spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T04:31:49.889Z","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T04:31:49.895Z","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f"}4{"parentUuid":null,"isSidechain":false,"promptId":"245893dc-704e-4aec-9422-07ac9c5a458f","type":"user","message":{"role":"user","content":"Begin"},"uuid":"ab17ad64-2c57-4951-9820-23a46c2ba796","timestamp":"2026-10-30T04:31:49.900Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0105","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f","shapeHash":"9d4c2b4b808da87f5f3014d7dcaf3767f9b2cf99db791ca587fb3813c11301cc","timestamp":"2026-10-30T04:31:49.914Z","version":"2.1.314","shape":{"system":[{"type":"text","text":"You are Claude Code, Anthropic's official CLI for Claude, running within the Claude Agent SDK."},{"type":"text","text":"CWD: /Users/simon-pierreboucher/wp29/runs/RC0105\nDate: 2026-10-30\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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the user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0105","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f"}12{"parentUuid":"77ddfae4-063f-43ff-b699-d99778b7e866","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo533TVMBfLBD7myXCA","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":2603,"cache_read_input_tokens":1504,"output_tokens":139,"output_tokens_details":{"thinking_tokens":23},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2603},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":472,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXo52jroj7hVAsHKfyuR","requestRef":"e8b6d5c8-7339-4514-8707-a83d04544606","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"67d569a2-e0aa-4338-a7d8-4e5c4933c6e6","timestamp":"2026-10-30T04:31:52.310Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0105","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"67d569a2-e0aa-4338-a7d8-4e5c4933c6e6","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo533TVMBfLBD7myXCA","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01E77JT2PRdJ9fT6rC3r7rRb","name":"Bash","input":{"command":"cat TASK.md; 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The estimand for month t is g_t = 100 × (P_t / P_{t−12} − 1) in percent, where P is the index level taken from the most recent data vintage in which that month is available; the summary estimands are the mean of g_t over the window, the sub-period means for 2001-01 … 2013-12 and 2014-01 … 2026-09, a few named months, and the months of the maximum and minimum growth.\n\n## Inputs\n- `data/cpi_vintage_2016-07-15.csv` — observed data: CPIAUCSL as published by BLS/FRED in the ALFRED vintage of 2016-07-15, months 1947-01 to 2016-06 (834 rows). Columns `date` (ISO, first day of month), `cpi` (index, 1982–84 = 100). Source, retrieval date, license and SHA-256 in `data/README.md`.\n- `data/cpi_vintage_2026-10-28.csv` — observed data: CPIAUCSL in the ALFRED vintage of 2026-10-28, extract restricted to months 2014-01 to 2026-09 (153 rows). Same columns. An empty `cpi` cell means that the source reports no value for that month.\n- `data/cpi_assembled.csv` — a single file assembled from the two extracts above (columns `date`, `cpi`, `vintage`), intended to cover 1947-01 to 2026-09. It was assembled by hand and may contain assembly errors; the two vintage files are the authoritative inputs.\n\n## Information-availability rules\n- For each month, the index level to use is the value from the most recent vintage in which that month is available (the 2026-10-28 vintage where it covers the month, the 2016-07-15 vintage otherwise).\n- Growth rates must only be computed from index levels that exist in the sources; do not interpolate or impute missing index levels. Months for which g_t cannot be computed are reported as missing and counted.\n- Use only the files under `data/`.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F1-conf-01\" |\n| `first_growth_month` | string | YYYY-MM | first month of the window with a growth rate (expected \"2001-01\" if computable) |\n| `last_month` | string | YYYY-MM | last month of the window for which an index level exists in the sources |\n| `n_months_growth` | integer | months | months in the window with a computable g_t |\n| `n_months_missing_growth` | integer | months | months in the window without a computable g_t |\n| `n_analysed` | integer | months | same as `n_months_growth` |\n| `mean_growth_12m` | number | percent | mean of g_t over the window |\n| `mean_growth_12m_2001_2013` | number | percent | baseline: mean of g_t over 2001-01 … 2013-12 |\n| `mean_growth_12m_2014_2026` | number | percent | mean of g_t over 2014-01 … 2026-09 |\n| `growth_2014_06`, `growth_2016_07`, `growth_2020_05`, `growth_2022_06`, `growth_2026_09` | number | percent | g_t for those months |\n| `max_growth_12m`, `max_growth_month` | number, string | percent, YYYY-MM | maximum g_t in the window and its month |\n| `min_growth_12m`, `min_growth_month` | number, string | percent, YYYY-MM | minimum g_t in the window and its month |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | every data problem detected and how it was handled (empty list if none) |\n| `n_input_rows` | object | rows | {file name: number of data rows read (excluding the header)} for every file under `data/` that was read |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/analytic.csv` — one row per month of the window with a computable growth rate, columns `date` (YYYY-MM-01), `cpi`, `cpi_lag12`, `vintage_used`, `growth_12m`; exactly `n_analysed` rows.\n- `out/exclusions.csv` — the exclusion ledger with columns `step`, `rule`, `n_before`, `n_after`, `n_removed`, `rationale`; one row per step from the rows you start from to the `n_analysed` rows of `out/analytic.csv` (`n_before − n_removed = n_after` on every row; each step's `n_before` equals the previous step's `n_after`).\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/analytic.csv` and `out/exclusions.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 32\ndrwxr-xr-x   6 simon-pierreboucher  staff   192 oct.  29 23:41 .\ndrwxr-xr-x  71 simon-pierreboucher  staff  2272 oct.  30 00:31 ..\n-rw-r--r--   1 simon-pierreboucher  staff   731 oct.  29 23:41 bundle.json\ndrwxr-xr-x   6 simon-pierreboucher  staff   192 oct.  29 23:31 data\n-rw-r--r--   1 simon-pierreboucher  staff  1980 oct.  29 23:31 gate_spec.json\n-rw-r--r--   1 simon-pierreboucher  staff  4969 oct.  29 23:31 TASK.md\ndata:\ntotal 104\ndrwxr-xr-x  6 simon-pierreboucher  staff    192 oct.  29 23:31 .\ndrwxr-xr-x  6 simon-pierreboucher  staff    192 oct.  29 23:41 ..\n-rw-r--r--  1 simon-pierreboucher  staff  28281 oct.  29 23:30 cpi_assembled.csv\n-rw-r--r--  1 simon-pierreboucher  staff  15416 oct.  29 23:30 cpi_vintage_2016-07-15.csv\n-rw-r--r--  1 simon-pierreboucher  staff   2909 oct.  29 23:30 cpi_vintage_2026-10-28.csv\n-rw-r--r--  1 simon-pierreboucher  staff   1909 oct.  29 23:31 README.md","is_error":true,"tool_use_id":"toolu_01E77JT2PRdJ9fT6rC3r7rRb"}]},"uuid":"8b5d56bd-b6b2-484f-b199-e93adfc7e13d","timestamp":"2026-10-30T04:31:53.012Z","toolUseResult":"Error: Exit code 1\n# F1-conf-01 — 12-month CPI inflation from two data vintages\n\n## Scientific question\nCompute the 12-month growth rate of the U.S. consumer price index (CPIAUCSL, seasonally adjusted) for every month from 2001-01 through 2026-09 and summarise it. The estimand for month t is g_t = 100 × (P_t / P_{t−12} − 1) in percent, where P is the index level taken from the most recent data vintage in which that month is available; the summary estimands are the mean of g_t over the window, the sub-period means for 2001-01 … 2013-12 and 2014-01 … 2026-09, a few named months, and the months of the maximum and minimum growth.\n\n## Inputs\n- `data/cpi_vintage_2016-07-15.csv` — observed data: CPIAUCSL as published by BLS/FRED in the ALFRED vintage of 2016-07-15, months 1947-01 to 2016-06 (834 rows). Columns `date` (ISO, first day of month), `cpi` (index, 1982–84 = 100). Source, retrieval date, license and SHA-256 in `data/README.md`.\n- `data/cpi_vintage_2026-10-28.csv` — observed data: CPIAUCSL in the ALFRED vintage of 2026-10-28, extract restricted to months 2014-01 to 2026-09 (153 rows). Same columns. An empty `cpi` cell means that the source reports no value for that month.\n- `data/cpi_assembled.csv` — a single file assembled from the two extracts above (columns `date`, `cpi`, `vintage`), intended to cover 1947-01 to 2026-09. It was assembled by hand and may contain assembly errors; the two vintage files are the authoritative inputs.\n\n## Information-availability rules\n- For each month, the index level to use is the value from the most recent vintage in which that month is available (the 2026-10-28 vintage where it covers the month, the 2016-07-15 vintage otherwise).\n- Growth rates must only be computed from index levels that exist in the sources; do not interpolate or impute missing index levels. Months for which g_t cannot be computed are reported as missing and counted.\n- Use only the files under `data/`.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F1-conf-01\" |\n| `first_growth_month` | string | YYYY-MM | first month of the window with a growth rate (expected \"2001-01\" if computable) |\n| `last_month` | string | YYYY-MM | last month of the window for which an index level exists in the sources |\n| `n_months_growth` | integer | months | months in the window with a computable g_t |\n| `n_months_missing_growth` | integer | months | months in the window without a computable g_t |\n| `n_analysed` | integer | months | same as `n_months_growth` |\n| `mean_growth_12m` | number | percent | mean of g_t over the window |\n| `mean_growth_12m_2001_2013` | number | percent | baseline: mean of g_t over 2001-01 … 2013-12 |\n| `mean_growth_12m_2014_2026` | number | percent | mean of g_t over 2014-01 … 2026-09 |\n| `growth_2014_06`, `growth_2016_07`, `growth_2020_05`, `growth_2022_06`, `growth_2026_09` | number | percent | g_t for those months |\n| `max_growth_12m`, `max_growth_month` | number, string | percent, YYYY-MM | maximum g_t in the window and its month |\n| `min_growth_12m`, `min_growth_month` | number, string | percent, YYYY-MM | minimum g_t in the window and its month |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | every data problem detected and how it was handled (empty list if none) |\n| `n_input_rows` | object | rows | {file name: number of data rows read (excluding the header)} for every file under `data/` that was read |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/analytic.csv` — one row per month of the window with a computable growth rate, columns `date` (YYYY-MM-01), `cpi`, `cpi_lag12`, `vintage_used`, `growth_12m`; exactly `n_analysed` rows.\n- `out/exclusions.csv` — the exclusion ledger with columns `step`, `rule`, `n_before`, `n_after`, `n_removed`, `rationale`; one row per step from the rows you start from to the `n_analysed` rows of `out/analytic.csv` (`n_before − n_removed = n_after` on every row; each step's `n_before` equals the previous step's `n_after`).\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/analytic.csv` and `out/exclusions.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 32\ndrwxr-xr-x   6 simon-pierreboucher  staff   192 oct.  29 23:41 .\ndrwxr-xr-x  71 simon-pierreboucher  staff  2272 oct.  30 00:31 ..\n-rw-r--r--   1 simon-pierreboucher  staff   731 oct.  29 23:41 bundle.json\ndrwxr-xr-x   6 simon-pierreboucher  staff   192 oct.  29 23:31 data\n-rw-r--r--   1 simon-pierreboucher  staff  1980 oct.  29 23:31 gate_spec.json\n-rw-r--r--   1 simon-pierreboucher  staff  4969 oct.  29 23:31 TASK.md\ndata:\ntotal 104\ndrwxr-xr-x  6 simon-pierreboucher  staff    192 oct.  29 23:31 .\ndrwxr-xr-x  6 simon-pierreboucher  staff    192 oct.  29 23:41 ..\n-rw-r--r--  1 simon-pierreboucher  staff  28281 oct.  29 23:30 cpi_assembled.csv\n-rw-r--r--  1 simon-pierreboucher  staff  15416 oct.  29 23:30 cpi_vintage_2016-07-15.csv\n-rw-r--r--  1 simon-pierreboucher  staff   2909 oct.  29 23:30 cpi_vintage_2026-10-28.csv\n-rw-r--r--  1 simon-pierreboucher  staff   1909 oct.  29 23:31 README.md","sandboxed":false,"sourceToolAssistantUUID":"1d493cfb-2cf5-4ed8-b930-c33014c670bd","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0105","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f","version":"2.1.314","gitBranch":"HEAD"}15{"type":"api-request-blob","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f","hash":"0b6ff0cc8538595d2ba35d7f66c72406b7a9f3f884d9eeff6a72ab1e338f4559","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01E77JT2PRdJ9fT6rC3r7rRb","name":"Bash","input":{"command":"cat TASK.md; 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Terms: FRED/ALFRED terms of use (BLS data are in the public domain; attribution to BLS and FRED requested). Observed data, not simulated.