1{"type":"atis-latch","atis":"","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T04:30:10.501Z","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T04:30:10.506Z","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56"}4{"parentUuid":null,"isSidechain":false,"promptId":"8f565a57-70f1-433b-b044-ef7f6d25465c","type":"user","message":{"role":"user","content":"Begin"},"uuid":"e2a56a68-efca-48d2-ba28-3353e532e6a6","timestamp":"2026-10-30T04:30:10.509Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","shapeHash":"feb5c91d53bbada2e1da572f056120317de4cfdbadce4316c811c4f5cc3165bd","timestamp":"2026-10-30T04:30:10.520Z","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/RC0100\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\nSEPARATE REVIEW (additional)\n- After the gate passes, a separate reviewer session that has not seen your work in progress will examine `TASK.md` and everything under `out/` and may return a list of issues (file `out/review/review.json`). You will then be resumed once to address them: fix what is justified, explain in `out/report.md` (Limitations and open issues) what you disagree with, re-run the gate, and update `out/claims.json` and `out/state/phases.json`. The reviewer has no reference answers; its role is to challenge unsupported claims, violated information rules, unverified computations, and inconsistencies between your report and your results.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. Never use words like \"complex\" or \"risk\" in the description - just describe what it does.\n\nSay what the command does in plain words: do not echo the command's text, its flags, or file paths - the user reads this description, often without seeing the command.\n\nFor simple commands (git, npm, standard CLI tools), keep it brief (5-10 words):\n- ls → \"List files in current directory\"\n- git status → \"Show working tree status\"\n- npm install → \"Install package dependencies\"\n\nFor commands that are harder to parse at a glance (piped commands, obscure flags, etc.), add enough context to clarify what it does:\n- find . -name \"*.tmp\" -exec rm {} \\; → \"Find and delete all .tmp files recursively\"\n- git reset --hard origin/main → \"Discard all local changes and match remote main\"\n- curl -s url | jq '.data[]' → \"Fetch JSON from URL and extract data array elements\"","type":"string"},"dangerouslyDisableSandbox":{"description":"Set this to true to dangerously override sandbox mode and run commands without sandboxing.","type":"boolean"}},"required":["command"],"additionalProperties":false}},{"name":"Edit","description":"modify file contents in place","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"file_path":{"description":"The absolute path to the file to modify","type":"string"},"old_string":{"description":"The text to replace","type":"string"},"new_string":{"description":"The text to replace it with (must be different from old_string)","type":"string"},"replace_all":{"description":"Replace all occurrences of old_string (default false)","default":false,"type":"boolean"}},"required":["file_path","old_string","new_string"],"additionalProperties":false}},{"name":"Read","description":"read files, images, PDFs, notebooks","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"file_path":{"description":"The absolute path to the file to read","type":"string"},"offset":{"description":"The line number to start reading from. Only provide if the file is too large to read at once","type":"integer","minimum":0,"maximum":9007199254740991},"limit":{"description":"The number of lines to read. Only provide if the file is too large to read at once.","type":"integer","exclusiveMinimum":0,"maximum":9007199254740991},"pages":{"description":"Page range for PDF files (e.g., \"1-5\", \"3\", \"10-20\"). Only applicable to PDF files. Maximum 20 pages per request.","type":"string"},"allow_large":{"description":"Set to true to read a text file, or a line range of one, that is over the usual size limits, up to what still fits in your context. Only use this when you genuinely need all of it or the user asked for the whole file; otherwise read it in parts with offset and limit, or search it.","type":"boolean"}},"required":["file_path"],"additionalProperties":false}}]}}6{"type":"api-request-blob","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","hash":"d43ab0e0b8ec9db790a6459cb2f4bc4ffce02825ca05063162023be60912ced9","message":{"role":"system","content":[],"output_config":{"effort":"medium"}}}7{"type":"api-request","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","id":"73a12abf-cdac-42a1-8e7e-20ac8561c996","timestamp":"2026-10-30T04:30:10.520Z","version":"2.1.314","querySource":"sdk","shapeHash":"feb5c91d53bbada2e1da572f056120317de4cfdbadce4316c811c4f5cc3165bd","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"}},"keep":0,"tail":["252097215a6af3e04abe3f3c5a1cce73f5f388825c554cef2907d381aaf4102e","d43ab0e0b8ec9db790a6459cb2f4bc4ffce02825ca05063162023be60912ced9"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",0,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"acfd8c7f093a08a986a364589040896b968e00dcf4ed443670906e229949dcd5"}8{"parentUuid":"e2a56a68-efca-48d2-ba28-3353e532e6a6","isSidechain":false,"attachment":{"type":"session_context","context":{}},"type":"attachment","uuid":"7ad73f62-28e3-4e10-9ca4-bb0ff0944fe9","timestamp":"2026-10-30T04:30:10.513Z","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}9{"parentUuid":"7ad73f62-28e3-4e10-9ca4-bb0ff0944fe9","isSidechain":false,"attachment":{"type":"date","date":"2026-10-30"},"type":"attachment","uuid":"9c697822-bbc7-4252-b554-cd47c46fc443","timestamp":"2026-10-30T04:30:10.513Z","rendered":[{"content":"<system-reminder>\nToday's date is 2026-10-30.\n</system-reminder>"}],"renderedRole":"system","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}10{"parentUuid":"9c697822-bbc7-4252-b554-cd47c46fc443","isSidechain":false,"attachment":{"type":"remote_session_change","url":null,"commit":"Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>","pr":"🤖 Generated with [Claude Code](https://claude.com/claude-code)","sendUserFileHint":false,"managedCommit":false,"managedPr":false},"type":"attachment","uuid":"e35eca75-bc34-4c25-97ca-c76f53992de1","timestamp":"2026-10-30T04:30:10.514Z","rendered":[{"content":"<system-reminder>\nAttribution for git commits and pull requests you create from here on (this replaces Claude Code's own earlier attribution guidance, such as a previous copy of this reminder; 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/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56"}12{"parentUuid":"e35eca75-bc34-4c25-97ca-c76f53992de1","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnwem5JiNrPZZTvoBSC","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":2811,"cache_read_input_tokens":1504,"output_tokens":134,"output_tokens_details":{"thinking_tokens":19},"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":2811},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":426,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXnweVCLbCFRvhnC9nDv","requestRef":"73a12abf-cdac-42a1-8e7e-20ac8561c996","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e5eaf2dd-0021-41e6-b54b-51a4cf13cabc","timestamp":"2026-10-30T04:30:12.032Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"e5eaf2dd-0021-41e6-b54b-51a4cf13cabc","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnwem5JiNrPZZTvoBSC","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01BM6BePoFMrsWhyjNRDUpmx","name":"Bash","input":{"command":"cat