22	+ version: 2.10.8
	23	+ typescript:
	24	+ specifier: ^5.5.4
	25	+ version: 5.9.3
	26	+ typescript-eslint:
	27	+ specifier: ^8.0.0
	28	+ version: 8.66.0(eslint@9.39.5(jiti@1.21.7))(typescript@5.9.3)
	29	+
	30	+ apps/web:
	31	+ dependencies:
	32	+ '@airiskindex/db':
	33	+ specifier: workspace:*
	34	+ version: link:../../packages/db
	35	+ '@airiskindex/scoring':
	36	+ specifier: workspace:*
	37	+ version: link:../../packages/scoring
	38	+ '@airiskindex/ui':
	39	+ specifier: workspace:*
	40	+ version: link:../../packages/ui
	41	+ next:
	42	+ specifier: ^14.2.5
	43	+ version: 14.2.35(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
	44	+ react:
	45	+ specifier: ^18.3.1
	46	+ version: 18.3.1
	47	+ react-dom:
	48	+ specifier: ^18.3.1
	49	+ version: 18.3.1(react@18.3.1)
	50	+ devDependencies:
	51	+ '@types/node':
	52	+ specifier: ^20.14.11
	53	+ version: 20.19.43
	54	+ '@types/react':
	55	+ specifier: ^18.3.3
	56	+ version: 18.3.31
	57	+ '@types/react-dom':
	58	+ specifier: ^18.3.0
	59	+ version: 18.3.7(@types/react@18.3.31)
	60	+ autoprefixer:
	61	+ specifier: ^10.4.19
	62	+ version: 10.5.4(postcss@8.5.25)
	63	+ postcss:
	64	+ specifier: ^8.4.39
	65	+ version: 8.5.25
	66	+ tailwindcss:
	67	+ specifier: ^3.4.6
	68	+ version: 3.4.19(tsx@4.23.6)
	69	+ typescript:
	70	+ specifier: ^5.5.4
	71	+ version: 5.9.3
	72	+
	73	+ apps/worker:
	74	+ dependencies:
	75	+ '@airiskindex/db':
	76	+ specifier: workspace:*
	77	+ version: link:../../packages/db
	78	+ '@airiskindex/scoring':
	79	+ specifier: workspace:*
	80	+ version: link:../../packages/scoring
	81	+ '@anthropic-ai/sdk':
	82	+ specifier: '>=0.32.1 <1'
	83	+ version: 0.115.0(zod@3.25.76)
	84	+ bullmq:
	85	+ specifier: ^5.8.7
	86	+ version: 5.81.3
	87	+ ioredis:
	88	+ specifier: ^5.4.1
	89	+ version: 5.11.1
	90	+ zod:
	91	+ specifier: ^3.23.8
	92	+ version: 3.25.76
	93	+ devDependencies:
	94	+ '@types/node':
	95	+ specifier: ^20.14.11
	96	+ version: 20.19.43
	97	+ tsx:
	98	+ specifier: ^4.16.2
	99	+ version: 4.23.6
	100	+ typescript:
	101	+ specifier: ^5.5.4
	102	+ version: 5.9.3
	103	+
	104	+ packages/config: {}
	105	+
	106	+ packages/db:
	107	+ dependencies:
	108	+ '@prisma/client':
	109	+ specifier: ^5.17.0
	110	+ version: 5.22.0(prisma@5.22.0)
	111	+ devDependencies:
	112	+ '@types/node':
	113	+ specifier: ^20.14.11
	114	+ version: 20.19.43
	115	+ prisma:
	116	+ specifier: ^5.17.0
	117	+ version: 5.22.0
	118	+ tsx:
	119	+ specifier: ^4.16.2
	120	+ version: 4.23.6
	121	+ typescript:
	122	+ specifier: ^5.5.4
	123	+ version: 5.9.3
	124	+
	125	+ packages/scoring:
	126	+ devDependencies:
	127	+ '@types/node':
	128	+ specifier: ^20.14.11
	129	+ version: 20.19.43
	130	+ fast-check:
	131	+ specifier: ^3.20.0
	132	+ version: 3.23.2
	133	+ typescript:
	134	+ specifier: ^5.5.4
	135	+ version: 5.9.3
	136	+ vitest:
	137	+ specifier: ^2.0.4
	138	+ version: 2.1.9(@types/node@20.19.43)
	139	+
	140	+ packages/ui:
	141	+ devDependencies:
	142	+ '@types/react':
	143	+ specifier: ^18.3.3
	144	+ version: 18.3.31
	145	+ react:
	146	+ specifier: ^18.3.1
	147	+ version: 18.3.1
	148	+ typescript:
	149	+ specifier: ^5.5.4
	150	+ version: 5.9.3
	151	+
	152	+packages:
	153	+
	154	+ '@alloc/quick-lru@5.2.0':
	155	+ resolution: {integrity: sha512-UrcABB+4bUrFABwbluTIBErXwvbsU/V7TZWfmbgJfbkwiBuziS9gxdODUyuiecfdGQ85jglMW6juS3+z5TsKLw==}
	156	+ engines: {node: '>=10'}
	157	+
	158	+ '@anthropic-ai/sdk@0.115.0':
	159	+ resolution: {integrity: sha512-BJrFIVyjNuU8lfDyIJTvlRYzgQg+zEl78BxE7fq8esULsGz9IRQvGtW5spq3tydmtjQb/GFdooKGdGsetpx+lQ==}
	160	+ hasBin: true
	161	+ peerDependencies:
	162	+ zod: ^3.25.0 \|\| ^4.0.0
	163	+ peerDependenciesMeta:
	164	+ zod:
	165	+ optional: true
	166	+
	167	+ '@babel/runtime@7.29.7':
	168	+ resolution: {integrity: sha512-Nq8OhGWiZIZGV6hLHoyAKLLcJihP/xFeBMGJoUrxTX2psI8dCifzLhZISFb+VWS3wFMRDmCGw5R+dOySCqPLhw==}
	169	+ engines: {node: '>=6.9.0'}
	170	+
	171	+ '@esbuild/aix-ppc64@0.21.5':
	172	+ resolution: {integrity: sha512-1SDgH6ZSPTlggy1yI6+Dbkiz8xzpHJEVAlF/AM1tHPLsf5STom9rwtjE4hKAF20FfXXNTFqEYXyJNWh1GiZedQ==}
	173	+ engines: {node: '>=12'}
	174	+ cpu: [ppc64]
	175	+ os: [aix]
	176	+
	177	+ '@esbuild/aix-ppc64@0.28.1':
	178	+ resolution: {integrity: sha512-Svl7tq8k/08+p6CXPpRjQ1fKX+1odH/BQbb48fV6fj3CWHhsoIOoY87w1oHXm0qEpkIK3ZfVgp0hed3XBXzXMQ==}
	179	+ engines: {node: '>=18'}
	180	+ cpu: [ppc64]
	181	+ os: [aix]
	182	+
	183	+ '@esbuild/android-arm64@0.21.5':
	184	+ resolution: {integrity: sha512-c0uX9VAUBQ7dTDCjq+wdyGLowMdtR/GoC2U5IYk/7D1H1JYC0qseD7+11iMP2mRLN9RcCMRcjC4YMclCzGwS/A==}
	185	+ engines: {node: '>=12'}
	186	+ cpu: [arm64]
	187	+ os: [android]
	188	+
	189	+ '@esbuild/android-arm64@0.28.1':
	190	+ resolution: {integrity: sha512-34EGEbCIAgosYz6goLcopX6Mo7NyGv9tfwEM2/7Ce2VcVRk568iSvniGWcUXIy7wEDR1wzolcxcriFVrWYcwBg==}
	191	+ engines: {node: '>=18'}
	192	+ cpu: [arm64]
	193	+ os: [android]
	194	+
	195	+ '@esbuild/android-arm@0.21.5':
	196	+ resolution: {integrity: sha512-vCPvzSjpPHEi1siZdlvAlsPxXl7WbOVUBBAowWug4rJHb68Ox8KualB+1ocNvT5fjv6wpkX6o/iEpbDrf68zcg==}
	197	+ engines: {node: '>=12'}
	198	+ cpu: [arm]
	199	+ os: [android]
	200	+
	201	+ '@esbuild/android-arm@0.28.1':
	202	+ resolution: {integrity: sha512-0k2F129Xdio1TdJfzJ8sy1Q47vUD2NnwdhiAf7drUN1EBTfPf4hsFCtmMgu/6m8JSzsBrlmVjudMBQqOfG8usQ==}
	203	+ engines: {node: '>=18'}
	204	+ cpu: [arm]
	205	+ os: [android]
	206	+
	207	+ '@esbuild/android-x64@0.21.5':
	208	+ resolution: {integrity: sha512-D7aPRUUNHRBwHxzxRvp856rjUHRFW1SdQATKXH2hqA0kAZb1hKmi02OpYRacl0TxIGz/ZmXWlbZgjwWYaCakTA==}
	209	+ engines: {node: '>=12'}
	210	+ cpu: [x64]
	211	+ os: [android]
	212	+
	213	+ '@esbuild/android-x64@0.28.1':
	214	+ resolution: {integrity: sha512-dbwY7ltSMDWsRatcRpCnES4F+im88OCUgGZjy52shC7GqHRE/cYlxNbB4Z4UpJswpcc4Qxd2oE/ufM0p61IKng==}
	215	+ engines: {node: '>=18'}
	216	+ cpu: [x64]
	217	+ os: [android]
	218	+
	219	+ '@esbuild/darwin-arm64@0.21.5':
	220	+ resolution: {integrity: sha512-DwqXqZyuk5AiWWf3UfLiRDJ5EDd49zg6O9wclZ7kUMv2WRFr4HKjXp/5t8JZ11QbQfUS6/cRCKGwYhtNAY88kQ==}
	221	+ engines: {node: '>=12'}
	222	+ cpu: [arm64]
	223	+ os: [darwin]
	224	+
	225	+ '@esbuild/darwin-arm64@0.28.1':
	226	+ resolution: {integrity: sha512-TZbWkQY7kvTAXbXUT7uVACR5cMHsDiSz9z7ZKAX/RTq/WJEk3QyRr0wZpNhBDX+/0CtdqUIJlOiodQcta6tY3Q==}
	227	+ engines: {node: '>=18'}
	228	+ cpu: [arm64]
	229	+ os: [darwin]
	230	+
	231	+ '@esbuild/darwin-x64@0.21.5':
	232	+ resolution: {integrity: sha512-se/JjF8NlmKVG4kNIuyWMV/22ZaerB+qaSi5MdrXtd6R08kvs2qCN4C09miupktDitvh8jRFflwGFBQcxZRjbw==}
	233	+ engines: {node: '>=12'}
	234	+ cpu: [x64]
	235	+ os: [darwin]
	236	+
	237	+ '@esbuild/darwin-x64@0.28.1':
	238	+ resolution: {integrity: sha512-zfdzgK9ACBNZLI/CyHTOx81SyNbM6YXn7rxSgX97VjyiPl9W1i4Ka4fgKECEoFCKGpvBj5qArWIGgQjOwkgskQ==}
	239	+ engines: {node: '>=18'}
	240	+ cpu: [x64]
	241	+ os: [darwin]
	242	+
	243	+ '@esbuild/freebsd-arm64@0.21.5':
	244	+ resolution: {integrity: sha512-5JcRxxRDUJLX8JXp/wcBCy3pENnCgBR9bN6JsY4OmhfUtIHe3ZW0mawA7+RDAcMLrMIZaf03NlQiX9DGyB8h4g==}
	245	+ engines: {node: '>=12'}
	246	+ cpu: [arm64]
	247	+ os: [freebsd]
	248	+
	249	+ '@esbuild/freebsd-arm64@0.28.1':
	250	+ resolution: {integrity: sha512-wG2EA8ENdEI0qhkSZMjfqrdY+ziCYCPMmtZjjIwOmXFjmyzEHn+UUxk5of+SYsjtfs3VpnlC7QLzSI5hY/rOAw==}
	251	+ engines: {node: '>=18'}
	252	+ cpu: [arm64]
	253	+ os: [freebsd]
	254	+
	255	+ '@esbuild/freebsd-x64@0.21.5':
	256	+ resolution: {integrity: sha512-J95kNBj1zkbMXtHVH29bBriQygMXqoVQOQYA+ISs0/2l3T9/kj42ow2mpqerRBxDJnmkUDCaQT/dfNXWX/ZZCQ==}
	257	+ engines: {node: '>=12'}
	258	+ cpu: [x64]
	259	+ os: [freebsd]
	260	+
	261	+ '@esbuild/freebsd-x64@0.28.1':
	262	+ resolution: {integrity: sha512-i7dZ9vQgnvSCzi/rYCXNgtF/U+eKZNJBzu3eTQbRgHnM7tNSizLOkRFAl3qzVc/Op/u5YkHHa4pf/3DOYHthLQ==}
	263	+ engines: {node: '>=18'}
	264	+ cpu: [x64]
	265	+ os: [freebsd]
	266	+
	267	+ '@esbuild/linux-arm64@0.21.5':
	268	+ resolution: {integrity: sha512-ibKvmyYzKsBeX8d8I7MH/TMfWDXBF3db4qM6sy+7re0YXya+K1cem3on9XgdT2EQGMu4hQyZhan7TeQ8XkGp4Q==}
	269	+ engines: {node: '>=12'}
	270	+ cpu: [arm64]
	271	+ os: [linux]
	272	+
	273	+ '@esbuild/linux-arm64@0.28.1':
	274	+ resolution: {integrity: sha512-yHs+0uc8+nvEAfAfxrWQKK5peSNzBc4PegcMO0EJ2hT71uA7vB8Ihg2e77R2P7SG5uYjPbHlLLmve4LLLRCf0g==}
	275	+ engines: {node: '>=18'}
	276	+ cpu: [arm64]
	277	+ os: [linux]
	278	+
	279	+ '@esbuild/linux-arm@0.21.5':
	280	+ resolution: {integrity: sha512-bPb5AHZtbeNGjCKVZ9UGqGwo8EUu4cLq68E95A53KlxAPRmUyYv2D6F0uUI65XisGOL1hBP5mTronbgo+0bFcA==}
	281	+ engines: {node: '>=12'}
	282	+ cpu: [arm]
	283	+ os: [linux]
	284	+
	285	+ '@esbuild/linux-arm@0.28.1':
	286	+ resolution: {integrity: sha512-qVXBOHQS+d5Y722GwJzJUtOLlX7km3CraOaGormF1pDtPd2C/l1SHRPgjLunLGe51Sh5YYWKMFDyV4SxgMQYTQ==}
	287	+ engines: {node: '>=18'}
	288	+ cpu: [arm]
	289	+ os: [linux]
	290	+
	291	+ '@esbuild/linux-ia32@0.21.5':
	292	+ resolution: {integrity: sha512-YvjXDqLRqPDl2dvRODYmmhz4rPeVKYvppfGYKSNGdyZkA01046pLWyRKKI3ax8fbJoK5QbxblURkwK/MWY18Tg==}
	293	+ engines: {node: '>=12'}
	294	+ cpu: [ia32]
	295	+ os: [linux]
	296	+
	297	+ '@esbuild/linux-ia32@0.28.1':
	298	+ resolution: {integrity: sha512-d1z4ZuP0ajrfz/FhGT4vv278rX8KnPPJx8i5+AtK7TYbx9Le9F1hyzurZpkEyjkGa9dUGhQow4C1NmeGvqxN2w==}
	299	+ engines: {node: '>=18'}
	300	+ cpu: [ia32]
	301	+ os: [linux]
	302	+
	303	+ '@esbuild/linux-loong64@0.21.5':
	304	+ resolution: {integrity: sha512-uHf1BmMG8qEvzdrzAqg2SIG/02+4/DHB6a9Kbya0XDvwDEKCoC8ZRWI5JJvNdUjtciBGFQ5PuBlpEOXQj+JQSg==}
	305	+ engines: {node: '>=12'}
	306	+ cpu: [loong64]
	307	+ os: [linux]
	308	+
	309	+ '@esbuild/linux-loong64@0.28.1':
	310	+ resolution: {integrity: sha512-M5sRjUVZrkm1OAPR3dlOYzNmN+loZKGVi1VUQGrwuqLcbR6qeAz+famMhjASeH3YVKvZz+zT1jlh/keC3Rj/lg==}
	311	+ engines: {node: '>=18'}
	312	+ cpu: [loong64]
	313	+ os: [linux]
	314	+
	315	+ '@esbuild/linux-mips64el@0.21.5':
	316	+ resolution: {integrity: sha512-IajOmO+KJK23bj52dFSNCMsz1QP1DqM6cwLUv3W1QwyxkyIWecfafnI555fvSGqEKwjMXVLokcV5ygHW5b3Jbg==}
	317	+ engines: {node: '>=12'}
	318	+ cpu: [mips64el]
	319	+ os: [linux]
	320	+
	321	+ '@esbuild/linux-mips64el@0.28.1':
	322	+ resolution: {integrity: sha512-mRObBZeHh2OxcBFPWE/FjylkRgZdYuiTR3vaTozquCGOH14iP9oN4x4Ge81CoIDYQrXmIxpFumJBu5MtZpnQJQ==}
	323	+ engines: {node: '>=18'}
	324	+ cpu: [mips64el]
	325	+ os: [linux]
	326	+
	327	+ '@esbuild/linux-ppc64@0.21.5':
	328	+ resolution: {integrity: sha512-1hHV/Z4OEfMwpLO8rp7CvlhBDnjsC3CttJXIhBi+5Aj5r+MBvy4egg7wCbe//hSsT+RvDAG7s81tAvpL2XAE4w==}
	329	+ engines: {node: '>=12'}
	330	+ cpu: [ppc64]
	331	+ os: [linux]
	332	+
	333	+ '@esbuild/linux-ppc64@0.28.1':
	334	+ resolution: {integrity: sha512-slScBsMAb3GFDcdrCgLwZtPYRoH2H/youv10QiZyRjmsP48fznoveWytSgCI/R0ZcUgpc0ZhIUEx6LHts8yrfQ==}
	335	+ engines: {node: '>=18'}
	336	+ cpu: [ppc64]
	337	+ os: [linux]
	338	+
	339	+ '@esbuild/linux-riscv64@0.21.5':
	340	+ resolution: {integrity: sha512-2HdXDMd9GMgTGrPWnJzP2ALSokE/0O5HhTUvWIbD3YdjME8JwvSCnNGBnTThKGEB91OZhzrJ4qIIxk/SBmyDDA==}
	341	+ engines: {node: '>=12'}
	342	+ cpu: [riscv64]
	343	+ os: [linux]
	344	+
	345	+ '@esbuild/linux-riscv64@0.28.1':
	346	+ resolution: {integrity: sha512-kw0owk1o0GFETUJyW0jc0G4Yzs0BHZn0JDZ8JRT088vjJYX777BAs1fDGxAC+q831qOs2DTC96mNsG2opdfyyQ==}
	347	+ engines: {node: '>=18'}
	348	+ cpu: [riscv64]
	349	+ os: [linux]
	350	+
	351	+ '@esbuild/linux-s390x@0.21.5':
	352	+ resolution: {integrity: sha512-zus5sxzqBJD3eXxwvjN1yQkRepANgxE9lgOW2qLnmr8ikMTphkjgXu1HR01K4FJg8h1kEEDAqDcZQtbrRnB41A==}
	353	+ engines: {node: '>=12'}
	354	+ cpu: [s390x]
	355	+ os: [linux]
	356	+
	357	+ '@esbuild/linux-s390x@0.28.1':
	358	+ resolution: {integrity: sha512-/lAIjX8aYFRByhh6L5rYtPEDRqa9de/4V/juOXcta5frjvzXO4/sqEtyytse0g3zZFuWu5cDN0MkLz2qRDD2Ag==}
	359	+ engines: {node: '>=18'}
	360	+ cpu: [s390x]
	361	+ os: [linux]
	362	+
	363	+ '@esbuild/linux-x64@0.21.5':
	364	+ resolution: {integrity: sha512-1rYdTpyv03iycF1+BhzrzQJCdOuAOtaqHTWJZCWvijKD2N5Xu0TtVC8/+1faWqcP9iBCWOmjmhoH94dH82BxPQ==}
	365	+ engines: {node: '>=12'}
	366	+ cpu: [x64]
	367	+ os: [linux]
	368	+
	369	+ '@esbuild/linux-x64@0.28.1':
	370	+ resolution: {integrity: sha512-u/anNYF2mmVOEDwLtnQ1wOr3EZ9sTNGLWrsYGYwHWzGA3Si84IOkHXlbWTD1NB+9/1lcnweYKO54uhxZydNzfA==}
	371	+ engines: {node: '>=18'}
	372	+ cpu: [x64]
	373	+ os: [linux]
	374	+
	375	+ '@esbuild/netbsd-arm64@0.28.1':
	376	+ resolution: {integrity: sha512-oks0DYbLwWMmaakTsCb+zL4E+aHRVLom9IJZOAthMQEPiQmydXHkziYEsGYRx0uNV/IjEKGAV941JzH02pflqw==}
	377	+ engines: {node: '>=18'}
	378	+ cpu: [arm64]
	379	+ os: [netbsd]
	380	+
	381	+ '@esbuild/netbsd-x64@0.21.5':
	382	+ resolution: {integrity: sha512-Woi2MXzXjMULccIwMnLciyZH4nCIMpWQAs049KEeMvOcNADVxo0UBIQPfSmxB3CWKedngg7sWZdLvLczpe0tLg==}
	383	+ engines: {node: '>=12'}
	384	+ cpu: [x64]
	385	+ os: [netbsd]
	386	+
	387	+ '@esbuild/netbsd-x64@0.28.1':
	388	+ resolution: {integrity: sha512-aeL6lAnN89Hz43Mlh1G8ARasbuoYvSITDEx0tHh5b7jJnHcssqgjy9Yx430GDpmCa6OyrKoS0aNRjKundRizGg==}
	389	+ engines: {node: '>=18'}
	390	+ cpu: [x64]
	391	+ os: [netbsd]
	392	+
	393	+ '@esbuild/openbsd-arm64@0.28.1':
	394	+ resolution: {integrity: sha512-MEFJe5C3R8pwXdZ5Y21oo6m7ePiS0d9pWucn99O/wvyJZChoIQKrQDxKrGeW8F5+T0okTHesAmDeiHDTIq0V/Q==}
	395	+ engines: {node: '>=18'}
	396	+ cpu: [arm64]
	397	+ os: [openbsd]
	398	+
	399	+ '@esbuild/openbsd-x64@0.21.5':
	400	+ resolution: {integrity: sha512-HLNNw99xsvx12lFBUwoT8EVCsSvRNDVxNpjZ7bPn947b8gJPzeHWyNVhFsaerc0n3TsbOINvRP2byTZ5LKezow==}
	401	+ engines: {node: '>=12'}
	402	+ cpu: [x64]
	403	+ os: [openbsd]
	404	+
	405	+ '@esbuild/openbsd-x64@0.28.1':
	406	+ resolution: {integrity: sha512-i/ZLIOafE0Z8cI/XANJAixoJL/uRAoS2xOA3rb0xN+KK0K177cMAsQYkzHtBrtMXAKuAc7HGgcWiZ/sRC1Nxgw==}
	407	+ engines: {node: '>=18'}
	408	+ cpu: [x64]
	409	+ os: [openbsd]
	410	+
	411	+ '@esbuild/openharmony-arm64@0.28.1':
	412	+ resolution: {integrity: sha512-ge+Z7EXFNt2BO1oAMsVpiQ8EwndV9i1xXerAeTIK7AtPs3bKFXQM7nlRxDSIUIMeueR1CNXxqztLzdNeReKBJg==}
	413	+ engines: {node: '>=18'}
	414	+ cpu: [arm64]
	415	+ os: [openharmony]
	416	+
	417	+ '@esbuild/sunos-x64@0.21.5':
	418	+ resolution: {integrity: sha512-6+gjmFpfy0BHU5Tpptkuh8+uw3mnrvgs+dSPQXQOv3ekbordwnzTVEb4qnIvQcYXq6gzkyTnoZ9dZG+D4garKg==}
	419	+ engines: {node: '>=12'}
	420	+ cpu: [x64]
	421	+ os: [sunos]
	422	+
	423	+ '@esbuild/sunos-x64@0.28.1':
	424	+ resolution: {integrity: sha512-BEjgtECkL3vY+SaSQ6nzVfiALUeFxpawyp8Jmf5PtYhf1Ug40N1h/hxlhts+f1FvSvarEigdxS3BlSMI2PJLcQ==}
	425	+ engines: {node: '>=18'}
	426	+ cpu: [x64]
	427	+ os: [sunos]
	428	+
	429	+ '@esbuild/win32-arm64@0.21.5':
	430	+ resolution: {integrity: sha512-Z0gOTd75VvXqyq7nsl93zwahcTROgqvuAcYDUr+vOv8uHhNSKROyU961kgtCD1e95IqPKSQKH7tBTslnS3tA8A==}
	431	+ engines: {node: '>=12'}
	432	+ cpu: [arm64]
	433	+ os: [win32]
	434	+
	435	+ '@esbuild/win32-arm64@0.28.1':
	436	+ resolution: {integrity: sha512-lCv9eK/H6ZJWbE7bh2nw54CZ9M2nupBxJcTsdk/QQnWkdSjKGuxmmH8/GWrlT1eMmZfn4dGcCjRte397WqfQXA==}
	437	+ engines: {node: '>=18'}
	438	+ cpu: [arm64]
	439	+ os: [win32]
	440	+
	441	+ '@esbuild/win32-ia32@0.21.5':
	442	+ resolution: {integrity: sha512-SWXFF1CL2RVNMaVs+BBClwtfZSvDgtL//G/smwAc5oVK/UPu2Gu9tIaRgFmYFFKrmg3SyAjSrElf0TiJ1v8fYA==}
	443	+ engines: {node: '>=12'}
	444	+ cpu: [ia32]
	445	+ os: [win32]
	446	+
	447	+ '@esbuild/win32-ia32@0.28.1':
	448	+ resolution: {integrity: sha512-zvb/mB2bSCoJOpoCBgYKKpX6YM6mJBlBUVUtVj41DlZJVEB6/0CKlRYxP5wWl1C1ILiCoAU5wZZ4q1P3qeS6Eg==}
	449	+ engines: {node: '>=18'}
	450	+ cpu: [ia32]
	451	+ os: [win32]
	452	+
	453	+ '@esbuild/win32-x64@0.21.5':
	454	+ resolution: {integrity: sha512-tQd/1efJuzPC6rCFwEvLtci/xNFcTZknmXs98FYDfGE4wP9ClFV98nyKrzJKVPMhdDnjzLhdUyMX4PsQAPjwIw==}
	455	+ engines: {node: '>=12'}