\n\n| file | content | source URL | retrieved (UTC) | SHA-256 of the raw download |\n|---|---|---|---|---|\n| `cpi_vintage_2016-07-15.csv` | vintage of 2016-07-15 (values as published on that date), months 1947-01 … 2016-06, 834 rows | https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=CPIAUCSL&vintage_date=2016-07-15 | 2026-10-30T03:22Z | 74c7c4e0e3fa26059421d9620abe12d57df9a7786012acf39d694766cb854f6d |\n| `cpi_vintage_2026-10-28.csv` | vintage of 2026-10-28 (latest), extract restricted to months 2014-01 … 2026-09, 153 rows | https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=CPIAUCSL&vintage_date=2026-10-28&cosd=2014-01-01 | 2026-10-30T03:24Z | 480c9f9931a48e3194b73a8f6b65b325a51e185232456c1222a260dde64f9730 |\n| `cpi_assembled.csv` | a single monthly file assembled by the task author from the two extracts above, intended to cover 1947-01 … 2026-09 with a `vintage` column recording which extract each value came from | assembled from the two files above | 2026-10-30 | — |\n\nColumns of the vintage files: `date` (first day of the month, ISO), `cpi` (index level, 3 decimals). The assembled file adds `vintage` (the vintage date of the extract the value was taken from). The raw downloads were reformatted (two columns, ISO dates) without changing any value. An empty `cpi` cell means the source publishes no value for that month. The assembled file was produced by hand and may contain assembly errors.\n{\n  \"task_id\": \"F1-conf-01\",\n  \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/analytic.csv\", \"out/exclusions.csv\"],\n  \"results_keys\": {\n    \"task_id\": {\"type\": \"string\", \"nullable\": false},\n    \"first_growth_month\": {\"type\": \"string\", \"nullable\": false},\n    \"last_month\": {\"type\": \"string\", \"nullable\": false},\n    \"n_months_growth\": {\"type\": \"integer\", \"nullable\": false},\n    \"n_months_missing_growth\": {\"type\": \"integer\", \"nullable\": false},\n    \"n_analysed\": {\"type\": \"integer\", \"nullable\": false},\n    \"mean_growth_12m\": {\"type\": \"number\", \"nullable\": false},\n    \"mean_growth_12m_2001_2013\": {\"type\": \"number\", \"nullable\": false},\n    \"mean_growth_12m_2014_2026\": {\"type\": \"number\", \"nullable\": false},\n    \"growth_2014_06\": {\"type\": \"number\", \"nullable\": true},\n    \"growth_2016_07\": {\"type\": \"number\", \"nullable\": true},\n    \"growth_2020_05\": {\"type\": \"number\", \"nullable\": true},\n    \"growth_2022_06\": {\"type\": \"number\", \"nullable\": true},\n    \"growth_2026_09\": {\"type\": \"number\", \"nullable\": true},\n    \"max_growth_12m\": {\"type\": \"number\", \"nullable\": false},\n    \"max_growth_month\": {\"type\": \"string\", \"nullable\": false},\n    \"min_growth_12m\": {\"type\": \"number\", \"nullable\": false},\n    \"min_growth_month\": {\"type\": \"string\", \"nullable\": false},\n    \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n    \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n    \"data_issues\": {\"type\": \"array\", \"nullable\": false},\n    \"n_input_rows\": {\"type\": \"object\", \"nullable\": false},\n    \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n  },\n  \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n  \"family_checks\": [\"exclusion_ledger_sums\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\"],\n  \"ci_pairs\": [],\n  \"positive_keys\": [],\n  \"chronology_pairs\": [],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 1e-6\n}\n{\n  \"task_id\": \"F1-conf-01\",\n  \"family\": \"F1\",\n  \"split\": \"conf\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:41:11.620523+00:00\",\n  \"files\": {\n    \"TASK.md\": \"a1445e54705eb87a7873503e75c51e4f37ad8594693f10839f681fb9238ca91a\",\n    \"data/README.md\": \"ac136bff13bbdb5afac60e0404212ea1f73f992bd5cca1c1e7a6599df78f8965\",\n    \"data/cpi_assembled.csv\": \"ff732b201a525d6fb171d4e8b2d470f986863c1547a1928e42bb197c0777d44d\",\n    \"data/cpi_vintage_2016-07-15.csv\": \"281fbcdd6a6eeb3e9cb7c1f20476e6bd1f7b925bf140606fe46538fe83f2c83e\",\n    \"data/cpi_vintage_2026-10-28.csv\": \"8220d5b4d70ca32fc2c7b1975651c515967db8a87053cf725159c63ed274cfba\",\n    \"gate_spec.json\": \"2435fd30a9f36563592bbfefe3c379975747affc40c42d521c3227215aaf7619\"\n  }\n}ff732b201a525d6fb171d4e8b2d470f986863c1547a1928e42bb197c0777d44d  cpi_assembled.csv\n281fbcdd6a6eeb3e9cb7c1f20476e6bd1f7b925bf140606fe46538fe83f2c83e  cpi_vintage_2016-07-15.csv\n8220d5b4d70ca32fc2c7b1975651c515967db8a87053cf725159c63ed274cfba  cpi_vintage_2026-10-28.csv\n     958 cpi_assembled.csv\n     835 cpi_vintage_2016-07-15.csv\n     154 cpi_vintage_2026-10-28.csv\n    1947 total\n==> cpi_assembled.csv <==\ndate,cpi,vintage\n1947-01-01,21.480,2016-07-15\n1947-02-01,21.620,2016-07-15\n\n==> cpi_vintage_2016-07-15.csv <==\ndate,cpi\n1947-01-01,21.480\n1947-02-01,21.620\n\n==> cpi_vintage_2026-10-28.csv <==\ndate,cpi\n2014-01-01,235.288\n2014-02-01,235.547\n2024-04-01,313.023\n2024-05-01,313.175\n2024-06-01,313.044\n2024-07-01,313.569\n2024-08-01,314.062\n2024-09-01,314.732\n2024-10-01,315.631\n2024-11-01,316.528\n2024-12-01,317.604\n2025-01-01,318.961\n2025-02-01,319.679\n2025-03-01,319.785\n2025-04-01,320.302\n2025-05-01,320.620\n2025-06-01,321.435\n2025-07-01,322.169\n2025-08-01,323.291\n2025-09-01,324.245\n2025-10-01,\n2025-11-01,325.063\n2025-12-01,326.031\n2026-01-01,326.588\n2026-02-01,327.460\n2026-03-01,330.293\n2026-04-01,332.407\n2026-05-01,333.979\n2026-06-01,332.568\n2026-07-01,332.813\n2026-08-01,334.131\n2026-09-01,336.298","is_error":false}]},"uuid":"635a20e6-23a8-4234-b2de-ad501e6875f3","timestamp":"2026-10-30T04:31:55.291Z","toolUseResult":{"stdout":"# Data provenance — F1-conf-01\n\nAll three files concern the same series: **CPIAUCSL**, Consumer Price Index for All Urban Consumers: All Items in U.S. City Average, seasonally adjusted, index 1982–1984 = 100, monthly, produced by the U.S. Bureau of Labor Statistics and distributed by the Federal Reserve Bank of St. Louis (FRED / ALFRED). Terms: FRED/ALFRED terms of use (BLS data are in the public domain; attribution to BLS and FRED requested). Observed data, not simulated.\n\n| file | content | source URL | retrieved (UTC) | SHA-256 of the raw download |\n|---|---|---|---|---|\n| `cpi_vintage_2016-07-15.csv` | vintage of 2016-07-15 (values as published on that date), months 1947-01 … 2016-06, 834 rows | https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=CPIAUCSL&vintage_date=2016-07-15 | 2026-10-30T03:22Z | 74c7c4e0e3fa26059421d9620abe12d57df9a7786012acf39d694766cb854f6d |\n| `cpi_vintage_2026-10-28.csv` | vintage of 2026-10-28 (latest), extract restricted to months 2014-01 … 2026-09, 153 rows | https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=CPIAUCSL&vintage_date=2026-10-28&cosd=2014-01-01 | 2026-10-30T03:24Z | 480c9f9931a48e3194b73a8f6b65b325a51e185232456c1222a260dde64f9730 |\n| `cpi_assembled.csv` | a single monthly file assembled by the task author from the two extracts above, intended to cover 1947-01 … 2026-09 with a `vintage` column recording which extract each value came from | assembled from the two files above | 2026-10-30 | — |\n\nColumns of the vintage files: `date` (first day of the month, ISO), `cpi` (index level, 3 decimals). The assembled file adds `vintage` (the vintage date of the extract the value was taken from). The raw downloads were reformatted (two columns, ISO dates) without changing any value. An empty `cpi` cell means the source publishes no value for that month. The assembled file was produced by hand and may contain assembly errors.\n{\n  \"task_id\": \"F1-conf-01\",\n  \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/analytic.csv\", \"out/exclusions.csv\"],\n  \"results_keys\": {\n    \"task_id\": {\"type\": \"string\", \"nullable\": false},\n    \"first_growth_month\": {\"type\": \"string\", \"nullable\": false},\n    \"last_month\": {\"type\": \"string\", \"nullable\": false},\n    \"n_months_growth\": {\"type\": \"integer\", \"nullable\": false},\n    \"n_months_missing_growth\": {\"type\": \"integer\", \"nullable\": false},\n    \"n_analysed\": {\"type\": \"integer\", \"nullable\": false},\n    \"mean_growth_12m\": {\"type\": \"number\", \"nullable\": false},\n    \"mean_growth_12m_2001_2013\": {\"type\": \"number\", \"nullable\": false},\n    \"mean_growth_12m_2014_2026\": {\"type\": \"number\", \"nullable\": false},\n    \"growth_2014_06\": {\"type\": \"number\", \"nullable\": true},\n    \"growth_2016_07\": {\"type\": \"number\", \"nullable\": true},\n    \"growth_2020_05\": {\"type\": \"number\", \"nullable\": true},\n    \"growth_2022_06\": {\"type\": \"number\", \"nullable\": true},\n    \"growth_2026_09\": {\"type\": \"number\", \"nullable\": true},\n    \"max_growth_12m\": {\"type\": \"number\", \"nullable\": false},\n    \"max_growth_month\": {\"type\": \"string\", \"nullable\": false},\n    \"min_growth_12m\": {\"type\": \"number\", \"nullable\": false},\n    \"min_growth_month\": {\"type\": \"string\", \"nullable\": false},\n    \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n    \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n    \"data_issues\": {\"type\": \"array\", \"nullable\": false},\n    \"n_input_rows\": {\"type\": \"object\", \"nullable\": false},\n    \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n  },\n  \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n  \"family_checks\": [\"exclusion_ledger_sums\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\"],\n  \"ci_pairs\": [],\n  \"positive_keys\": [],\n  \"chronology_pairs\": [],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 1e-6\n}\n{\n  \"task_id\": \"F1-conf-01\",\n  \"family\": \"F1\",\n  \"split\": \"conf\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:41:11.620523+00:00\",\n  \"files\": {\n    \"TASK.md\": \"a1445e54705eb87a7873503e75c51e4f37ad8594693f10839f681fb9238ca91a\",\n    \"data/README.md\": \"ac136bff13bbdb5afac60e0404212ea1f73f992bd5cca1c1e7a6599df78f8965\",\n    \"data/cpi_assembled.csv\": \"ff732b201a525d6fb171d4e8b2d470f986863c1547a1928e42bb197c0777d44d\",\n    \"data/cpi_vintage_2016-07-15.csv\": \"281fbcdd6a6eeb3e9cb7c1f20476e6bd1f7b925bf140606fe46538fe83f2c83e\",\n    \"data/cpi_vintage_2026-10-28.csv\": \"8220d5b4d70ca32fc2c7b1975651c515967db8a87053cf725159c63ed274cfba\",\n    \"gate_spec.json\": \"2435fd30a9f36563592bbfefe3c379975747affc40c42d521c3227215aaf7619\"\n  }\n}ff732b201a525d6fb171d4e8b2d470f986863c1547a1928e42bb197c0777d44d  cpi_assembled.csv\n281fbcdd6a6eeb3e9cb7c1f20476e6bd1f7b925bf140606fe46538fe83f2c83e  cpi_vintage_2016-07-15.csv\n8220d5b4d70ca32fc2c7b1975651c515967db8a87053cf725159c63ed274cfba  cpi_vintage_2026-10-28.csv\n     958 cpi_assembled.csv\n     835 cpi_vintage_2016-07-15.csv\n     154 cpi_vintage_2026-10-28.csv\n    1947 total\n==> cpi_assembled.csv <==\ndate,cpi,vintage\n1947-01-01,21.480,2016-07-15\n1947-02-01,21.620,2016-07-15\n\n==> cpi_vintage_2016-07-15.csv <==\ndate,cpi\n1947-01-01,21.480\n1947-02-01,21.620\n\n==> cpi_vintage_2026-10-28.csv <==\ndate,cpi\n2014-01-01,235.288\n2014-02-01,235.547\n2024-04-01,313.023\n2024-05-01,313.175\n2024-06-01,313.044\n2024-07-01,313.569\n2024-08-01,314.062\n2024-09-01,314.732\n2024-10-01,315.631\n2024-11-01,316.528\n2024-12-01,317.604\n2025-01-01,318.961\n2025-02-01,319.679\n2025-03-01,319.785\n2025-04-01,320.302\n2025-05-01,320.620\n2025-06-01,321.435\n2025-07-01,322.169\n2025-08-01,323.291\n2025-09-01,324.245\n2025-10-01,\n2025-11-01,325.063\n2025-12-01,326.031\n2026-01-01,326.588\n2026-02-01,327.460\n2026-03-01,330.293\n2026-04-01,332.407\n2026-05-01,333.979\n2026-06-01,332.568\n2026-07-01,332.813\n2026-08-01,334.131\n2026-09-01,336.298","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"7ce910ef-47e1-4678-8142-4af511885c14","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0105/data","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f","version":"2.1.314","gitBranch":"HEAD"}21{"type":"api-request-blob","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f","hash":"d37cf5d0ef0ed0a885f0f704ff6cc5140f5a0ef9eebbffcc4f35e97d792cffb8","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_012iKWvy6khpFTzHqeJ23HRp","name":"Bash","input":{"command":"cat data/README.md gate_spec.json bundle.json; cd data; shasum -a 256 *.csv; wc -l *.csv; head -3 *.csv; cat cpi_vintage_2026-10-28.csv | tail -30","description":"Inspect README, spec and data 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data; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nfor f in ['cpi_vintage_2016-07-15.csv','cpi_vintage_2026-10-28.csv','cpi_assembled.csv']:\n    d=pd.read_csv(f,dtype=str,keep_default_na=False)\n    print(f,d.shape,d.columns.tolist())\n    print(' dup dates',d.date.duplicated().sum(),'empty cpi',(d.cpi=='').sum(), 'min',d.date.min(),'max',d.date.max())\n    dt=pd.to_datetime(d.date,errors='coerce'); print(' bad dates',dt.isna().sum())\n    full=pd.date_range(dt.min(),dt.max(),freq='MS'); print(' missing months',sorted(set(full)-set(dt))[:10], 'sorted',dt.is_monotonic_increasing)\n    v=pd.to_numeric(d.cpi,errors='coerce'); print(' nonnum',(v.isna()&(d.cpi!