TASK.md; ls -R | head -50; ls out/state 2>/dev/null && cat out/state/log.md","description":"Read task and list workspace"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":2811,"cache_read_input_tokens":1504,"output_tokens":134,"output_tokens_details":{"thinking_tokens":19},"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":2811},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXnweVCLbCFRvhnC9nDv","requestRef":"73a12abf-cdac-42a1-8e7e-20ac8561c996","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d3d6e7b4-01b0-4c11-a3ff-ddb8f64c274a","timestamp":"2026-10-30T04:30:12.734Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}14{"parentUuid":"d3d6e7b4-01b0-4c11-a3ff-ddb8f64c274a","isSidechain":false,"promptId":"8f565a57-70f1-433b-b044-ef7f6d25465c","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"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.\nbundle.json\ndata\ngate_spec.json\nTASK.md\n\n./data:\ncpi_assembled.csv\ncpi_vintage_2016-07-15.csv\ncpi_vintage_2026-10-28.csv\nREADME.md","is_error":true,"tool_use_id":"toolu_01BM6BePoFMrsWhyjNRDUpmx"}]},"uuid":"dad84bb2-feac-4960-a57c-7a5c9d341f54","timestamp":"2026-10-30T04:30:12.816Z","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.\nbundle.json\ndata\ngate_spec.json\nTASK.md\n\n./data:\ncpi_assembled.csv\ncpi_vintage_2016-07-15.csv\ncpi_vintage_2026-10-28.csv\nREADME.md","sandboxed":false,"sourceToolAssistantUUID":"d3d6e7b4-01b0-4c11-a3ff-ddb8f64c274a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}15{"type":"api-request-blob","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","hash":"90b5865e387d02154d7f2237607e35a9c0a990f7fa48eb7259fb5b4e12a80588","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01BM6BePoFMrsWhyjNRDUpmx","name":"Bash","input":{"command":"cat TASK.md; ls -R | head -50; ls out/state 2>/dev/null && cat out/state/log.md","description":"Read task and list workspace"}}]}}16{"type":"api-request","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","id":"72cac84f-9104-4c3a-bdd9-e6c0a92ab649","timestamp":"2026-10-30T04:30:12.819Z","version":"2.1.314","querySource":"sdk","shapeHash":"feb5c91d53bbada2e1da572f056120317de4cfdbadce4316c811c4f5cc3165bd","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":"73a12abf-cdac-42a1-8e7e-20ac8561c996","keep":2,"tail":["90b5865e387d02154d7f2237607e35a9c0a990f7fa48eb7259fb5b4e12a80588","218207b11f659ff72975a87171a681d4248e424771da62db24756dd722c23153"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"3141ba9825d77d3121645e81c9be7c2ee2e918065d3aea85dd8be7dfb4ed771a"}17{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"dad84bb2-feac-4960-a57c-7a5c9d341f54","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56"}18{"type":"atis-latch","atis":"","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56"}19{"parentUuid":"dad84bb2-feac-4960-a57c-7a5c9d341f54","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnwp5ehvcGvdkdP8nRY","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011rLND39H1hKT43SB3yTetm","name":"Bash","input":{"command":"cat data/README.md; cat gate_spec.json | head -50; cd data; sha256sum * 2>/dev/null || shasum -a 256 *; wc -l *.csv; cat cpi_vintage_2026-10-28.csv; head -3 cpi_vintage_2016-07-15.csv; tail -3 cpi_vintage_2016-07-15.csv","description":"Inspect data files and readme"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2175,"cache_read_input_tokens":4315,"output_tokens":742,"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":2175},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXnwono4xcUsN3Tbpfau","requestRef":"72cac84f-9104-4c3a-bdd9-e6c0a92ab649","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"07a7c7d4-6ee8-4bfd-a1fd-44c1144fc1f6","timestamp":"2026-10-30T04:30:15.245Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100/data","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}20{"parentUuid":"07a7c7d4-6ee8-4bfd-a1fd-44c1144fc1f6","isSidechain":false,"promptId":"8f565a57-70f1-433b-b044-ef7f6d25465c","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011rLND39H1hKT43SB3yTetm","type":"tool_result","content":"# 