	456	+ cpu: [x64]
	457	+ os: [win32]
	458	+
	459	+ '@esbuild/win32-x64@0.28.1':
	460	+ resolution: {integrity: sha512-bm4Mowrv+GXMlpWX++EcXw/iLyd1o3+bJkC2DkWXYVvgZCqD/bSj9ctZeAMC3cIxgjRVR2Dufaiu4YPxr5gW1A==}
	461	+ engines: {node: '>=18'}
	462	+ cpu: [x64]
	463	+ os: [win32]
	464	+
	465	+ '@eslint-community/eslint-utils@4.10.1':
	466	+ resolution: {integrity: sha512-cuadcxVFE8sDK6iWJbs8Sn0av2Nrh2QSGQhVlBW9AaAHqHwjWsZHT8LJ4hFGPh7ASBV2deFdM7H/DPjulmh8rg==}
	467	+ engines: {node: ^12.22.0 \|\| ^14.17.0 \|\| >=16.0.0}
	468	+ peerDependencies:
	469	+ eslint: ^6.0.0 \|\| ^7.0.0 \|\| >=8.0.0
	470	+
	471	+ '@eslint-community/regexpp@4.12.2':
	472	+ resolution: {integrity: sha512-EriSTlt5OC9/7SXkRSCAhfSxxoSUgBm33OH+IkwbdpgoqsSsUg7y3uh+IICI/Qg4BBWr3U2i39RpmycbxMq4ew==}
	473	+ engines: {node: ^12.0.0 \|\| ^14.0.0 \|\| >=16.0.0}
	474	+
	475	+ '@eslint/config-array@0.21.2':
	476	+ resolution: {integrity: sha512-nJl2KGTlrf9GjLimgIru+V/mzgSK0ABCDQRvxw5BjURL7WfH5uoWmizbH7QB6MmnMBd8cIC9uceWnezL1VZWWw==}
	477	+ engines: {node: ^18.18.0 \|\| ^20.9.0 \|\| >=21.1.0}
	478	+
	479	+ '@eslint/config-helpers@0.4.2':
	480	+ resolution: {integrity: sha512-gBrxN88gOIf3R7ja5K9slwNayVcZgK6SOUORm2uBzTeIEfeVaIhOpCtTox3P6R7o2jLFwLFTLnC7kU/RGcYEgw==}
	481	+ engines: {node: ^18.18.0 \|\| ^20.9.0 \|\| >=21.1.0}
	482	+
	483	+ '@eslint/core@0.17.0':
	484	+ resolution: {integrity: sha512-yL/sLrpmtDaFEiUj1osRP4TI2MDz1AddJL+jZ7KSqvBuliN4xqYY54IfdN8qD8Toa6g1iloph1fxQNkjOxrrpQ==}
	485	+ engines: {node: ^18.18.0 \|\| ^20.9.0 \|\| >=21.1.0}
	486	+
	487	+ '@eslint/eslintrc@3.3.6':
	488	+ resolution: {integrity: sha512-l2Ul9PrHsPCKcEY/ac7VgFj9D80C7S68sOKc618SyHDPK36s1XcFebXY0iTzUVn4Yq+YbwvSnDmCz9yxjX+QrA==}
	489	+ engines: {node: ^18.18.0 \|\| ^20.9.0 \|\| >=21.1.0}
	490	+
	491	+ '@eslint/js@9.39.5':
	492	+ resolution: {integrity: sha512-QywQuszQh77pIXCsq998c8hbhSTI/azTty1Z6N53dmAudKHhy573j3yvRLsX2BSp8YpLtoCEG8E9DJe+8zUh4A==}
	493	+ engines: {node: ^18.18.0 \|\| ^20.9.0 \|\| >=21.1.0}
	494	+
	495	+ '@eslint/object-schema@2.1.7':
	496	+ resolution: {integrity: sha512-VtAOaymWVfZcmZbp6E2mympDIHvyjXs/12LqWYjVw6qjrfF+VK+fyG33kChz3nnK+SU5/NeHOqrTEHS8sXO3OA==}
	497	+ engines: {node: ^18.18.0 \|\| ^20.9.0 \|\| >=21.1.0}
	498	+
	499	+ '@eslint/plugin-kit@0.4.1':
	500	+ resolution: {integrity: sha512-43/qtrDUokr7LJqoF2c3+RInu/t4zfrpYdoSDfYyhg52rwLV6TnOvdG4fXm7IkSB3wErkcmJS9iEhjVtOSEjjA==}
	501	+ engines: {node: ^18.18.0 \|\| ^20.9.0 \|\| >=21.1.0}
	502	+
	503	+ '@humanfs/core@0.19.2':
	504	+ resolution: {integrity: sha512-UhXNm+CFMWcbChXywFwkmhqjs3PRCmcSa/hfBgLIb7oQ5HNb1wS0icWsGtSAUNgefHeI+eBrA8I1fxmbHsGdvA==}
	505	+ engines: {node: '>=18.18.0'}
	506	+
	507	+ '@humanfs/node@0.16.8':
	508	+ resolution: {integrity: sha512-gE1eQNZ3R++kTzFUpdGlpmy8kDZD/MLyHqDwqjkVQI0JMdI1D51sy1H958PNXYkM2rAac7e5/CnIKZrHtPh3BQ==}
	509	+ engines: {node: '>=18.18.0'}
	510	+
	511	+ '@humanfs/types@0.15.0':
	512	+ resolution: {integrity: sha512-ZZ1w0aoQkwuUuC7Yf+7sdeaNfqQiiLcSRbfI08oAxqLtpXQr9AIVX7Ay7HLDuiLYAaFPu8oBYNq/QIi9URHJ3Q==}
	513	+ engines: {node: '>=18.18.0'}
	514	+
	515	+ '@humanwhocodes/module-importer@1.0.1':
	516	+ resolution: {integrity: sha512-bxveV4V8v5Yb4ncFTT3rPSgZBOpCkjfK0y4oVVVJwIuDVBRMDXrPyXRL988i5ap9m9bnyEEjWfm5WkBmtffLfA==}
	517	+ engines: {node: '>=12.22'}
	518	+
	519	+ '@humanwhocodes/retry@0.4.3':
	520	+ resolution: {integrity: sha512-bV0Tgo9K4hfPCek+aMAn81RppFKv2ySDQeMoSZuvTASywNTnVJCArCZE2FWqpvIatKu7VMRLWlR1EazvVhDyhQ==}
	521	+ engines: {node: '>=18.18'}
	522	+
	523	+ '@ioredis/commands@1.10.0':
	524	+ resolution: {integrity: sha512-UmeW7z4LfctwoQ5wkhVzgq8tXkreED2xZGpX+Bg+zA+WJFZCT6c062AfCK/Dfk81xZnnwdhJCUMkitihRaoC2Q==}
	525	+
	526	+ '@jridgewell/gen-mapping@0.3.13':
	527	+ resolution: {integrity: sha512-2kkt/7niJ6MgEPxF0bYdQ6etZaA+fQvDcLKckhy1yIQOzaoKjBBjSj63/aLVjYE3qhRt5dvM+uUyfCg6UKCBbA==}
	528	+
	529	+ '@jridgewell/resolve-uri@3.1.2':
	530	+ resolution: {integrity: sha512-bRISgCIjP20/tbWSPWMEi54QVPRZExkuD9lJL+UIxUKtwVJA8wW1Trb1jMs1RFXo1CBTNZ/5hpC9QvmKWdopKw==}
	531	+ engines: {node: '>=6.0.0'}
	532	+
	533	+ '@jridgewell/sourcemap-codec@1.5.5':
	534	+ resolution: {integrity: sha512-cYQ9310grqxueWbl+WuIUIaiUaDcj7WOq5fVhEljNVgRfOUhY9fy2zTvfoqWsnebh8Sl70VScFbICvJnLKB0Og==}
	535	+
	536	+ '@jridgewell/trace-mapping@0.3.31':
	537	+ resolution: {integrity: sha512-zzNR+SdQSDJzc8joaeP8QQoCQr8NuYx2dIIytl1QeBEZHJ9uW6hebsrYgbz8hJwUQao3TWCMtmfV8Nu1twOLAw==}
	538	+
	539	+ '@msgpackr-extract/msgpackr-extract-darwin-arm64@3.0.4':
	540	+ resolution: {integrity: sha512-LCkGo6JDfaBhgST7UpPWgNgLINpcpabaHfyz5OBx75nUYxBsaEPxjnyNjWpeb/xBup/682QnBfRBy2/LvPutZQ==}
	541	+ cpu: [arm64]
	542	+ os: [darwin]
	543	+
	544	+ '@msgpackr-extract/msgpackr-extract-darwin-x64@3.0.4':
	545	+ resolution: {integrity: sha512-zExlW9zUJKZH/tOtVMttwjKa4Xm/3KcNjnE3dPN92uCktwavMxpgCA3MoJK/DOnTWsQgo224OaST27/mPNAf+w==}
	546	+ cpu: [x64]
	547	+ os: [darwin]
	548	+
	549	+ '@msgpackr-extract/msgpackr-extract-linux-arm64@3.0.4':
	550	+ resolution: {integrity: sha512-dgX0P/9wGPJeHFBG+ZmhgE6bmtMt7NP5CRBGyyktpopdk/mW4POnrpQsSLtKI1dwpc+pPLuXHDh6vvskyQE/sw==}
	551	+ cpu: [arm64]
	552	+ os: [linux]
	553	+
	554	+ '@msgpackr-extract/msgpackr-extract-linux-arm@3.0.4':
	555	+ resolution: {integrity: sha512-Tg3yX65f5GbtXLkrYEHE5oibZG9epyYWas7FogTTEJeDEF9JlXJzKgXaNhT3UXlTOeA+AfZpYZYZ0uPj7Cfquw==}
	556	+ cpu: [arm]
	557	+ os: [linux]
	558	+
	559	+ '@msgpackr-extract/msgpackr-extract-linux-x64@3.0.4':
	560	+ resolution: {integrity: sha512-8TNXMEjJc3QEy7R/x1INhgiU+XakDAFUzBhaz7+Rbrs8NH5UQeHQxxmzsSBJGyV6I1jW79undiQm8tOI+D+8FQ==}
	561	+ cpu: [x64]
	562	+ os: [linux]
	563	+
	564	+ '@msgpackr-extract/msgpackr-extract-win32-x64@3.0.4':
	565	+ resolution: {integrity: sha512-CmCXPQrkbwExx3j946/PtHWHbYJiCRBRDl4BlkRQcJB/YOwQxJRTpoo7aTsortjgoJ1x7opzTSxn7C+ASSLVjQ==}
	566	+ cpu: [x64]
	567	+ os: [win32]
	568	+
	569	+ '@napi-rs/lzma-linux-x64-gnu@1.5.1':
	570	+ resolution: {integrity: sha512-oTXEIha4SsuXdTA4Iyskj0kpdx2yVXdhd75c2v3xGrHFfVMsbhTPZU/nMPL4sWKo4pBHm3aucLaqGlF696dTyQ==}
	571	+ engines: {node: ^22.20 \|\| ^24.12 \|\| >=25}
	572	+ cpu: [x64]
	573	+ os: [linux]
	574	+
	575	+ '@next/env@14.2.35':
	576	+ resolution: {integrity: sha512-DuhvCtj4t9Gwrx80dmz2F4t/zKQ4ktN8WrMwOuVzkJfBilwAwGr6v16M5eI8yCuZ63H9TTuEU09Iu2HqkzFPVQ==}
	577	+
	578	+ '@next/swc-darwin-arm64@14.2.33':
	579	+ resolution: {integrity: sha512-HqYnb6pxlsshoSTubdXKu15g3iivcbsMXg4bYpjL2iS/V6aQot+iyF4BUc2qA/J/n55YtvE4PHMKWBKGCF/+wA==}
	580	+ engines: {node: '>= 10'}
	581	+ cpu: [arm64]
	582	+ os: [darwin]
	583	+
	584	+ '@next/swc-darwin-x64@14.2.33':
	585	+ resolution: {integrity: sha512-8HGBeAE5rX3jzKvF593XTTFg3gxeU4f+UWnswa6JPhzaR6+zblO5+fjltJWIZc4aUalqTclvN2QtTC37LxvZAA==}
	586	+ engines: {node: '>= 10'}
	587	+ cpu: [x64]
	588	+ os: [darwin]
	589	+
	590	+ '@next/swc-linux-arm64-gnu@14.2.33':
	591	+ resolution: {integrity: sha512-JXMBka6lNNmqbkvcTtaX8Gu5by9547bukHQvPoLe9VRBx1gHwzf5tdt4AaezW85HAB3pikcvyqBToRTDA4DeLw==}
	592	+ engines: {node: '>= 10'}
	593	+ cpu: [arm64]
	594	+ os: [linux]
	595	+
	596	+ '@next/swc-linux-arm64-musl@14.2.33':
	597	+ resolution: {integrity: sha512-Bm+QulsAItD/x6Ih8wGIMfRJy4G73tu1HJsrccPW6AfqdZd0Sfm5Imhgkgq2+kly065rYMnCOxTBvmvFY1BKfg==}
	598	+ engines: {node: '>= 10'}
	599	+ cpu: [arm64]
	600	+ os: [linux]
	601	+
	602	+ '@next/swc-linux-x64-gnu@14.2.33':
	603	+ resolution: {integrity: sha512-FnFn+ZBgsVMbGDsTqo8zsnRzydvsGV8vfiWwUo1LD8FTmPTdV+otGSWKc4LJec0oSexFnCYVO4hX8P8qQKaSlg==}
	604	+ engines: {node: '>= 10'}
	605	+ cpu: [x64]
	606	+ os: [linux]
	607	+
	608	+ '@next/swc-linux-x64-musl@14.2.33':
	609	+ resolution: {integrity: sha512-345tsIWMzoXaQndUTDv1qypDRiebFxGYx9pYkhwY4hBRaOLt8UGfiWKr9FSSHs25dFIf8ZqIFaPdy5MljdoawA==}
	610	+ engines: {node: '>= 10'}
	611	+ cpu: [x64]
	612	+ os: [linux]
	613	+
	614	+ '@next/swc-win32-arm64-msvc@14.2.33':
	615	+ resolution: {integrity: sha512-nscpt0G6UCTkrT2ppnJnFsYbPDQwmum4GNXYTeoTIdsmMydSKFz9Iny2jpaRupTb+Wl298+Rh82WKzt9LCcqSQ==}
	616	+ engines: {node: '>= 10'}
	617	+ cpu: [arm64]
	618	+ os: [win32]
	619	+
	620	+ '@next/swc-win32-ia32-msvc@14.2.33':
	621	+ resolution: {integrity: sha512-pc9LpGNKhJ0dXQhZ5QMmYxtARwwmWLpeocFmVG5Z0DzWq5Uf0izcI8tLc+qOpqxO1PWqZ5A7J1blrUIKrIFc7Q==}
	622	+ engines: {node: '>= 10'}
	623	+ cpu: [ia32]
	624	+ os: [win32]
	625	+
	626	+ '@next/swc-win32-x64-msvc@14.2.33':
	627	+ resolution: {integrity: sha512-nOjfZMy8B94MdisuzZo9/57xuFVLHJaDj5e/xrduJp9CV2/HrfxTRH2fbyLe+K9QT41WBLUd4iXX3R7jBp0EUg==}
	628	+ engines: {node: '>= 10'}
	629	+ cpu: [x64]
	630	+ os: [win32]
	631	+
	632	+ '@nodelib/fs.scandir@2.1.5':
	633	+ resolution: {integrity: sha512-vq24Bq3ym5HEQm2NKCr3yXDwjc7vTsEThRDnkp2DK9p1uqLR+DHurm/NOTo0KG7HYHU7eppKZj3MyqYuMBf62g==}
	634	+ engines: {node: '>= 8'}
	635	+
	636	+ '@nodelib/fs.stat@2.0.5':
	637	+ resolution: {integrity: sha512-RkhPPp2zrqDAQA/2jNhnztcPAlv64XdhIp7a7454A5ovI7Bukxgt7MX7udwAu3zg1DcpPU0rz3VV1SeaqvY4+A==}
	638	+ engines: {node: '>= 8'}
	639	+
	640	+ '@nodelib/fs.walk@1.2.8':
	641	+ resolution: {integrity: sha512-oGB+UxlgWcgQkgwo8GcEGwemoTFt3FIO9ababBmaGwXIoBKZ+GTy0pP185beGg7Llih/NSHSV2XAs1lnznocSg==}
	642	+ engines: {node: '>= 8'}
	643	+
	644	+ '@prisma/client@5.22.0':
	645	+ resolution: {integrity: sha512-M0SVXfyHnQREBKxCgyo7sffrKttwE6R8PMq330MIUF0pTwjUhLbW84pFDlf06B27XyCR++VtjugEnIHdr07SVA==}
	646	+ engines: {node: '>=16.13'}
	647	+ peerDependencies:
	648	+ prisma: '*'
	649	+ peerDependenciesMeta:
	650	+ prisma:
	651	+ optional: true
	652	+
	653	+ '@prisma/debug@5.22.0':
	654	+ resolution: {integrity: sha512-AUt44v3YJeggO2ZU5BkXI7M4hu9BF2zzH2iF2V5pyXT/lRTyWiElZ7It+bRH1EshoMRxHgpYg4VB6rCM+mG5jQ==}
	655	+
	656	+ '@prisma/engines-version@5.22.0-44.605197351a3c8bdd595af2d2a9bc3025bca48ea2':
	657	+ resolution: {integrity: sha512-2PTmxFR2yHW/eB3uqWtcgRcgAbG1rwG9ZriSvQw+nnb7c4uCr3RAcGMb6/zfE88SKlC1Nj2ziUvc96Z379mHgQ==}
	658	+
	659	+ '@prisma/engines@5.22.0':
	660	+ resolution: {integrity: sha512-UNjfslWhAt06kVL3CjkuYpHAWSO6L4kDCVPegV6itt7nD1kSJavd3vhgAEhjglLJJKEdJ7oIqDJ+yHk6qO8gPA==}
	661	+
	662	+ '@prisma/fetch-engine@5.22.0':
	663	+ resolution: {integrity: sha512-bkrD/Mc2fSvkQBV5EpoFcZ87AvOgDxbG99488a5cexp5Ccny+UM6MAe/UFkUC0wLYD9+9befNOqGiIJhhq+HbA==}
	664	+
	665	+ '@prisma/get-platform@5.22.0':
	666	+ resolution: {integrity: sha512-pHhpQdr1UPFpt+zFfnPazhulaZYCUqeIcPpJViYoq9R+D/yw4fjE+CtnsnKzPYm0ddUbeXUzjGVGIRVgPDCk4Q==}
	667	+
	668	+ '@rollup/rollup-android-arm-eabi@4.62.4':
	669	+ resolution: {integrity: sha512-RrPokAb7dmbxFoeO3TloqHyOjgye8RkBhSqmp4aJMIex4c9r46ZstPnleDQOq1t46VOVjwIuwNogIqbodV1Vvg==}
	670	+ cpu: [arm]
	671	+ os: [android]
	672	+
	673	+ '@rollup/rollup-android-arm64@4.62.4':
	674	+ resolution: {integrity: sha512-JKuJc+pnpks2pjy7L/N3v/cAkZxYlnmuZoD840ldbMI5KDbC4iO9NKwPKYdjYFCMAIIlBzYSFHxIJVYzRo2/8A==}
	675	+ cpu: [arm64]
	676	+ os: [android]
	677	+
	678	+ '@rollup/rollup-darwin-arm64@4.62.4':
	679	+ resolution: {integrity: sha512-krw5uS2STmvJ02x0uTXHbqQNuz+9eZ1iw+qXk9dmW2gvV4jV7O2hEoOnuhFrpOPiel1mBFtqbxYZZtC46hXLOw==}
	680	+ cpu: [arm64]
	681	+ os: [darwin]
	682	+
	683	+ '@rollup/rollup-darwin-x64@4.62.4':
	684	+ resolution: {integrity: sha512-wsTxtgApb4PrOsNJIm0FZ1h3WvCC+k9uxLJ4ad75hgoS4NiRes2SoJFlDAyMwiUY8IssDqGcHbXuN0sx1tfF1A==}
	685	+ cpu: [x64]
	686	+ os: [darwin]
	687	+
	688	+ '@rollup/rollup-freebsd-arm64@4.62.4':
	689	+ resolution: {integrity: sha512-GUOnQlyZe3yAXhWOtOMsn5Qkrv5E5mZXa0thbARWi5Ei2szlVXJFQhddZ4HbAzh8q92w5twp+CQvs/eFanz9YQ==}
	690	+ cpu: [arm64]
	691	+ os: [freebsd]
	692	+
	693	+ '@rollup/rollup-freebsd-x64@4.62.4':
	694	+ resolution: {integrity: sha512-/Y7f3QuxjzPKsjA/rfEDa3+0vXqyjmJ50Ln8dPpCmWkKTrUoWHG1cWhTqaAMLob2m2nESWuC7yGrREz019Ztqg==}
	695	+ cpu: [x64]
	696	+ os: [freebsd]
	697	+
	698	+ '@rollup/rollup-linux-arm-gnueabihf@4.62.4':
	699	+ resolution: {integrity: sha512-81wiiX3v7aqy+T+bT61TJ78yJjRquqFFTTbAPt08imfQQzkPIW8t6aJbkTagtCCrXMNc9D66+geqlK7ydLPNqA==}
	700	+ cpu: [arm]
	701	+ os: [linux]
	702	+
	703	+ '@rollup/rollup-linux-arm-musleabihf@4.62.4':
	704	+ resolution: {integrity: sha512-9kmDIvNZqdoHOBZgNtpTBeLWYO/LVipM3H/j62P8848/l/VPEQL6N3uxU9pvP1oZAsXyC2MEnFP3ovRjo7WYNQ==}
	705	+ cpu: [arm]
	706	+ os: [linux]
	707	+
	708	+ '@rollup/rollup-linux-arm64-gnu@4.62.4':
	709	+ resolution: {integrity: sha512-CcnXHWnXg69g+DX5VWL3FHts3qMRN2uVEHX+BZvGLdd07/gXkn3ePjYtO1LDJvxkGKVHMclKBRa1QUTH+6toYQ==}
	710	+ cpu: [arm64]
	711	+ os: [linux]
	712	+
	713	+ '@rollup/rollup-linux-arm64-musl@4.62.4':
	714	+ resolution: {integrity: sha512-iFOibiHnTRuhrWLlRsOQFdZJJIa7S8OwkneJr4ocALP16u5yk6lWLINFwhHaEqBFMsKDUZofLkGos7+CPzGB3g==}
	715	+ cpu: [arm64]
	716	+ os: [linux]
	717	+
	718	+ '@rollup/rollup-linux-loong64-gnu@4.62.4':
	719	+ resolution: {integrity: sha512-XnWYMI7euHlb5a871xPja+Gm7DRCFU+FGRrtS2sMq9N8FvqtpagUy6gD4YOemC5MRk9xbh8+jYMEJbigFQwsgA==}
	720	+ cpu: [loong64]
	721	+ os: [linux]
	722	+
	723	+ '@rollup/rollup-linux-loong64-musl@4.62.4':
	724	+ resolution: {integrity: sha512-qGDAlO0U8xedCcsdRm9oaoQY8DAx/QT7uIxJWhCdx0ceIWX783UC9QSYkdpzAe29wNiVfp24+bZdQmn49o45SQ==}
	725	+ cpu: [loong64]
	726	+ os: [linux]
	727	+
	728	+ '@rollup/rollup-linux-ppc64-gnu@4.62.4':
	729	+ resolution: {integrity: sha512-ru4H6ezD7ysA5EiEK6qkkaEb4modH8CTej6kUy/gQi20u3kB3G7Zn8snXXkeJSCOFKG/rbPPtM/+9Wgas1961w==}
	730	+ cpu: [ppc64]
	731	+ os: [linux]
	732	+
	733	+ '@rollup/rollup-linux-ppc64-musl@4.62.4':
	734	+ resolution: {integrity: sha512-2W4MO5WQVJnbJaZdvDb9rhBDuFU1nKIepPFpJUBsTh2k1YY2g+ODViaWuyOAjQ5cOP7NvrvLzt3wvHOoiAvc7w==}
	735	+ cpu: [ppc64]
	736	+ os: [linux]
	737	+
	738	+ '@rollup/rollup-linux-riscv64-gnu@4.62.4':
	739	+ resolution: {integrity: sha512-+fxjfuoAmVMCYV5QyjoIpu0cp5DOiOTeqYFk1AVaxGr+/ravWLX89XfQmptsoWcaVy/TGf2hexzbUOrCQIL1CQ==}
	740	+ cpu: [riscv64]
	741	+ os: [linux]
	742	+
	743	+ '@rollup/rollup-linux-riscv64-musl@4.62.4':
	744	+ resolution: {integrity: sha512-jTn8JfHGL4djjFxPuM06LmNUJDsst2jeVlsd9OmIH6zc5sC9K6rIuO4YajXatLUpBmBKl6b35ro1QZocLi+tcA==}
	745	+ cpu: [riscv64]
	746	+ os: [linux]
	747	+
	748	+ '@rollup/rollup-linux-s390x-gnu@4.62.4':
	749	+ resolution: {integrity: sha512-oCJCJL4pXsoDcP2QZ+JVlPTIRc6266zsIaeJJsWImmF7HO0W8nb6HuSgZlMWxJwaPf8ehbSw8yo0EUw925hKsA==}
	750	+ cpu: [s390x]
	751	+ os: [linux]
	752	+
	753	+ '@rollup/rollup-linux-x64-gnu@4.62.4':
	754	+ resolution: {integrity: sha512-W69hukhZ3KKNRCaMIEzKvcFye42hh0FE1+YoYaf5+Ikacuftoco6yO/xouz0hc5d5W/s3yBro5jRiuEE/Q5vUw==}
	755	+ cpu: [x64]
	756	+ os: [linux]
	757	+
	758	+ '@rollup/rollup-linux-x64-musl@4.62.4':
	759	+ resolution: {integrity: sha512-qiXbGG2jkjXhzXpsFZSR2Xpb8DN/UaxYsbb/STbuR/6fpaDgRmmaq1B/LmtF2wQFOFOSsK2jdE0RZ3a0zHn4QA==}
	760	+ cpu: [x64]
	761	+ os: [linux]
	762	+
	763	+ '@rollup/rollup-openbsd-x64@4.62.4':
	764	+ resolution: {integrity: sha512-nWeM//hxv8mIo6jD7Hu4o48DVmV9pbV6gsKaWU+4NFyqHoPKwrkRiZGLKUhOBk8qNmDmpwFtPKg80Bo/Tn4xiQ==}
	765	+ cpu: [x64]
	766	+ os: [openbsd]
	767	+
	768	+ '@rollup/rollup-openharmony-arm64@4.62.4':
	769	+ resolution: {integrity: sha512-s62SQ/vgsRSvMwDkOEfTqfgASF0f26ZNaQuTA6Aok5lrikf89yI2W0gFHvZb2Jpgc6N8JnOKZgCK2iciO3CsxQ==}
	770	+ cpu: [arm64]
	771	+ os: [openharmony]
	772	+
	773	+ '@rollup/rollup-win32-arm64-msvc@4.62.4':
	774	+ resolution: {integrity: sha512-J6wGf8TVGbXJq+HH+ttTvrcfNKPbuZecV6KT1B8I18BC5IURUh5kl4Yl5OEP5eFIUoI5BWxCsyYMhFsDx8kekw==}
	775	+ cpu: [arm64]
	776	+ os: [win32]
	777	+
	778	+ '@rollup/rollup-win32-ia32-msvc@4.62.4':
	779	+ resolution: {integrity: sha512-zmfrQd/0wu6oJs8Vq8KwY/YtsKSsLtKe/HwAP4Wqy8LhWjeT55fHRAkOhYQ12wI3ayS4Tt12d5CDRD7N96SAYQ==}
	780	+ cpu: [ia32]
	781	+ os: [win32]
	782	+
	783	+ '@rollup/rollup-win32-x64-gnu@4.62.4':