='')).sum(), v.describe().to_dict())\n    r=v.pct_change(fill_method=None); print(' big moves', d[(r.abs()>0.03)].to_string())\n    if 'vintage' in d: print(d.vintage.value_counts())\n\"\n","description":"Audit data 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no such file or directory: data\ncpi_vintage_2016-07-15.csv (834, 2) ['date', 'cpi']\n dup dates 0 empty cpi 0 min 1947-01-01 max 2016-06-01\n bad dates 0\n missing months [] sorted True\n nonnum 0 {'count': 834.0, 'mean': 103.09592925659473, 'std': 72.92510164077056, 'min': 21.48, '25%': 30.9875, '50%': 93.25, '75%': 164.7, 'max': 239.927}\n big moves Empty DataFrame\nColumns: [date, cpi]\nIndex: []\ncpi_vintage_2026-10-28.csv (153, 2) ['date', 'cpi']\n dup dates 0 empty cpi 1 min 2014-01-01 max 2026-09-01\n bad dates 0\n missing months [] sorted True\n nonnum 0 {'count': 152.0, 'mean': 272.40488157894737, 'std': 32.630718623000526, 'min': 234.747, '25%': 243.976, '50%': 258.491, '75%': 304.16275, 'max': 336.298}\n big moves Empty DataFrame\nColumns: [date, cpi]\nIndex: []\ncpi_assembled.csv (957, 3) ['date', 'cpi', 'vintage']\n dup dates 0 empty cpi 1 min 1947-01-01 max 2026-10-01\n bad dates 0\n missing months [Timestamp('2016-07-01 00:00:00')] sorted True\n nonnum 0 {'count': 956.0, 'mean': 125.80659728033473, 'std': 91.03898483553499, 'min': 21.48, '25%': 32.894999999999996, '50%': 110.30000000000001, '75%': 202.82500000000002, 'max': 336.298}\n big moves Empty DataFrame\nColumns: [date, cpi, vintage]\nIndex: []\nvintage\n2016-07-15    834\n2026-10-28    123\nName: count, dtype: int64","is_error":false}]},"uuid":"79f5dd1c-3f48-493d-b7a8-67f7aca0287f","timestamp":"2026-10-30T04:32:02.716Z","toolUseResult":{"stdout":"(eval):cd:1: no such file or directory: data\ncpi_vintage_2016-07-15.csv (834, 2) ['date', 'cpi']\n dup dates 0 empty cpi 0 min 1947-01-01 max 2016-06-01\n bad dates 0\n missing months [] sorted True\n nonnum 0 {'count': 834.0, 'mean': 103.09592925659473, 'std': 72.92510164077056, 'min': 21.48, '25%': 30.9875, '50%': 93.25, '75%': 164.7, 'max': 239.927}\n big moves Empty DataFrame\nColumns: [date, cpi]\nIndex: []\ncpi_vintage_2026-10-28.csv (153, 2) ['date', 'cpi']\n dup dates 0 empty cpi 1 min 2014-01-01 max 2026-09-01\n bad dates 0\n missing months [] sorted True\n nonnum 0 {'count': 152.0, 'mean': 272.40488157894737, 'std': 32.630718623000526, 'min': 234.747, '25%': 243.976, '50%': 258.491, '75%': 304.16275, 'max': 336.298}\n big moves Empty DataFrame\nColumns: [date, cpi]\nIndex: []\ncpi_assembled.csv (957, 3) ['date', 'cpi', 'vintage']\n dup dates 0 empty cpi 1 min 1947-01-01 max 2026-10-01\n bad dates 0\n missing months [Timestamp('2016-07-01 00:00:00')] sorted True\n nonnum 0 {'count': 956.0, 'mean': 125.80659728033473, 'std': 91.03898483553499, 'min': 21.48, '25%': 32.894999999999996, '50%': 110.30000000000001, '75%': 202.82500000000002, 'max': 336.298}\n big moves Empty DataFrame\nColumns: [date, cpi, vintage]\nIndex: []\nvintage\n2016-07-15    834\n2026-10-28    123\nName: count, dtype: 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data; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nfor f in ['cpi_vintage_2016-07-15.csv','cpi_vintage_2026-10-28.csv','cpi_assembled.csv']:\n    d=pd.read_csv(f,dtype=str,keep_default_na=False)\n    print(f,d.shape,d.columns.tolist())\n    print(' dup dates',d.date.duplicated().sum(),'empty cpi',(d.cpi=='').sum(), 'min',d.date.min(),'max',d.date.max())\n    dt=pd.to_datetime(d.date,errors='coerce'); print(' bad dates',dt.isna().sum())\n    full=pd.date_range(dt.min(),dt.max(),freq='MS'); print(' missing months',sorted(set(full)-set(dt))[:10], 'sorted',dt.is_monotonic_increasing)\n    v=pd.to_numeric(d.cpi,errors='coerce'); print(' nonnum',(v.isna()&(d.cpi!='')).sum(), v.describe().to_dict())\n    r=v.pct_change(fill_method=None); print(' big moves', d[(r.abs()>0.03)].to_string())\n    if 'vintage' in d: print(d.vintage.value_counts())\n\"\n","description":"Audit data files"}}]}}27{"type":"api-request","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f","id":"5c4ee576-0183-45ab-95be-ba4e7af51f44","timestamp":"2026-10-30T04:32:02.720Z","version":"2.1.314","querySource":"sdk","shapeHash":"9d4c2b4b808da87f5f3014d7dcaf3767f9b2cf99db791ca587fb3813c11301cc","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"88b80760-1915-4034-b927-9fdf9d84ecb9","keep":6,"tail":["5e22785c769492f8c1fc4479599e60611d3b264fe2984ffd384804c5e1740fda","6466f4906f5ae250a7f5a500dccdad09fbbeb4ed3362dc4c611136a838e0c756"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"479a879c130c57cb690d0de2f386f49ef4052299759b004d8cd326717d90a2f4"}28{"parentUuid":"79f5dd1c-3f48-493d-b7a8-67f7aca0287f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo5umgZdi8ApsVDotH1","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1459,"cache_read_input_tokens":9735,"output_tokens":621,"output_tokens_details":{"thinking_tokens":38},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1459},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":370,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXo5uUpYeVB2hhceV7vb","requestRef":"5c4ee576-0183-45ab-95be-ba4e7af51f44","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"16c2fa66-834a-4f91-9039-a860f831334a","timestamp":"2026-10-30T04:32:04.490Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0105/data","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"16c2fa66-834a-4f91-9039-a860f831334a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo5umgZdi8ApsVDotH1","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01V8HrFtbnoZDgRUd7CKuj4p","name":"Bash","input":{"command":"pwd; cd /Users/simon-pierreboucher/wp29/runs/RC0105; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\na=pd.read_csv('data/cpi_vintage_2016-07-15.csv',parse_dates=['date']).set_index('date').cpi\nb=pd.read_csv('data/cpi_vintage_2026-10-28.csv',parse_dates=['date']).set_index('date').cpi\ns=pd.read_csv('data/cpi_assembled.csv',parse_dates=['date'],dtype={'vintage':str}).set_index('date')\nprint(s[s.cpi.isna()]); print(s.tail(3))\nov=a.index.intersection(b.index); print('overlap',len(ov)); d=(b[ov]-a[ov]); print(d.describe())\nx=s[s.vintage=='2016-07-15']; print('assembled 2016 rows range',x.index.min(),x.index.max())\nprint('assembled 2016 vals != a:', (x.cpi-a.reindex(x.index)).abs().gt(1e-9).sum())\ny=s[s.vintage=='2026-10-28']; print(y.index.min(),y.index.max())\nbb=b.reindex(y.index); m=~((y.cpi-bb).abs()<1e-9)&~(y.cpi.isna()&bb.isna()); print(pd.DataFrame({'asm':y.cpi[m],'b':bb[m]}))\n\"","description":"Compare assembled file to vintage files"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1459,"cache_read_input_tokens":9735,"output_tokens":621,"output_tokens_details":{"thinking_tokens":38},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1459},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXo5uUpYeVB2hhceV7vb","requestRef":"5c4ee576-0183-45ab-95be-ba4e7af51f44","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"22d57733-a43a-4459-a0ae-c5c68fa91400","timestamp":"2026-10-30T04:32:08.703Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0105/data","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f","version":"2.1.314","gitBranch":"HEAD"}30{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"22d57733-a43a-4459-a0ae-c5c68fa91400","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f"}31{"type":"atis-latch","atis":"","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f"}32{"parentUuid":"22d57733-a43a-4459-a0ae-c5c68fa91400","isSidechain":false,"promptId":"245893dc-704e-4aec-9422-07ac9c5a458f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01V8HrFtbnoZDgRUd7CKuj4p","type":"tool_result","content":"/Users/simon-pierreboucher/wp29/runs/RC0105/data\n            cpi     vintage\ndate                       \n2025-11-01  NaN  2026-10-28\n                cpi     vintage\ndate                           \n2026-08-01  332.813  2026-10-28\n2026-09-01  334.131  2026-10-28\n2026-10-01  336.298  2026-10-28\noverlap 30\ncount    30.000000\nmean     -0.001300\nstd       0.223576\nmin      -0.455000\n25%      -0.210750\n50%       0.017000\n75%       0.156750\nmax       0.330000\nName: cpi, dtype: float64\nassembled 2016 rows range 1947-01-01 00:00:00 2016-06-01 00:00:00\nassembled 2016 vals != a: 0\n2016-08-01 00:00:00 2026-10-01 00:00:00\n                asm        b\ndate                        \n2016-08-01  240.101  240.545\n2016-09-01  240.545  241.176\n2016-10-01  241.176  241.741\n2016-11-01  241.741  242.026\n2016-12-01  242.026  242.637\n...             ...      ...\n2026-06-01  333.979  332.568\n2026-07-01  332.568  332.813\n2026-08-01  332.813  334.131\n2026-09-01  334.131  336.298\n2026-10-01  336.298      NaN\n\n[123 rows x 2 columns]","is_error":false}]},"uuid":"046ce9a1-594f-4e79-81b1-51c8fef71581","timestamp":"2026-10-30T04:32:09.012Z","toolUseResult":{"stdout":"/Users/simon-pierreboucher/wp29/runs/RC0105/data\n            cpi     vintage\ndate                       \n2025-11-01  NaN  2026-10-28\n                cpi     vintage\ndate                           \n2026-08-01  332.813  2026-10-28\n2026-09-01  334.131  2026-10-28\n2026-10-01  336.298  2026-10-28\noverlap 30\ncount    30.000000\nmean     -0.001300\nstd       0.223576\nmin      -0.455000\n25%      -0.210750\n50%       0.017000\n75%       0.156750\nmax       0.330000\nName: cpi, dtype: float64\nassembled 2016 rows range 1947-01-01 00:00:00 2016-06-01 00:00:00\nassembled 2016 vals != a: 0\n2016-08-01 00:00:00 2026-10-01 00:00:00\n                asm        b\ndate                        \n2016-08-01  240.101  240.545\n2016-09-01  240.545  241.176\n2016-10-01  241.176  241.741\n2016-11-01  241.741  242.026\n2016-12-01  242.026  242.637\n...             ...      ...\n2026-06-01  333.979  332.568\n2026-07-01  332.568  332.813\n2026-08-01  332.813  334.131\n2026-09-01  334.131  336.298\n2026-10-01  336.298      NaN\n\n[123 rows x 2 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cd /Users/simon-pierreboucher/wp29/runs/RC0105; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\na=pd.read_csv('data/cpi_vintage_2016-07-15.csv',parse_dates=['date']).set_index('date').cpi\nb=pd.read_csv('data/cpi_vintage_2026-10-28.csv',parse_dates=['date']).set_index('date').cpi\ns=pd.read_csv('data/cpi_assembled.csv',parse_dates=['date'],dtype={'vintage':str}).set_index('date')\nprint(s[s.cpi.isna()]); print(s.tail(3))\nov=a.index.intersection(b.index); print('overlap',len(ov)); d=(b[ov]-a[ov]); print(d.describe())\nx=s[s.vintage=='2016-07-15']; print('assembled 2016 rows range',x.index.min(),x.index.max())\nprint('assembled 2016 vals != a:', (x.cpi-a.reindex(x.index)).abs().gt(1e-9).sum())\ny=s[s.vintage=='2026-10-28']; print(y.index.min(),y.index.max())\nbb=b.reindex(y.index); m=~((y.cpi-bb).abs()<1e-9)&~(y.cpi.isna()&bb.isna()); print(pd.DataFrame({'asm':y.cpi[m],'b':bb[m]}))\n\"","description":"Compare assembled file to vintage 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-p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract — F1-conf-01\n**Question.** 12-month growth of CPIAUCSL (SA) for each month 2001-01..2026-09 (309 months), and summaries.\n**Estimand.** g_t = 100*(P_t/P_{t-12} - 1), with P taken from the most recent vintage covering the month: 2026-10-28 vintage for 2014-01..2026-09, 2016-07-15 vintage for months before 2014-01. Lags for 2001-01 reach back to 2000-01 (2016 vintage). Mixing vintages across t and t-12 (e.g. 2014-06 vs 2013-06) is what the rule prescribes; it is meaningful under the assumption that revisions between vintages (seasonal factor updates) are small.\n**Summaries.** mean over window; means 2001-01..2013-12 and 2014-01..2026-09; g for 2014-06, 2016-07, 2020-05, 2022-06, 2026-09; max/min and months.\n**Outputs.** out/results.json (keys per TASK.md table), out/analytic.csv (date,cpi,cpi_lag12,vintage_used,growth_12m; n_analysed rows), out/exclusions.csv, out/report.md (6 sections), out/analysis.py, out/claims.json.\n**Info rules.** Latest vintage wins; no interpolation/imputation; missing g_t counted; only data/ files.\n**Invalidity.** Using the hand-assembled file as source if it has errors; imputing missing levels; using older vintage where newer exists.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > log.md","description":"Write contract, phase ledger, 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-p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract — F1-conf-01\n**Question.