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}\nff732b201a525d6fb171d4e8b2d470f986863c1547a1928e42bb197c0777d44d cpi_assembled.csv\n281fbcdd6a6eeb3e9cb7c1f20476e6bd1f7b925bf140606fe46538fe83f2c83e cpi_vintage_2016-07-15.csv\n8220d5b4d70ca32fc2c7b1975651c515967db8a87053cf725159c63ed274cfba cpi_vintage_2026-10-28.csv\nac136bff13bbdb5afac60e0404212ea1f73f992bd5cca1c1e7a6599df78f8965 README.md\n 958 cpi_assembled.csv\n 835 cpi_vintage_2016-07-15.csv\n 154 cpi_vintage_2026-10-28.csv\n 1947 total\ndate,cpi\n2014-01-01,235.288\n2014-02-01,235.547\n2014-03-01,236.028\n2014-04-01,236.468\n2014-05-01,236.918\n2014-06-01,237.231\n2014-07-01,237.498\n2014-08-01,237.460\n2014-09-01,237.477\n2014-10-01,237.430\n2014-11-01,236.983\n2014-12-01,236.252\n2015-01-01,234.747\n2015-02-01,235.342\n2015-03-01,235.976\n2015-04-01,236.222\n2015-05-01,237.001\n2015-06-01,237.657\n2015-07-01,238.034\n2015-08-01,238.033\n2015-09-01,237.498\n2015-10-01,237.733\n2015-11-01,238.017\n2015-12-01,237.761\n2016-01-01,237.652\n2016-02-01,237.336\n2016-03-01,238.080\n2016-04-01,238.992\n2016-05-01,239.557\n2016-06-01,240.222\n2016-07-01,240.101\n2016-08-01,240.545\n2016-09-01,241.176\n2016-10-01,241.741\n2016-11-01,242.026\n2016-12-01,242.637\n2017-01-01,243.618\n2017-02-01,244.006\n2017-03-01,243.892\n2017-04-01,244.193\n2017-05-01,244.004\n2017-06-01,244.163\n2017-07-01,244.243\n2017-08-01,245.183\n2017-09-01,246.435\n2017-10-01,246.626\n2017-11-01,247.284\n2017-12-01,247.805\n2018-01-01,248.859\n2018-02-01,249.529\n2018-03-01,249.577\n2018-04-01,250.227\n2018-05-01,250.792\n2018-06-01,251.018\n2018-07-01,251.214\n2018-08-01,251.663\n2018-09-01,252.182\n2018-10-01,252.772\n2018-11-01,252.594\n2018-12-01,252.767\n2019-01-01,252.561\n2019-02-01,253.319\n2019-03-01,254.277\n2019-04-01,255.233\n2019-05-01,255.296\n2019-06-01,255.213\n2019-07-01,255.802\n2019-08-01,256.036\n2019-09-01,256.430\n2019-10-01,257.155\n2019-11-01,257.879\n2019-12-01,258.630\n2020-01-01,259.127\n2020-02-01,259.250\n2020-03-01,258.076\n2020-04-01,256.032\n2020-05-01,255.802\n2020-06-01,257.042\n2020-07-01,258.352\n2020-08-01,259.316\n2020-09-01,259.997\n2020-10-01,260.319\n2020-11-01,260.911\n2020-12-01,262.045\n2021-01-01,262.687\n2021-02-01,263.579\n2021-03-01,264.961\n2021-04-01,266.614\n2021-05-01,268.383\n2021-06-01,270.654\n2021-07-01,271.903\n2021-08-01,272.676\n2021-09-01,273.910\n2021-10-01,276.550\n2021-11-01,278.919\n2021-12-01,280.845\n2022-01-01,282.543\n2022-02-01,284.500\n2022-03-01,287.674\n2022-04-01,288.561\n2022-05-01,291.298\n2022-06-01,294.957\n2022-07-01,294.913\n2022-08-01,295.097\n2022-09-01,296.349\n2022-10-01,298.007\n2022-11-01,298.786\n2022-12-01,298.832\n2023-01-01,300.420\n2023-02-01,301.450\n2023-03-01,301.821\n2023-04-01,302.845\n2023-05-01,303.334\n2023-06-01,304.014\n2023-07-01,304.609\n2023-08-01,306.082\n2023-09-01,307.276\n2023-10-01,307.696\n2023-11-01,308.148\n2023-12-01,308.741\n2024-01-01,309.698\n2024-02-01,310.967\n2024-03-01,312.345\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\ndate,cpi\n1947-01-01,21.480\n1947-02-01,21.620\n2016-04-01,238.890\n2016-05-01,239.410\n2016-06-01,239.927","is_error":false}]},"uuid":"d6c5da1d-72ba-4997-9e85-1d929f5292a6","timestamp":"2026-10-30T04:30:15.310Z","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}\nff732b201a525d6fb171d4e8b2d470f986863c1547a1928e42bb197c0777d44d