	784	+ resolution: {integrity: sha512-qPzHqdj9rfUD+w79dtE07zi/kFwKyCJqplp5K5ygeLTp7jLpAoc16OAH39HSmRC9UpozaecsleI8uAdEj6v2yw==}
	785	+ cpu: [x64]
	786	+ os: [win32]
	787	+
	788	+ '@rollup/rollup-win32-x64-msvc@4.62.4':
	789	+ resolution: {integrity: sha512-zD6NdeWEByGE9QF9vCrlJ5YQB4oq9q91kPZS37Jwj5hOkvR1lTBSpsKhKDw4IJtbQ35LsTS1HD9DZYGKIshU1Q==}
	790	+ cpu: [x64]
	791	+ os: [win32]
	792	+
	793	+ '@stablelib/base64@1.0.1':
	794	+ resolution: {integrity: sha512-1bnPQqSxSuc3Ii6MhBysoWCg58j97aUjuCSZrGSmDxNqtytIi0k8utUenAwTZN4V5mXXYGsVUI9zeBqy+jBOSQ==}
	795	+
	796	+ '@swc/counter@0.1.3':
	797	+ resolution: {integrity: sha512-e2BR4lsJkkRlKZ/qCHPw9ZaSxc0MVUd7gtbtaB7aMvHeJVYe8sOB8DBZkP2DtISHGSku9sCK6T6cnY0CtXrOCQ==}
	798	+
	799	+ '@swc/helpers@0.5.5':
	800	+ resolution: {integrity: sha512-KGYxvIOXcceOAbEk4bi/dVLEK9z8sZ0uBB3Il5b1rhfClSpcX0yfRO0KmTkqR2cnQDymwLB+25ZyMzICg/cm/A==}
	801	+
	802	+ '@turbo/darwin-64@2.10.8':
	803	+ resolution: {integrity: sha512-po+7rfJfUnFXjWlcoN2RwhErgzCdRtBc1T26vYPcywHlggmCQiQe1uWaE4j+BibI2uY9/2pDoFzMN0rmSaPFOw==}
	804	+ cpu: [x64]
	805	+ os: [darwin]
	806	+
	807	+ '@turbo/darwin-arm64@2.10.8':
	808	+ resolution: {integrity: sha512-+zB2btDJ00lnPRuqOvpVvgl4x34k/djZQGZTTCfjn7JgNCl8QFY5Njo5+dqkY1g/+9gbbsnAvWm9CmJg9ebcXA==}
	809	+ cpu: [arm64]
	810	+ os: [darwin]
	811	+
	812	+ '@turbo/linux-64@2.10.8':
	813	+ resolution: {integrity: sha512-K1dxqiVisyN7cViVsfQLs6xscQbYuI8aO2nbUhFURDACgEDfZRdP/b4CCxeosBJpcMfhYyiibWqJorCnvz9kKg==}
	814	+ cpu: [x64]
	815	+ os: [android, linux]
	816	+
	817	+ '@turbo/linux-arm64@2.10.8':
	818	+ resolution: {integrity: sha512-Gi77ibVnrE1fEmvr+/wBD/yvRqhwp/RQuCp2+//lv1U1wNFFyVg0V7Wj8FG9FXPFAw5QHReo8rxc9+wBSDZjzA==}
	819	+ cpu: [arm64]
	820	+ os: [android, linux]
	821	+
	822	+ '@turbo/windows-64@2.10.8':
	823	+ resolution: {integrity: sha512-znnLO1haJPYTHoKMKwlAvlkjRiYbbhBzME6wIGaMd+fwir23U6jVd1ecaTWWi1fbnRVqxMfgDBKseQ/hLKb83g==}
	824	+ cpu: [x64]
	825	+ os: [win32]
	826	+
	827	+ '@turbo/windows-arm64@2.10.8':
	828	+ resolution: {integrity: sha512-VN30vh3b3Czh2WzYHNTfF1FE0YMZ5aHsLO8dBMGHJewA6792wX6iJR8ZxlzFW6WdOu0gEAKIvlYhfyT81Wkm4Q==}
	829	+ cpu: [arm64]
	830	+ os: [win32]
	831	+
	832	+ '@types/estree@1.0.9':
	833	+ resolution: {integrity: sha512-GhdPgy1el4/ImP05X05Uw4cw2/M93BCUmnEvWZNStlCzEKME4Fkk+YpoA5OiHNQmoS7Cafb8Xa3Pya8m1Qrzeg==}
	834	+
	835	+ '@types/json-schema@7.0.15':
	836	+ resolution: {integrity: sha512-5+fP8P8MFNC+AyZCDxrB2pkZFPGzqQWUzpSeuuVLvm8VMcorNYavBqoFcxK8bQz4Qsbn4oUEEem4wDLfcysGHA==}
	837	+
	838	+ '@types/node@20.19.43':
	839	+ resolution: {integrity: sha512-6oYBAi5ikg4Pl+kGsoYtawUMBT2zZMCvPNF7pVLnHZfd1zf38DRiWn/gT01RYCdUqkv7Fhr+C9ot4/tb+2sVvA==}
	840	+
	841	+ '@types/prop-types@15.7.15':
	842	+ resolution: {integrity: sha512-F6bEyamV9jKGAFBEmlQnesRPGOQqS2+Uwi0Em15xenOxHaf2hv6L8YCVn3rPdPJOiJfPiCnLIRyvwVaqMY3MIw==}
	843	+
	844	+ '@types/react-dom@18.3.7':
	845	+ resolution: {integrity: sha512-MEe3UeoENYVFXzoXEWsvcpg6ZvlrFNlOQ7EOsvhI3CfAXwzPfO8Qwuxd40nepsYKqyyVQnTdEfv68q91yLcKrQ==}
	846	+ peerDependencies:
	847	+ '@types/react': ^18.0.0
	848	+
	849	+ '@types/react@18.3.31':
	850	+ resolution: {integrity: sha512-vfEqpXTvwT91yhmwdfouStN2hSKwTvyRs8qpLfADyrq/kxDw0hZM7Wk9Ug1FELj8hIby+S/+kQCSRFF32nv2Qw==}
	851	+
	852	+ '@typescript-eslint/eslint-plugin@8.66.0':
	853	+ resolution: {integrity: sha512-p088eaGrzYz1s+7cov0aMOCkNGTJlVxF4jgubf28c8L0Cv9Rloj8YBHnv4hXLq6IIEE1AsjNWavO+k+8kP2Y0A==}
	854	+ engines: {node: ^18.18.0 \|\| ^20.9.0 \|\| >=21.1.0}
	855	+ peerDependencies:
	856	+ '@typescript-eslint/parser': ^8.66.0
	857	+ eslint: ^8.57.0 \|\| ^9.0.0 \|\| ^10.0.0
	858	+ typescript: '>=4.8.4 <6.1.0'
	859	+
	860	+ '@typescript-eslint/parser@8.66.0':
	861	+ resolution: {integrity: sha512-X6ypGChaWYk6PBtUg2BwuTZEFFcHJAtGTVJ9/lCTOufhZ4i9fNolQNnktq+kkMCwMj7V8Svsq7+TxSDslmhE0g==}
	862	+ engines: {node: ^18.18.0 \|\| ^20.9.0 \|\| >=21.1.0}
	863	+ peerDependencies:
	864	+ eslint: ^8.57.0 \|\| ^9.0.0 \|\| ^10.0.0
	865	+ typescript: '>=4.8.4 <6.1.0'
	866	+
	867	+ '@typescript-eslint/project-service@8.66.0':
	868	+ resolution: {integrity: sha512-7MthGPTt4BP69lSryqpqq8HQqxuzynssckL/jyDyk3+TNMQ3y2jFWkptCrktWvBrP+EH787Nl5N5Qpw7WZg+5g==}
	869	+ engines: {node: ^18.18.0 \|\| ^20.9.0 \|\| >=21.1.0}
	870	+ peerDependencies:
	871	+ typescript: '>=4.8.4 <6.1.0'
	872	+
	873	+ '@typescript-eslint/scope-manager@8.66.0':
	874	+ resolution: {integrity: sha512-8TGcH25j9zqJ/IULB/ppyhRvxA8QYfFEZ7nfbg6/BN9spDgb8fPWQXlE5l8TWBL50EtUx007uZ1o9VOwrq2/9g==}
	875	+ engines: {node: ^18.18.0 \|\| ^20.9.0 \|\| >=21.1.0}
	876	+
	877	+ '@typescript-eslint/tsconfig-utils@8.66.0':
	878	+ resolution: {integrity: sha512-9D5gLYZG4rOjcoag8MQ/fWI8WqA9wcPDyOGyWtWFhvM1lHRbliqUSPIY5J3zqCU1tvSwzXxnnjhQhz5Ne7mJ4g==}
	879	+ engines: {node: ^18.18.0 \|\| ^20.9.0 \|\| >=21.1.0}
	880	+ peerDependencies:
	881	+ typescript: '>=4.8.4 <6.1.0'
	882	+
	883	+ '@typescript-eslint/type-utils@8.66.0':
	884	+ resolution: {integrity: sha512-LG2dWfjZQQp0ADtAu/EWJVayefGL2UEZ3CDeI44D9v3rXB/WYUqE/jpO28KrEKul5AySrmI+Zh1v6v+xW2U9+g==}
	885	+ engines: {node: ^18.18.0 \|\| ^20.9.0 \|\| >=21.1.0}
	886	+ peerDependencies:
	887	+ eslint: ^8.57.0 \|\| ^9.0.0 \|\| ^10.0.0
	888	+ typescript: '>=4.8.4 <6.1.0'
	889	+
	890	+ '@typescript-eslint/types@8.66.0':
	891	+ resolution: {integrity: sha512-H6gcYaSDOyvL3AD/jHUtUFo2jqGgn/F6nuyuZSu0QTesxL+cP4dQoIMrODRofuJC09g64+WgZ6tE19Y1N2YIFQ==}
	892	+ engines: {node: ^18.18.0 \|\| ^20.9.0 \|\| >=21.1.0}
	893	+
	894	+ '@typescript-eslint/typescript-estree@8.66.0':
	895	+ resolution: {integrity: sha512-8/x4INiiQb10jGgXYD7116/zQ+OL84ZIFn0za68wwFHCanT/VLbBEroWht8RV8fn0/ZCAoazHLQgwUC0UQcDfg==}
	896	+ engines: {node: ^18.18.0 \|\| ^20.9.0 \|\| >=21.1.0}
	897	+ peerDependencies:
	898	+ typescript: '>=4.8.4 <6.1.0'
	899	+
	900	+ '@typescript-eslint/utils@8.66.0':
	901	+ resolution: {integrity: sha512-jasearZPolBw5NJNYGMwxzHMF83niVWmMU1VdHzG1CyfI2VS7f7nZltnKtHcg20hW+7Uo5GfK4MeDPoU3qI8EA==}
	902	+ engines: {node: ^18.18.0 \|\| ^20.9.0 \|\| >=21.1.0}
	903	+ peerDependencies:
	904	+ eslint: ^8.57.0 \|\| ^9.0.0 \|\| ^10.0.0
	905	+ typescript: '>=4.8.4 <6.1.0'
	906	+
	907	+ '@typescript-eslint/visitor-keys@8.66.0':
	908	+ resolution: {integrity: sha512-dkKR8q+lKciskj1Y3vthHktl+3cMLWGyVUP23bRiPZ5O9BRT++4EqDDV+TVeIKBL1VXVEqrJlz8MYbcnvJcAlg==}
	909	+ engines: {node: ^18.18.0 \|\| ^20.9.0 \|\| >=21.1.0}
	910	+
	911	+ '@vitest/expect@2.1.9':
	912	+ resolution: {integrity: sha512-UJCIkTBenHeKT1TTlKMJWy1laZewsRIzYighyYiJKZreqtdxSos/S1t+ktRMQWu2CKqaarrkeszJx1cgC5tGZw==}
	913	+
	914	+ '@vitest/mocker@2.1.9':
	915	+ resolution: {integrity: sha512-tVL6uJgoUdi6icpxmdrn5YNo3g3Dxv+IHJBr0GXHaEdTcw3F+cPKnsXFhli6nO+f/6SDKPHEK1UN+k+TQv0Ehg==}
	916	+ peerDependencies:
	917	+ msw: ^2.4.9
	918	+ vite: ^5.0.0
	919	+ peerDependenciesMeta:
	920	+ msw:
	921	+ optional: true
	922	+ vite:
	923	+ optional: true
	924	+
	925	+ '@vitest/pretty-format@2.1.9':
	926	+ resolution: {integrity: sha512-KhRIdGV2U9HOUzxfiHmY8IFHTdqtOhIzCpd8WRdJiE7D/HUcZVD0EgQCVjm+Q9gkUXWgBvMmTtZgIG48wq7sOQ==}
	927	+
	928	+ '@vitest/runner@2.1.9':
	929	+ resolution: {integrity: sha512-ZXSSqTFIrzduD63btIfEyOmNcBmQvgOVsPNPe0jYtESiXkhd8u2erDLnMxmGrDCwHCCHE7hxwRDCT3pt0esT4g==}
	930	+
	931	+ '@vitest/snapshot@2.1.9':
	932	+ resolution: {integrity: sha512-oBO82rEjsxLNJincVhLhaxxZdEtV0EFHMK5Kmx5sJ6H9L183dHECjiefOAdnqpIgT5eZwT04PoggUnW88vOBNQ==}
	933	+
	934	+ '@vitest/spy@2.1.9':
	935	+ resolution: {integrity: sha512-E1B35FwzXXTs9FHNK6bDszs7mtydNi5MIfUWpceJ8Xbfb1gBMscAnwLbEu+B44ed6W3XjL9/ehLPHR1fkf1KLQ==}
	936	+
	937	+ '@vitest/utils@2.1.9':
	938	+ resolution: {integrity: sha512-v0psaMSkNJ3A2NMrUEHFRzJtDPFn+/VWZ5WxImB21T9fjucJRmS7xCS3ppEnARb9y11OAzaD+P2Ps+b+BGX5iQ==}
	939	+
	940	+ acorn-jsx@5.3.2:
	941	+ resolution: {integrity: sha512-rq9s+JNhf0IChjtDXxllJ7g41oZk5SlXtp0LHwyA5cejwn7vKmKp4pPri6YEePv2PU65sAsegbXtIinmDFDXgQ==}
	942	+ peerDependencies:
	943	+ acorn: ^6.0.0 \|\| ^7.0.0 \|\| ^8.0.0
	944	+
	945	+ acorn@8.18.0:
	946	+ resolution: {integrity: sha512-lGq+9yr1/GuAWaVYIHRjvvySG5/4VfKIvC8EWxStPdcDh/Ka7FG3twP6v4d5BkravUilhIAsG4Qj83t02LWUPQ==}
	947	+ engines: {node: '>=0.4.0'}
	948	+ hasBin: true
	949	+
	950	+ ajv@6.15.0:
	951	+ resolution: {integrity: sha512-fgFx7Hfoq60ytK2c7DhnF8jIvzYgOMxfugjLOSMHjLIPgenqa7S7oaagATUq99mV6IYvN2tRmC0wnTYX6iPbMw==}
	952	+
	953	+ ansi-styles@4.3.0:
	954	+ resolution: {integrity: sha512-zbB9rCJAT1rbjiVDb2hqKFHNYLxgtk8NURxZ3IZwD3F6NtxbXZQCnnSi1Lkx+IDohdPlFp222wVALIheZJQSEg==}
	955	+ engines: {node: '>=8'}
	956	+
	957	+ any-promise@1.3.0:
	958	+ resolution: {integrity: sha512-7UvmKalWRt1wgjL1RrGxoSJW/0QZFIegpeGvZG9kjp8vrRu55XTHbwnqq2GpXm9uLbcuhxm3IqX9OB4MZR1b2A==}
	959	+
	960	+ anymatch@3.1.3:
	961	+ resolution: {integrity: sha512-KMReFUr0B4t+D+OBkjR3KYqvocp2XaSzO55UcB6mgQMd3KbcE+mWTyvVV7D/zsdEbNnV6acZUutkiHQXvTr1Rw==}
	962	+ engines: {node: '>= 8'}
	963	+
	964	+ arg@5.0.2:
	965	+ resolution: {integrity: sha512-PYjyFOLKQ9y57JvQ6QLo8dAgNqswh8M1RMJYdQduT6xbWSgK36P/Z/v+p888pM69jMMfS8Xd8F6I1kQ/I9HUGg==}
	966	+
	967	+ argparse@2.0.1:
	968	+ resolution: {integrity: sha512-8+9WqebbFzpX9OR+Wa6O29asIogeRMzcGtAINdpMHHyAg10f05aSFVBbcEqGf/PXw1EjAZ+q2/bEBg3DvurK3Q==}
	969	+
	970	+ assertion-error@2.0.1:
	971	+ resolution: {integrity: sha512-Izi8RQcffqCeNVgFigKli1ssklIbpHnCYc6AknXGYoB6grJqyeby7jv12JUQgmTAnIDnbck1uxksT4dzN3PWBA==}
	972	+ engines: {node: '>=12'}
	973	+
	974	+ autoprefixer@10.5.4:
	975	+ resolution: {integrity: sha512-MaU0U/za7N3r6brxD4YB/l4NSrFzLPlANv6wEuQVaIPlD3L4W9rFcQPbL/EilY9BHhHvhfcz3gInDLrEtWT4EA==}
	976	+ engines: {node: ^10 \|\| ^12 \|\| >=14}
	977	+ hasBin: true
	978	+ peerDependencies:
	979	+ postcss: ^8.1.0
	980	+
	981	+ balanced-match@1.0.2:
	982	+ resolution: {integrity: sha512-3oSeUO0TMV67hN1AmbXsK4yaqU7tjiHlbxRDZOpH0KW9+CeX4bRAaX0Anxt0tx2MrpRpWwQaPwIlISEJhYU5Pw==}
	983	+
	984	+ balanced-match@4.0.4:
	985	+ resolution: {integrity: sha512-BLrgEcRTwX2o6gGxGOCNyMvGSp35YofuYzw9h1IMTRmKqttAZZVU67bdb9Pr2vUHA8+j3i2tJfjO6C6+4myGTA==}
	986	+ engines: {node: 18 \|\| 20 \|\| >=22}
	987	+
	988	+ baseline-browser-mapping@2.11.12:
	989	+ resolution: {integrity: sha512-r7WnVImvVCeFpf2DOXfy41aPWzeNg3H/A2X4dKmy1QL0MSyyk/e7z8ihJ3N6Nn2PsdhkVlqnEfnUE4a05P2aTA==}
	990	+ engines: {node: '>=6.0.0'}
	991	+ hasBin: true
	992	+
	993	+ binary-extensions@2.3.0:
	994	+ resolution: {integrity: sha512-Ceh+7ox5qe7LJuLHoY0feh3pHuUDHAcRUeyL2VYghZwfpkNIy/+8Ocg0a3UuSoYzavmylwuLWQOf3hl0jjMMIw==}
	995	+ engines: {node: '>=8'}
	996	+
	997	+ brace-expansion@1.1.18:
	998	+ resolution: {integrity: sha512-Edep/X9fGqVNmzKBVsDYIOtD+z1tuezV70LBjdCst9Tqu76lsnvRiZ6oTic1n+/BIwX6QDGAO94PN4N2SADvtw==}
	999	+
	1000	+ brace-expansion@5.0.9:
	1001	+ resolution: {integrity: sha512-ScQ4IuvIEF1TMlP7Zt+vjJ//9zlPb2SDcxWxM3bk8s6t6GGdJ7KO1dCcTidOPJKePW30LE/2cT7wCyPho9/Wxg==}
	1002	+ engines: {node: 20 \|\| >=22}
	1003	+
	1004	+ braces@3.0.3:
	1005	+ resolution: {integrity: sha512-yQbXgO/OSZVD2IsiLlro+7Hf6Q18EJrKSEsdoMzKePKXct3gvD8oLcOQdIzGupr5Fj+EDe8gO/lxc1BzfMpxvA==}
	1006	+ engines: {node: '>=8'}
	1007	+
	1008	+ browserslist@4.28.7:
	1009	+ resolution: {integrity: sha512-JxV13hNrFxqjOc8alRbq9dK1MM79NEXYpma2B2J4wAtpWS5zIEIKqWPGCl7N4o7Uc7B7itylh7SuDujATRyyTw==}
	1010	+ engines: {node: ^6 \|\| ^7 \|\| ^8 \|\| ^9 \|\| ^10 \|\| ^11 \|\| ^12 \|\| >=13.7}
	1011	+ hasBin: true
	1012	+
	1013	+ bullmq@5.81.3:
	1014	+ resolution: {integrity: sha512-Q7uEH2G92rVjX3Yl2qw3+6VEO15uxehC6DfeTHQBQTehddcRDSDuquD9zuvDjxiCffXsPUaBwrRlvlbVcgKs7g==}
	1015	+ engines: {node: '>=12.22.0'}
	1016	+ peerDependencies:
	1017	+ redis: '>=5.0.0'
	1018	+ peerDependenciesMeta:
	1019	+ redis:
	1020	+ optional: true
	1021	+
	1022	+ busboy@1.6.0:
	1023	+ resolution: {integrity: sha512-8SFQbg/0hQ9xy3UNTB0YEnsNBbWfhf7RtnzpL7TkBiTBRfrQ9Fxcnz7VJsleJpyp6rVLvXiuORqjlHi5q+PYuA==}
	1024	+ engines: {node: '>=10.16.0'}
	1025	+
	1026	+ cac@6.7.14:
	1027	+ resolution: {integrity: sha512-b6Ilus+c3RrdDk+JhLKUAQfzzgLEPy6wcXqS7f/xe1EETvsDP6GORG7SFuOs6cID5YkqchW/LXZbX5bc8j7ZcQ==}
	1028	+ engines: {node: '>=8'}
	1029	+
	1030	+ callsites@3.1.0:
	1031	+ resolution: {integrity: sha512-P8BjAsXvZS+VIDUI11hHCQEv74YT67YUi5JJFNWIqL235sBmjX4+qx9Muvls5ivyNENctx46xQLQ3aTuE7ssaQ==}
	1032	+ engines: {node: '>=6'}
	1033	+
	1034	+ camelcase-css@2.0.1:
	1035	+ resolution: {integrity: sha512-QOSvevhslijgYwRx6Rv7zKdMF8lbRmx+uQGx2+vDc+KI/eBnsy9kit5aj23AgGu3pa4t9AgwbnXWqS+iOY+2aA==}
	1036	+ engines: {node: '>= 6'}
	1037	+
	1038	+ caniuse-lite@1.0.30001806:
	1039	+ resolution: {integrity: sha512-72Cuvd95zbSYPKq6Fhg8eDJRlzgWDf7/mtoZv6Qe/DYNCEBdNxoA3+rZAU2ZhGCpZlns3EssFavaZomckT5Uuw==}
	1040	+
	1041	+ chai@5.3.3:
	1042	+ resolution: {integrity: sha512-4zNhdJD/iOjSH0A05ea+Ke6MU5mmpQcbQsSOkgdaUMJ9zTlDTD/GYlwohmIE2u0gaxHYiVHEn1Fw9mZ/ktJWgw==}
	1043	+ engines: {node: '>=18'}
	1044	+
	1045	+ chalk@4.1.2:
	1046	+ resolution: {integrity: sha512-oKnbhFyRIXpUuez8iBMmyEa4nbj4IOQyuhc/wy9kY7/WVPcwIO9VA668Pu8RkO7+0G76SLROeyw9CpQ061i4mA==}
	1047	+ engines: {node: '>=10'}
	1048	+
	1049	+ check-error@2.1.3:
	1050	+ resolution: {integrity: sha512-PAJdDJusoxnwm1VwW07VWwUN1sl7smmC3OKggvndJFadxxDRyFJBX/ggnu/KE4kQAB7a3Dp8f/YXC1FlUprWmA==}
	1051	+ engines: {node: '>= 16'}
	1052	+
	1053	+ chokidar@3.6.0:
	1054	+ resolution: {integrity: sha512-7VT13fmjotKpGipCW9JEQAusEPE+Ei8nl6/g4FBAmIm0GOOLMua9NDDo/DWp0ZAxCr3cPq5ZpBqmPAQgDda2Pw==}
	1055	+ engines: {node: '>= 8.10.0'}
	1056	+
	1057	+ client-only@0.0.1:
	1058	+ resolution: {integrity: sha512-IV3Ou0jSMzZrd3pZ48nLkT9DA7Ag1pnPzaiQhpW7c3RbcqqzvzzVu+L8gfqMp/8IM2MQtSiqaCxrrcfu8I8rMA==}
	1059	+
	1060	+ cluster-key-slot@1.1.1:
	1061	+ resolution: {integrity: sha512-rwHwUfXL40Chm1r08yrhU3qpUvdVlgkKNeyeGPOxnW8/SyVDvgRaed/Uz54AqWNaTCAThlj6QAs3TZcKI0xDEw==}
	1062	+ engines: {node: '>=0.10.0'}
	1063	+
	1064	+ color-convert@2.0.1:
	1065	+ resolution: {integrity: sha512-RRECPsj7iu/xb5oKYcsFHSppFNnsj/52OVTRKb4zP5onXwVF3zVmmToNcOfGC+CRDpfK/U584fMg38ZHCaElKQ==}
	1066	+ engines: {node: '>=7.0.0'}
	1067	+
	1068	+ color-name@1.1.4:
	1069	+ resolution: {integrity: sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA==}
	1070	+
	1071	+ commander@4.1.1:
	1072	+ resolution: {integrity: sha512-NOKm8xhkzAjzFx8B2v5OAHT+u5pRQc2UCa2Vq9jYL/31o2wi9mxBA7LIFs3sV5VSC49z6pEhfbMULvShKj26WA==}
	1073	+ engines: {node: '>= 6'}
	1074	+
	1075	+ concat-map@0.0.1:
	1076	+ resolution: {integrity: sha512-/Srv4dswyQNBfohGpz9o6Yb3Gz3SrUDqBH5rTuhGR7ahtlbYKnVxw2bCFMRljaA7EXHaXZ8wsHdodFvbkhKmqg==}
	1077	+
	1078	+ cron-parser@4.9.0:
	1079	+ resolution: {integrity: sha512-p0SaNjrHOnQeR8/VnfGbmg9te2kfyYSQ7Sc/j/6DtPL3JQvKxmjO9TSjNFpujqV3vEYYBvNNvXSxzyksBWAx1Q==}
	1080	+ engines: {node: '>=12.0.0'}
	1081	+ deprecated: v4 is no longer maintained, upgrade to v5
	1082	+
	1083	+ cross-spawn@7.0.6:
	1084	+ resolution: {integrity: sha512-uV2QOWP2nWzsy2aMp8aRibhi9dlzF5Hgh5SHaB9OiTGEyDTiJJyx0uy51QXdyWbtAHNua4XJzUKca3OzKUd3vA==}
	1085	+ engines: {node: '>= 8'}
	1086	+
	1087	+ cssesc@3.0.0:
	1088	+ resolution: {integrity: sha512-/Tb/JcjK111nNScGob5MNtsntNM1aCNUDipB/TkwZFhyDrrE47SOx/18wF2bbjgc3ZzCSKW1T5nt5EbFoAz/Vg==}
	1089	+ engines: {node: '>=4'}
	1090	+ hasBin: true
	1091	+
	1092	+ csstype@3.2.3:
	1093	+ resolution: {integrity: sha512-z1HGKcYy2xA8AGQfwrn0PAy+PB7X/GSj3UVJW9qKyn43xWa+gl5nXmU4qqLMRzWVLFC8KusUX8T/0kCiOYpAIQ==}
	1094	+
	1095	+ debug@4.4.3:
	1096	+ resolution: {integrity: sha512-RGwwWnwQvkVfavKVt22FGLw+xYSdzARwm0ru6DhTVA3umU5hZc28V3kO4stgYryrTlLpuvgI9GiijltAjNbcqA==}
	1097	+ engines: {node: '>=6.0'}
	1098	+ peerDependencies:
	1099	+ supports-color: '*'
	1100	+ peerDependenciesMeta:
	1101	+ supports-color:
	1102	+ optional: true
	1103	+
	1104	+ deep-eql@5.0.2:
	1105	+ resolution: {integrity: sha512-h5k/5U50IJJFpzfL6nO9jaaumfjO/f2NjK/oYB2Djzm4p9L+3T9qWpZqZ2hAbLPuuYq9wrU08WQyBTL5GbPk5Q==}