** 12-month growth of CPIAUCSL (SA) for each month 2001-01..2026-09 (309 months), and summaries.\n**Estimand.** g_t = 100*(P_t/P_{t-12} - 1), with P taken from the most recent vintage covering the month: 2026-10-28 vintage for 2014-01..2026-09, 2016-07-15 vintage for months before 2014-01. Lags for 2001-01 reach back to 2000-01 (2016 vintage). Mixing vintages across t and t-12 (e.g. 2014-06 vs 2013-06) is what the rule prescribes; it is meaningful under the assumption that revisions between vintages (seasonal factor updates) are small.\n**Summaries.** mean over window; means 2001-01..2013-12 and 2014-01..2026-09; g for 2014-06, 2016-07, 2020-05, 2022-06, 2026-09; max/min and months.\n**Outputs.** out/results.json (keys per TASK.md table), out/analytic.csv (date,cpi,cpi_lag12,vintage_used,growth_12m; n_analysed rows), out/exclusions.csv, out/report.md (6 sections), out/analysis.py, out/claims.json.\n**Info rules.** Latest vintage wins; no interpolation/imputation; missing g_t counted; only data/ files.\n**Invalidity.** Using the hand-assembled file as source if it has errors; imputing missing levels; using older vintage where newer exists.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > log.md","description":"Write contract, phase ledger, 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out/state && cat > data_audit.md <<'EOF'\n# Data audit (P1)\n| file | SHA-256 | data rows | cols | date range | empty cpi | dup dates | gaps |\n|---|---|---|---|---|---|---|---|\n| cpi_vintage_2016-07-15.csv | 281fbcdd6a6eeb3e9cb7c1f20476e6bd1f7b925bf140606fe46538fe83f2c83e | 834 | date,cpi | 1947-01..2016-06 | 0 | 0 | none |\n| cpi_vintage_2026-10-28.csv | 8220d5b4d70ca32fc2c7b1975651c515967db8a87053cf725159c63ed274cfba | 153 | date,cpi | 2014-01..2026-09 | 1 (2025-10) | 0 | none |\n| cpi_assembled.csv | ff732b201a525d6fb171d4e8b2d470f986863c1547a1928e42bb197c0777d44d | 957 | date,cpi,vintage | 1947-01..2026-10 | 1 (2025-11) | 0 | 2016-07 absent |\n\nSHA-256 values match bundle.json. (README lists hashes of the *raw downloads*, before reformatting; they differ from the reformatted files, as the README explains.)\nUnits: index 1982-84=100, SA, monthly. No non-numeric values, no month-on-month moves >3%.\n\n## Anomalies\n1. 2026-10-28 vintage has no value for 2025-10 (empty cell; source publishes none). => g_2025-10 not computable (no imputation). 2026-10 would also lack lag but is outside the window.\n2. Vintages overlap 2014-01..2016-06 (30 months); values differ (revisions, mean diff -0.0013, range -0.455..+0.330). Rule: use 2026-10-28 vintage.\n3. cpi_assembled.csv has assembly errors: (a) uses the 2016-07-15 vintage for 2014-01..2016-06 instead of the latest vintage; (b) 2016-07 missing; (c) from 2016-08 on, every value is shifted one month later (assembled value at month m = 2026 vintage value at m-1), so the empty cell appears at 2025-11 and the file runs to 2026-10. \nHandling: assembled file is NOT used for computation; read only for audit/cross-check. 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(README lists hashes of the *raw downloads*, before reformatting; they differ from the reformatted files, as the README explains.)\nUnits: index 1982-84=100, SA, monthly. No non-numeric values, no month-on-month moves >3%.\n\n## Anomalies\n1. 2026-10-28 vintage has no value for 2025-10 (empty cell; source publishes none). => g_2025-10 not computable (no imputation). 2026-10 would also lack lag but is outside the window.\n2. Vintages overlap 2014-01..2016-06 (30 months); values differ (revisions, mean diff -0.0013, range -0.455..+0.330). Rule: use 2026-10-28 vintage.\n3. cpi_assembled.csv has assembly errors: (a) uses the 2016-07-15 vintage for 2014-01..2016-06 instead of the latest vintage; (b) 2016-07 missing; (c) from 2016-08 on, every value is shifted one month later (assembled value at month m = 2026 vintage value at m-1), so the empty cell appears at 2025-11 and the file runs to 2026-10. \nHandling: assembled file is NOT used for computation; read only for audit/cross-check. The two vintage files are authoritative.\n\n## Availability\nNo input violates the rules; the 2026-10-28 vintage is the latest, and both vintages are in data/.\nEOF\necho \"- 2026-10-30 P1: audit done; decision: ignore cpi_assembled.csv for computation (shifted/wrong vintage).\" >> log.md","description":"Write data audit"}}]}}44{"type":"api-request","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f","id":"5c95a188-aac3-42c0-8c1c-d17fe613e931","timestamp":"2026-10-30T04:32:31.163Z","version":"2.1.314","querySource":"sdk","shapeHash":"9d4c2b4b808da87f5f3014d7dcaf3767f9b2cf99db791ca587fb3813c11301cc","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"89275368-951e-4af5-8696-26116f25d882","keep":12,"tail":["30d7017dfb1b0bbae7e8dde86b07602cff4f74b18265e64ec8efe304a87ad1c7","f72521c9114b563c547c41d97d7f284a7aadd58cdced9af0cd9d5ff67ce859d0"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"3093135df4f5b13973f5b54dddb101ed4e785d82f6da26d83c43f993ac24d768"}45{"parentUuid":"516d1fbe-a8fb-407f-8d1d-df068048b910","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo81N6DFpY5gGyzzEC7","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01G7nW38tDXGqH9uWqwUVqAs","name":"Bash","input":{"command":"cat > data_audit.md <<'EOF'\n# Data audit (P1)\n| file | SHA-256 | data rows | cols | date range | empty cpi | dup dates | gaps |\n|---|---|---|---|---|---|---|---|\n| cpi_vintage_2016-07-15.csv | 281fbcdd6a6eeb3e9cb7c1f20476e6bd1f7b925bf140606fe46538fe83f2c83e | 834 | date,cpi | 1947-01..2016-06 | 0 | 0 | none |\n| cpi_vintage_2026-10-28.csv | 8220d5b4d70ca32fc2c7b1975651c515967db8a87053cf725159c63ed274cfba | 153 | date,cpi | 2014-01..2026-09 | 1 (2025-10) | 0 | none |\n| cpi_assembled.csv | ff732b201a525d6fb171d4e8b2d470f986863c1547a1928e42bb197c0777d44d | 957 | date,cpi,vintage | 1947-01..2026-10 | 1 (2025-11) | 0 | 2016-07 absent |\n\nSHA-256 values match bundle.json. (README lists hashes of the *raw downloads*, before reformatting; they differ from the reformatted files, as the README explains.)\nUnits: index 1982-84=100, SA, monthly. No non-numeric values, no month-on-month moves >3%.\n\n## Anomalies\n1. 2026-10-28 vintage has no value for 2025-10 (empty cell; source publishes none). => g_2025-10 not computable (no imputation). 2026-10 would also lack lag but is outside the window.\n2. Vintages overlap 2014-01..2016-06 (30 months); values differ (revisions, mean diff -0.0013, range -0.455..+0.330). Rule: use 2026-10-28 vintage.\n3. cpi_assembled.csv has assembly errors: (a) uses the 2016-07-15 vintage for 2014-01..2016-06 instead of the latest vintage; (b) 2016-07 missing; (c) from 2016-08 on, every value is shifted one month later (assembled value at month m = 2026 vintage value at m-1), so the empty cell appears at 2025-11 and the file runs to 2026-10. \nHandling: assembled file is NOT used for computation; read only for audit/cross-check. The two vintage files are authoritative.\n\n## Availability\nNo input violates the rules; the 2026-10-28 vintage is the latest, and both vintages are in data/.\nEOF\necho \"- 2026-10-30 P1: audit done; decision: ignore cpi_assembled.csv for computation (shifted/wrong vintage).\" >> log.md","description":"Write data audit"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1196,"cache_read_input_tokens":13286,"output_tokens":1020,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1196},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXo816DdP7UNqsiyN2aY","requestRef":"5c95a188-aac3-42c0-8c1c-d17fe613e931","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"18745268-d71b-48fb-a233-432b749e6ad0","timestamp":"2026-10-30T04:32:40.054Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0105/out/state","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f","version":"2.1.314","gitBranch":"HEAD"}46{"parentUuid":"18745268-d71b-48fb-a233-432b749e6ad0","isSidechain":false,"promptId":"245893dc-704e-4aec-9422-07ac9c5a458f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01G7nW38tDXGqH9uWqwUVqAs","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"e6447df5-1d6c-4e43-9839-45f4205025ab","timestamp":"2026-10-30T04:32:40.103Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"18745268-d71b-48fb-a233-432b749e6ad0","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0105/out/state","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f","version":"2.1.314","gitBranch":"HEAD"}47{"type":"api-request-blob","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f","hash":"c93eb27f828e20aa6c3c51e075e27fcc19ad26797f5cdeadddc338250b5921a5","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01G7nW38tDXGqH9uWqwUVqAs","name":"Bash","input":{"command":"cat > data_audit.md <<'EOF'\n# Data audit (P1)\n| file | SHA-256 | data rows | cols | date range | empty cpi | dup dates | gaps |\n|---|---|---|---|---|---|---|---|\n| cpi_vintage_2016-07-15.csv | 281fbcdd6a6eeb3e9cb7c1f20476e6bd1f7b925bf140606fe46538fe83f2c83e | 834 | date,cpi | 1947-01..2016-06 | 0 | 0 | none |\n| cpi_vintage_2026-10-28.csv | 8220d5b4d70ca32fc2c7b1975651c515967db8a87053cf725159c63ed274cfba | 153 | date,cpi | 2014-01..2026-09 | 1 (2025-10) | 0 | none |\n| cpi_assembled.csv | ff732b201a525d6fb171d4e8b2d470f986863c1547a1928e42bb197c0777d44d | 957 | date,cpi,vintage | 1947-01..2026-10 | 1 (2025-11) | 0 | 2016-07 absent |\n\nSHA-256 values match bundle.json. (README lists hashes of the *raw downloads*, before reformatting; they differ from the reformatted files, as the README explains.)\nUnits: index 1982-84=100, SA, monthly. No non-numeric values, no month-on-month moves >3%.\n\n## Anomalies\n1. 2026-10-28 vintage has no value for 2025-10 (empty cell; source publishes none). => g_2025-10 not computable (no imputation). 2026-10 would also lack lag but is outside the window.\n2. Vintages overlap 2014-01..2016-06 (30 months); values differ (revisions, mean diff -0.0013, range -0.455..+0.330). Rule: use 2026-10-28 vintage.\n3. cpi_assembled.csv has assembly errors: (a) uses the 2016-07-15 vintage for 2014-01..2016-06 instead of the latest vintage; (b) 2016-07 missing; (c) from 2016-08 on, every value is shifted one month later (assembled value at month m = 2026 vintage value at m-1), so the empty cell appears at 2025-11 and the file runs to 2026-10. \nHandling: assembled file is NOT used for computation; read only for audit/cross-check. The two vintage files are authoritative.\n\n## Availability\nNo input violates the rules; the 2026-10-28 vintage is the latest, and both vintages are in data/.\nEOF\necho \"- 2026-10-30 P1: audit done; decision: ignore cpi_assembled.csv for computation (shifted/wrong vintage).\" >> log.md","description":"Write data 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/Users/simon-pierreboucher/wp29/runs/RC0105 && cat > out/analysis.py <<'EOF'\n\"\"\"F1-conf-01: 12-month CPI inflation from two ALFRED vintages.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/analytic.csv, out/exclusions.csv. No randomness.\"\"\"\nimport json\nimport numpy as np\nimport pandas as pd\n\nOLD, NEW, ASM = \"cpi_vintage_2016-07-15.csv\", \"cpi_vintage_2026-10-28.csv\", \"cpi_assembled.csv\"\nW0, W1 = pd.Timestamp(\"2001-01-01\"), pd.Timestamp(\"2026-09-01\")\n\n\ndef load(f):\n    d = pd.read_csv(\"data/\" + f, dtype={\"date\": str})\n    d[\"date\"] = pd.to_datetime(d[\"date\"])\n    return d\n\n\nold, new, asm = load(OLD), load(NEW), load(ASM)\nfor d in (old, new):\n    assert not d[\"date\"].duplicated().any()\nn_input_rows = {OLD: len(old), NEW: len(new), ASM: len(asm)}\n\n# Latest-vintage rule: a month is \"covered\" by the 2026 vintage if it appears in that extract\n# (rows 2014-01..2026-09). An empty cell there means the source reports no value; we do NOT\n# fall back to the older vintage (it does not cover 2025-10 anyway) and do not impute.