	1106	+ engines: {node: '>=6'}
	1107	+
	1108	+ deep-is@0.1.4:
	1109	+ resolution: {integrity: sha512-oIPzksmTg4/MriiaYGO+okXDT7ztn/w3Eptv/+gSIdMdKsJo0u4CfYNFJPy+4SKMuCqGw2wxnA+URMg3t8a/bQ==}
	1110	+
	1111	+ denque@2.1.0:
	1112	+ resolution: {integrity: sha512-HVQE3AAb/pxF8fQAoiqpvg9i3evqug3hoiwakOyZAwJm+6vZehbkYXZ0l4JxS+I3QxM97v5aaRNhj8v5oBhekw==}
	1113	+ engines: {node: '>=0.10'}
	1114	+
	1115	+ detect-libc@2.1.2:
	1116	+ resolution: {integrity: sha512-Btj2BOOO83o3WyH59e8MgXsxEQVcarkUOpEYrubB0urwnN10yQ364rsiByU11nZlqWYZm05i/of7io4mzihBtQ==}
	1117	+ engines: {node: '>=8'}
	1118	+
	1119	+ didyoumean@1.2.2:
	1120	+ resolution: {integrity: sha512-gxtyfqMg7GKyhQmb056K7M3xszy/myH8w+B4RT+QXBQsvAOdc3XymqDDPHx1BgPgsdAA5SIifona89YtRATDzw==}
	1121	+
	1122	+ dlv@1.1.3:
	1123	+ resolution: {integrity: sha512-+HlytyjlPKnIG8XuRG8WvmBP8xs8P71y+SKKS6ZXWoEgLuePxtDoUEiH7WkdePWrQ5JBpE6aoVqfZfJUQkjXwA==}
	1124	+
	1125	+ electron-to-chromium@1.5.401:
	1126	+ resolution: {integrity: sha512-H6ViHN68nGYlChEvlIU67fn8O2/tpbWQPwck98yaJmh+08LSvHiydzDQ6oXNccLU3kNRVIRS9A4mA7CG+i6fLQ==}
	1127	+
	1128	+ es-errors@1.3.0:
	1129	+ resolution: {integrity: sha512-Zf5H2Kxt2xjTvbJvP2ZWLEICxA6j+hAmMzIlypy4xcBg1vKVnx89Wy0GbS+kf5cwCVFFzdCFh2XSCFNULS6csw==}
	1130	+ engines: {node: '>= 0.4'}
	1131	+
	1132	+ es-module-lexer@1.7.0:
	1133	+ resolution: {integrity: sha512-jEQoCwk8hyb2AZziIOLhDqpm5+2ww5uIE6lkO/6jcOCusfk6LhMHpXXfBLXTZ7Ydyt0j4VoUQv6uGNYbdW+kBA==}
	1134	+
	1135	+ esbuild@0.21.5:
	1136	+ resolution: {integrity: sha512-mg3OPMV4hXywwpoDxu3Qda5xCKQi+vCTZq8S9J/EpkhB2HzKXq4SNFZE3+NK93JYxc8VMSep+lOUSC/RVKaBqw==}
	1137	+ engines: {node: '>=12'}
	1138	+ hasBin: true
	1139	+
	1140	+ esbuild@0.28.1:
	1141	+ resolution: {integrity: sha512-HrJrvZv5ayxBzPfwphOoNzkzOIIlifzk0KJrGK2c8R4+LKpMtpYLQeUdjnwjWv/LZlkH2laZk+4w78pi99D4Vw==}
	1142	+ engines: {node: '>=18'}
	1143	+ hasBin: true
	1144	+
	1145	+ escalade@3.2.0:
	1146	+ resolution: {integrity: sha512-WUj2qlxaQtO4g6Pq5c29GTcWGDyd8itL8zTlipgECz3JesAiiOKotd8JU6otB3PACgG6xkJUyVhboMS+bje/jA==}
	1147	+ engines: {node: '>=6'}
	1148	+
	1149	+ escape-string-regexp@4.0.0:
	1150	+ resolution: {integrity: sha512-TtpcNJ3XAzx3Gq8sWRzJaVajRs0uVxA2YAkdb1jm2YkPz4G6egUFAyA3n5vtEIZefPk5Wa4UXbKuS5fKkJWdgA==}
	1151	+ engines: {node: '>=10'}
	1152	+
	1153	+ eslint-scope@8.4.0:
	1154	+ resolution: {integrity: sha512-sNXOfKCn74rt8RICKMvJS7XKV/Xk9kA7DyJr8mJik3S7Cwgy3qlkkmyS2uQB3jiJg6VNdZd/pDBJu0nvG2NlTg==}
	1155	+ engines: {node: ^18.18.0 \|\| ^20.9.0 \|\| >=21.1.0}
	1156	+
	1157	+ eslint-visitor-keys@3.4.3:
	1158	+ resolution: {integrity: sha512-wpc+LXeiyiisxPlEkUzU6svyS1frIO3Mgxj1fdy7Pm8Ygzguax2N3Fa/D/ag1WqbOprdI+uY6wMUl8/a2G+iag==}
	1159	+ engines: {node: ^12.22.0 \|\| ^14.17.0 \|\| >=16.0.0}
	1160	+
	1161	+ eslint-visitor-keys@4.2.1:
	1162	+ resolution: {integrity: sha512-Uhdk5sfqcee/9H/rCOJikYz67o0a2Tw2hGRPOG2Y1R2dg7brRe1uG0yaNQDHu+TO/uQPF/5eCapvYSmHUjt7JQ==}
	1163	+ engines: {node: ^18.18.0 \|\| ^20.9.0 \|\| >=21.1.0}
	1164	+
	1165	+ eslint-visitor-keys@5.0.1:
	1166	+ resolution: {integrity: sha512-tD40eHxA35h0PEIZNeIjkHoDR4YjjJp34biM0mDvplBe//mB+IHCqHDGV7pxF+7MklTvighcCPPZC7ynWyjdTA==}
	1167	+ engines: {node: ^20.19.0 \|\| ^22.13.0 \|\| >=24}
	1168	+
	1169	+ eslint@9.39.5:
	1170	+ resolution: {integrity: sha512-DgZS62aPLXKlnxILS/AYCoRvHaZeXceIzlXPkkGGzJWSow1aEk0lbTlxUSlyjC8jcaKxAdOnTDz+o1JFSBsyjw==}
	1171	+ engines: {node: ^18.18.0 \|\| ^20.9.0 \|\| >=21.1.0}
	1172	+ hasBin: true
	1173	+ peerDependencies:
	1174	+ jiti: '*'
	1175	+ peerDependenciesMeta:
	1176	+ jiti:
	1177	+ optional: true
	1178	+
	1179	+ espree@10.4.0:
	1180	+ resolution: {integrity: sha512-j6PAQ2uUr79PZhBjP5C5fhl8e39FmRnOjsD5lGnWrFU8i2G776tBK7+nP8KuQUTTyAZUwfQqXAgrVH5MbH9CYQ==}
	1181	+ engines: {node: ^18.18.0 \|\| ^20.9.0 \|\| >=21.1.0}
	1182	+
	1183	+ esquery@1.7.0:
	1184	+ resolution: {integrity: sha512-Ap6G0WQwcU/LHsvLwON1fAQX9Zp0A2Y6Y/cJBl9r/JbW90Zyg4/zbG6zzKa2OTALELarYHmKu0GhpM5EO+7T0g==}
	1185	+ engines: {node: '>=0.10'}
	1186	+
	1187	+ esrecurse@4.3.0:
	1188	+ resolution: {integrity: sha512-KmfKL3b6G+RXvP8N1vr3Tq1kL/oCFgn2NYXEtqP8/L3pKapUA4G8cFVaoF3SU323CD4XypR/ffioHmkti6/Tag==}
	1189	+ engines: {node: '>=4.0'}
	1190	+
	1191	+ estraverse@5.3.0:
	1192	+ resolution: {integrity: sha512-MMdARuVEQziNTeJD8DgMqmhwR11BRQ/cBP+pLtYdSTnf3MIO8fFeiINEbX36ZdNlfU/7A9f3gUw49B3oQsvwBA==}
	1193	+ engines: {node: '>=4.0'}
	1194	+
	1195	+ estree-walker@3.0.3:
	1196	+ resolution: {integrity: sha512-7RUKfXgSMMkzt6ZuXmqapOurLGPPfgj6l9uRZ7lRGolvk0y2yocc35LdcxKC5PQZdn2DMqioAQ2NoWcrTKmm6g==}
	1197	+
	1198	+ esutils@2.0.3:
	1199	+ resolution: {integrity: sha512-kVscqXk4OCp68SZ0dkgEKVi6/8ij300KBWTJq32P/dYeWTSwK41WyTxalN1eRmA5Z9UU/LX9D7FWSmV9SAYx6g==}
	1200	+ engines: {node: '>=0.10.0'}
	1201	+
	1202	+ expect-type@1.4.0:
	1203	+ resolution: {integrity: sha512-KfYbmpRm0VbLjEvVa9yGwCi9GI34xvi7A/HXYWQO65CSD2u3MczUJSuwXKFIxlGsgBQizV9q5J9NHj4VG0n+pA==}
	1204	+ engines: {node: '>=12.0.0'}
	1205	+
	1206	+ fast-check@3.23.2:
	1207	+ resolution: {integrity: sha512-h5+1OzzfCC3Ef7VbtKdcv7zsstUQwUDlYpUTvjeUsJAssPgLn7QzbboPtL5ro04Mq0rPOsMzl7q5hIbRs2wD1A==}
	1208	+ engines: {node: '>=8.0.0'}
	1209	+
	1210	+ fast-deep-equal@3.1.3:
	1211	+ resolution: {integrity: sha512-f3qQ9oQy9j2AhBe/H9VC91wLmKBCCU/gDOnKNAYG5hswO7BLKj09Hc5HYNz9cGI++xlpDCIgDaitVs03ATR84Q==}
	1212	+
	1213	+ fast-glob@3.3.3:
	1214	+ resolution: {integrity: sha512-7MptL8U0cqcFdzIzwOTHoilX9x5BrNqye7Z/LuC7kCMRio1EMSyqRK3BEAUD7sXRq4iT4AzTVuZdhgQ2TCvYLg==}
	1215	+ engines: {node: '>=8.6.0'}
	1216	+
	1217	+ fast-json-stable-stringify@2.1.0:
	1218	+ resolution: {integrity: sha512-lhd/wF+Lk98HZoTCtlVraHtfh5XYijIjalXck7saUtuanSDyLMxnHhSXEDJqHxD7msR8D0uCmqlkwjCV8xvwHw==}
	1219	+
	1220	+ fast-levenshtein@2.0.6:
	1221	+ resolution: {integrity: sha512-DCXu6Ifhqcks7TZKY3Hxp3y6qphY5SJZmrWMDrKcERSOXWQdMhU9Ig/PYrzyw/ul9jOIyh0N4M0tbC5hodg8dw==}
	1222	+
	1223	+ fast-sha256@1.3.0:
	1224	+ resolution: {integrity: sha512-n11RGP/lrWEFI/bWdygLxhI+pVeo1ZYIVwvvPkW7azl/rOy+F3HYRZ2K5zeE9mmkhQppyv9sQFx0JM9UabnpPQ==}
	1225	+
	1226	+ fastq@1.20.1:
	1227	+ resolution: {integrity: sha512-GGToxJ/w1x32s/D2EKND7kTil4n8OVk/9mycTc4VDza13lOvpUZTGX3mFSCtV9ksdGBVzvsyAVLM6mHFThxXxw==}
	1228	+
	1229	+ fdir@6.5.0:
	1230	+ resolution: {integrity: sha512-tIbYtZbucOs0BRGqPJkshJUYdL+SDH7dVM8gjy+ERp3WAUjLEFJE+02kanyHtwjWOnwrKYBiwAmM0p4kLJAnXg==}
	1231	+ engines: {node: '>=12.0.0'}
	1232	+ peerDependencies:
	1233	+ picomatch: ^3 \|\| ^4
	1234	+ peerDependenciesMeta:
	1235	+ picomatch:
	1236	+ optional: true
	1237	+
	1238	+ file-entry-cache@8.0.0:
	1239	+ resolution: {integrity: sha512-XXTUwCvisa5oacNGRP9SfNtYBNAMi+RPwBFmblZEF7N7swHYQS6/Zfk7SRwx4D5j3CH211YNRco1DEMNVfZCnQ==}
	1240	+ engines: {node: '>=16.0.0'}
	1241	+
	1242	+ fill-range@7.1.1:
	1243	+ resolution: {integrity: sha512-YsGpe3WHLK8ZYi4tWDg2Jy3ebRz2rXowDxnld4bkQB00cc/1Zw9AWnC0i9ztDJitivtQvaI9KaLyKrc+hBW0yg==}
	1244	+ engines: {node: '>=8'}
	1245	+
	1246	+ find-up@5.0.0:
	1247	+ resolution: {integrity: sha512-78/PXT1wlLLDgTzDs7sjq9hzz0vXD+zn+7wypEe4fXQxCmdmqfGsEPQxmiCSQI3ajFV91bVSsvNtrJRiW6nGng==}
	1248	+ engines: {node: '>=10'}
	1249	+
	1250	+ flat-cache@4.0.1:
	1251	+ resolution: {integrity: sha512-f7ccFPK3SXFHpx15UIGyRJ/FJQctuKZ0zVuN3frBo4HnK3cay9VEW0R6yPYFHC0AgqhukPzKjq22t5DmAyqGyw==}
	1252	+ engines: {node: '>=16'}
	1253	+
	1254	+ flatted@3.4.4:
	1255	+ resolution: {integrity: sha512-5+ybhBZANEJxaH3X5evAFatUxLfEHSr7n6kYJ+1Qd0mUqr4eu9gIf6GDbWHf8RJijHrjjO8G+la14SlL2SeS1Q==}
	1256	+
	1257	+ fraction.js@5.3.4:
	1258	+ resolution: {integrity: sha512-1X1NTtiJphryn/uLQz3whtY6jK3fTqoE3ohKs0tT+Ujr1W59oopxmoEh7Lu5p6vBaPbgoM0bzveAW4Qi5RyWDQ==}
	1259	+
	1260	+ fsevents@2.3.3:
	1261	+ resolution: {integrity: sha512-5xoDfX+fL7faATnagmWPpbFtwh/R77WmMMqqHGS65C3vvB0YHrgF+B1YmZ3441tMj5n63k0212XNoJwzlhffQw==}
	1262	+ engines: {node: ^8.16.0 \|\| ^10.6.0 \|\| >=11.0.0}
	1263	+ os: [darwin]
	1264	+
	1265	+ function-bind@1.1.2:
	1266	+ resolution: {integrity: sha512-7XHNxH7qX9xG5mIwxkhumTox/MIRNcOgDrxWsMt2pAr23WHp6MrRlN7FBSFpCpr+oVO0F744iUgR82nJMfG2SA==}
	1267	+
	1268	+ glob-parent@5.1.2:
	1269	+ resolution: {integrity: sha512-AOIgSQCepiJYwP3ARnGx+5VnTu2HBYdzbGP45eLw1vr3zB3vZLeyed1sC9hnbcOc9/SrMyM5RPQrkGz4aS9Zow==}
	1270	+ engines: {node: '>= 6'}
	1271	+
	1272	+ glob-parent@6.0.2:
	1273	+ resolution: {integrity: sha512-XxwI8EOhVQgWp6iDL+3b0r86f4d6AX6zSU55HfB4ydCEuXLXc5FcYeOu+nnGftS4TEju/11rt4KJPTMgbfmv4A==}
	1274	+ engines: {node: '>=10.13.0'}
	1275	+
	1276	+ globals@14.0.0:
	1277	+ resolution: {integrity: sha512-oahGvuMGQlPw/ivIYBjVSrWAfWLBeku5tpPE2fOPLi+WHffIWbuh2tCjhyQhTBPMf5E9jDEH4FOmTYgYwbKwtQ==}
	1278	+ engines: {node: '>=18'}
	1279	+
	1280	+ graceful-fs@4.2.11:
	1281	+ resolution: {integrity: sha512-RbJ5/jmFcNNCcDV5o9eTnBLJ/HszWV0P73bc+Ff4nS/rJj+YaS6IGyiOL0VoBYX+l1Wrl3k63h/KrH+nhJ0XvQ==}
	1282	+
	1283	+ has-flag@4.0.0:
	1284	+ resolution: {integrity: sha512-EykJT/Q1KjTWctppgIAgfSO0tKVuZUjhgMr17kqTumMl6Afv3EISleU7qZUzoXDFTAHTDC4NOoG/ZxU3EvlMPQ==}
	1285	+ engines: {node: '>=8'}
	1286	+
	1287	+ hasown@2.0.4:
	1288	+ resolution: {integrity: sha512-T2UbfbBEF32wiepXIsMlTW9+dDYC6wMh/t/vYA4tuOMKqWz/n3vr1NFSxQiyP+zk2mXsoMA/i/7qV6LKut1t1A==}
	1289	+ engines: {node: '>= 0.4'}
	1290	+
	1291	+ ignore@5.3.2:
	1292	+ resolution: {integrity: sha512-hsBTNUqQTDwkWtcdYI2i06Y/nUBEsNEDJKjWdigLvegy8kDuJAS8uRlpkkcQpyEXL0Z/pjDy5HBmMjRCJ2gq+g==}
	1293	+ engines: {node: '>= 4'}
	1294	+
	1295	+ ignore@7.0.6:
	1296	+ resolution: {integrity: sha512-BAg6QkE8W+TuQLrrw0Ugr7HegXduRuuj8/ti2kSOc+jz1dmx8/WNcjr6XGnq5YpDWxFwwaavqD0+jIUOKelTsw==}
	1297	+ engines: {node: '>= 4'}
	1298	+
	1299	+ import-fresh@3.3.1:
	1300	+ resolution: {integrity: sha512-TR3KfrTZTYLPB6jUjfx6MF9WcWrHL9su5TObK4ZkYgBdWKPOFoSoQIdEuTuR82pmtxH2spWG9h6etwfr1pLBqQ==}
	1301	+ engines: {node: '>=6'}
	1302	+
	1303	+ imurmurhash@0.1.4:
	1304	+ resolution: {integrity: sha512-JmXMZ6wuvDmLiHEml9ykzqO6lwFbof0GG4IkcGaENdCRDDmMVnny7s5HsIgHCbaq0w2MyPhDqkhTUgS2LU2PHA==}
	1305	+ engines: {node: '>=0.8.19'}
	1306	+
	1307	+ ioredis@5.11.1:
	1308	+ resolution: {integrity: sha512-ehuGcf94bQXhfagULNXrJdfnWO38v070jxSx/qE87Kjzmu2fU7ro5EFAb+OPituLqgfyuQaym5DlrNydW2sJ9A==}
	1309	+ engines: {node: '>=12.22.0'}
	1310	+
	1311	+ is-binary-path@2.1.0:
	1312	+ resolution: {integrity: sha512-ZMERYes6pDydyuGidse7OsHxtbI7WVeUEozgR/g7rd0xUimYNlvZRE/K2MgZTjWy725IfelLeVcEM97mmtRGXw==}
	1313	+ engines: {node: '>=8'}
	1314	+
	1315	+ is-core-module@2.16.2:
	1316	+ resolution: {integrity: sha512-evOr8xfXKxE6qSR0hSXL2r3sd7ALj8+7jQEUvPYcm5sgZFdJ+AYzT6yNmJenvIYQBgIGwfwz08sL8zoL7yq2BA==}
	1317	+ engines: {node: '>= 0.4'}
	1318	+
	1319	+ is-extglob@2.1.1:
	1320	+ resolution: {integrity: sha512-SbKbANkN603Vi4jEZv49LeVJMn4yGwsbzZworEoyEiutsN3nJYdbO36zfhGJ6QEDpOZIFkDtnq5JRxmvl3jsoQ==}
	1321	+ engines: {node: '>=0.10.0'}
	1322	+
	1323	+ is-glob@4.0.3:
	1324	+ resolution: {integrity: sha512-xelSayHH36ZgE7ZWhli7pW34hNbNl8Ojv5KVmkJD4hBdD3th8Tfk9vYasLM+mXWOZhFkgZfxhLSnrwRr4elSSg==}
	1325	+ engines: {node: '>=0.10.0'}
	1326	+
	1327	+ is-number@7.0.0:
	1328	+ resolution: {integrity: sha512-41Cifkg6e8TylSpdtTpeLVMqvSBEVzTttHvERD741+pnZ8ANv0004MRL43QKPDlK9cGvNp6NZWZUBlbGXYxxng==}
	1329	+ engines: {node: '>=0.12.0'}
	1330	+
	1331	+ isexe@2.0.0:
	1332	+ resolution: {integrity: sha512-RHxMLp9lnKHGHRng9QFhRCMbYAcVpn69smSGcq3f36xjgVVWThj4qqLbTLlq7Ssj8B+fIQ1EuCEGI2lKsyQeIw==}
	1333	+
	1334	+ jiti@1.21.7:
	1335	+ resolution: {integrity: sha512-/imKNG4EbWNrVjoNC/1H5/9GFy+tqjGBHCaSsN+P2RnPqjsLmv6UD3Ej+Kj8nBWaRAwyk7kK5ZUc+OEatnTR3A==}
	1336	+ hasBin: true
	1337	+
	1338	+ js-tokens@4.0.0:
	1339	+ resolution: {integrity: sha512-RdJUflcE3cUzKiMqQgsCu06FPu9UdIJO0beYbPhHN4k6apgJtifcoCtT9bcxOpYBtpD2kCM6Sbzg4CausW/PKQ==}
	1340	+
	1341	+ js-yaml@4.3.1:
	1342	+ resolution: {integrity: sha512-CY6crGq313MX8GkwvB7tzgp99vjQxY1++5y10/BKN/GUfHqWaOGQMNZkBvqSzsZKWk/ijwHlWzzkLulsGHhjWQ==}
	1343	+ hasBin: true
	1344	+
	1345	+ json-buffer@3.0.1:
	1346	+ resolution: {integrity: sha512-4bV5BfR2mqfQTJm+V5tPPdf+ZpuhiIvTuAB5g8kcrXOZpTT/QwwVRWBywX1ozr6lEuPdbHxwaJlm9G6mI2sfSQ==}
	1347	+
	1348	+ json-schema-to-ts@3.1.1:
	1349	+ resolution: {integrity: sha512-+DWg8jCJG2TEnpy7kOm/7/AxaYoaRbjVB4LFZLySZlWn8exGs3A4OLJR966cVvU26N7X9TWxl+Jsw7dzAqKT6g==}
	1350	+ engines: {node: '>=16'}
	1351	+
	1352	+ json-schema-traverse@0.4.1:
	1353	+ resolution: {integrity: sha512-xbbCH5dCYU5T8LcEhhuh7HJ88HXuW3qsI3Y0zOZFKfZEHcpWiHU/Jxzk629Brsab/mMiHQti9wMP+845RPe3Vg==}
	1354	+
	1355	+ json-stable-stringify-without-jsonify@1.0.1:
	1356	+ resolution: {integrity: sha512-Bdboy+l7tA3OGW6FjyFHWkP5LuByj1Tk33Ljyq0axyzdk9//JSi2u3fP1QSmd1KNwq6VOKYGlAu87CisVir6Pw==}
	1357	+
	1358	+ keyv@4.5.4:
	1359	+ resolution: {integrity: sha512-oxVHkHR/EJf2CNXnWxRLW6mg7JyCCUcG0DtEGmL2ctUo1PNTin1PUil+r/+4r5MpVgC/fn1kjsx7mjSujKqIpw==}
	1360	+
	1361	+ levn@0.4.1:
	1362	+ resolution: {integrity: sha512-+bT2uH4E5LGE7h/n3evcS/sQlJXCpIp6ym8OWJ5eV6+67Dsql/LaaT7qJBAt2rzfoa/5QBGBhxDix1dMt2kQKQ==}
	1363	+ engines: {node: '>= 0.8.0'}
	1364	+
	1365	+ lilconfig@3.1.3:
	1366	+ resolution: {integrity: sha512-/vlFKAoH5Cgt3Ie+JLhRbwOsCQePABiU3tJ1egGvyQ+33R/vcwM2Zl2QR/LzjsBeItPt3oSVXapn+m4nQDvpzw==}
	1367	+ engines: {node: '>=14'}
	1368	+
	1369	+ lines-and-columns@1.2.4:
	1370	+ resolution: {integrity: sha512-7ylylesZQ/PV29jhEDl3Ufjo6ZX7gCqJr5F7PKrqc93v7fzSymt1BpwEU8nAUXs8qzzvqhbjhK5QZg6Mt/HkBg==}
	1371	+
	1372	+ locate-path@6.0.0:
	1373	+ resolution: {integrity: sha512-iPZK6eYjbxRu3uB4/WZ3EsEIMJFMqAoopl3R+zuq0UjcAm/MO6KCweDgPfP3elTztoKP3KtnVHxTn2NHBSDVUw==}
	1374	+ engines: {node: '>=10'}
	1375	+
	1376	+ lodash.merge@4.6.2:
	1377	+ resolution: {integrity: sha512-0KpjqXRVvrYyCsX1swR/XTK0va6VQkQM6MNo7PqW77ByjAhoARA8EfrP1N4+KlKj8YS0ZUCtRT/YUuhyYDujIQ==}
	1378	+
	1379	+ loose-envify@1.4.0:
	1380	+ resolution: {integrity: sha512-lyuxPGr/Wfhrlem2CL/UcnUc1zcqKAImBDzukY7Y5F/yQiNdko6+fRLevlw1HgMySw7f611UIY408EtxRSoK3Q==}
	1381	+ hasBin: true
	1382	+
	1383	+ loupe@3.2.1:
	1384	+ resolution: {integrity: sha512-CdzqowRJCeLU72bHvWqwRBBlLcMEtIvGrlvef74kMnV2AolS9Y8xUv1I0U/MNAWMhBlKIoyuEgoJ0t/bbwHbLQ==}
	1385	+
	1386	+ luxon@3.7.2:
	1387	+ resolution: {integrity: sha512-vtEhXh/gNjI9Yg1u4jX/0YVPMvxzHuGgCm6tC5kZyb08yjGWGnqAjGJvcXbqQR2P3MyMEFnRbpcdFS6PBcLqew==}
	1388	+ engines: {node: '>=12'}
	1389	+
	1390	+ magic-string@0.30.21:
	1391	+ resolution: {integrity: sha512-vd2F4YUyEXKGcLHoq+TEyCjxueSeHnFxyyjNp80yg0XV4vUhnDer/lvvlqM/arB5bXQN5K2/3oinyCRyx8T2CQ==}
	1392	+
	1393	+ merge2@1.4.1:
	1394	+ resolution: {integrity: sha512-8q7VEgMJW4J8tcfVPy8g09NcQwZdbwFEqhe/WZkoIzjn/3TGDwtOCYtXGxA3O8tPzpczCCDgv+P2P5y00ZJOOg==}
	1395	+ engines: {node: '>= 8'}
	1396	+
	1397	+ micromatch@4.0.8:
	1398	+ resolution: {integrity: sha512-PXwfBhYu0hBCPw8Dn0E+WDYb7af3dSLVWKi3HGv84IdF4TyFoC0ysxFd0Goxw7nSv4T/PzEJQxsYsEiFCKo2BA==}
	1399	+ engines: {node: '>=8.6'}
	1400	+
	1401	+ minimatch@10.2.6:
	1402	+ resolution: {integrity: sha512-vpLQEs+VLCr1nU0BXS07maYoFwlDAH0gngQuuttxIwutDFEMHq2blX+8vpgxDdK3J1PwjCJiep77OitTZ4Ll1A==}
	1403	+ engines: {node: 18 \|\| 20 \|\| >=22}
	1404	+
	1405	+ minimatch@3.1.5:
	1406	+ resolution: {integrity: sha512-VgjWUsnnT6n+NUk6eZq77zeFdpW2LWDzP6zFGrCbHXiYNul5Dzqk2HHQ5uFH2DNW5Xbp8+jVzaeNt94ssEEl4w==}
	1407	+
	1408	+ ms@2.1.3:
	1409	+ resolution: {integrity: sha512-6FlzubTLZG3J2a/NVCAleEhjzq5oxgHyaCU9yYXvcLsvoVaHJq/s5xXI6/XXP6tz7R9xAOtHnSO/tXtF3WRTlA==}
	1410	+
	1411	+ msgpackr-extract@3.0.4:
	1412	+ resolution: {integrity: sha512-4kmO/MdyUIkLIvTPr8VHLil4AtoKIoniWPIEk5+CDy0xnWC84azhSFmuJ7PxZdsYtiP5kEeQsORAVIeMgxT+Hw==}
	1413	+ hasBin: true
	1414	+
	1415	+ msgpackr@2.0.5:
	1416	+ resolution: {integrity: sha512-cef05H/dSYpLpqp3sj/qyZh5vhUYCalnaLO7j1yOmpsR0y/XwLVtK7r5gn+U/F7CTEfMowcGhlUQJDLcLf7jcA==}
	1417	+
	1418	+ mz@2.7.0:
	1419	+ resolution: {integrity: sha512-z81GNO7nnYMEhrGh9LeymoE4+Yr0Wn5McHIZMK5cfQCl+NDX08sCZgUc9/6MHni9IWuFLm1Z3HTCXu2z9fN62Q==}
	1420	+
	1421	+ nanoid@3.3.17:
	1422	+ resolution: {integrity: sha512-xQLf0A3HOMlgHq0n247/LRuAOYmB7dXJ/DvAxGvsSBij45XtBSmQycu+F8ODbHwns/XyFZagyL1+J0Offw1E0g==}
	1423	+ engines: {node: ^10 \|\| ^12 \|\| ^13.7 \|\| ^14 \|\| >=15.0.1}
	1424	+ hasBin: true
	1425	+
	1426	+ natural-compare@1.4.0:
	1427	+ resolution: {integrity: sha512-OWND8ei3VtNC9h7V60qff3SVobHr996CTwgxubgyQYEpg290h9J0buyECNNJexkFm5sOajh5G116RYA1c8ZMSw==}
	1428	+
	1429	+ next@14.2.35:
	1430	+ resolution: {integrity: sha512-KhYd2Hjt/O1/1aZVX3dCwGXM1QmOV4eNM2UTacK5gipDdPN/oHHK/4oVGy7X8GMfPMsUTUEmGlsy0EY1YGAkig==}
	1431	+ engines: {node: '>=18.17.0'}
	1432	+ hasBin: true
	1433	+ peerDependencies:
	1434	+ '@opentelemetry/api': ^1.1.0
	1435	+ '@playwright/test': ^1.41.2
	1436	+ react: ^18.2.0
	1437	+ react-dom: ^18.2.0
	1438	+ sass: ^1.3.0
	1439	+ peerDependenciesMeta:
	1440	+ '@opentelemetry/api':
	1441	+ optional: true
	1442	+ '@playwright/test':
	1443	+ optional: true
	1444	+ sass:
	1445	+ optional: true
	1446	+
	1447	+ node-abort-controller@3.1.1:
	1448	+ resolution: {integrity: sha512-AGK2yQKIjRuqnc6VkX2Xj5d+QW8xZ87pa1UK6yA6ouUyuxfHuMP6umE5QK7UmTeOAymo+Zx1Fxiuw9rVx8taHQ==}
	1449	+
	1450	+ node-gyp-build-optional-packages@5.2.2:
	1451	+ resolution: {integrity: sha512-s+w+rBWnpTMwSFbaE0UXsRlg7hU4FjekKU4eyAih5T8nJuNZT1nNsskXpxmeqSK9UzkBl6UgRlnKc8hz8IEqOw==}
	1452	+ hasBin: true
	1453	+
	1454	+ node-releases@2.0.52:
	1455	+ resolution: {integrity: sha512-MRlTqhAfoMx/4mhEbPo3Hi02g9LJZaJkka69V6h67Cb1gjrAG0jsTE4CZX1eptNx+VCAwJmfpnDIF4P0Nh1A7A==}
	1456	+ engines: {node: '>=18'}
	1457	+
	1458	+ normalize-path@3.0.0:
	1459	+ resolution: {integrity: sha512-6eZs5Ls3WtCisHWp9S2GUy8dqkpGi4BVSz3GaqiE6ezub0512ESztXUwUB6C6IKbQkY2Pnb/mD4WYojCRwcwLA==}
	1460	+ engines: {node: '>=0.10.0'}
	1461	+
	1462	+ object-assign@4.1.1:
	1463	+ resolution: {integrity: sha512-rJgTQnkUnH1sFw8yT6VSU3zD3sWmu6sZhIseY8VX+GRu3P6F7Fu+JNDoXfklElbLJSnc3FUQHVe4cU5hj+BcUg==}
	1464	+ engines: {node: '>=0.10.0'}
	1465	+
	1466	+ object-hash@3.0.0:
	1467	+ resolution: {integrity: sha512-RSn9F68PjH9HqtltsSnqYC1XXoWe9Bju5+213R98cNGttag9q9yAOTzdbsqvIa7aNm5WffBZFpWYr2aWrklWAw==}
	1468	+ engines: {node: '>= 6'}
	1469	+
	1470	+ optionator@0.9.4:
	1471	+ resolution: {integrity: sha512-6IpQ7mKUxRcZNLIObR0hz7lxsapSSIYNZJwXPGeF0mTVqGKFIXj1DQcMoT22S3ROcLyY/rz0PWaWZ9ayWmad9g==}
	1472	+ engines: {node: '>= 0.8.0'}
	1473	+
	1474	+ p-limit@3.1.0:
	1475	+ resolution: {integrity: sha512-TYOanM3wGwNGsZN2cVTYPArw454xnXj5qmWF1bEoAc4+cU/ol7GVh7odevjp1FNHduHc3KZMcFduxU5Xc6uJRQ==}
	1476	+ engines: {node: '>=10'}
	1477	+
	1478	+ p-locate@5.0.0:
	1479	+ resolution: {integrity: sha512-LaNjtRWUBY++zB5nE/NwcaoMylSPk+S+ZHNB1TzdbMJMny6dynpAGt7X/tl/QYq3TIeE6nxHppbo2LGymrG5Pw==}
	1480	+ engines: {node: '>=10'}
	1481	+
	1482	+ parent-module@1.0.1:
	1483	+ resolution: {integrity: sha512-GQ2EWRpQV8/o+Aw8YqtfZZPfNRWZYkbidE9k5rpl/hC3vtHHBfGm2Ifi6qWV+coDGkrUKZAxE3Lot5kcsRlh+g==}
	1484	+ engines: {node: '>=6'}
	1485	+
	1486	+ path-exists@4.0.0:
	1487	+ resolution: {integrity: sha512-ak9Qy5Q7jYb2Wwcey5Fpvg2KoAc/ZIhLSLOSBmRmygPsGwkVVt0fZa0qrtMz+m6tJTAHfZQ8FnmB4MG4LWy7/w==}
	1488	+ engines: {node: '>=8'}
	1489	+
	1490	+ path-key@3.1.1:
	1491	+ resolution: {integrity: sha512-ojmeN0qd+y0jszEtoY48r0Peq5dwMEkIlCOu6Q5f41lfkswXuKtYrhgoTpLnyIcHm24Uhqx+5Tqm2InSwLhE6Q==}
	1492	+ engines: {node: '>=8'}
	1493	+
	1494	+ path-parse@1.0.7:
	1495	+ resolution: {integrity: sha512-LDJzPVEEEPR+y48z93A0Ed0yXb8pAByGWo/k5YYdYgpY2/2EsOsksJrq7lOHxryrVOn1ejG6oAp8ahvOIQD8sw==}
	1496	+
	1497	+ pathe@1.1.2:
	1498	+ resolution: {integrity: sha512-whLdWMYL2TwI08hn8/ZqAbrVemu0LNaNNJZX73O6qaIdCTfXutsLhMkjdENX0qhsQ9uIimo4/aQOmXkoon2nDQ==}
	1499	+
	1500	+ pathval@2.0.1:
	1501	+ resolution: {integrity: sha512-//nshmD55c46FuFw26xV/xFAaB5HF9Xdap7HJBBnrKdAd6/GxDBaNA1870O79+9ueg61cZLSVc+OaFlfmObYVQ==}
	1502	+ engines: {node: '>= 14.16'}
	1503	+
	1504	+ picocolors@1.1.1:
	1505	+ resolution: {integrity: sha512-xceH2snhtb5M9liqDsmEw56le376mTZkEX/jEb/RxNFyegNul7eNslCXP9FDj/Lcu0X8KEyMceP2ntpaHrDEVA==}
	1506	+
	1507	+ picomatch@2.3.2:
	1508	+ resolution: {integrity: sha512-V7+vQEJ06Z+c5tSye8S+nHUfI51xoXIXjHQ99cQtKUkQqqO1kO/KCJUfZXuB47h/YBlDhah2H3hdUGXn8ie0oA==}
	1509	+ engines: {node: '>=8.6'}
	1510	+
	1511	+ picomatch@4.0.5:
	1512	+ resolution: {integrity: sha512-RvwwcruNjI1ncT5xRakeyS9Lf8lcItv34KD+aif+VH9kduAyfYBipGh12274xtenIPZ119/R9BdTBa8gAwSh0A==}
	1513	+ engines: {node: '>=12'}
	1514	+
	1515	+ pify@2.3.0:
	1516	+ resolution: {integrity: sha512-udgsAY+fTnvv7kI7aaxbqwWNb0AHiB0qBO89PZKPkoTmGOgdbrHDKD+0B2X4uTfJ/FT1R09r9gTsjUjNJotuog==}
	1517	+ engines: {node: '>=0.10.0'}
	1518	+
	1519	+ pirates@4.0.7:
	1520	+ resolution: {integrity: sha512-TfySrs/5nm8fQJDcBDuUng3VOUKsd7S+zqvbOTiGXHfxX4wK31ard+hoNuvkicM/2YFzlpDgABOevKSsB4G/FA==}
	1521	+ engines: {node: '>= 6'}
	1522	+
	1523	+ postcss-import@15.1.0:
	1524	+ resolution: {integrity: sha512-hpr+J05B2FVYUAXHeK1YyI267J/dDDhMU6B6civm8hSY1jYJnBXxzKDKDswzJmtLHryrjhnDjqqp/49t8FALew==}
	1525	+ engines: {node: '>=14.0.0'}
	1526	+ peerDependencies:
	1527	+ postcss: ^8.0.0
	1528	+
	1529	+ postcss-js@4.1.0:
	1530	+ resolution: {integrity: sha512-oIAOTqgIo7q2EOwbhb8UalYePMvYoIeRY2YKntdpFQXNosSu3vLrniGgmH9OKs/qAkfoj5oB3le/7mINW1LCfw==}
	1531	+ engines: {node: ^12 \|\| ^14 \|\| >= 16}
	1532	+ peerDependencies:
	1533	+ postcss: ^8.4.21
	1534	+
	1535	+ postcss-load-config@6.0.1:
	1536	+ resolution: {integrity: sha512-oPtTM4oerL+UXmx+93ytZVN82RrlY/wPUV8IeDxFrzIjXOLF1pN+EmKPLbubvKHT2HC20xXsCAH2Z+CKV6Oz/g==}
	1537	+ engines: {node: '>= 18'}
	1538	+ peerDependencies:
	1539	+ jiti: '>=1.21.0'
	1540	+ postcss: '>=8.0.9'
	1541	+ tsx: ^4.8.1
	1542	+ yaml: ^2.4.2
	1543	+ peerDependenciesMeta:
	1544	+ jiti:
	1545	+ optional: true
	1546	+ postcss:
	1547	+ optional: true
	1548	+ tsx:
	1549	+ optional: true
	1550	+ yaml:
	1551	+ optional: true
	1552	+
	1553	+ postcss-nested@6.2.0:
	1554	+ resolution: {integrity: sha512-HQbt28KulC5AJzG+cZtj9kvKB93CFCdLvog1WFLf1D+xmMvPGlBstkpTEZfK5+AN9hfJocyBFCNiqyS48bpgzQ==}
	1555	+ engines: {node: '>=12.0'}
	1556	+ peerDependencies:
	1557	+ postcss: ^8.2.14
	1558	+
	1559	+ postcss-selector-parser@6.1.4:
	1560	+ resolution: {integrity: sha512-bIoJLOmjCO1S9XdY/DcnR5hJxvrDir1PbGChrzXG3vw0/FOliy/fA3dmdhQ441kah4gKv+TwckGzex6wNS5cnQ==}
	1561	+ engines: {node: '>=4'}
	1562	+
	1563	+ postcss-value-parser@4.2.0:
	1564	+ resolution: {integrity: sha512-1NNCs6uurfkVbeXG4S8JFT9t19m45ICnif8zWLd5oPSZ50QnwMfK+H3jv408d4jw/7Bttv5axS5IiHoLaVNHeQ==}
	1565	+
	1566	+ postcss@8.4.31:
	1567	+ resolution: {integrity: sha512-PS08Iboia9mts/2ygV3eLpY5ghnUcfLV/EXTOW1E2qYxJKGGBUtNjN76FYHnMs36RmARn41bC0AZmn+rR0OVpQ==}
	1568	+ engines: {node: ^10 \|\| ^12 \|\| >=14}
	1569	+
	1570	+ postcss@8.5.25:
	1571	+ resolution: {integrity: sha512-DTPx3RWSSnWyzLxQnlH0rJP+EW5ekl16ZU4/psbIhA0e53kJfdgaN5vKM+xP7yJtXVu+nfdVFmlgFDEKAe4Pyw==}
	1572	+ engines: {node: ^10 \|\| ^12 \|\| >=14}
	1573	+
	1574	+ prelude-ls@1.2.1:
	1575	+ resolution: {integrity: sha512-vkcDPrRZo1QZLbn5RLGPpg/WmIQ65qoWWhcGKf/b5eplkkarX0m9z8ppCat4mlOqUsWpyNuYgO3VRyrYHSzX5g==}
	1576	+ engines: {node: '>= 0.8.0'}
	1577	+
	1578	+ prettier@3.9.6:
	1579	+ resolution: {integrity: sha512-OpN0zzVdiaiAhxpuuj5efpIS4sY9j7bY6uR5mnj5yPzGkdkjNKSJeUThPb60Jw29QuAZgA4o+/iB49kFiaBX6g==}
	1580	+ engines: {node: '>=14'}
	1581	+ hasBin: true
	1582	+
	1583	+ prisma@5.22.0:
	1584	+ resolution: {integrity: sha512-vtpjW3XuYCSnMsNVBjLMNkTj6OZbudcPPTPYHqX0CJfpcdWciI1dM8uHETwmDxxiqEwCIE6WvXucWUetJgfu/A==}
	1585	+ engines: {node: '>=16.13'}
	1586	+ hasBin: true
	1587	+
	1588	+ punycode@2.3.1:
	1589	+ resolution: {integrity: sha512-vYt7UD1U9Wg6138shLtLOvdAu+8DsC/ilFtEVHcH+wydcSpNE20AfSOduf6MkRFahL5FY7X1oU7nKVZFtfq8Fg==}
	1590	+ engines: {node: '>=6'}
	1591	+
	1592	+ pure-rand@6.1.0:
	1593	+ resolution: {integrity: sha512-bVWawvoZoBYpp6yIoQtQXHZjmz35RSVHnUOTefl8Vcjr8snTPY1wnpSPMWekcFwbxI6gtmT7rSYPFvz71ldiOA==}
	1594	+
	1595	+ queue-microtask@1.2.3:
	1596	+ resolution: {integrity: sha512-NuaNSa6flKT5JaSYQzJok04JzTL1CA6aGhv5rfLW3PgqA+M2ChpZQnAC8h8i4ZFkBS8X5RqkDBHA7r4hej3K9A==}
	1597	+
	1598	+ react-dom@18.3.1:
	1599	+ resolution: {integrity: sha512-5m4nQKp+rZRb09LNH59GM4BxTh9251/ylbKIbpe7TpGxfJ+9kv6BLkLBXIjjspbgbnIBNqlI23tRnTWT0snUIw==}
	1600	+ peerDependencies:
	1601	+ react: ^18.3.1
	1602	+
	1603	+ react@18.3.1:
	1604	+ resolution: {integrity: sha512-wS+hAgJShR0KhEvPJArfuPVN1+Hz1t0Y6n5jLrGQbkb4urgPE/0Rve+1kMB1v/oWgHgm4WIcV+i7F2pTVj+2iQ==}
	1605	+ engines: {node: '>=0.10.0'}
	1606	+
	1607	+ read-cache@1.0.0:
	1608	+ resolution: {integrity: sha512-Owdv/Ft7IjOgm/i0xvNDZ1LrRANRfew4b2prF3OWMQLxLfu3bS8FVhCsrSCMK4lR56Y9ya+AThoTpDCTxCmpRA==}
	1609	+
	1610	+ readdirp@3.6.0:
	1611	+ resolution: {integrity: sha512-hOS089on8RduqdbhvQ5Z37A0ESjsqz6qnRcffsMU3495FuTdqSm+7bhJ29JvIOsBDEEnan5DPu9t3To9VRlMzA==}
	1612	+ engines: {node: '>=8.10.0'}
	1613	+
	1614	+ redis-errors@1.2.0:
	1615	+ resolution: {integrity: sha512-1qny3OExCf0UvUV/5wpYKf2YwPcOqXzkwKKSmKHiE6ZMQs5heeE/c8eXK+PNllPvmjgAbfnsbpkGZWy8cBpn9w==}
	1616	+ engines: {node: '>=4'}
	1617	+
	1618	+ redis-parser@3.0.0:
	1619	+ resolution: {integrity: sha512-DJnGAeenTdpMEH6uAJRK/uiyEIH9WVsUmoLwzudwGJUwZPp80PDBWPHXSAGNPwNvIXAbe7MSUB1zQFugFml66A==}
	1620	+ engines: {node: '>=4'}
	1621	+
	1622	+ resolve-from@4.0.0:
	1623	+ resolution: {integrity: sha512-pb/MYmXstAkysRFx8piNI1tGFNQIFA3vkE3Gq4EuA1dF6gHp/+vgZqsCGJapvy8N3Q+4o7FwvquPJcnZ7RYy4g==}
	1624	+ engines: {node: '>=4'}
	1625	+
	1626	+ resolve@1.22.12:
	1627	+ resolution: {integrity: sha512-TyeJ1zif53BPfHootBGwPRYT1RUt6oGWsaQr8UyZW/eAm9bKoijtvruSDEmZHm92CwS9nj7/fWttqPCgzep8CA==}
	1628	+ engines: {node: '>= 0.4'}
	1629	+ hasBin: true
	1630	+
	1631	+ reusify@1.1.0:
	1632	+ resolution: {integrity: sha512-g6QUff04oZpHs0eG5p83rFLhHeV00ug/Yf9nZM6fLeUrPguBTkTQOdpAWWspMh55TZfVQDPaN3NQJfbVRAxdIw==}
	1633	+ engines: {iojs: '>=1.0.0', node: '>=0.10.0'}
	1634	+
	1635	+ rollup@4.62.4:
	1636	+ resolution: {integrity: sha512-RXOqwaPsBGjMNMa4sQjDjHieHEZDFoj/Rdr46l2MU5DfEs16wHJPC2RPTPHWhNl+M3aI472LLqFkFKut4SblOg==}
	1637	+ engines: {node: '>=18.0.0', npm: '>=8.0.0'}
	1638	+ hasBin: true
	1639	+
	1640	+ run-parallel@1.2.0:
	1641	+ resolution: {integrity: sha512-5l4VyZR86LZ/lDxZTR6jqL8AFE2S0IFLMP26AbjsLVADxHdhB/c0GUsH+y39UfCi3dzz8OlQuPmnaJOMoDHQBA==}
	1642	+
	1643	+ scheduler@0.23.2:
	1644	+ resolution: {integrity: sha512-UOShsPwz7NrMUqhR6t0hWjFduvOzbtv7toDH1/hIrfRNIDBnnBWd0CwJTGvTpngVlmwGCdP9/Zl/tVrDqcuYzQ==}
	1645	+
	1646	+ semver@7.8.5:
	1647	+ resolution: {integrity: sha512-Y7/KDsb8LjooZpwaqGyulO6DQlksgCncchHGk+sZIY4SBvUocMBEFH5Ur1fI4dV+Jvl0w6cjvucaIi40puRioA==}
	1648	+ engines: {node: '>=10'}
	1649	+ hasBin: true
	1650	+
	1651	+ shebang-command@2.0.0:
	1652	+ resolution: {integrity: sha512-kHxr2zZpYtdmrN1qDjrrX/Z1rR1kG8Dx+gkpK1G4eXmvXswmcE1hTWBWYUzlraYw1/yZp6YuDY77YtvbN0dmDA==}
	1653	+ engines: {node: '>=8'}
	1654	+
	1655	+ shebang-regex@3.0.0:
	1656	+ resolution: {integrity: sha512-7++dFhtcx3353uBaq8DDR4NuxBetBzC7ZQOhmTQInHEd6bSrXdiEyzCvG07Z44UYdLShWUyXt5M/yhz8ekcb1A==}
	1657	+ engines: {node: '>=8'}
	1658	+
	1659	+ siginfo@2.0.0:
	1660	+ resolution: {integrity: sha512-ybx0WO1/8bSBLEWXZvEd7gMW3Sn3JFlW3TvX1nREbDLRNQNaeNN8WK0meBwPdAaOI7TtRRRJn/Es1zhrrCHu7g==}
	1661	+
	1662	+ source-map-js@1.2.1:
	1663	+ resolution: {integrity: sha512-UXWMKhLOwVKb728IUtQPXxfYU+usdybtUrK/8uGE8CQMvrhOpwvzDBwj0QhSL7MQc7vIsISBG8VQ8+IDQxpfQA==}
	1664	+ engines: {node: '>=0.10.0'}
	1665	+
	1666	+ stackback@0.0.2:
	1667	+ resolution: {integrity: sha512-1XMJE5fQo1jGH6Y/7ebnwPOBEkIEnT4QF32d5R1+VXdXveM0IBMJt8zfaxX1P3QhVwrYe+576+jkANtSS2mBbw==}
	1668	+
	1669	+ standard-as-callback@2.1.0:
	1670	+ resolution: {integrity: sha512-qoRRSyROncaz1z0mvYqIE4lCd9p2R90i6GxW3uZv5ucSu8tU7B5HXUP1gG8pVZsYNVaXjk8ClXHPttLyxAL48A==}
	1671	+
	1672	+ standardwebhooks@1.0.0:
	1673	+ resolution: {integrity: sha512-BbHGOQK9olHPMvQNHWul6MYlrRTAOKn03rOe4A8O3CLWhNf4YHBqq2HJKKC+sfqpxiBY52pNeesD6jIiLDz8jg==}
	1674	+
	1675	+ std-env@3.10.0:
	1676	+ resolution: {integrity: sha512-5GS12FdOZNliM5mAOxFRg7Ir0pWz8MdpYm6AY6VPkGpbA7ZzmbzNcBJQ0GPvvyWgcY7QAhCgf9Uy89I03faLkg==}
	1677	+
	1678	+ streamsearch@1.1.0:
	1679	+ resolution: {integrity: sha512-Mcc5wHehp9aXz1ax6bZUyY5afg9u2rv5cqQI3mRrYkGC8rW2hM02jWuwjtL++LS5qinSyhj2QfLyNsuc+VsExg==}
	1680	+ engines: {node: '>=10.0.0'}
	1681	+
	1682	+ strip-json-comments@3.1.1:
	1683	+ resolution: {integrity: sha512-6fPc+R4ihwqP6N/aIv2f1gMH8lOVtWQHoqC4yK6oSDVVocumAsfCqjkXnqiYMhmMwS/mEHLp7Vehlt3ql6lEig==}
	1684	+ engines: {node: '>=8'}
	1685	+
	1686	+ styled-jsx@5.1.1:
	1687	+ resolution: {integrity: sha512-pW7uC1l4mBZ8ugbiZrcIsiIvVx1UmTfw7UkC3Um2tmfUq9Bhk8IiyEIPl6F8agHgjzku6j0xQEZbfA5uSgSaCw==}
	1688	+ engines: {node: '>= 12.0.0'}
	1689	+ peerDependencies:
	1690	+ '@babel/core': '*'
	1691	+ babel-plugin-macros: '*'
	1692	+ react: '>= 16.8.0 \|\| 17.x.x \|\| ^18.0.0-0'
	1693	+ peerDependenciesMeta:
	1694	+ '@babel/core':
	1695	+ optional: true
	1696	+ babel-plugin-macros:
	1697	+ optional: true
	1698	+
	1699	+ sucrase@3.35.1:
	1700	+ resolution: {integrity: sha512-DhuTmvZWux4H1UOnWMB3sk0sbaCVOoQZjv8u1rDoTV0HTdGem9hkAZtl4JZy8P2z4Bg0nT+YMeOFyVr4zcG5Tw==}
	1701	+ engines: {node: '>=16 \|\| 14 >=14.17'}
	1702	+ hasBin: true
	1703	+
	1704	+ supports-color@7.2.0:
	1705	+ resolution: {integrity: sha512-qpCAvRl9stuOHveKsn7HncJRvv501qIacKzQlO/+Lwxc9+0q2wLyv4Dfvt80/DPn2pqOBsJdDiogXGR9+OvwRw==}
	1706	+ engines: {node: '>=8'}
	1707	+
	1708	+ supports-preserve-symlinks-flag@1.0.0:
	1709	+ resolution: {integrity: sha512-ot0WnXS9fgdkgIcePe6RHNk1WA8+muPa6cSjeR3V8K27q9BB1rTE3R1p7Hv0z1ZyAc8s6Vvv8DIyWf681MAt0w==}
	1710	+ engines: {node: '>= 0.4'}
	1711	+
	1712	+ tailwindcss@3.4.19:
	1713	+ resolution: {integrity: sha512-3ofp+LL8E+pK/JuPLPggVAIaEuhvIz4qNcf3nA1Xn2o/7fb7s/TYpHhwGDv1ZU3PkBluUVaF8PyCHcm48cKLWQ==}
	1714	+ engines: {node: '>=14.0.0'}
	1715	+ hasBin: true
	1716	+
	1717	+ thenify-all@1.6.0:
	1718	+ resolution: {integrity: sha512-RNxQH/qI8/t3thXJDwcstUO4zeqo64+Uy/+sNVRBx4Xn2OX+OZ9oP+iJnNFqplFra2ZUVeKCSa2oVWi3T4uVmA==}
	1719	+ engines: {node: '>=0.8'}
	1720	+
	1721	+ thenify@3.3.1:
	1722	+ resolution: {integrity: sha512-RVZSIV5IG10Hk3enotrhvz0T9em6cyHBLkH/YAZuKqd8hRkKhSfCGIcP2KUY0EPxndzANBmNllzWPwak+bheSw==}
	1723	+
	1724	+ tinybench@2.9.0:
	1725	+ resolution: {integrity: sha512-0+DUvqWMValLmha6lr4kD8iAMK1HzV0/aKnCtWb9v9641TnP/MFb7Pc2bxoxQjTXAErryXVgUOfv2YqNllqGeg==}
	1726	+
	1727	+ tinyexec@0.3.2:
	1728	+ resolution: {integrity: sha512-KQQR9yN7R5+OSwaK0XQoj22pwHoTlgYqmUscPYoknOoWCWfj/5/ABTMRi69FrKU5ffPVh5QcFikpWJI/P1ocHA==}
	1729	+
	1730	+ tinyglobby@0.2.17:
	1731	+ resolution: {integrity: sha512-wXR/dYpcqKmfWpEdZjiKJOwCNFndD0DMnrW/cYjVGttEkBfVgcLFHoNrlj47mjOVic9yyNu65alsgF4NQyTa2g==}
	1732	+ engines: {node: '>=12.0.0'}
	1733	+
	1734	+ tinypool@1.1.1:
	1735	+ resolution: {integrity: sha512-Zba82s87IFq9A9XmjiX5uZA/ARWDrB03OHlq+Vw1fSdt0I+4/Kutwy8BP4Y/y/aORMo61FQ0vIb5j44vSo5Pkg==}
	1736	+ engines: {node: ^18.0.0 \|\| >=20.0.0}
	1737	+
	1738	+ tinyrainbow@1.2.0:
	1739	+ resolution: {integrity: sha512-weEDEq7Z5eTHPDh4xjX789+fHfF+P8boiFB+0vbWzpbnbsEr/GRaohi/uMKxg8RZMXnl1ItAi/IUHWMsjDV7kQ==}
	1740	+ engines: {node: '>=14.0.0'}
	1741	+
	1742	+ tinyspy@3.0.2:
	1743	+ resolution: {integrity: sha512-n1cw8k1k0x4pgA2+9XrOkFydTerNcJ1zWCO5Nn9scWHTD+5tp8dghT2x1uduQePZTZgd3Tupf+x9BxJjeJi77Q==}
	1744	+ engines: {node: '>=14.0.0'}
	1745	+
	1746	+ to-regex-range@5.0.1:
	1747	+ resolution: {integrity: sha512-65P7iz6X5yEr1cwcgvQxbbIw7Uk3gOy5dIdtZ4rDveLqhrdJP+Li/Hx6tyK0NEb+2GCyneCMJiGqrADCSNk8sQ==}
	1748	+ engines: {node: '>=8.0'}
	1749	+
	1750	+ ts-algebra@2.0.0:
	1751	+ resolution: {integrity: sha512-FPAhNPFMrkwz76P7cdjdmiShwMynZYN6SgOujD1urY4oNm80Ou9oMdmbR45LotcKOXoy7wSmHkRFE6Mxbrhefw==}
	1752	+
	1753	+ ts-api-utils@2.5.0:
	1754	+ resolution: {integrity: sha512-OJ/ibxhPlqrMM0UiNHJ/0CKQkoKF243/AEmplt3qpRgkW8VG7IfOS41h7V8TjITqdByHzrjcS/2si+y4lIh8NA==}
	1755	+ engines: {node: '>=18.12'}
	1756	+ peerDependencies:
	1757	+ typescript: '>=4.8.4'
	1758	+
	1759	+ ts-interface-checker@0.1.13:
	1760	+ resolution: {integrity: sha512-Y/arvbn+rrz3JCKl9C4kVNfTfSm2/mEp5FSz5EsZSANGPSlQrpRI5M4PKF+mJnE52jOO90PnPSc3Ur3bTQw0gA==}
	1761	+
	1762	+ tslib@2.8.1:
	1763	+ resolution: {integrity: sha512-oJFu94HQb+KVduSUQL7wnpmqnfmLsOA/nAh6b6EH0wCEoK0/mPeXU6c3wKDV83MkOuHPRHtSXKKU99IBazS/2w==}
	1764	+
	1765	+ tsx@4.23.6:
	1766	+ resolution: {integrity: sha512-D/YYGUDqKlLvXhM5fBBbiENaGICxLfU4viHnZEkgmgplnDFa+Kczy34VV7AmLJgdzisv0I/J3zitfC26JH3GXg==}
	1767	+ engines: {node: '>=18.0.0'}
	1768	+ hasBin: true
	1769	+
	1770	+ turbo@2.10.8:
	1771	+ resolution: {integrity: sha512-9+8YX5QOkGXzZxcIykTHgaooRHGMWO+jfdyRK0o+rN0U7hBIig2MrJ8r/aNzIPDPhdA73SGb0O+tIztaModTMg==}
	1772	+ hasBin: true
	1773	+
	1774	+ type-check@0.4.0:
	1775	+ resolution: {integrity: sha512-XleUoc9uwGXqjWwXaUTZAmzMcFZ5858QA2vvx1Ur5xIcixXIP+8LnFDgRplU30us6teqdlskFfu+ae4K79Ooew==}
	1776	+ engines: {node: '>= 0.8.0'}
	1777	+
	1778	+ typescript-eslint@8.66.0:
	1779	+ resolution: {integrity: sha512-QlEbBPz/RuJ1XUHj29nm3t0F/O/cSlEnntozqPOYHnnTGAXFamnMBu5i9Vn6vhUPHGAjR+Vl+5J8vPN/BMUrJw==}
	1780	+ engines: {node: ^18.18.0 \|\| ^20.9.0 \|\| >=21.1.0}
	1781	+ peerDependencies:
	1782	+ eslint: ^8.57.0 \|\| ^9.0.0 \|\| ^10.0.0
	1783	+ typescript: '>=4.8.4 <6.1.0'
	1784	+
	1785	+ typescript@5.9.3:
	1786	+ resolution: {integrity: sha512-jl1vZzPDinLr9eUt3J/t7V6FgNEw9QjvBPdysz9KfQDD41fQrC2Y4vKQdiaUpFT4bXlb1RHhLpp8wtm6M5TgSw==}
	1787	+ engines: {node: '>=14.17'}
	1788	+ hasBin: true
	1789	+
	1790	+ undici-types@6.21.0:
	1791	+ resolution: {integrity: sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ==}
	1792	+
	1793	+ update-browserslist-db@1.2.3:
	1794	+ resolution: {integrity: sha512-Js0m9cx+qOgDxo0eMiFGEueWztz+d4+M3rGlmKPT+T4IS/jP4ylw3Nwpu6cpTTP8R1MAC1kF4VbdLt3ARf209w==}
	1795	+ hasBin: true
	1796	+ peerDependencies:
	1797	+ browserslist: '>= 4.21.0'
	1798	+
	1799	+ uri-js@4.4.1:
	1800	+ resolution: {integrity: sha512-7rKUyy33Q1yc98pQ1DAmLtwX109F7TIfWlW1Ydo8Wl1ii1SeHieeh0HHfPeL2fMXK6z0s8ecKs9frCuLJvndBg==}
	1801	+
	1802	+ util-deprecate@1.0.2:
	1803	+ resolution: {integrity: sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw==}
	1804	+
	1805	+ vite-node@2.1.9:
	1806	+ resolution: {integrity: sha512-AM9aQ/IPrW/6ENLQg3AGY4K1N2TGZdR5e4gu/MmmR2xR3Ll1+dib+nook92g4TV3PXVyeyxdWwtaCAiUL0hMxA==}
	1807	+ engines: {node: ^18.0.0 \|\| >=20.0.0}
	1808	+ hasBin: true
	1809	+
	1810	+ vite@5.4.21:
	1811	+ resolution: {integrity: sha512-o5a9xKjbtuhY6Bi5S3+HvbRERmouabWbyUcpXXUA1u+GNUKoROi9byOJ8M0nHbHYHkYICiMlqxkg1KkYmm25Sw==}
	1812	+ engines: {node: ^18.0.0 \|\| >=20.0.0}
	1813	+ hasBin: true
	1814	+ peerDependencies:
	1815	+ '@types/node': ^18.0.0 \|\| >=20.0.0
	1816	+ less: '*'
	1817	+ lightningcss: ^1.21.0
	1818	+ sass: '*'
	1819	+ sass-embedded: '*'
	1820	+ stylus: '*'
	1821	+ sugarss: '*'
	1822	+ terser: ^5.4.0
	1823	+ peerDependenciesMeta:
	1824	+ '@types/node':
	1825	+ optional: true
	1826	+ less:
	1827	+ optional: true
	1828	+ lightningcss:
	1829	+ optional: true
	1830	+ sass:
	1831	+ optional: true
	1832	+ sass-embedded:
	1833	+ optional: true
	1834	+ stylus:
	1835	+ optional: true
	1836	+ sugarss:
	1837	+ optional: true
	1838	+ terser:
	1839	+ optional: true
	1840	+
	1841	+ vitest@2.1.9:
	1842	+ resolution: {integrity: sha512-MSmPM9REYqDGBI8439mA4mWhV5sKmDlBKWIYbA3lRb2PTHACE0mgKwA8yQ2xq9vxDTuk4iPrECBAEW2aoFXY0Q==}
	1843	+ engines: {node: ^18.0.0 \|\| >=20.0.0}
	1844	+ hasBin: true
	1845	+ peerDependencies:
	1846	+ '@edge-runtime/vm': '*'
	1847	+ '@types/node': ^18.0.0 \|\| >=20.0.0
	1848	+ '@vitest/browser': 2.1.9
	1849	+ '@vitest/ui': 2.1.9
	1850	+ happy-dom: '*'
	1851	+ jsdom: '*'
	1852	+ peerDependenciesMeta:
	1853	+ '@edge-runtime/vm':
	1854	+ optional: true
	1855	+ '@types/node':
	1856	+ optional: true
	1857	+ '@vitest/browser':
	1858	+ optional: true
	1859	+ '@vitest/ui':
	1860	+ optional: true
	1861	+ happy-dom:
	1862	+ optional: true
	1863	+ jsdom:
	1864	+ optional: true
	1865	+
	1866	+ which@2.0.2:
	1867	+ resolution: {integrity: sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA==}
	1868	+ engines: {node: '>= 8'}
	1869	+ hasBin: true
	1870	+
	1871	+ why-is-node-running@2.3.0:
	1872	+ resolution: {integrity: sha512-hUrmaWBdVDcxvYqnyh09zunKzROWjbZTiNy8dBEjkS7ehEDQibXJ7XvlmtbwuTclUiIyN+CyXQD4Vmko8fNm8w==}
	1873	+ engines: {node: '>=8'}
	1874	+ hasBin: true
	1875	+
	1876	+ word-wrap@1.2.5:
	1877	+ resolution: {integrity: sha512-BN22B5eaMMI9UMtjrGd5g5eCYPpCPDUy0FJXbYsaT5zYxjFOckS53SQDE3pWkVoWpHXVb3BrYcEN4Twa55B5cA==}
	1878	+ engines: {node: '>=0.10.0'}
	1879	+
	1880	+ yocto-queue@0.1.0:
	1881	+ resolution: {integrity: sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==}
	1882	+ engines: {node: '>=10'}
	1883	+
	1884	+ zod@3.25.76:
	1885	+ resolution: {integrity: sha512-gzUt/qt81nXsFGKIFcC3YnfEAx5NkunCfnDlvuBSSFS02bcXu4Lmea0AFIUwbLWxWPx3d9p8S5QoaujKcNQxcQ==}
	1886	+
	1887	+snapshots:
	1888	+
	1889	+ '@alloc/quick-lru@5.2.0': {}
	1890	+
	1891	+ '@anthropic-ai/sdk@0.115.0(zod@3.25.76)':
	1892	+ dependencies:
	1893	+ json-schema-to-ts: 3.1.1
	1894	+ standardwebhooks: 1.0.0
	1895	+ optionalDependencies:
	1896	+ zod: 3.25.76
	1897	+
	1898	+ '@babel/runtime@7.29.7': {}
	1899	+
	1900	+ '@esbuild/aix-ppc64@0.21.5':
	1901	+ optional: true
	1902	+
	1903	+ '@esbuild/aix-ppc64@0.28.1':
	1904	+ optional: true
	1905	+
	1906	+ '@esbuild/android-arm64@0.21.5':
	1907	+ optional: true
	1908	+
	1909	+ '@esbuild/android-arm64@0.28.1':
	1910	+ optional: true
	1911	+
	1912	+ '@esbuild/android-arm@0.21.5':
	1913	+ optional: true
	1914	+
	1915	+ '@esbuild/android-arm@0.28.1':
	1916	+ optional: true
	1917	+
	1918	+ '@esbuild/android-x64@0.21.5':
	1919	+ optional: true
	1920	+
	1921	+ '@esbuild/android-x64@0.28.1':
	1922	+ optional: true
	1923	+
	1924	+ '@esbuild/darwin-arm64@0.21.5':
	1925	+ optional: true
	1926	+
	1927	+ '@esbuild/darwin-arm64@0.28.1':
	1928	+ optional: true
	1929	+
	1930	+ '@esbuild/darwin-x64@0.21.5':
	1931	+ optional: true
	1932	+
	1933	+ '@esbuild/darwin-x64@0.28.1':
	1934	+ optional: true
	1935	+
	1936	+ '@esbuild/freebsd-arm64@0.21.5':
	1937	+ optional: true
	1938	+
	1939	+ '@esbuild/freebsd-arm64@0.28.1':
	1940	+ optional: true
	1941	+
	1942	+ '@esbuild/freebsd-x64@0.21.5':
	1943	+ optional: true
	1944	+
	1945	+ '@esbuild/freebsd-x64@0.28.1':
	1946	+ optional: true
	1947	+
	1948	+ '@esbuild/linux-arm64@0.21.5':
	1949	+ optional: true
	1950	+
	1951	+ '@esbuild/linux-arm64@0.28.1':
	1952	+ optional: true
	1953	+
	1954	+ '@esbuild/linux-arm@0.21.5':
	1955	+ optional: true
	1956	+
	1957	+ '@esbuild/linux-arm@0.28.1':
	1958	+ optional: true
	1959	+
	1960	+ '@esbuild/linux-ia32@0.21.5':
	1961	+ optional: true
	1962	+
	1963	+ '@esbuild/linux-ia32@0.28.1':
	1964	+ optional: true
	1965	+
	1966	+ '@esbuild/linux-loong64@0.21.5':
	1967	+ optional: true
	1968	+
	1969	+ '@esbuild/linux-loong64@0.28.1':
	1970	+ optional: true
	1971	+
	1972	+ '@esbuild/linux-mips64el@0.21.5':
	1973	+ optional: true
	1974	+
	1975	+ '@esbuild/linux-mips64el@0.28.1':
	1976	+ optional: true
	1977	+
	1978	+ '@esbuild/linux-ppc64@0.21.5':
	1979	+ optional: true
	1980	+
	1981	+ '@esbuild/linux-ppc64@0.28.1':
	1982	+ optional: true
	1983	+
	1984	+ '@esbuild/linux-riscv64@0.21.5':
	1985	+ optional: true
	1986	+
	1987	+ '@esbuild/linux-riscv64@0.28.1':
	1988	+ optional: true
	1989	+
	1990	+ '@esbuild/linux-s390x@0.21.5':
	1991	+ optional: true
	1992	+
	1993	+ '@esbuild/linux-s390x@0.28.1':
	1994	+ optional: true
	1995	+
	1996	+ '@esbuild/linux-x64@0.21.5':
	1997	+ optional: true
	1998	+
	1999	+ '@esbuild/linux-x64@0.28.1':
	2000	+ optional: true
	2001	+
	2002	+ '@esbuild/netbsd-arm64@0.28.1':
	2003	+ optional: true
	2004	+
	2005	+ '@esbuild/netbsd-x64@0.21.5':
	2006	+ optional: true
	2007	+
	2008	+ '@esbuild/netbsd-x64@0.28.1':
	2009	+ optional: true
	2010	+
	2011	+ '@esbuild/openbsd-arm64@0.28.1':
	2012	+ optional: true
	2013	+
	2014	+ '@esbuild/openbsd-x64@0.21.5':
	2015	+ optional: true
	2016	+
	2017	+ '@esbuild/openbsd-x64@0.28.1':
	2018	+ optional: true
	2019	+
	2020	+ '@esbuild/openharmony-arm64@0.28.1':
	2021	+ optional: true
	2022	+
	2023	+ '@esbuild/sunos-x64@0.21.5':
	2024	+ optional: true
	2025	+
	2026	+ '@esbuild/sunos-x64@0.28.1':
	2027	+ optional: true
	2028	+
	2029	+ '@esbuild/win32-arm64@0.21.5':
	2030	+ optional: true
	2031	+
	2032	+ '@esbuild/win32-arm64@0.28.1':
	2033	+ optional: true
	2034	+
	2035	+ '@esbuild/win32-ia32@0.21.5':
	2036	+ optional: true
	2037	+
	2038	+ '@esbuild/win32-ia32@0.28.1':
	2039	+ optional: true
	2040	+
	2041	+ '@esbuild/win32-x64@0.21.5':
	2042	+ optional: true
	2043	+
	2044	+ '@esbuild/win32-x64@0.28.1':
	2045	+ optional: true
	2046	+
	2047	+ '@eslint-community/eslint-utils@4.10.1(eslint@9.39.5(jiti@1.21.7))':
	2048	+ dependencies:
	2049	+ eslint: 9.39.5(jiti@1.21.7)
	2050	+ eslint-visitor-keys: 3.4.3
	2051	+
	2052	+ '@eslint-community/regexpp@4.12.2': {}
	2053	+
	2054	+ '@eslint/config-array@0.21.2':
	2055	+ dependencies:
	2056	+ '@eslint/object-schema': 2.1.7
	2057	+ debug: 4.4.3
	2058	+ minimatch: 3.1.5
	2059	+ transitivePeerDependencies:
	2060	+ - supports-color
	2061	+
	2062	+ '@eslint/config-helpers@0.4.2':
	2063	+ dependencies:
	2064	+ '@eslint/core': 0.17.0
	2065	+
	2066	+ '@eslint/core@0.17.0':
	2067	+ dependencies:
	2068	+ '@types/json-schema': 7.0.15
	2069	+
	2070	+ '@eslint/eslintrc@3.3.6':
	2071	+ dependencies:
	2072	+ ajv: 6.15.0
	2073	+ debug: 4.4.3
	2074	+ espree: 10.4.0
	2075	+ globals: 14.0.0
	2076	+ ignore: 5.3.2
	2077	+ import-fresh: 3.3.1
	2078	+ js-yaml: 4.3.1
	2079	+ minimatch: 3.1.5
	2080	+ strip-json-comments: 3.1.1
	2081	+ transitivePeerDependencies:
	2082	+ - supports-color
	2083	+
	2084	+ '@eslint/js@9.39.5': {}
	2085	+
	2086	+ '@eslint/object-schema@2.1.7': {}
	2087	+
	2088	+ '@eslint/plugin-kit@0.4.1':
	2089	+ dependencies:
	2090	+ '@eslint/core': 0.17.0
	2091	+ levn: 0.4.1
	2092	+
	2093	+ '@humanfs/core@0.19.2':
	2094	+ dependencies:
	2095	+ '@humanfs/types': 0.15.0
	2096	+
	2097	+ '@humanfs/node@0.16.8':
	2098	+ dependencies:
	2099	+ '@humanfs/core': 0.19.2
	2100	+ '@humanfs/types': 0.15.0
	2101	+ '@humanwhocodes/retry': 0.4.3
	2102	+
	2103	+ '@humanfs/types@0.15.0': {}
	2104	+
	2105	+ '@humanwhocodes/module-importer@1.0.1': {}
	2106	+
	2107	+ '@humanwhocodes/retry@0.4.3': {}
	2108	+
	2109	+ '@ioredis/commands@1.10.0': {}
	2110	+
	2111	+ '@jridgewell/gen-mapping@0.3.13':
	2112	+ dependencies:
	2113	+ '@jridgewell/sourcemap-codec': 1.5.5
	2114	+ '@jridgewell/trace-mapping': 0.3.31
	2115	+
	2116	+ '@jridgewell/resolve-uri@3.1.2': {}
	2117	+
	2118	+ '@jridgewell/sourcemap-codec@1.5.5': {}
	2119	+
	2120	+ '@jridgewell/trace-mapping@0.3.31':
	2121	+ dependencies:
	2122	+ '@jridgewell/resolve-uri': 3.1.2
	2123	+ '@jridgewell/sourcemap-codec': 1.5.5
	2124	+
	2125	+ '@msgpackr-extract/msgpackr-extract-darwin-arm64@3.0.4':
	2126	+ optional: true
	2127	+
	2128	+ '@msgpackr-extract/msgpackr-extract-darwin-x64@3.0.4':
	2129	+ optional: true
	2130	+
	2131	+ '@msgpackr-extract/msgpackr-extract-linux-arm64@3.0.4':
	2132	+ optional: true
	2133	+
	2134	+ '@msgpackr-extract/msgpackr-extract-linux-arm@3.0.4':
	2135	+ optional: true
	2136	+
	2137	+ '@msgpackr-extract/msgpackr-extract-linux-x64@3.0.4':
	2138	+ optional: true
	2139	+
	2140	+ '@msgpackr-extract/msgpackr-extract-win32-x64@3.0.4':
	2141	+ optional: true
	2142	+
	2143	+ '@napi-rs/lzma-linux-x64-gnu@1.5.1':
	2144	+ optional: true
	2145	+
	2146	+ '@next/env@14.2.35': {}
	2147	+
	2148	+ '@next/swc-darwin-arm64@14.2.33':
	2149	+ optional: true
	2150	+
	2151	+ '@next/swc-darwin-x64@14.2.33':
	2152	+ optional: true
	2153	+
	2154	+ '@next/swc-linux-arm64-gnu@14.2.33':
	2155	+ optional: true
	2156	+
	2157	+ '@next/swc-linux-arm64-musl@14.2.33':
	2158	+ optional: true
	2159	+
	2160	+ '@next/swc-linux-x64-gnu@14.2.33':
	2161	+ optional: true
	2162	+
	2163	+ '@next/swc-linux-x64-musl@14.2.33':
	2164	+ optional: true
	2165	+
	2166	+ '@next/swc-win32-arm64-msvc@14.2.33':
	2167	+ optional: true
	2168	+
	2169	+ '@next/swc-win32-ia32-msvc@14.2.33':
	2170	+ optional: true
	2171	+
	2172	+ '@next/swc-win32-x64-msvc@14.2.33':
	2173	+ optional: true
	2174	+
	2175	+ '@nodelib/fs.scandir@2.1.5':
	2176	+ dependencies:
	2177	+ '@nodelib/fs.stat': 2.0.5
	2178	+ run-parallel: 1.2.0
	2179	+
	2180	+ '@nodelib/fs.stat@2.0.5': {}
	2181	+
	2182	+ '@nodelib/fs.walk@1.2.8':
	2183	+ dependencies:
	2184	+ '@nodelib/fs.scandir': 2.1.5
	2185	+ fastq: 1.20.1
	2186	+
	2187	+ '@prisma/client@5.22.0(prisma@5.22.0)':
	2188	+ optionalDependencies:
	2189	+ prisma: 5.22.0
	2190	+
	2191	+ '@prisma/debug@5.22.0': {}
	2192	+
	2193	+ '@prisma/engines-version@5.22.0-44.605197351a3c8bdd595af2d2a9bc3025bca48ea2': {}
	2194	+
	2195	+ '@prisma/engines@5.22.0':
	2196	+ dependencies:
	2197	+ '@prisma/debug': 5.22.0
	2198	+ '@prisma/engines-version': 5.22.0-44.605197351a3c8bdd595af2d2a9bc3025bca48ea2
	2199	+ '@prisma/fetch-engine': 5.22.0
	2200	+ '@prisma/get-platform': 5.22.0
	2201	+
	2202	+ '@prisma/fetch-engine@5.22.0':
	2203	+ dependencies:
	2204	+ '@prisma/debug': 5.22.0
	2205	+ '@prisma/engines-version': 5.22.0-44.605197351a3c8bdd595af2d2a9bc3025bca48ea2
	2206	+ '@prisma/get-platform': 5.22.0
	2207	+
	2208	+ '@prisma/get-platform@5.22.0':
	2209	+ dependencies:
	2210	+ '@prisma/debug': 5.22.0
	2211	+
	2212	+ '@rollup/rollup-android-arm-eabi@4.62.4':
	2213	+ optional: true
	2214	+
	2215	+ '@rollup/rollup-android-arm64@4.62.4':
	2216	+ optional: true
	2217	+
	2218	+ '@rollup/rollup-darwin-arm64@4.62.4':
	2219	+ optional: true
	2220	+
	2221	+ '@rollup/rollup-darwin-x64@4.62.4':
	2222	+ optional: true
	2223	+
	2224	+ '@rollup/rollup-freebsd-arm64@4.62.4':
	2225	+ optional: true
	2226	+
	2227	+ '@rollup/rollup-freebsd-x64@4.62.4':
	2228	+ optional: true
	2229	+
	2230	+ '@rollup/rollup-linux-arm-gnueabihf@4.62.4':
	2231	+ optional: true
	2232	+
	2233	+ '@rollup/rollup-linux-arm-musleabihf@4.62.4':
	2234	+ optional: true
	2235	+
	2236	+ '@rollup/rollup-linux-arm64-gnu@4.62.4':
	2237	+ optional: true
	2238	+
	2239	+ '@rollup/rollup-linux-arm64-musl@4.62.4':
	2240	+ optional: true
	2241	+
	2242	+ '@rollup/rollup-linux-loong64-gnu@4.62.4':
	2243	+ optional: true
	2244	+
	2245	+ '@rollup/rollup-linux-loong64-musl@4.62.4':
	2246	+ optional: true
	2247	+
	2248	+ '@rollup/rollup-linux-ppc64-gnu@4.62.4':
	2249	+ optional: true
	2250	+
	2251	+ '@rollup/rollup-linux-ppc64-musl@4.62.4':
	2252	+ optional: true
	2253	+
	2254	+ '@rollup/rollup-linux-riscv64-gnu@4.62.4':
	2255	+ optional: true
	2256	+
	2257	+ '@rollup/rollup-linux-riscv64-musl@4.62.4':
	2258	+ optional: true
	2259	+
	2260	+ '@rollup/rollup-linux-s390x-gnu@4.62.4':
	2261	+ optional: true
	2262	+
	2263	+ '@rollup/rollup-linux-x64-gnu@4.62.4':
	2264	+ optional: true
	2265	+
	2266	+ '@rollup/rollup-linux-x64-musl@4.62.4':
	2267	+ optional: true
	2268	+
	2269	+ '@rollup/rollup-openbsd-x64@4.62.4':
	2270	+ optional: true
	2271	+
	2272	+ '@rollup/rollup-openharmony-arm64@4.62.4':
	2273	+ optional: true
	2274	+
	2275	+ '@rollup/rollup-win32-arm64-msvc@4.62.4':