\no = old.set_index(\"date\")[\"cpi\"]\nnw = new.set_index(\"date\")[\"cpi\"]\nmonths = pd.date_range(min(o.index.min(), nw.index.min()), max(o.index.max(), nw.index.max()), freq=\"MS\")\nlevel = pd.Series(np.nan, index=months)\nvint = pd.Series(None, index=months, dtype=object)\nlevel.loc[o.index] = o.values\nvint.loc[o.index] = \"2016-07-15\"\nlevel.loc[nw.index] = nw.values\nvint.loc[nw.index] = \"2026-10-28\"\n\nwindow = pd.date_range(W0, W1, freq=\"MS\")\nlag = level.shift(12)\ng = 100.0 * (level / lag - 1.0)\ndf = pd.DataFrame({\"cpi\": level, \"cpi_lag12\": lag, \"vintage_used\": vint, \"growth_12m\": g}).loc[window]\nok = df[\"growth_12m\"].notna()\nanalytic = df[ok].copy()\nmissing_months = [d.strftime(\"%Y-%m\") for d in df.index[~ok]]\n\n# Exclusion ledger\nexcl = [\n    dict(step=1, rule=\"calendar months 2001-01..2026-09 (window)\", n_before=len(window), n_after=len(window),\n         n_removed=0, rationale=\"starting set: every month of the estimation window\"),\n]\nn_lvl = int(df[\"cpi\"].notna().sum())\nexcl.append(dict(step=2, rule=\"drop months with no index level P_t in latest covering vintage\",\n                 n_before=len(window), n_after=n_lvl, n_removed=len(window) - n_lvl,\n                 rationale=\"no interpolation/imputation allowed (e.g. 2025-10 empty in 2026-10-28 vintage)\"))\nn_ok = int(ok.sum())\nexcl.append(dict(step=3, rule=\"drop months with no lagged level P_{t-12}\", n_before=n_lvl, n_after=n_ok,\n                 n_removed=n_lvl - n_ok, rationale=\"g_t requires both P_t and P_{t-12}\"))\npd.DataFrame(excl).to_csv(\"out/exclusions.csv\", index=False)\n\nout_csv = analytic.reset_index().rename(columns={\"index\": \"date\"})\nout_csv[\"date\"] = out_csv[\"date\"].dt.strftime(\"%Y-%m-01\")\nout_csv.to_csv(\"out/analytic.csv\", index=False, float_format=\"%.10g\")\n\nG = analytic[\"growth_12m\"]\n\n\ndef gm(m):\n    t = pd.Timestamp(m + \"-01\")\n    v = G.get(t, np.nan)\n    return None if pd.isna(v) else round(float(v), 10)\n\n\ndef r(x):\n    return round(float(x), 10)\n\n\n# Data issues detected (computed, not hard-coded where possible)\nissues = []\nempty_new = [d.strftime(\"%Y-%m\") for d in nw.index[nw.isna()]]\nif empty_new:\n    issues.append({\"issue\": f\"{NEW}: empty cpi for {', '.join(empty_new)} (source reports no value)\",\n                   \"handling\": \"level treated as missing, not imputed; g_t for that month reported missing \"\n                               \"(and any month t whose t-12 is that month; inside the window: none)\"})\nov = o.index.intersection(nw.index)\ndif = (nw[ov] - o[ov]).dropna()\nif len(dif) and (dif.abs() > 1e-9).any():\n    issues.append({\"issue\": f\"vintages overlap for {len(ov)} months ({ov.min():%Y-%m}..{ov.max():%Y-%m}) with revised values \"\n                            f\"(max abs diff {dif.abs().max():.3f} index points)\",\n                   \"handling\": \"2026-10-28 vintage used for overlap months per latest-vintage rule\"})\na = asm.set_index(\"date\")\na_old = a[a[\"vintage\"].astype(str) == \"2016-07-15\"]\na_new = a[a[\"vintage\"].astype(str) == \"2026-10-28\"]\nasm_missing = [d.strftime(\"%Y-%m\") for d in months if d not in a.index]\nif (a_old.index >= nw.index.min()).any():\n    issues.append({\"issue\": f\"{ASM}: months {nw.index.min():%Y-%m}..{a_old.index.max():%Y-%m} taken from the older 2016-07-15 vintage although the 2026-10-28 vintage covers them\",\n                   \"handling\": \"assembled file not used; levels taken directly from the vintage files\"})\nif asm_missing:\n    issues.append({\"issue\": f\"{ASM}: month(s) {', '.join(asm_missing)} absent\",\n                   \"handling\": \"assembled file not used; levels taken directly from the vintage files\"})\nshift_match = (a_new[\"cpi\"].values[:-0 or None] is not None)\nnw_shift = nw.shift(1, freq=\"MS\")\ncommon = a_new.index.intersection(nw_shift.index)\nsame_shift = np.allclose(a_new.loc[common, \"cpi\"].fillna(-1), nw_shift.loc[common].fillna(-1))\nif same_shift:\n    issues.append({\"issue\": f\"{ASM}: rows labelled 2026-10-28 ({a_new.index.min():%Y-%m}..{a_new.index.max():%Y-%m}) are shifted one month late (value at month m equals the 2026-10-28 vintage value for m-1); file extends to {a.index.max():%Y-%m}, beyond the source\",\n                   \"handling\": \"assembled file not used; levels taken directly from the vintage files\"})\n\nlast_month = level.loc[window].dropna().index.max().strftime(\"%Y-%m\")\nimax, imin = G.idxmax(), G.idxmin()\nn_miss = len(window) - n_ok\nres = {\n    \"task_id\": \"F1-conf-01\",\n    \"first_growth_month\": G.index.min().strftime(\"%Y-%m\"),\n    \"last_month\": last_month,\n    \"n_months_growth\": n_ok,\n    \"n_months_missing_growth\": n_miss,\n    \"n_analysed\": n_ok,\n    \"mean_growth_12m\": r(G.mean()),\n    \"mean_growth_12m_2001_2013\": r(G[:\"2013-12-01\"].mean()),\n    \"mean_growth_12m_2014_2026\": r(G[\"2014-01-01\":].mean()),\n    \"growth_2014_06\": gm(\"2014-06\"),\n    \"growth_2016_07\": gm(\"2016-07\"),\n    \"growth_2020_05\": gm(\"2020-05\"),\n    \"growth_2022_06\": gm(\"2022-06\"),\n    \"growth_2026_09\": gm(\"2026-09\"),\n    \"max_growth_12m\": r(G.max()),\n    \"max_growth_month\": imax.strftime(\"%Y-%m\"),\n    \"min_growth_12m\": r(G.min()),\n    \"min_growth_month\": imin.strftime(\"%Y-%m\"),\n    \"feasibility\": \"complete\" if n_miss == 0 else \"partial\",\n    \"feasibility_reason\": None if n_miss == 0 else\n        f\"g_t not computable for {n_miss} month(s) of the window ({', '.join(missing_months)}): index level absent in the source; not imputed per the task rules\",\n    \"missing_growth_months\": missing_months,\n    \"n_months_window\": len(window),\n    \"n_months_growth_2001_2013\": int(G[:\"2013-12-01\"].notna().sum()),\n    \"n_months_growth_2014_2026\": int(G[\"2014-01-01\":].notna().sum()),\n    \"sd_growth_12m\": r(G.std(ddof=1)),\n    \"data_issues\": issues,\n    \"n_input_rows\": n_input_rows,\n    \"random_seed\": None,\n}\nwith open(\"out/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"n_analysed\", \"n_months_missing_growth\", \"mean_growth_12m\", \"feasibility\"]}))\nEOF\nsed -i '' '/shift_match = /d' out/analysis.py\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/exclusions.csv && head -3 out/analytic.csv && wc -l out/analytic.csv","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1003,"cache_read_input_tokens":14482,"output_tokens":3489,"output_tokens_details":{"thinking_tokens":106},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1003},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXo8fJ8KFRvupTFiAFYW","requestRef":"2fa59248-347b-47f7-96ee-472a23006d4d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"720fcda1-32da-4d2f-b112-8ac77a885fbc","timestamp":"2026-10-30T04:33:08.017Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0105/out/state","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f","version":"2.1.314","gitBranch":"HEAD"}53{"parentUuid":"720fcda1-32da-4d2f-b112-8ac77a885fbc","isSidechain":false,"promptId":"245893dc-704e-4aec-9422-07ac9c5a458f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01ANw577Q8mGCBxKaPhADdBx","type":"tool_result","content":"{\"n_analysed\": 308, \"n_months_missing_growth\": 1, \"mean_growth_12m\": 2.5741201901, \"feasibility\": \"partial\"}\n{\n  \"task_id\": \"F1-conf-01\",\n  \"first_growth_month\": \"2001-01\",\n  \"last_month\": \"2026-09\",\n  \"n_months_growth\": 308,\n  \"n_months_missing_growth\": 1,\n  \"n_analysed\": 308,\n  \"mean_growth_12m\": 2.5741201901,\n  \"mean_growth_12m_2001_2013\": 2.3612153974,\n  \"mean_growth_12m_2014_2026\": 2.7926277404,\n  \"growth_2014_06\": 2.0976342439,\n  \"growth_2016_07\": 0.8683633431,\n  \"growth_2020_05\": 0.1982013036,\n  \"growth_2022_06\": 8.9793611031,\n  \"growth_2026_09\": 3.7172508443,\n  \"max_growth_12m\": 8.9793611031,\n  \"max_growth_month\": \"2022-06\",\n  \"min_growth_12m\": -1.9587610038,\n  \"min_growth_month\": \"2009-07\",\n  \"feasibility\": \"partial\",\n  \"feasibility_reason\": \"g_t not computable for 1 month(s) of the window (2025-10): index level absent in the source; not imputed per the task rules\",\n  \"missing_growth_months\": [\n    \"2025-10\"\n  ],\n  \"n_months_window\": 309,\n  \"n_months_growth_2001_2013\": 156,\n  \"n_months_growth_2014_2026\": 152,\n  \"sd_growth_12m\": 1.7407883259,\n  \"data_issues\": [\n    {\n      \"issue\": \"cpi_vintage_2026-10-28.csv: empty cpi for 2025-10 (source reports no value)\",\n      \"handling\": \"level treated as missing, not imputed; g_t for that month reported missing (and any month t whose t-12 is that month; inside the window: none)\"\n    },\n    {\n      \"issue\": \"vintages overlap for 30 months (2014-01..2016-06) with revised values (max abs diff 0.455 index points)\",\n      \"handling\": \"2026-10-28 vintage used for overlap months per latest-vintage rule\"\n    },\n    {\n      \"issue\": \"cpi_assembled.csv: months 2014-01..2016-06 taken from the older 2016-07-15 vintage although the 2026-10-28 vintage covers them\",\n      \"handling\": \"assembled file not used; levels taken directly from the vintage files\"\n    },\n    {\n      \"issue\": \"cpi_assembled.csv: month(s) 2016-07 absent\",\n      \"handling\": \"assembled file not used; levels taken directly from the vintage files\"\n    },\n    {\n      \"issue\": \"cpi_assembled.csv: rows labelled 2026-10-28 (2016-08..2026-10) are shifted one month late (value at month m equals the 2026-10-28 vintage value for m-1); file extends to 2026-10, beyond the source\",\n      \"handling\": \"assembled file not used; levels taken directly from the vintage files\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"cpi_vintage_2016-07-15.csv\": 834,\n    \"cpi_vintage_2026-10-28.csv\": 153,\n    \"cpi_assembled.csv\": 957\n  },\n  \"random_seed\": null\n}step,rule,n_before,n_after,n_removed,rationale\n1,calendar months 2001-01..2026-09 (window),309,309,0,starting set: every month of the estimation window\n2,drop months with no index level P_t in latest covering vintage,309,308,1,no interpolation/imputation allowed (e.g. 2025-10 empty in 2026-10-28 vintage)\n3,drop months with no lagged level P_{t-12},308,308,0,g_t requires both P_t and P_{t-12}\ndate,cpi,cpi_lag12,vintage_used,growth_12m\n2001-01-01,175.6,169.3,2016-07-15,3.721204962\n2001-02-01,176,170,2016-07-15,3.529411765\n     309 out/analytic.csv","is_error":false}]},"uuid":"de6b4bcf-0456-4ab1-8895-cd951485c599","timestamp":"2026-10-30T04:33:08.347Z","toolUseResult":{"stdout":"{\"n_analysed\": 308, \"n_months_missing_growth\": 1, \"mean_growth_12m\": 2.5741201901, \"feasibility\": \"partial\"}\n{\n  \"task_id\": \"F1-conf-01\",\n  \"first_growth_month\": \"2001-01\",\n  \"last_month\": \"2026-09\",\n  \"n_months_growth\": 308,\n  \"n_months_missing_growth\": 1,\n  \"n_analysed\": 308,\n  \"mean_growth_12m\": 2.5741201901,\n  \"mean_growth_12m_2001_2013\": 2.3612153974,\n  \"mean_growth_12m_2014_2026\": 2.7926277404,\n  \"growth_2014_06\": 2.0976342439,\n  \"growth_2016_07\": 0.8683633431,\n  \"growth_2020_05\": 0.1982013036,\n  \"growth_2022_06\": 8.9793611031,\n  \"growth_2026_09\": 3.7172508443,\n  \"max_growth_12m\": 8.9793611031,\n  \"max_growth_month\": \"2022-06\",\n  \"min_growth_12m\": -1.9587610038,\n  \"min_growth_month\": \"2009-07\",\n  \"feasibility\": \"partial\",\n  \"feasibility_reason\": \"g_t not computable for 1 month(s) of the window (2025-10): index level absent in the source; not imputed per the task rules\",\n  \"missing_growth_months\": [\n    \"2025-10\"\n  ],\n  \"n_months_window\": 309,\n  \"n_months_growth_2001_2013\": 156,\n  \"n_months_growth_2014_2026\": 152,\n  \"sd_growth_12m\": 1.7407883259,\n  \"data_issues\": [\n    {\n      \"issue\": \"cpi_vintage_2026-10-28.csv: empty cpi for 2025-10 (source reports no value)\",\n      \"handling\": \"level treated as missing, not imputed; g_t for that month reported missing (and any month t whose t-12 is that month; inside the window: none)\"\n    },\n    {\n      \"issue\": \"vintages overlap for 30 months (2014-01..2016-06) with revised values (max abs diff 0.455 index points)\",\n      \"handling\": \"2026-10-28 vintage used for overlap months per latest-vintage rule\"\n    },\n    {\n      \"issue\": \"cpi_assembled.csv: months 2014-01..2016-06 taken from the older 2016-07-15 vintage although the 2026-10-28 vintage covers them\",\n      \"handling\": \"assembled file not used; levels taken directly from the vintage files\"\n    },\n    {\n      \"issue\": \"cpi_assembled.csv: month(s) 2016-07 absent\",\n      \"handling\": \"assembled file not used; levels taken directly from the vintage files\"\n    },\n    {\n      \"issue\": \"cpi_assembled.csv: rows labelled 2026-10-28 (2016-08..2026-10) are shifted one month late (value at month m equals the 2026-10-28 vintage value for m-1); file extends to 2026-10, beyond the source\",\n      \"handling\": \"assembled file not used; levels taken directly from the vintage files\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"cpi_vintage_2016-07-15.csv\": 834,\n    \"cpi_vintage_2026-10-28.csv\": 153,\n    \"cpi_assembled.csv\": 957\n  },\n  \"random_seed\": null\n}step,rule,n_before,n_after,n_removed,rationale\n1,calendar months 2001-01..2026-09 (window),309,309,0,starting set: every month of the estimation window\n2,drop months with no index level P_t in latest covering vintage,309,308,1,no interpolation/imputation allowed (e.g. 2025-10 empty in 2026-10-28 vintage)\n3,drop months with no lagged level P_{t-12},308,308,0,g_t requires both P_t and P_{t-12}\ndate,cpi,cpi_lag12,vintage_used,growth_12m\n2001-01-01,175.6,169.3,2016-07-15,3.721204962\n2001-02-01,176,170,2016-07-15,3.529411765\n     309 out/analytic.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"720fcda1-32da-4d2f-b112-8ac77a885fbc","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0105","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f","version":"2.1.314","gitBranch":"HEAD"}54{"type":"api-request-blob","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f","hash":"52a331aeb764c05a248586cdc06619d10ddb439b555b3a8710bd1db275540f21","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01ANw577Q8mGCBxKaPhADdBx","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0105 && cat > out/analysis.py <<'EOF'\n\"\"\"F1-conf-01: 12-month CPI inflation from two ALFRED vintages.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/analytic.csv, out/exclusions.csv. No randomness.\"\"\"\nimport json\nimport numpy as np\nimport pandas as pd\n\nOLD, NEW, ASM = \"cpi_vintage_2016-07-15.csv\", \"cpi_vintage_2026-10-28.csv\", \"cpi_assembled.csv\"\nW0, W1 = pd.Timestamp(\"2001-01-01\"), pd.Timestamp(\"2026-09-01\")\n\n\ndef load(f):\n    d = pd.read_csv(\"data/\" + f, dtype={\"date\": str})\n    d[\"date\"] = pd.to_datetime(d[\"date\"])\n    return d\n\n\nold, new, asm = load(OLD), load(NEW), load(ASM)\nfor d in (old, new):\n    assert not d[\"date\"].duplicated().any()\nn_input_rows = {OLD: len(old), NEW: len(new), ASM: len(asm)}\n\n# Latest-vintage rule: a month is \"covered\" by the 2026 vintage if it appears in that extract\n# (rows 2014-01..2026-09). An empty cell there means the source reports no value; we do NOT\n# fall back to the older vintage (it does not cover 2025-10 anyway) and do not impute.\no = old.set_index(\"date\")[\"cpi\"]\nnw = new.set_index(\"date\")[\"cpi\"]\nmonths = pd.date_range(min(o.index.min(), nw.index.min()), max(o.index.max(), nw.index.max()), freq=\"MS\")\nlevel = pd.Series(np.nan, index=months)\nvint = pd.Series(None, index=months, dtype=object)\nlevel.loc[o.index] = o.values\nvint.loc[o.index] = \"2016-07-15\"\nlevel.loc[nw.index] = nw.values\nvint.loc[nw.index] = \"2026-10-28\"\n\nwindow = pd.date_range(W0, W1, freq=\"MS\")\nlag = level.shift(12)\ng = 100.0 * (level / lag - 1.0)\ndf = pd.DataFrame({\"cpi\": level, \"cpi_lag12\": lag, \"vintage_used\": vint, \"growth_12m\": g}).loc[window]\nok = df[\"growth_12m\"].notna()\nanalytic = df[ok].copy()\nmissing_months = [d.strftime(\"%Y-%m\") for d in df.index[~ok]]\n\n# Exclusion ledger\nexcl = [\n    dict(step=1, rule=\"calendar months 2001-01..2026-09 (window)\", n_before=len(window), n_after=len(window),\n         n_removed=0, rationale=\"starting set: every month of the estimation window\"),\n]\nn_lvl = int(df[\"cpi\"].notna().sum())\nexcl.append(dict(step=2, rule=\"drop months with no index level P_t in latest covering vintage\",\n                 n_before=len(window), n_after=n_lvl, n_removed=len(window) - n_lvl,\n                 rationale=\"no interpolation/imputation allowed (e.g. 2025-10 empty in 2026-10-28 vintage)\"))\nn_ok = int(ok.sum())\nexcl.append(dict(step=3, rule=\"drop months with no lagged level P_{t-12}\", n_before=n_lvl, n_after=n_ok,\n                 n_removed=n_lvl - n_ok, rationale=\"g_t requires both P_t and P_{t-12}\"))\npd.DataFrame(excl).to_csv(\"out/exclusions.csv\", index=False)\n\nout_csv = analytic.reset_index().rename(columns={\"index\": \"date\"})\nout_csv[\"date\"] = out_csv[\"date\"].dt.strftime(\"%Y-%m-01\")\nout_csv.to_csv(\"out/analytic.csv\", index=False, float_format=\"%.10g\")\n\nG = analytic[\"growth_12m\"]\n\n\ndef gm(m):\n    t = pd.Timestamp(m + \"-01\")\n    v = G.get(t, np.nan)\n    return None if pd.isna(v) else round(float(v), 10)\n\n\ndef r(x):\n    return round(float(x), 10)\n\n\n# Data issues detected (computed, not hard-coded where possible)\nissues = []\nempty_new = [d.strftime(\"%Y-%m\") for d in nw.index[nw.isna()]]\nif empty_new:\n    issues.append({\"issue\": f\"{NEW}: empty cpi for {', '.join(empty_new)} (source reports no value)\",\n                   \"handling\": \"level treated as missing, not imputed; g_t for that month reported missing \"\n                               \"(and any month t whose t-12 is that month; inside the window: none)\"})\nov = o.index.intersection(nw.index)\ndif = (nw[ov] - o[ov]).dropna()\nif len(dif) and (dif.abs() > 1e-9).any():\n    issues.append({\"issue\": f\"vintages overlap for {len(ov)} months ({ov.min():%Y-%m}..{ov.max():%Y-%m}) with revised values \"\n                            f\"(max abs diff {dif.abs().max():.3f} index points)\",\n                   \"handling\": \"2026-10-28 vintage used for overlap months per latest-vintage rule\"})\na = asm.set_index(\"date\")\na_old = a[a[\"vintage\"].astype(str) == \"2016-07-15\"]\na_new = a[a[\"vintage\"].astype(str) == \"2026-10-28\"]\nasm_missing = [d.strftime(\"%Y-%m\") for d in months if d not in a.index]\nif (a_old.index >= nw.index.min()).any():\n    issues.append({\"issue\": f\"{ASM}: months {nw.index.min():%Y-%m}..{a_old.index.max():%Y-%m} taken from the older 2016-07-15 vintage although the 2026-10-28 vintage covers them\",\n                   \"handling\": \"assembled file not used; levels taken directly from the vintage files\"})\nif asm_missing:\n    issues.append({\"issue\": f\"{ASM}: month(s) {', '.join(asm_missing)} absent\",\n                   \"handling\": \"assembled file not used; levels taken directly from the vintage files\"})\nshift_match = (a_new[\"cpi\"].values[:-0 or None] is not None)\nnw_shift = nw.shift(1, freq=\"MS\")\ncommon = a_new.index.intersection(nw_shift.index)\nsame_shift = np.allclose(a_new.loc[common, \"cpi\"].fillna(-1), nw_shift.loc[common].fillna(-1))\nif same_shift:\n    issues.append({\"issue\": f\"{ASM}: rows labelled 2026-10-28 ({a_new.index.min():%Y-%m}..{a_new.index.max():%Y-%m}) are shifted one month late (value at month m equals the 2026-10-28 vintage value for m-1); file extends to {a.index.max():%Y-%m}, beyond the source\",\n                   \"handling\": \"assembled file not used; levels taken directly from the vintage files\"})\n\nlast_month = level.loc[window].dropna().index.max().strftime(\"%Y-%m\")\nimax, imin = G.idxmax(), G.idxmin()\nn_miss = len(window) - n_ok\nres = {\n    \"task_id\": \"F1-conf-01\",\n    \"first_growth_month\": G.index.min().strftime(\"%Y-%m\"),\n    \"last_month\": last_month,\n    \"n_months_growth\": n_ok,\n    \"n_months_missing_growth\": n_miss,\n    \"n_analysed\": n_ok,\n    \"mean_growth_12m\": r(G.mean()),\n    \"mean_growth_12m_2001_2013\": r(G[:\"2013-12-01\"].mean()),\n    \"mean_growth_12m_2014_2026\": r(G[\"2014-01-01\":].mean()),\n    \"growth_2014_06\": gm(\"2014-06\"),\n    \"growth_2016_07\": gm(\"2016-07\"),\n    \"growth_2020_05\": gm(\"2020-05\"),\n    \"growth_2022_06\": gm(\"2022-06\"),\n    \"growth_2026_09\": gm(\"2026-09\"),\n    \"max_growth_12m\": r(G.max()),\n    \"max_growth_month\": imax.strftime(\"%Y-%m\"),\n    \"min_growth_12m\": r(G.min()),\n    \"min_growth_month\": imin.strftime(\"%Y-%m\"),\n    \"feasibility\": \"complete\" if n_miss == 0 else \"partial\",\n    \"feasibility_reason\": None if n_miss == 0 else\n        f\"g_t not computable for {n_miss} month(s) of the window ({', '.join(missing_months)}): index level absent in the source; not imputed per the task rules\",\n    \"missing_growth_months\": missing_months,\n    \"n_months_window\": len(window),\n    \"n_months_growth_2001_2013\": int(G[:\"2013-12-01\"].notna().sum()),\n    \"n_months_growth_2014_2026\": int(G[\"2014-01-01\":].notna().sum()),\n    \"sd_growth_12m\": r(G.std(ddof=1)),\n    \"data_issues\": issues,\n    \"n_input_rows\": n_input_rows,\n    \"random_seed\": None,\n}\nwith open(\"out/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"n_analysed\", \"n_months_missing_growth\", \"mean_growth_12m\", \"feasibility\"]}))\nEOF\nsed -i '' '/shift_match = /d' out/analysis.py\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/exclusions.csv && head -3 out/analytic.csv && wc -l out/analytic.csv","description":"Write and run analysis 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e.n_after.iloc[-1]==r['n_analysed']))\nw=(156*r['mean_growth_12m_2001_2013']+152*r['mean_growth_12m_2014_2026'])/308\nc.append((\"overall mean = weighted sub-means\",abs(w-r['mean_growth_12m'])<1e-8))\ng=100*(a.cpi/a.cpi_lag12-1); c.append((\"growth recomputes from csv\",np.allclose(g,a.growth_12m,atol=1e-7)))\nc.append((\"min<=means<=max\",r['min_growth_12m']<=r['mean_growth_12m']<=r['max_growth_12m']))\nc.append((\"vintage_used 2026 for >=2014\",(a[a.date>='2014-01-01'].vintage_used=='2026-10-28').all() and (a[a.date<'2014-01-01'].vintage_used=='2016-07-15').all()))\nc.append((\"no 2025-10 in analytic\",'2025-10-01' not in set(a.date)))\nc.append((\"magnitudes plausible (-3<g<10)\",a.growth_12m.between(-3,10).all()))\nc.append((\"growth_2022_06 hand check 294.957/270.654\",abs(100*(294.957/270.654-1)-r['growth_2022_06'])<1e-8))\nfor n,ok in c: print(f\"- {n}: {'PASS' if ok else 'FAIL'}\")\nEOF","description":"Reproducibility and consistency 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analytic rows == n_analysed: PASS\n- n_growth+n_missing == 309: PASS\n- sub-period counts add: PASS\n- exclusion ledger rows consistent: PASS\n- overall mean = weighted sub-means: PASS\n- growth recomputes from csv: PASS\n- min<=means<=max: PASS\n- vintage_used 2026 for >=2014: PASS\n- no 2025-10 in analytic: PASS\n- magnitudes plausible (-3<g<10): PASS\n- growth_2022_06 hand check 294.957/270.654: PASS","is_error":false}]},"uuid":"5a9960e7-3557-456b-86d0-8700748255cd","timestamp":"2026-10-30T04:33:23.958Z","toolUseResult":{"stdout":"IDENTICAL\n- analytic rows == n_analysed: PASS\n- n_growth+n_missing == 309: PASS\n- sub-period counts add: PASS\n- exclusion ledger rows consistent: PASS\n- overall mean = weighted sub-means: PASS\n- growth recomputes from csv: PASS\n- min<=means<=max: PASS\n- vintage_used 2026 for >=2014: PASS\n- no 2025-10 in analytic: PASS\n- magnitudes plausible (-3<g<10): PASS\n- growth_2022_06 hand check 294.957/270.654: PASS","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"3108a8ea-ca93-4e2b-a671-4aa92c8313e2","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0105","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f","version":"2.1.314","gitBranch":"HEAD"}66{"type":"api-request-blob","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f","hash":"bf46d6f277db5f6da7bf77360e5b41a3bfe15331b2f3f9f4f3666b5404677670","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01KRswTTr5pePYGGhmte3RST","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; cp out/analytic.csv /tmp/a1.csv; rm out/results.json out/analytic.csv out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp out/results.json /tmp/r1.json && cmp out/analytic.csv /tmp/a1.csv && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport json,pandas as pd,numpy as np\nr=json.load(open('out/results.json')); a=pd.read_csv('out/analytic.csv'); e=pd.read_csv('out/exclusions.csv')\nc=[]\nc.append((\"analytic rows == n_analysed\",len(a)==r['n_analysed']))\nc.append((\"n_growth+n_missing == 309\",r['n_months_growth']+r['n_months_missing_growth']==309))\nc.append((\"sub-period counts add\",r['n_months_growth_2001_2013']+r['n_months_growth_2014_2026']==r['n_analysed']))\nc.append((\"exclusion ledger rows consistent\",all(e.n_before-e.n_removed==e.n_after) and all(e.n_before.iloc[1:].values==e.n_after.iloc[:-1].values) and e.n_after.iloc[-1]==r['n_analysed']))\nw=(156*r['mean_growth_12m_2001_2013']+152*r['mean_growth_12m_2014_2026'])/308\nc.append((\"overall mean = weighted sub-means\",abs(w-r['mean_growth_12m'])<1e-8))\ng=100*(a.cpi/a.cpi_lag12-1); c.append((\"growth recomputes from csv\",np.allclose(g,a.growth_12m,atol=1e-7)))\nc.append((\"min<=means<=max\",r['min_growth_12m']<=r['mean_growth_12m']<=r['max_growth_12m']))\nc.append((\"vintage_used 2026 for >=2014\",(a[a.date>='2014-01-01'].vintage_used=='2026-10-28').all() and (a[a.date<'2014-01-01'].vintage_used=='2016-07-15').all()))\nc.append((\"no 2025-10 in analytic\",'2025-10-01' not in set(a.date)))\nc.append((\"magnitudes plausible (-3<g<10)\",a.growth_12m.between(-3,10).all()))\nc.append((\"growth_2022_06 hand check 294.957/270.654\",abs(100*(294.957/270.654-1)-r['growth_2022_06'])<1e-8))\nfor n,ok in c: print(f\"- {n}: {'PASS' if ok else 'FAIL'}\")\nEOF","description":"Reproducibility and consistency