	2276	+ optional: true
	2277	+
	2278	+ '@rollup/rollup-win32-ia32-msvc@4.62.4':
	2279	+ optional: true
	2280	+
	2281	+ '@rollup/rollup-win32-x64-gnu@4.62.4':
	2282	+ optional: true
	2283	+
	2284	+ '@rollup/rollup-win32-x64-msvc@4.62.4':
	2285	+ optional: true
	2286	+
	2287	+ '@stablelib/base64@1.0.1': {}
	2288	+
	2289	+ '@swc/counter@0.1.3': {}
	2290	+
	2291	+ '@swc/helpers@0.5.5':
	2292	+ dependencies:
	2293	+ '@swc/counter': 0.1.3
	2294	+ tslib: 2.8.1
	2295	+
	2296	+ '@turbo/darwin-64@2.10.8':
	2297	+ optional: true
	2298	+
	2299	+ '@turbo/darwin-arm64@2.10.8':
	2300	+ optional: true
	2301	+
	2302	+ '@turbo/linux-64@2.10.8':
	2303	+ optional: true
	2304	+
	2305	+ '@turbo/linux-arm64@2.10.8':
	2306	+ optional: true
	2307	+
	2308	+ '@turbo/windows-64@2.10.8':
	2309	+ optional: true
	2310	+
	2311	+ '@turbo/windows-arm64@2.10.8':
	2312	+ optional: true
	2313	+
	2314	+ '@types/estree@1.0.9': {}
	2315	+
	2316	+ '@types/json-schema@7.0.15': {}
	2317	+
	2318	+ '@types/node@20.19.43':
	2319	+ dependencies:
	2320	+ undici-types: 6.21.0
	2321	+
	2322	+ '@types/prop-types@15.7.15': {}
	2323	+
	2324	+ '@types/react-dom@18.3.7(@types/react@18.3.31)':
	2325	+ dependencies:
	2326	+ '@types/react': 18.3.31
	2327	+
	2328	+ '@types/react@18.3.31':
	2329	+ dependencies:
	2330	+ '@types/prop-types': 15.7.15
	2331	+ csstype: 3.2.3
	2332	+
	2333	+ '@typescript-eslint/eslint-plugin@8.66.0(@typescript-eslint/parser@8.66.0(eslint@9.39.5(jiti@1.21.7))(typescript@5.9.3))(eslint@9.39.5(jiti@1.21.7))(typescript@5.9.3)':
	2334	+ dependencies:
	2335	+ '@eslint-community/regexpp': 4.12.2
	2336	+ '@typescript-eslint/parser': 8.66.0(eslint@9.39.5(jiti@1.21.7))(typescript@5.9.3)
	2337	+ '@typescript-eslint/scope-manager': 8.66.0
	2338	+ '@typescript-eslint/type-utils': 8.66.0(eslint@9.39.5(jiti@1.21.7))(typescript@5.9.3)
	2339	+ '@typescript-eslint/utils': 8.66.0(eslint@9.39.5(jiti@1.21.7))(typescript@5.9.3)
	2340	+ '@typescript-eslint/visitor-keys': 8.66.0
	2341	+ eslint: 9.39.5(jiti@1.21.7)
	2342	+ ignore: 7.0.6
	2343	+ natural-compare: 1.4.0
	2344	+ ts-api-utils: 2.5.0(typescript@5.9.3)
	2345	+ typescript: 5.9.3
	2346	+ transitivePeerDependencies:
	2347	+ - supports-color
	2348	+
	2349	+ '@typescript-eslint/parser@8.66.0(eslint@9.39.5(jiti@1.21.7))(typescript@5.9.3)':
	2350	+ dependencies:
	2351	+ '@typescript-eslint/scope-manager': 8.66.0
	2352	+ '@typescript-eslint/types': 8.66.0
	2353	+ '@typescript-eslint/typescript-estree': 8.66.0(typescript@5.9.3)
	2354	+ '@typescript-eslint/visitor-keys': 8.66.0
	2355	+ debug: 4.4.3
	2356	+ eslint: 9.39.5(jiti@1.21.7)
	2357	+ typescript: 5.9.3
	2358	+ transitivePeerDependencies:
	2359	+ - supports-color
	2360	+
	2361	+ '@typescript-eslint/project-service@8.66.0(typescript@5.9.3)':
	2362	+ dependencies:
	2363	+ '@typescript-eslint/tsconfig-utils': 8.66.0(typescript@5.9.3)
	2364	+ '@typescript-eslint/types': 8.66.0
	2365	+ debug: 4.4.3
	2366	+ typescript: 5.9.3
	2367	+ transitivePeerDependencies:
	2368	+ - supports-color
	2369	+
	2370	+ '@typescript-eslint/scope-manager@8.66.0':
	2371	+ dependencies:
	2372	+ '@typescript-eslint/types': 8.66.0
	2373	+ '@typescript-eslint/visitor-keys': 8.66.0
	2374	+
	2375	+ '@typescript-eslint/tsconfig-utils@8.66.0(typescript@5.9.3)':
	2376	+ dependencies:
	2377	+ typescript: 5.9.3
	2378	+
	2379	+ '@typescript-eslint/type-utils@8.66.0(eslint@9.39.5(jiti@1.21.7))(typescript@5.9.3)':
	2380	+ dependencies:
	2381	+ '@typescript-eslint/types': 8.66.0
	2382	+ '@typescript-eslint/typescript-estree': 8.66.0(typescript@5.9.3)
	2383	+ '@typescript-eslint/utils': 8.66.0(eslint@9.39.5(jiti@1.21.7))(typescript@5.9.3)
	2384	+ debug: 4.4.3
	2385	+ eslint: 9.39.5(jiti@1.21.7)
	2386	+ ts-api-utils: 2.5.0(typescript@5.9.3)
	2387	+ typescript: 5.9.3
	2388	+ transitivePeerDependencies:
	2389	+ - supports-color
	2390	+
	2391	+ '@typescript-eslint/types@8.66.0': {}
	2392	+
	2393	+ '@typescript-eslint/typescript-estree@8.66.0(typescript@5.9.3)':
	2394	+ dependencies:
	2395	+ '@typescript-eslint/project-service': 8.66.0(typescript@5.9.3)
	2396	+ '@typescript-eslint/tsconfig-utils': 8.66.0(typescript@5.9.3)
	2397	+ '@typescript-eslint/types': 8.66.0
	2398	+ '@typescript-eslint/visitor-keys': 8.66.0
	2399	+ debug: 4.4.3
	2400	+ minimatch: 10.2.6
	2401	+ semver: 7.8.5
	2402	+ tinyglobby: 0.2.17
	2403	+ ts-api-utils: 2.5.0(typescript@5.9.3)
	2404	+ typescript: 5.9.3
	2405	+ transitivePeerDependencies:
	2406	+ - supports-color
	2407	+
	2408	+ '@typescript-eslint/utils@8.66.0(eslint@9.39.5(jiti@1.21.7))(typescript@5.9.3)':
	2409	+ dependencies:
	2410	+ '@eslint-community/eslint-utils': 4.10.1(eslint@9.39.5(jiti@1.21.7))
	2411	+ '@typescript-eslint/scope-manager': 8.66.0
	2412	+ '@typescript-eslint/types': 8.66.0
	2413	+ '@typescript-eslint/typescript-estree': 8.66.0(typescript@5.9.3)
	2414	+ eslint: 9.39.5(jiti@1.21.7)
	2415	+ typescript: 5.9.3
	2416	+ transitivePeerDependencies:
	2417	+ - supports-color
	2418	+
	2419	+ '@typescript-eslint/visitor-keys@8.66.0':
	2420	+ dependencies:
	2421	+ '@typescript-eslint/types': 8.66.0
	2422	+ eslint-visitor-keys: 5.0.1
	2423	+
	2424	+ '@vitest/expect@2.1.9':
	2425	+ dependencies:
	2426	+ '@vitest/spy': 2.1.9
	2427	+ '@vitest/utils': 2.1.9
	2428	+ chai: 5.3.3
	2429	+ tinyrainbow: 1.2.0
	2430	+
	2431	+ '@vitest/mocker@2.1.9(vite@5.4.21(@types/node@20.19.43))':
	2432	+ dependencies:
	2433	+ '@vitest/spy': 2.1.9
	2434	+ estree-walker: 3.0.3
	2435	+ magic-string: 0.30.21
	2436	+ optionalDependencies:
	2437	+ vite: 5.4.21(@types/node@20.19.43)
	2438	+
	2439	+ '@vitest/pretty-format@2.1.9':
	2440	+ dependencies:
	2441	+ tinyrainbow: 1.2.0
	2442	+
	2443	+ '@vitest/runner@2.1.9':
	2444	+ dependencies:
	2445	+ '@vitest/utils': 2.1.9
	2446	+ pathe: 1.1.2
	2447	+
	2448	+ '@vitest/snapshot@2.1.9':
	2449	+ dependencies:
	2450	+ '@vitest/pretty-format': 2.1.9
	2451	+ magic-string: 0.30.21
	2452	+ pathe: 1.1.2
	2453	+
	2454	+ '@vitest/spy@2.1.9':
	2455	+ dependencies:
	2456	+ tinyspy: 3.0.2
	2457	+
	2458	+ '@vitest/utils@2.1.9':
	2459	+ dependencies:
	2460	+ '@vitest/pretty-format': 2.1.9
	2461	+ loupe: 3.2.1
	2462	+ tinyrainbow: 1.2.0
	2463	+
	2464	+ acorn-jsx@5.3.2(acorn@8.18.0):
	2465	+ dependencies:
	2466	+ acorn: 8.18.0
	2467	+
	2468	+ acorn@8.18.0: {}
	2469	+
	2470	+ ajv@6.15.0:
	2471	+ dependencies:
	2472	+ fast-deep-equal: 3.1.3
	2473	+ fast-json-stable-stringify: 2.1.0
	2474	+ json-schema-traverse: 0.4.1
	2475	+ uri-js: 4.4.1
	2476	+
	2477	+ ansi-styles@4.3.0:
	2478	+ dependencies:
	2479	+ color-convert: 2.0.1
	2480	+
	2481	+ any-promise@1.3.0: {}
	2482	+
	2483	+ anymatch@3.1.3:
	2484	+ dependencies:
	2485	+ normalize-path: 3.0.0
	2486	+ picomatch: 2.3.2
	2487	+
	2488	+ arg@5.0.2: {}
	2489	+
	2490	+ argparse@2.0.1: {}
	2491	+
	2492	+ assertion-error@2.0.1: {}
	2493	+
	2494	+ autoprefixer@10.5.4(postcss@8.5.25):
	2495	+ dependencies:
	2496	+ browserslist: 4.28.7
	2497	+ caniuse-lite: 1.0.30001806
	2498	+ fraction.js: 5.3.4
	2499	+ picocolors: 1.1.1
	2500	+ postcss: 8.5.25
	2501	+ postcss-value-parser: 4.2.0
	2502	+
	2503	+ balanced-match@1.0.2: {}
	2504	+
	2505	+ balanced-match@4.0.4: {}
	2506	+
	2507	+ baseline-browser-mapping@2.11.12: {}
	2508	+
	2509	+ binary-extensions@2.3.0: {}
	2510	+
	2511	+ brace-expansion@1.1.18:
	2512	+ dependencies:
	2513	+ balanced-match: 1.0.2
	2514	+ concat-map: 0.0.1
	2515	+
	2516	+ brace-expansion@5.0.9:
	2517	+ dependencies:
	2518	+ balanced-match: 4.0.4
	2519	+
	2520	+ braces@3.0.3:
	2521	+ dependencies:
	2522	+ fill-range: 7.1.1
	2523	+
	2524	+ browserslist@4.28.7:
	2525	+ dependencies:
	2526	+ baseline-browser-mapping: 2.11.12
	2527	+ caniuse-lite: 1.0.30001806
	2528	+ electron-to-chromium: 1.5.401
	2529	+ node-releases: 2.0.52
	2530	+ update-browserslist-db: 1.2.3(browserslist@4.28.7)
	2531	+
	2532	+ bullmq@5.81.3:
	2533	+ dependencies:
	2534	+ cron-parser: 4.9.0
	2535	+ ioredis: 5.11.1
	2536	+ msgpackr: 2.0.5
	2537	+ node-abort-controller: 3.1.1
	2538	+ semver: 7.8.5
	2539	+ tslib: 2.8.1
	2540	+ transitivePeerDependencies:
	2541	+ - supports-color
	2542	+
	2543	+ busboy@1.6.0:
	2544	+ dependencies:
	2545	+ streamsearch: 1.1.0
	2546	+
	2547	+ cac@6.7.14: {}
	2548	+
	2549	+ callsites@3.1.0: {}
	2550	+
	2551	+ camelcase-css@2.0.1: {}
	2552	+
	2553	+ caniuse-lite@1.0.30001806: {}
	2554	+
	2555	+ chai@5.3.3:
	2556	+ dependencies:
	2557	+ assertion-error: 2.0.1
	2558	+ check-error: 2.1.3
	2559	+ deep-eql: 5.0.2
	2560	+ loupe: 3.2.1
	2561	+ pathval: 2.0.1
	2562	+
	2563	+ chalk@4.1.2:
	2564	+ dependencies:
	2565	+ ansi-styles: 4.3.0
	2566	+ supports-color: 7.2.0
	2567	+
	2568	+ check-error@2.1.3: {}
	2569	+
	2570	+ chokidar@3.6.0:
	2571	+ dependencies:
	2572	+ anymatch: 3.1.3
	2573	+ braces: 3.0.3
	2574	+ glob-parent: 5.1.2
	2575	+ is-binary-path: 2.1.0
	2576	+ is-glob: 4.0.3
	2577	+ normalize-path: 3.0.0
	2578	+ readdirp: 3.6.0
	2579	+ optionalDependencies:
	2580	+ fsevents: 2.3.3
	2581	+
	2582	+ client-only@0.0.1: {}
	2583	+
	2584	+ cluster-key-slot@1.1.1: {}
	2585	+
	2586	+ color-convert@2.0.1:
	2587	+ dependencies:
	2588	+ color-name: 1.1.4
	2589	+
	2590	+ color-name@1.1.4: {}
	2591	+
	2592	+ commander@4.1.1: {}
	2593	+
	2594	+ concat-map@0.0.1: {}
	2595	+
	2596	+ cron-parser@4.9.0:
	2597	+ dependencies:
	2598	+ luxon: 3.7.2
	2599	+
	2600	+ cross-spawn@7.0.6:
	2601	+ dependencies:
	2602	+ path-key: 3.1.1
	2603	+ shebang-command: 2.0.0
	2604	+ which: 2.0.2
	2605	+
	2606	+ cssesc@3.0.0: {}
	2607	+
	2608	+ csstype@3.2.3: {}
	2609	+
	2610	+ debug@4.4.3:
	2611	+ dependencies:
	2612	+ ms: 2.1.3
	2613	+
	2614	+ deep-eql@5.0.2: {}
	2615	+
	2616	+ deep-is@0.1.4: {}
	2617	+
	2618	+ denque@2.1.0: {}
	2619	+
	2620	+ detect-libc@2.1.2:
	2621	+ optional: true
	2622	+
	2623	+ didyoumean@1.2.2: {}
	2624	+
	2625	+ dlv@1.1.3: {}
	2626	+
	2627	+ electron-to-chromium@1.5.401: {}
	2628	+
	2629	+ es-errors@1.3.0: {}
	2630	+
	2631	+ es-module-lexer@1.7.0: {}
	2632	+
	2633	+ esbuild@0.21.5:
	2634	+ optionalDependencies:
	2635	+ '@esbuild/aix-ppc64': 0.21.5
	2636	+ '@esbuild/android-arm': 0.21.5
	2637	+ '@esbuild/android-arm64': 0.21.5
	2638	+ '@esbuild/android-x64': 0.21.5
	2639	+ '@esbuild/darwin-arm64': 0.21.5
	2640	+ '@esbuild/darwin-x64': 0.21.5
	2641	+ '@esbuild/freebsd-arm64': 0.21.5
	2642	+ '@esbuild/freebsd-x64': 0.21.5
	2643	+ '@esbuild/linux-arm': 0.21.5
	2644	+ '@esbuild/linux-arm64': 0.21.5
	2645	+ '@esbuild/linux-ia32': 0.21.5
	2646	+ '@esbuild/linux-loong64': 0.21.5
	2647	+ '@esbuild/linux-mips64el': 0.21.5
	2648	+ '@esbuild/linux-ppc64': 0.21.5
	2649	+ '@esbuild/linux-riscv64': 0.21.5
	2650	+ '@esbuild/linux-s390x': 0.21.5
	2651	+ '@esbuild/linux-x64': 0.21.5
	2652	+ '@esbuild/netbsd-x64': 0.21.5
	2653	+ '@esbuild/openbsd-x64': 0.21.5
	2654	+ '@esbuild/sunos-x64': 0.21.5
	2655	+ '@esbuild/win32-arm64': 0.21.5
	2656	+ '@esbuild/win32-ia32': 0.21.5
	2657	+ '@esbuild/win32-x64': 0.21.5
	2658	+
	2659	+ esbuild@0.28.1:
	2660	+ optionalDependencies:
	2661	+ '@esbuild/aix-ppc64': 0.28.1
	2662	+ '@esbuild/android-arm': 0.28.1
	2663	+ '@esbuild/android-arm64': 0.28.1
	2664	+ '@esbuild/android-x64': 0.28.1
	2665	+ '@esbuild/darwin-arm64': 0.28.1
	2666	+ '@esbuild/darwin-x64': 0.28.1
	2667	+ '@esbuild/freebsd-arm64': 0.28.1
	2668	+ '@esbuild/freebsd-x64': 0.28.1
	2669	+ '@esbuild/linux-arm': 0.28.1
	2670	+ '@esbuild/linux-arm64': 0.28.1
	2671	+ '@esbuild/linux-ia32': 0.28.1
	2672	+ '@esbuild/linux-loong64': 0.28.1
	2673	+ '@esbuild/linux-mips64el': 0.28.1
	2674	+ '@esbuild/linux-ppc64': 0.28.1
	2675	+ '@esbuild/linux-riscv64': 0.28.1
	2676	+ '@esbuild/linux-s390x': 0.28.1
	2677	+ '@esbuild/linux-x64': 0.28.1
	2678	+ '@esbuild/netbsd-arm64': 0.28.1
	2679	+ '@esbuild/netbsd-x64': 0.28.1
	2680	+ '@esbuild/openbsd-arm64': 0.28.1
	2681	+ '@esbuild/openbsd-x64': 0.28.1
	2682	+ '@esbuild/openharmony-arm64': 0.28.1
	2683	+ '@esbuild/sunos-x64': 0.28.1
	2684	+ '@esbuild/win32-arm64': 0.28.1
	2685	+ '@esbuild/win32-ia32': 0.28.1
	2686	+ '@esbuild/win32-x64': 0.28.1
	2687	+
	2688	+ escalade@3.2.0: {}
	2689	+
	2690	+ escape-string-regexp@4.0.0: {}
	2691	+
	2692	+ eslint-scope@8.4.0:
	2693	+ dependencies:
	2694	+ esrecurse: 4.3.0
	2695	+ estraverse: 5.3.0
	2696	+
	2697	+ eslint-visitor-keys@3.4.3: {}
	2698	+
	2699	+ eslint-visitor-keys@4.2.1: {}
	2700	+
	2701	+ eslint-visitor-keys@5.0.1: {}
	2702	+
	2703	+ eslint@9.39.5(jiti@1.21.7):
	2704	+ dependencies:
	2705	+ '@eslint-community/eslint-utils': 4.10.1(eslint@9.39.5(jiti@1.21.7))
	2706	+ '@eslint-community/regexpp': 4.12.2
	2707	+ '@eslint/config-array': 0.21.2
	2708	+ '@eslint/config-helpers': 0.4.2
	2709	+ '@eslint/core': 0.17.0
	2710	+ '@eslint/eslintrc': 3.3.6
	2711	+ '@eslint/js': 9.39.5
	2712	+ '@eslint/plugin-kit': 0.4.1
	2713	+ '@humanfs/node': 0.16.8
	2714	+ '@humanwhocodes/module-importer': 1.0.1
	2715	+ '@humanwhocodes/retry': 0.4.3
	2716	+ '@types/estree': 1.0.9
	2717	+ ajv: 6.15.0
	2718	+ chalk: 4.1.2
	2719	+ cross-spawn: 7.0.6
	2720	+ debug: 4.4.3
	2721	+ escape-string-regexp: 4.0.0
	2722	+ eslint-scope: 8.4.0
	2723	+ eslint-visitor-keys: 4.2.1
	2724	+ espree: 10.4.0
	2725	+ esquery: 1.7.0
	2726	+ esutils: 2.0.3
	2727	+ fast-deep-equal: 3.1.3
	2728	+ file-entry-cache: 8.0.0
	2729	+ find-up: 5.0.0
	2730	+ glob-parent: 6.0.2
	2731	+ ignore: 5.3.2
	2732	+ imurmurhash: 0.1.4
	2733	+ is-glob: 4.0.3
	2734	+ json-stable-stringify-without-jsonify: 1.0.1
	2735	+ lodash.merge: 4.6.2
	2736	+ minimatch: 3.1.5
	2737	+ natural-compare: 1.4.0
	2738	+ optionator: 0.9.4
	2739	+ optionalDependencies:
	2740	+ jiti: 1.21.7
	2741	+ transitivePeerDependencies:
	2742	+ - supports-color
	2743	+
	2744	+ espree@10.4.0:
	2745	+ dependencies:
	2746	+ acorn: 8.18.0
	2747	+ acorn-jsx: 5.3.2(acorn@8.18.0)
	2748	+ eslint-visitor-keys: 4.2.1
	2749	+
	2750	+ esquery@1.7.0:
	2751	+ dependencies:
	2752	+ estraverse: 5.3.0
	2753	+
	2754	+ esrecurse@4.3.0:
	2755	+ dependencies:
	2756	+ estraverse: 5.3.0
	2757	+
	2758	+ estraverse@5.3.0: {}
	2759	+
	2760	+ estree-walker@3.0.3:
	2761	+ dependencies:
	2762	+ '@types/estree': 1.0.9
	2763	+
	2764	+ esutils@2.0.3: {}
	2765	+
	2766	+ expect-type@1.4.0: {}
	2767	+
	2768	+ fast-check@3.23.2:
	2769	+ dependencies:
	2770	+ pure-rand: 6.1.0
	2771	+
	2772	+ fast-deep-equal@3.1.3: {}
	2773	+
	2774	+ fast-glob@3.3.3:
	2775	+ dependencies:
	2776	+ '@nodelib/fs.stat': 2.0.5
	2777	+ '@nodelib/fs.walk': 1.2.8
	2778	+ glob-parent: 5.1.2
	2779	+ merge2: 1.4.1
	2780	+ micromatch: 4.0.8
	2781	+
	2782	+ fast-json-stable-stringify@2.1.0: {}
	2783	+
	2784	+ fast-levenshtein@2.0.6: {}
	2785	+
	2786	+ fast-sha256@1.3.0: {}
	2787	+
	2788	+ fastq@1.20.1:
	2789	+ dependencies:
	2790	+ reusify: 1.1.0
	2791	+
	2792	+ fdir@6.5.0(picomatch@4.0.5):
	2793	+ optionalDependencies:
	2794	+ picomatch: 4.0.5
	2795	+
	2796	+ file-entry-cache@8.0.0:
	2797	+ dependencies:
	2798	+ flat-cache: 4.0.1
	2799	+
	2800	+ fill-range@7.1.1:
	2801	+ dependencies:
	2802	+ to-regex-range: 5.0.1
	2803	+
	2804	+ find-up@5.0.0:
	2805	+ dependencies:
	2806	+ locate-path: 6.0.0
	2807	+ path-exists: 4.0.0
	2808	+
	2809	+ flat-cache@4.0.1:
	2810	+ dependencies:
	2811	+ flatted: 3.4.4
	2812	+ keyv: 4.5.4
	2813	+
	2814	+ flatted@3.4.4: {}
	2815	+
	2816	+ fraction.js@5.3.4: {}
	2817	+
	2818	+ fsevents@2.3.3:
	2819	+ optional: true
	2820	+
	2821	+ function-bind@1.1.2: {}
	2822	+
	2823	+ glob-parent@5.1.2:
	2824	+ dependencies:
	2825	+ is-glob: 4.0.3
	2826	+
	2827	+ glob-parent@6.0.2:
	2828	+ dependencies:
	2829	+ is-glob: 4.0.3
	2830	+
	2831	+ globals@14.0.0: {}
	2832	+
	2833	+ graceful-fs@4.2.11: {}
	2834	+
	2835	+ has-flag@4.0.0: {}
	2836	+
	2837	+ hasown@2.0.4:
	2838	+ dependencies:
	2839	+ function-bind: 1.1.2
	2840	+
	2841	+ ignore@5.3.2: {}
	2842	+
	2843	+ ignore@7.0.6: {}
	2844	+
	2845	+ import-fresh@3.3.1:
	2846	+ dependencies:
	2847	+ parent-module: 1.0.1
	2848	+ resolve-from: 4.0.0
	2849	+
	2850	+ imurmurhash@0.1.4: {}
	2851	+
	2852	+ ioredis@5.11.1:
	2853	+ dependencies:
	2854	+ '@ioredis/commands': 1.10.0
	2855	+ cluster-key-slot: 1.1.1
	2856	+ debug: 4.4.3
	2857	+ denque: 2.1.0
	2858	+ redis-errors: 1.2.0
	2859	+ redis-parser: 3.0.0
	2860	+ standard-as-callback: 2.1.0
	2861	+ transitivePeerDependencies:
	2862	+ - supports-color
	2863	+
	2864	+ is-binary-path@2.1.0:
	2865	+ dependencies:
	2866	+ binary-extensions: 2.3.0
	2867	+
	2868	+ is-core-module@2.16.2:
	2869	+ dependencies:
	2870	+ hasown: 2.0.4
	2871	+
	2872	+ is-extglob@2.1.1: {}
	2873	+
	2874	+ is-glob@4.0.3:
	2875	+ dependencies:
	2876	+ is-extglob: 2.1.1
	2877	+
	2878	+ is-number@7.0.0: {}
	2879	+
	2880	+ isexe@2.0.0: {}
	2881	+
	2882	+ jiti@1.21.7: {}
	2883	+
	2884	+ js-tokens@4.0.0: {}
	2885	+
	2886	+ js-yaml@4.3.1:
	2887	+ dependencies:
	2888	+ argparse: 2.0.1
	2889	+
	2890	+ json-buffer@3.0.1: {}
	2891	+
	2892	+ json-schema-to-ts@3.1.1:
	2893	+ dependencies:
	2894	+ '@babel/runtime': 7.29.7
	2895	+ ts-algebra: 2.0.0
	2896	+
	2897	+ json-schema-traverse@0.4.1: {}
	2898	+
	2899	+ json-stable-stringify-without-jsonify@1.0.1: {}
	2900	+
	2901	+ keyv@4.5.4:
	2902	+ dependencies:
	2903	+ json-buffer: 3.0.1
	2904	+
	2905	+ levn@0.4.1:
	2906	+ dependencies:
	2907	+ prelude-ls: 1.2.1
	2908	+ type-check: 0.4.0
	2909	+
	2910	+ lilconfig@3.1.3: {}
	2911	+
	2912	+ lines-and-columns@1.2.4: {}
	2913	+
	2914	+ locate-path@6.0.0:
	2915	+ dependencies:
	2916	+ p-locate: 5.0.0
	2917	+
	2918	+ lodash.merge@4.6.2: {}
	2919	+
	2920	+ loose-envify@1.4.0:
	2921	+ dependencies:
	2922	+ js-tokens: 4.0.0
	2923	+
	2924	+ loupe@3.2.1: {}
	2925	+
	2926	+ luxon@3.7.2: {}
	2927	+
	2928	+ magic-string@0.30.21:
	2929	+ dependencies:
	2930	+ '@jridgewell/sourcemap-codec': 1.5.5
	2931	+
	2932	+ merge2@1.4.1: {}
	2933	+
	2934	+ micromatch@4.0.8:
	2935	+ dependencies:
	2936	+ braces: 3.0.3
	2937	+ picomatch: 2.3.2
	2938	+
	2939	+ minimatch@10.2.6:
	2940	+ dependencies:
	2941	+ brace-expansion: 5.0.9
	2942	+
	2943	+ minimatch@3.1.5:
	2944	+ dependencies:
	2945	+ brace-expansion: 1.1.18
	2946	+
	2947	+ ms@2.1.3: {}
	2948	+
	2949	+ msgpackr-extract@3.0.4:
	2950	+ dependencies:
	2951	+ node-gyp-build-optional-packages: 5.2.2
	2952	+ optionalDependencies:
	2953	+ '@msgpackr-extract/msgpackr-extract-darwin-arm64': 3.0.4
	2954	+ '@msgpackr-extract/msgpackr-extract-darwin-x64': 3.0.4
	2955	+ '@msgpackr-extract/msgpackr-extract-linux-arm': 3.0.4
	2956	+ '@msgpackr-extract/msgpackr-extract-linux-arm64': 3.0.4
	2957	+ '@msgpackr-extract/msgpackr-extract-linux-x64': 3.0.4
	2958	+ '@msgpackr-extract/msgpackr-extract-win32-x64': 3.0.4
	2959	+ optional: true
	2960	+
	2961	+ msgpackr@2.0.5:
	2962	+ optionalDependencies:
	2963	+ msgpackr-extract: 3.0.4
	2964	+
	2965	+ mz@2.7.0:
	2966	+ dependencies:
	2967	+ any-promise: 1.3.0
	2968	+ object-assign: 4.1.1
	2969	+ thenify-all: 1.6.0
	2970	+
	2971	+ nanoid@3.3.17: {}
	2972	+
	2973	+ natural-compare@1.4.0: {}
	2974	+
	2975	+ next@14.2.35(react-dom@18.3.1(react@18.3.1))(react@18.3.1):
	2976	+ dependencies:
	2977	+ '@next/env': 14.2.35
	2978	+ '@swc/helpers': 0.5.5
	2979	+ busboy: 1.6.0
	2980	+ caniuse-lite: 1.0.30001806
	2981	+ graceful-fs: 4.2.11
	2982	+ postcss: 8.4.31
	2983	+ react: 18.3.1
	2984	+ react-dom: 18.3.1(react@18.3.1)
	2985	+ styled-jsx: 5.1.1(react@18.3.1)
	2986	+ optionalDependencies:
	2987	+ '@next/swc-darwin-arm64': 14.2.33
	2988	+ '@next/swc-darwin-x64': 14.2.33
	2989	+ '@next/swc-linux-arm64-gnu': 14.2.33
	2990	+ '@next/swc-linux-arm64-musl': 14.2.33
	2991	+ '@next/swc-linux-x64-gnu': 14.2.33
	2992	+ '@next/swc-linux-x64-musl': 14.2.33
	2993	+ '@next/swc-win32-arm64-msvc': 14.2.33
	2994	+ '@next/swc-win32-ia32-msvc': 14.2.33
	2995	+ '@next/swc-win32-x64-msvc': 14.2.33
	2996	+ transitivePeerDependencies:
	2997	+ - '@babel/core'
	2998	+ - babel-plugin-macros
	2999	+
	3000	+ node-abort-controller@3.1.1: {}
	3001	+
	3002	+ node-gyp-build-optional-packages@5.2.2:
	3003	+ dependencies:
	3004	+ detect-libc: 2.1.2
	3005	+ optional: true
	3006	+
	3007	+ node-releases@2.0.52: {}
	3008	+
	3009	+ normalize-path@3.0.0: {}
	3010	+
	3011	+ object-assign@4.1.1: {}
	3012	+
	3013	+ object-hash@3.0.0: {}
	3014	+
	3015	+ optionator@0.9.4:
	3016	+ dependencies:
	3017	+ deep-is: 0.1.4
	3018	+ fast-levenshtein: 2.0.6
	3019	+ levn: 0.4.1
	3020	+ prelude-ls: 1.2.1
	3021	+ type-check: 0.4.0
	3022	+ word-wrap: 1.2.5
	3023	+
	3024	+ p-limit@3.1.0:
	3025	+ dependencies:
	3026	+ yocto-queue: 0.1.0
	3027	+
	3028	+ p-locate@5.0.0:
	3029	+ dependencies:
	3030	+ p-limit: 3.1.0
	3031	+
	3032	+ parent-module@1.0.1:
	3033	+ dependencies:
	3034	+ callsites: 3.1.0
	3035	+
	3036	+ path-exists@4.0.0: {}
	3037	+
	3038	+ path-key@3.1.1: {}
	3039	+
	3040	+ path-parse@1.0.7: {}
	3041	+
	3042	+ pathe@1.1.2: {}
	3043	+
	3044	+ pathval@2.0.1: {}
	3045	+
	3046	+ picocolors@1.1.1: {}
	3047	+
	3048	+ picomatch@2.3.2: {}
	3049	+
	3050	+ picomatch@4.0.5: {}
	3051	+
	3052	+ pify@2.3.0: {}
	3053	+
	3054	+ pirates@4.0.7: {}
	3055	+
	3056	+ postcss-import@15.1.0(postcss@8.5.25):
	3057	+ dependencies:
	3058	+ postcss: 8.5.25
	3059	+ postcss-value-parser: 4.2.0
	3060	+ read-cache: 1.0.0
	3061	+ resolve: 1.22.12
	3062	+
	3063	+ postcss-js@4.1.0(postcss@8.5.25):
	3064	+ dependencies:
	3065	+ camelcase-css: 2.0.1
	3066	+ postcss: 8.5.25
	3067	+
	3068	+ postcss-load-config@6.0.1(jiti@1.21.7)(postcss@8.5.25)(tsx@4.23.6):
	3069	+ dependencies:
	3070	+ lilconfig: 3.1.3
	3071	+ optionalDependencies:
	3072	+ jiti: 1.21.7
	3073	+ postcss: 8.5.25
	3074	+ tsx: 4.23.6
	3075	+
	3076	+ postcss-nested@6.2.0(postcss@8.5.25):
	3077	+ dependencies:
	3078	+ postcss: 8.5.25
	3079	+ postcss-selector-parser: 6.1.4
	3080	+
	3081	+ postcss-selector-parser@6.1.4:
	3082	+ dependencies:
	3083	+ cssesc: 3.0.0
	3084	+ util-deprecate: 1.0.2
	3085	+
	3086	+ postcss-value-parser@4.2.0: {}
	3087	+
	3088	+ postcss@8.4.31:
	3089	+ dependencies:
	3090	+ nanoid: 3.3.17
	3091	+ picocolors: 1.1.1
	3092	+ source-map-js: 1.2.1
	3093	+
	3094	+ postcss@8.5.25:
	3095	+ dependencies:
	3096	+ nanoid: 3.3.17
	3097	+ picocolors: 1.1.1
	3098	+ source-map-js: 1.2.1
	3099	+
	3100	+ prelude-ls@1.2.1: {}
	3101	+
	3102	+ prettier@3.9.6: {}
	3103	+
	3104	+ prisma@5.22.0:
	3105	+ dependencies:
	3106	+ '@prisma/engines': 5.22.0
	3107	+ optionalDependencies:
	3108	+ fsevents: 2.3.3
	3109	+
	3110	+ punycode@2.3.1: {}
	3111	+
	3112	+ pure-rand@6.1.0: {}
	3113	+
	3114	+ queue-microtask@1.2.3: {}
	3115	+
	3116	+ react-dom@18.3.1(react@18.3.1):
	3117	+ dependencies:
	3118	+ loose-envify: 1.4.0
	3119	+ react: 18.3.1
	3120	+ scheduler: 0.23.2
	3121	+
	3122	+ react@18.3.1:
	3123	+ dependencies:
	3124	+ loose-envify: 1.4.0
	3125	+
	3126	+ read-cache@1.0.0:
	3127	+ dependencies:
	3128	+ pify: 2.3.0
	3129	+
	3130	+ readdirp@3.6.0:
	3131	+ dependencies:
	3132	+ picomatch: 2.3.2
	3133	+
	3134	+ redis-errors@1.2.0: {}
	3135	+
	3136	+ redis-parser@3.0.0:
	3137	+ dependencies:
	3138	+ redis-errors: 1.2.0
	3139	+
	3140	+ resolve-from@4.0.0: {}
	3141	+
	3142	+ resolve@1.22.12:
	3143	+ dependencies:
	3144	+ es-errors: 1.3.0
	3145	+ is-core-module: 2.16.2
	3146	+ path-parse: 1.0.7
	3147	+ supports-preserve-symlinks-flag: 1.0.0
	3148	+
	3149	+ reusify@1.1.0: {}
	3150	+
	3151	+ rollup@4.62.4:
	3152	+ dependencies:
	3153	+ '@types/estree': 1.0.9
	3154	+ optionalDependencies:
	3155	+ '@napi-rs/lzma-linux-x64-gnu': 1.5.1
	3156	+ '@rollup/rollup-android-arm-eabi': 4.62.4
	3157	+ '@rollup/rollup-android-arm64': 4.62.4
	3158	+ '@rollup/rollup-darwin-arm64': 4.62.4
	3159	+ '@rollup/rollup-darwin-x64': 4.62.4
	3160	+ '@rollup/rollup-freebsd-arm64': 4.62.4
	3161	+ '@rollup/rollup-freebsd-x64': 4.62.4
	3162	+ '@rollup/rollup-linux-arm-gnueabihf': 4.62.4
	3163	+ '@rollup/rollup-linux-arm-musleabihf': 4.62.4
	3164	+ '@rollup/rollup-linux-arm64-gnu': 4.62.4
	3165	+ '@rollup/rollup-linux-arm64-musl': 4.62.4
	3166	+ '@rollup/rollup-linux-loong64-gnu': 4.62.4
	3167	+ '@rollup/rollup-linux-loong64-musl': 4.62.4
	3168	+ '@rollup/rollup-linux-ppc64-gnu': 4.62.4
	3169	+ '@rollup/rollup-linux-ppc64-musl': 4.62.4
	3170	+ '@rollup/rollup-linux-riscv64-gnu': 4.62.4
	3171	+ '@rollup/rollup-linux-riscv64-musl': 4.62.4
	3172	+ '@rollup/rollup-linux-s390x-gnu': 4.62.4
	3173	+ '@rollup/rollup-linux-x64-gnu': 4.62.4
	3174	+ '@rollup/rollup-linux-x64-musl': 4.62.4
	3175	+ '@rollup/rollup-openbsd-x64': 4.62.4
	3176	+ '@rollup/rollup-openharmony-arm64': 4.62.4
	3177	+ '@rollup/rollup-win32-arm64-msvc': 4.62.4
	3178	+ '@rollup/rollup-win32-ia32-msvc': 4.62.4
	3179	+ '@rollup/rollup-win32-x64-gnu': 4.62.4
	3180	+ '@rollup/rollup-win32-x64-msvc': 4.62.4
	3181	+ fsevents: 2.3.3
	3182	+
	3183	+ run-parallel@1.2.0:
	3184	+ dependencies:
	3185	+ queue-microtask: 1.2.3
	3186	+
	3187	+ scheduler@0.23.2:
	3188	+ dependencies:
	3189	+ loose-envify: 1.4.0
	3190	+
	3191	+ semver@7.8.5: {}
	3192	+
	3193	+ shebang-command@2.0.0:
	3194	+ dependencies:
	3195	+ shebang-regex: 3.0.0
	3196	+
	3197	+ shebang-regex@3.0.0: {}
	3198	+
	3199	+ siginfo@2.0.0: {}
	3200	+
	3201	+ source-map-js@1.2.1: {}
	3202	+
	3203	+ stackback@0.0.2: {}
	3204	+
	3205	+ standard-as-callback@2.1.0: {}
	3206	+
	3207	+ standardwebhooks@1.0.0:
	3208	+ dependencies:
	3209	+ '@stablelib/base64': 1.0.1
	3210	+ fast-sha256: 1.3.0
	3211	+
	3212	+ std-env@3.10.0: {}
	3213	+
	3214	+ streamsearch@1.1.0: {}
	3215	+
	3216	+ strip-json-comments@3.1.1: {}
	3217	+
	3218	+ styled-jsx@5.1.1(react@18.3.1):
	3219	+ dependencies:
	3220	+ client-only: 0.0.1
	3221	+ react: 18.3.1
	3222	+
	3223	+ sucrase@3.35.1:
	3224	+ dependencies:
	3225	+ '@jridgewell/gen-mapping': 0.3.13
	3226	+ commander: 4.1.1
	3227	+ lines-and-columns: 1.2.4
	3228	+ mz: 2.7.0
	3229	+ pirates: 4.0.7
	3230	+ tinyglobby: 0.2.17
	3231	+ ts-interface-checker: 0.1.13
	3232	+
	3233	+ supports-color@7.2.0:
	3234	+ dependencies:
	3235	+ has-flag: 4.0.0
	3236	+
	3237	+ supports-preserve-symlinks-flag@1.0.0: {}
	3238	+
	3239	+ tailwindcss@3.4.19(tsx@4.23.6):
	3240	+ dependencies:
	3241	+ '@alloc/quick-lru': 5.2.0
	3242	+ arg: 5.0.2
	3243	+ chokidar: 3.6.0
	3244	+ didyoumean: 1.2.2
	3245	+ dlv: 1.1.3
	3246	+ fast-glob: 3.3.3
	3247	+ glob-parent: 6.0.2
	3248	+ is-glob: 4.0.3
	3249	+ jiti: 1.21.7
	3250	+ lilconfig: 3.1.3
	3251	+ micromatch: 4.0.8
	3252	+ normalize-path: 3.0.0
	3253	+ object-hash: 3.0.0
	3254	+ picocolors: 1.1.1
	3255	+ postcss: 8.5.25
	3256	+ postcss-import: 15.1.0(postcss@8.5.25)
	3257	+ postcss-js: 4.1.0(postcss@8.5.25)
	3258	+ postcss-load-config: 6.0.1(jiti@1.21.7)(postcss@8.5.25)(tsx@4.23.6)
	3259	+ postcss-nested: 6.2.0(postcss@8.5.25)
	3260	+ postcss-selector-parser: 6.1.4
	3261	+ resolve: 1.22.12
	3262	+ sucrase: 3.35.1
	3263	+ transitivePeerDependencies:
	3264	+ - tsx
	3265	+ - yaml
	3266	+
	3267	+ thenify-all@1.6.0:
	3268	+ dependencies:
	3269	+ thenify: 3.3.1
	3270	+
	3271	+ thenify@3.3.1:
	3272	+ dependencies:
	3273	+ any-promise: 1.3.0
	3274	+
	3275	+ tinybench@2.9.0: {}
	3276	+
	3277	+ tinyexec@0.3.2: {}
	3278	+
	3279	+ tinyglobby@0.2.17:
	3280	+ dependencies:
	3281	+ fdir: 6.5.0(picomatch@4.0.5)
	3282	+ picomatch: 4.0.5
	3283	+
	3284	+ tinypool@1.1.1: {}
	3285	+
	3286	+ tinyrainbow@1.2.0: {}
	3287	+
	3288	+ tinyspy@3.0.2: {}
	3289	+
	3290	+ to-regex-range@5.0.1:
	3291	+ dependencies:
	3292	+ is-number: 7.0.0
	3293	+
	3294	+ ts-algebra@2.0.0: {}
	3295	+
	3296	+ ts-api-utils@2.5.0(typescript@5.9.3):
	3297	+ dependencies:
	3298	+ typescript: 5.9.3
	3299	+
	3300	+ ts-interface-checker@0.1.13: {}
	3301	+
	3302	+ tslib@2.8.1: {}
	3303	+
	3304	+ tsx@4.23.6:
	3305	+ dependencies:
	3306	+ esbuild: 0.28.1
	3307	+ optionalDependencies:
	3308	+ fsevents: 2.3.3
	3309	+
	3310	+ turbo@2.10.8:
	3311	+ optionalDependencies:
	3312	+ '@turbo/darwin-64': 2.10.8
	3313	+ '@turbo/darwin-arm64': 2.10.8
	3314	+ '@turbo/linux-64': 2.10.8
	3315	+ '@turbo/linux-arm64': 2.10.8
	3316	+ '@turbo/windows-64': 2.10.8
	3317	+ '@turbo/windows-arm64': 2.10.8
	3318	+
	3319	+ type-check@0.4.0:
	3320	+ dependencies:
	3321	+ prelude-ls: 1.2.1
	3322	+
	3323	+ typescript-eslint@8.66.0(eslint@9.39.5(jiti@1.21.7))(typescript@5.9.3):
	3324	+ dependencies:
	3325	+ '@typescript-eslint/eslint-plugin': 8.66.0(@typescript-eslint/parser@8.66.0(eslint@9.39.5(jiti@1.21.7))(typescript@5.9.3))(eslint@9.39.5(jiti@1.21.7))(typescript@5.9.3)
	3326	+ '@typescript-eslint/parser': 8.66.0(eslint@9.39.5(jiti@1.21.7))(typescript@5.9.3)
	3327	+ '@typescript-eslint/typescript-estree': 8.66.0(typescript@5.9.3)
	3328	+ '@typescript-eslint/utils': 8.66.0(eslint@9.39.5(jiti@1.21.7))(typescript@5.9.3)
	3329	+ eslint: 9.39.5(jiti@1.21.7)
	3330	+ typescript: 5.9.3
	3331	+ transitivePeerDependencies:
	3332	+ - supports-color
	3333	+
	3334	+ typescript@5.9.3: {}
	3335	+
	3336	+ undici-types@6.21.0: {}
	3337	+
	3338	+ update-browserslist-db@1.2.3(browserslist@4.28.7):
	3339	+ dependencies:
	3340	+ browserslist: 4.28.7
	3341	+ escalade: 3.2.0
	3342	+ picocolors: 1.1.1
	3343	+
	3344	+ uri-js@4.4.1:
	3345	+ dependencies:
	3346	+ punycode: 2.3.1
	3347	+
	3348	+ util-deprecate@1.0.2: {}
	3349	+
	3350	+ vite-node@2.1.9(@types/node@20.19.43):
	3351	+ dependencies:
	3352	+ cac: 6.7.14
	3353	+ debug: 4.4.3
	3354	+ es-module-lexer: 1.7.0
	3355	+ pathe: 1.1.2
	3356	+ vite: 5.4.21(@types/node@20.19.43)
	3357	+ transitivePeerDependencies:
	3358	+ - '@types/node'
	3359	+ - less
	3360	+ - lightningcss
	3361	+ - sass
	3362	+ - sass-embedded
	3363	+ - stylus
	3364	+ - sugarss
	3365	+ - supports-color
	3366	+ - terser
	3367	+
	3368	+ vite@5.4.21(@types/node@20.19.43):
	3369	+ dependencies:
	3370	+ esbuild: 0.21.5
	3371	+ postcss: 8.5.25
	3372	+ rollup: 4.62.4
	3373	+ optionalDependencies:
	3374	+ '@types/node': 20.19.43
	3375	+ fsevents: 2.3.3
	3376	+
	3377	+ vitest@2.1.9(@types/node@20.19.43):
	3378	+ dependencies:
	3379	+ '@vitest/expect': 2.1.9
	3380	+ '@vitest/mocker': 2.1.9(vite@5.4.21(@types/node@20.19.43))
	3381	+ '@vitest/pretty-format': 2.1.9
	3382	+ '@vitest/runner': 2.1.9
	3383	+ '@vitest/snapshot': 2.1.9
	3384	+ '@vitest/spy': 2.1.9
	3385	+ '@vitest/utils': 2.1.9
	3386	+ chai: 5.3.3
	3387	+ debug: 4.4.3
	3388	+ expect-type: 1.4.0
	3389	+ magic-string: 0.30.21
	3390	+ pathe: 1.1.2
	3391	+ std-env: 3.10.0
	3392	+ tinybench: 2.9.0
	3393	+ tinyexec: 0.3.2
	3394	+ tinypool: 1.1.1
	3395	+ tinyrainbow: 1.2.0
	3396	+ vite: 5.4.21(@types/node@20.19.43)
	3397	+ vite-node: 2.1.9(@types/node@20.19.43)
	3398	+ why-is-node-running: 2.3.0
	3399	+ optionalDependencies:
	3400	+ '@types/node': 20.19.43
	3401	+ transitivePeerDependencies:
	3402	+ - less
	3403	+ - lightningcss
	3404	+ - msw
	3405	+ - sass
	3406	+ - sass-embedded
	3407	+ - stylus
	3408	+ - sugarss
	3409	+ - supports-color
	3410	+ - terser
	3411	+
	3412	+ which@2.0.2:
	3413	+ dependencies:
	3414	+ isexe: 2.0.0
	3415	+
	3416	+ why-is-node-running@2.3.0:
	3417	+ dependencies:
	3418	+ siginfo: 2.0.0
	3419	+ stackback: 0.0.2
	3420	+
	3421	+ word-wrap@1.2.5: {}
	3422	+
	3423	+ yocto-queue@0.1.0: {}
	3424	+
	3425	+ zod@3.25.76: {}

added pnpm-workspace.yaml +3 −0

		`@@ -0,0 +1,3 @@`
	1	+packages:
	2	+ - "apps/*"
	3	+ - "packages/*"

added turbo.json +19 −0

		`@@ -0,0 +1,19 @@`
	1	+{
	2	+ "$schema": "https://turbo.build/schema.json",
	3	+ "tasks": {
	4	+ "build": {
	5	+ "dependsOn": ["^build"],
	6	+ "outputs": [".next/", "!.next/cache/", "dist/**"]
	7	+ },
	8	+ "dev": {
	9	+ "cache": false,
	10	+ "persistent": true
	11	+ },
	12	+ "test": {
	13	+ "outputs": []
	14	+ },
	15	+ "typecheck": {
	16	+ "outputs": []
	17	+ }
	18	+ }
	19	+}
	20