checks"}}]}}67{"type":"api-request","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f","id":"a1e5a43a-0555-45de-8c23-25d333e6ba3b","timestamp":"2026-10-30T04:33:23.961Z","version":"2.1.314","querySource":"sdk","shapeHash":"9d4c2b4b808da87f5f3014d7dcaf3767f9b2cf99db791ca587fb3813c11301cc","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"9a169207-da05-4f97-b714-c8cd172e17c2","keep":20,"tail":["bf46d6f277db5f6da7bf77360e5b41a3bfe15331b2f3f9f4f3666b5404677670","82c7ee642b36458bc24e27705013e0f9b01e2fc74c02ef1b7fa775d212eed311"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",21,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"b6e2503282cb1c191c6037af50bf9ac1e3a893e3517915bbb4261da35c7b2375"}68{"parentUuid":"5a9960e7-3557-456b-86d0-8700748255cd","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoBu7RBciCaCy8D6MdZ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_019Lay5V32veMV3CNDaR7rhr","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4), 2026-10-30\n- Clean re-run (deleted outputs, re-ran analysis.py): results.json and analytic.csv byte-identical: PASS\n- analytic rows == n_analysed (308): PASS\n- n_growth + n_missing == 309 window months: PASS\n- sub-period counts 156 + 152 == 308: PASS\n- exclusion ledger n_before-n_removed=n_after, chained, ends at n_analysed: PASS\n- overall mean equals count-weighted sub-period means: PASS\n- growth_12m recomputes from cpi/cpi_lag12 in analytic.csv: PASS\n- min <= mean <= max: PASS\n- vintage_used = 2026-10-28 for months >= 2014-01, 2016-07-15 before: PASS\n- 2025-10 absent from analytic.csv (no imputation): PASS\n- magnitudes plausible (all g in (-3, 10) %): PASS\n- hand check growth_2022_06 = 100*(294.957/270.654-1), growth_2014_06 = 100*(237.231/232.357-1), min 2009-07 = 100*(214.726/219.016-1) from raw files: PASS\n- Baseline comparison: mean_growth_12m_2001_2013 (baseline, 2.36) vs 2014-2026 (2.79); overall 2.57 lies between: PASS\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np='out/state/phases.json'; s=json.load(open(p))\ns['P1']={\"status\":\"passed\",\"checks\":[\"sha256 computed & matched bundle.json\",\"row/col counts, gaps, dups, empties recorded\",\"assembled file compared to vintages: errors found, file excluded from computation\"],\"notes\":\"see data_audit.md\"}\ns['P2']={\"status\":\"passed\",\"checks\":[\"baseline mean_growth_12m_2001_2013 computed with same g_t rule\"],\"notes\":\"\"}\ns['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, deterministic, no RNG (random_seed null)\"],\"notes\":\"\"}\ns['P4']={\"status\":\"running\",\"checks\":[\"clean re-run identical\",\"11 consistency checks PASS (checks.md)\"],\"notes\":\"\"}\njson.dump(s,open(p,'w'),indent=1)\nEOF\necho \"- 2026-10-30 P2,P3 passed; P4 checks pass, running gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Record checks and run 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code 1\nPGAR GATE pgar_gate/1.0 — FAILED (10 of 40 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] file:out/analytic.csv: present\n  [ok] file:out/exclusions.csv: present\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:first_growth_month: ok\n  [ok] key:last_month: ok\n  [ok] key:n_months_growth: ok\n  [ok] key:n_months_missing_growth: ok\n  [ok] key:n_analysed: ok\n  [ok] key:mean_growth_12m: ok\n  [ok] key:mean_growth_12m_2001_2013: ok\n  [ok] key:mean_growth_12m_2014_2026: ok\n  [ok] key:growth_2014_06: ok\n  [ok] key:growth_2016_07: ok\n  [ok] key:growth_2020_05: ok\n  [ok] key:growth_2022_06: ok\n  [ok] key:growth_2026_09: ok\n  [ok] key:max_growth_12m: ok\n  [ok] key:max_growth_month: ok\n  [ok] key:min_growth_12m: ok\n  [ok] key:min_growth_month: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] exclusion_ledger: 3 steps consistent\n  [ok] reproducibility: regenerated in 0.3s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0105/out/state/gate_receipts/20261030T043332.546566Z.json","is_error":true,"tool_use_id":"toolu_019Lay5V32veMV3CNDaR7rhr"}]},"uuid":"0b4b10d2-795a-4fa0-85fe-aa2de7cbe511","timestamp":"2026-10-30T04:33:32.553Z","toolUseResult":"Error: Exit code 1\nPGAR GATE pgar_gate/1.0 — FAILED (10 of 40 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] file:out/analytic.csv: present\n  [ok] file:out/exclusions.csv: present\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:first_growth_month: ok\n  [ok] key:last_month: ok\n  [ok] key:n_months_growth: ok\n  [ok] key:n_months_missing_growth: ok\n  [ok] key:n_analysed: ok\n  [ok] key:mean_growth_12m: ok\n  [ok] key:mean_growth_12m_2001_2013: ok\n  [ok] key:mean_growth_12m_2014_2026: ok\n  [ok] key:growth_2014_06: ok\n  [ok] key:growth_2016_07: ok\n  [ok] key:growth_2020_05: ok\n  [ok] key:growth_2022_06: ok\n  [ok] key:growth_2026_09: ok\n  [ok] key:max_growth_12m: ok\n  [ok] key:max_growth_month: ok\n  [ok] key:min_growth_12m: ok\n  [ok] key:min_growth_month: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] exclusion_ledger: 3 steps consistent\n  [ok] reproducibility: regenerated in 0.3s; identical within tolerance\nreceipt: 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n_missing == 309 window months: PASS\n- sub-period counts 156 + 152 == 308: PASS\n- exclusion ledger n_before-n_removed=n_after, chained, ends at n_analysed: PASS\n- overall mean equals count-weighted sub-period means: PASS\n- growth_12m recomputes from cpi/cpi_lag12 in analytic.csv: PASS\n- min <= mean <= max: PASS\n- vintage_used = 2026-10-28 for months >= 2014-01, 2016-07-15 before: PASS\n- 2025-10 absent from analytic.csv (no imputation): PASS\n- magnitudes plausible (all g in (-3, 10) %): PASS\n- hand check growth_2022_06 = 100*(294.957/270.654-1), growth_2014_06 = 100*(237.231/232.357-1), min 2009-07 = 100*(214.726/219.016-1) from raw files: PASS\n- Baseline comparison: mean_growth_12m_2001_2013 (baseline, 2.36) vs 2014-2026 (2.79); overall 2.57 lies between: PASS\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np='out/state/phases.json'; s=json.load(open(p))\ns['P1']={\"status\":\"passed\",\"checks\":[\"sha256 computed & matched bundle.json\",\"row/col counts, gaps, dups, empties recorded\",\"assembled file compared to vintages: errors found, file excluded from computation\"],\"notes\":\"see data_audit.md\"}\ns['P2']={\"status\":\"passed\",\"checks\":[\"baseline mean_growth_12m_2001_2013 computed with same g_t rule\"],\"notes\":\"\"}\ns['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, deterministic, no RNG (random_seed null)\"],\"notes\":\"\"}\ns['P4']={\"status\":\"running\",\"checks\":[\"clean re-run identical\",\"11 consistency checks PASS (checks.md)\"],\"notes\":\"\"}\njson.dump(s,open(p,'w'),indent=1)\nEOF\necho \"- 2026-10-30 P2,P3 passed; P4 checks pass, running gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Record checks and run gate"}}]}}71{"type":"api-request","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f","id":"d2ea66ee-1577-4877-8886-0744764300c1","timestamp":"2026-10-30T04:33:32.558Z","version":"2.1.314","querySource":"sdk","shapeHash":"9d4c2b4b808da87f5f3014d7dcaf3767f9b2cf99db791ca587fb3813c11301cc","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"a1e5a43a-0555-45de-8c23-25d333e6ba3b","keep":22,"tail":["7321ab3d021a5f412f9a8aae822a6ac97316651daafd6959c6930819d3f1bed7","5b0523715db316afbe19ab66b39333c210cc11fc3a04c4b9b2d5ee08607cd488"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",23,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"821f55d99fc4e0a1fdb74cfb6c9a6627196e6b691c339d2503a8175550ebebc9"}72{"parentUuid":"0b4b10d2-795a-4fa0-85fe-aa2de7cbe511","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoCXsVv2ZRCKa54X6Ji","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1821,"cache_read_input_tokens":22489,"output_tokens":3088,"output_tokens_details":{"thinking_tokens":45},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1821},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":397,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXoCXb8zntnSeavNaDoJ","requestRef":"d2ea66ee-1577-4877-8886-0744764300c1","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"48cc9f57-58f3-4fa0-b796-80b35551afdd","timestamp":"2026-10-30T04:33:34.267Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0105","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f","version":"2.1.314","gitBranch":"HEAD"}73{"parentUuid":"48cc9f57-58f3-4fa0-b796-80b35551afdd","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoCXsVv2ZRCKa54X6Ji","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017chDcH85d3mCXv7xJgie6c","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F1-conf-01 — 12-month CPI inflation from two data vintages\n\n## Question\n12-month growth g_t = 100×(P_t/P_{t−12} − 1) of CPIAUCSL (SA) for every month 2001-01 … 2026-09, with P_t taken from the most recent vintage covering month t (2026-10-28 vintage for 2014-01 … 2026-09, 2016-07-15 vintage before), plus summaries.\n\n## Data and exclusions\nInputs read (`n_input_rows`): `cpi_vintage_2016-07-15.csv` (834 rows), `cpi_vintage_2026-10-28.csv` (153 rows), `cpi_assembled.csv` (957 rows, read only for audit). Hashes and schemas are in `out/state/data_audit.md`. Problems found (`data_issues`):\n- The 2026-10-28 vintage has an empty value for 2025-10. I left it missing and did not impute it.\n- The two vintages overlap for 2014-01 … 2016-06 and the values differ because of revisions. For these months I used the 2026-10-28 values.\n- `cpi_assembled.csv` has assembly errors: it takes the old vintage for 2014-01 … 2016-06, it has no row for 2016-07, and every row from 2016-08 on is placed one month late (the file runs to 2026-10 and the empty cell sits at 2025-11). So I did not use it for any computation. All levels come from the two vintage files.\n\nExclusion ledger (`out/exclusions.csv`): the 309 window months, minus 1 month with no level P_t (2025-10), minus 0 months with no lagged level, leaves 308 analysed months (`n_analysed`, `n_months_growth`; `n_months_missing_growth` = 1; `missing_growth_months`).\n\n## Method\n`out/analysis.py` builds one monthly level series. It fills months from the 2016-07-15 vintage first and then overwrites them with every month that appears in the 2026-10-28 extract, keeping the empty cell as missing. It then computes g_t as defined above and takes simple means and extremes over the computable months. The method uses no randomness (`random_seed` = null). The baseline is the 2001–2013 sub-period mean (`mean_growth_12m_2001_2013`), computed with the same rule. For the 12 months 2014-01 … 2014-12, P_t comes from the 2026 vintage and P_{t−12} from the 2016 vintage (`n_months_mixed_vintage`). The latest-vintage rule requires this.\n\n## Results\n- First growth month `first_growth_month` = 2001-01; last month with a level `last_month` = 2026-09.\n- Mean 12-month growth over the window: 2.574 % (`mean_growth_12m`). For 2001-01 … 2013-12 it is 2.361 % (`mean_growth_12m_2001_2013`, 156 months, `n_months_growth_2001_2013`), and for 2014-01 … 2026-09 it is 2.793 % (`mean_growth_12m_2014_2026`, 152 months, `n_months_growth_2014_2026`).\n- Named months: 2014-06 2.098 % (`growth_2014_06`), 2016-07 0.868 % (`growth_2016_07`), 2020-05 0.198 % (`growth_2020_05`), 2022-06 8.979 % (`growth_2022_06`), 2026-09 3.717 % (`growth_2026_09`).\n- Maximum 8.979 % in 2022-06 (`max_growth_12m`, `max_growth_month`); minimum −1.959 % in 2009-07 (`min_growth_12m`, `min_growth_month`).\n- `feasibility` = \"partial\": g_t cannot be computed for 2025-10 (`feasibility_reason`).\n\n## Uncertainty\nThese are exact descriptive quantities computed from published index levels. There is no sampling uncertainty to report. The standard deviation of g_t over the window is 1.741 pp (`sd_growth_12m`); it describes how much inflation varied, not how precise the mean is. The main remaining sensitivity is data revision. Within the 2014-01 … 2016-06 overlap, the vintages differ by up to 0.455 index points (`data_issues`), so values and means that rely on the 2016 vintage (pre-2014 levels) may differ somewhat from what current data would give. I did not quantify this sensitivity.\n\n## Limitations and open issues\n- 2025-10 has no published level in the source, so its g_t is missing and the window mean covers 308 of 309 months (`n_months_window`). With no imputation, 2026-10 would also lack a computable g_t, but it falls outside the window.\n- Levels before 2014 come from the 2016-07-15 vintage. Later seasonal-factor revisions to those months are not reflected because the provided 2026 extract starts at 2014-01.\n- In 12 months (2014) the two levels in the ratio come from different vintages (`n_months_mixed_vintage`). The task's rule requires this.\n- The hand-assembled file was set aside because of the errors listed above. Using it would have shifted every post-2016 level by one month.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Over 2001-01..2026-09, 308 of 309 months have a computable 12-month growth rate; 2025-10 is missing because the source publishes no level.\",\"results_keys\":[\"n_analysed\",\"n_months_growth\",\"n_months_missing_growth\",\"missing_growth_months\",\"n_months_window\",\"feasibility\",\"feasibility_reason\"],\"status\":\"supported\",\"caveat\":\"no imputation, per task rules\"},\n {\"id\":\"C2\",\"text\":\"Mean 12-month CPI inflation over the window is about 2.57%, 2.36% in 2001-2013 and 2.79% in 2014-2026.\",\"results_keys\":[\"mean_growth_12m\",\"mean_growth_12m_2001_2013\",\"mean_growth_12m_2014_2026\",\"n_months_growth_2001_2013\",\"n_months_growth_2014_2026\"],\"status\":\"supported\",\"caveat\":\"pre-2014 levels from the 2016 vintage; 2014-2026 mean excludes 2025-10\"},\n {\"id\":\"C3\",\"text\":\"Maximum 12-month growth is 8.98% in 2022-06; minimum is -1.96% in 2009-07.\",\"results_keys\":[\"max_growth_12m\",\"max_growth_month\",\"min_growth_12m\",\"min_growth_month\",\"growth_2022_06\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C4\",\"text\":\"Named-month growth rates: 2014-06 2.10%, 2016-07 0.87%, 2020-05 0.20%, 2026-09 3.72%.\",\"results_keys\":[\"growth_2014_06\",\"growth_2016_07\",\"growth_2020_05\",\"growth_2026_09\"],\"status\":\"supported\",\"caveat\":\"2014-06 mixes the 2026 vintage (t) and 2016 vintage (t-12)\"},\n {\"id\":\"C5\",\"text\":\"The hand-assembled file contains assembly errors (old vintage for 2014-2016, missing 2016-07, one-month shift from 2016-08) and was not used for computation.\",\"results_keys\":[\"data_issues\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C6\",\"text\":\"Twelve months (2014) use levels from different vintages for t and t-12.\",\"results_keys\":[\"n_months_mixed_vintage\"],\"status\":\"supported\",\"caveat\":\"required by the latest-vintage rule\"},\n {\"id\":\"C7\",\"text\":\"Standard deviation of 12-month growth over the window is about 1.74 pp.\",\"results_keys\":[\"sd_growth_12m\"],\"status\":\"supported\",\"caveat\":\"descriptive dispersion, not sampling uncertainty\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np='out/state/phases.json'; s=json.load(open(p))\ns['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run identical (cmp)\",\"11 consistency checks PASS (checks.md)\",\"gate run: receipt out/state/gate_receipts/20261030T043332.546566Z.json — passed=false only for P5 artifacts (report, claims, phase terminal); reproducibility & exclusion ledger ok\"],\"notes\":\"\"}\ns['P5']={\"status\":\"passed\",\"checks\":[\"report.md with 6 sections, each number cites a results key\",\"claims.json written, keys exist\",\"final gate run (see receipt list)\"],\"notes\":\"\"}\njson.dump(s,open(p,'w'),indent=1)\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v \"\\[ok\\]\"","description":"Write 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> out/report.md <<'EOF'\n# F1-conf-01 — 12-month CPI inflation from two data vintages\n\n## Question\n12-month growth g_t = 100×(P_t/P_{t−12} − 1) of CPIAUCSL (SA) for every month 2001-01 … 2026-09, with P_t taken from the most recent vintage covering month t (2026-10-28 vintage for 2014-01 … 2026-09, 2016-07-15 vintage before), plus summaries.\n\n## Data and exclusions\nInputs read (`n_input_rows`): `cpi_vintage_2016-07-15.csv` (834 rows), `cpi_vintage_2026-10-28.csv` (153 rows), `cpi_assembled.csv` (957 rows, read only for audit). Hashes and schemas are in `out/state/data_audit.md`. Problems found (`data_issues`):\n- The 2026-10-28 vintage has an empty value for 2025-10. I left it missing and did not impute it.\n- The two vintages overlap for 2014-01 … 2016-06 and the values differ because of revisions. For these months I used the 2026-10-28 values.\n- `cpi_assembled.csv` has assembly errors: it takes the old vintage for 2014-01 … 2016-06, it has no row for 2016-07, and every row from 2016-08 on is placed one month late (the file runs to 2026-10 and the empty cell sits at 2025-11). So I did not use it for any computation. All levels come from the two vintage files.\n\nExclusion ledger (`out/exclusions.csv`): the 309 window months, minus 1 month with no level P_t (2025-10), minus 0 months with no lagged level, leaves 308 analysed months (`n_analysed`, `n_months_growth`; `n_months_missing_growth` = 1; `missing_growth_months`).\n\n## Method\n`out/analysis.py` builds one monthly level series. It fills months from the 2016-07-15 vintage first and then overwrites them with every month that appears in the 2026-10-28 extract, keeping the empty cell as missing. It then computes g_t as defined above and takes simple means and extremes over the computable months. The method uses no randomness (`random_seed` = null). The baseline is the 2001–2013 sub-period mean (`mean_growth_12m_2001_2013`), computed with the same rule. For the 12 months 2014-01 … 2014-12, P_t comes from the 2026 vintage and P_{t−12} from the 2016 vintage (`n_months_mixed_vintage`). The latest-vintage rule requires this.\n\n## Results\n- First growth month `first_growth_month` = 2001-01; last month with a level `last_month` = 2026-09.\n- Mean 12-month growth over the window: 2.574 % (`mean_growth_12m`). For 2001-01 … 2013-12 it is 2.361 % (`mean_growth_12m_2001_2013`, 156 months, `n_months_growth_2001_2013`), and for 2014-01 … 2026-09 it is 2.793 % (`mean_growth_12m_2014_2026`, 152 months, `n_months_growth_2014_2026`).\n- Named months: 2014-06 2.098 % (`growth_2014_06`), 2016-07 0.868 % (`growth_2016_07`), 2020-05 0.198 % (`growth_2020_05`), 2022-06 8.979 % (`growth_2022_06`), 2026-09 3.717 % (`growth_2026_09`).\n- Maximum 8.979 % in 2022-06 (`max_growth_12m`, `max_growth_month`); minimum −1.959 % in 2009-07 (`min_growth_12m`, `min_growth_month`).\n- `feasibility` = \"partial\": g_t cannot be computed for 2025-10 (`feasibility_reason`).\n\n## Uncertainty\nThese are exact descriptive quantities computed from published index levels. There is no sampling uncertainty to report. The standard deviation of g_t over the window is 1.741 pp (`sd_growth_12m`); it describes how much inflation varied, not how precise the mean is. The main remaining sensitivity is data revision. Within the 2014-01 … 2016-06 overlap, the vintages differ by up to 0.455 index points (`data_issues`), so values and means that rely on the 2016 vintage (pre-2014 levels) may differ somewhat from what current data would give. I did not quantify this sensitivity.\n\n## Limitations and open issues\n- 2025-10 has no published level in the source, so its g_t is missing and the window mean covers 308 of 309 months (`n_months_window`). With no imputation, 2026-10 would also lack a computable g_t, but it falls outside the window.\n- Levels before 2014 come from the 2016-07-15 vintage. Later seasonal-factor revisions to those months are not reflected because the provided 2026 extract starts at 2014-01.\n- In 12 months (2014) the two levels in the ratio come from different vintages (`n_months_mixed_vintage`). The task's rule requires this.\n- The hand-assembled file was set aside because of the errors listed above. Using it would have shifted every post-2016 level by one month.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Over 2001-01..2026-09, 308 of 309 months have a computable 12-month growth rate; 2025-10 is missing because the source publishes no level.\",\"results_keys\":[\"n_analysed\",\"n_months_growth\",\"n_months_missing_growth\",\"missing_growth_months\",\"n_months_window\",\"feasibility\",\"feasibility_reason\"],\"status\":\"supported\",\"caveat\":\"no imputation, per task rules\"},\n {\"id\":\"C2\",\"text\":\"Mean 12-month CPI inflation over the window is about 2.57%, 2.36% in 2001-2013 and 2.79% in 2014-2026.\",\"results_keys\":[\"mean_growth_12m\",\"mean_growth_12m_2001_2013\",\"mean_growth_12m_2014_2026\",\"n_months_growth_2001_2013\",\"n_months_growth_2014_2026\"],\"status\":\"supported\",\"caveat\":\"pre-2014 levels from the 2016 vintage; 2014-2026 mean excludes 2025-10\"},\n {\"id\":\"C3\",\"text\":\"Maximum 12-month growth is 8.98% in 2022-06; minimum is -1.96% in 2009-07.\",\"results_keys\":[\"max_growth_12m\",\"max_growth_month\",\"min_growth_12m\",\"min_growth_month\",\"growth_2022_06\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C4\",\"text\":\"Named-month growth rates: 2014-06 2.10%, 2016-07 0.87%, 2020-05 0.20%, 2026-09 3.72%.\",\"results_keys\":[\"growth_2014_06\",\"growth_2016_07\",\"growth_2020_05\",\"growth_2026_09\"],\"status\":\"supported\",\"caveat\":\"2014-06 mixes the 2026 vintage (t) and 2016 vintage (t-12)\"},\n {\"id\":\"C5\",\"text\":\"The hand-assembled file contains assembly errors (old vintage for 2014-2016, missing 2016-07, one-month shift from 2016-08) and was not used for computation.\",\"results_keys\":[\"data_issues\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C6\",\"text\":\"Twelve months (2014) use levels from different vintages for t and t-12.\",\"results_keys\":[\"n_months_mixed_vintage\"],\"status\":\"supported\",\"caveat\":\"required by the latest-vintage rule\"},\n {\"id\":\"C7\",\"text\":\"Standard deviation of 12-month growth over the window is about 1.74 pp.\",\"results_keys\":[\"sd_growth_12m\"],\"status\":\"supported\",\"caveat\":\"descriptive dispersion, not sampling uncertainty\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np='out/state/phases.json'; s=json.load(open(p))\ns['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run identical (cmp)\",\"11 consistency checks PASS (checks.md)\",\"gate run: receipt out/state/gate_receipts/20261030T043332.546566Z.json — passed=false only for P5 artifacts (report, claims, phase terminal); reproducibility & exclusion ledger ok\"],\"notes\":\"\"}\ns['P5']={\"status\":\"passed\",\"checks\":[\"report.md with 6 sections, each number cites a results key\",\"claims.json written, keys exist\",\"final gate run (see receipt list)\"],\"notes\":\"\"}\njson.dump(s,open(p,'w'),indent=1)\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v \"\\[ok\\]\"","description":"Write report, claims, finalize ledger, run gate"}}]}}78{"type":"api-request","sessionId":"851ae3fd-4879-45f5-b2ed-80e2c2a93c1f","id":"ee9e3b76-de4c-4469-9a69-70e591e55ad1","timestamp":"2026-10-30T04:33:58.087Z","version":"2.1.314","querySource":"sdk","shapeHash":"9d4c2b4b808da87f5f3014d7dcaf3767f9b2cf99db791ca587fb3813c11301cc","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"d2ea66ee-1577-4877-8886-0744764300c1","keep":24,"tail":["31afd263ce5b25788ef14333c6a0a8186a06a7fcf47170ecc17c439df4e91eac","c75a755abf4b2f26085f4966145c04c77ef5505ec3338ca2e299e360a89f7aeb"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",25,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"a9ddd14e8b565440736337ec9f992023a6633a191213afdb8019016e856a8c2a"}79{"parentUuid":"706990cd-5ca8-47e4-8502-77a0b615b251","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoEQu74gZJAhubhRMhc","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01RG7uyTReAneaBHtixoYHpa","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np='out/state/phases.json'; 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The result is \"partial\": 308 of the 309 months have a growth rate, because October 2025 has no published index value. The final gate check passed, and re-running `out/analysis.py` from scratch reproduces the results exactly.\n\n**Main figures**\n- **Average growth over the whole window:** 2.574%.\n- **By sub-period:** 2.361% for 2001–2013 (156 months) and 2.793% for 2014–2026 (152 months).\n- **Highest:** 8.979% in 2022-06.\n- **Lowest:** −1.959% in 2009-07.\n- **Named months:**\n\n| Month | 12-month growth |\n|---|---|\n| 2014-06 | 2.098% |\n| 2016-07 | 0.868% |\n| 2020-05 | 0.198% |\n| 2022-06 | 8.979% |\n| 2026-09 | 3.717% |\n\n**Data problems and how I handled them**\n- **October 2025 is blank in the 2026 vintage.** As the task rules require, I didn't fill it in, so that month has no growth rate. September 2026 is the last month with an index value.\n- **The two vintages overlap for 2014-01 to 2016-06 and disagree** by up to 0.455 index points, because of later revisions. I used the newer 2026 values for those months.\n- **The hand-assembled file (`cpi_assembled.csv`) has errors, so I didn't use it for any calculation.**\n  - It uses old-vintage values for 2014–2016.\n  - It has no row for July 2016.\n  - From August 2016 on, every value sits one month late, which is why it runs to October 2026 and shows the blank at November 2025.\n  - All index values came directly from the two vintage files instead.\n\n**Limitations**\n- Index values before 2014 come from the 2016 vintage, because the 2026 file only starts in 2014. Any later revisions to those earlier months aren't reflected, and I didn't estimate how much that might change the results.\n- For the 12 months of 2014, the current month comes from the 2026 vintage but the month a year earlier comes from the 2016 vintage. The task's \"latest vintage\" rule requires this.\n- These are exact calculations on published figures, so there is no sampling uncertainty to report.\n\nEverything is in `out/`. Open `report.md` first; `analysis.py` regenerates `results.json`, `analytic.csv` and `exclusions.csv`. 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