cpi_assembled.csv\n281fbcdd6a6eeb3e9cb7c1f20476e6bd1f7b925bf140606fe46538fe83f2c83e cpi_vintage_2016-07-15.csv\n8220d5b4d70ca32fc2c7b1975651c515967db8a87053cf725159c63ed274cfba cpi_vintage_2026-10-28.csv\nac136bff13bbdb5afac60e0404212ea1f73f992bd5cca1c1e7a6599df78f8965 README.md\n 958 cpi_assembled.csv\n 835 cpi_vintage_2016-07-15.csv\n 154 cpi_vintage_2026-10-28.csv\n 1947 total\ndate,cpi\n2014-01-01,235.288\n2014-02-01,235.547\n2014-03-01,236.028\n2014-04-01,236.468\n2014-05-01,236.918\n2014-06-01,237.231\n2014-07-01,237.498\n2014-08-01,237.460\n2014-09-01,237.477\n2014-10-01,237.430\n2014-11-01,236.983\n2014-12-01,236.252\n2015-01-01,234.747\n2015-02-01,235.342\n2015-03-01,235.976\n2015-04-01,236.222\n2015-05-01,237.001\n2015-06-01,237.657\n2015-07-01,238.034\n2015-08-01,238.033\n2015-09-01,237.498\n2015-10-01,237.733\n2015-11-01,238.017\n2015-12-01,237.761\n2016-01-01,237.652\n2016-02-01,237.336\n2016-03-01,238.080\n2016-04-01,238.992\n2016-05-01,239.557\n2016-06-01,240.222\n2016-07-01,240.101\n2016-08-01,240.545\n2016-09-01,241.176\n2016-10-01,241.741\n2016-11-01,242.026\n2016-12-01,242.637\n2017-01-01,243.618\n2017-02-01,244.006\n2017-03-01,243.892\n2017-04-01,244.193\n2017-05-01,244.004\n2017-06-01,244.163\n2017-07-01,244.243\n2017-08-01,245.183\n2017-09-01,246.435\n2017-10-01,246.626\n2017-11-01,247.284\n2017-12-01,247.805\n2018-01-01,248.859\n2018-02-01,249.529\n2018-03-01,249.577\n2018-04-01,250.227\n2018-05-01,250.792\n2018-06-01,251.018\n2018-07-01,251.214\n2018-08-01,251.663\n2018-09-01,252.182\n2018-10-01,252.772\n2018-11-01,252.594\n2018-12-01,252.767\n2019-01-01,252.561\n2019-02-01,253.319\n2019-03-01,254.277\n2019-04-01,255.233\n2019-05-01,255.296\n2019-06-01,255.213\n2019-07-01,255.802\n2019-08-01,256.036\n2019-09-01,256.430\n2019-10-01,257.155\n2019-11-01,257.879\n2019-12-01,258.630\n2020-01-01,259.127\n2020-02-01,259.250\n2020-03-01,258.076\n2020-04-01,256.032\n2020-05-01,255.802\n2020-06-01,257.042\n2020-07-01,258.352\n2020-08-01,259.316\n2020-09-01,259.997\n2020-10-01,260.319\n2020-11-01,260.911\n2020-12-01,262.045\n2021-01-01,262.687\n2021-02-01,263.579\n2021-03-01,264.961\n2021-04-01,266.614\n2021-05-01,268.383\n2021-06-01,270.654\n2021-07-01,271.903\n2021-08-01,272.676\n2021-09-01,273.910\n2021-10-01,276.550\n2021-11-01,278.919\n2021-12-01,280.845\n2022-01-01,282.543\n2022-02-01,284.500\n2022-03-01,287.674\n2022-04-01,288.561\n2022-05-01,291.298\n2022-06-01,294.957\n2022-07-01,294.913\n2022-08-01,295.097\n2022-09-01,296.349\n2022-10-01,298.007\n2022-11-01,298.786\n2022-12-01,298.832\n2023-01-01,300.420\n2023-02-01,301.450\n2023-03-01,301.821\n2023-04-01,302.845\n2023-05-01,303.334\n2023-06-01,304.014\n2023-07-01,304.609\n2023-08-01,306.082\n2023-09-01,307.276\n2023-10-01,307.696\n2023-11-01,308.148\n2023-12-01,308.741\n2024-01-01,309.698\n2024-02-01,310.967\n2024-03-01,312.345\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\ndate,cpi\n1947-01-01,21.480\n1947-02-01,21.620\n2016-04-01,238.890\n2016-05-01,239.410\n2016-06-01,239.927","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"07a7c7d4-6ee8-4bfd-a1fd-44c1144fc1f6","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100/data","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}21{"parentUuid":"d6c5da1d-72ba-4997-9e85-1d929f5292a6","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnwp5ehvcGvdkdP8nRY","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01D9QHFRx9dgoURxq8mLBPq1","name":"Bash","input":{"command":"cd data; head -3 cpi_assembled.csv; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\na=pd.read_csv('cpi_vintage_2016-07-15.csv');b=pd.read_csv('cpi_vintage_2026-10-28.csv');c=pd.read_csv('cpi_assembled.csv')\nfor n,d in [('a',a),('b',b),('c',c)]:\n print(n,d.shape,d.date.duplicated().sum(),d.cpi.isna().sum(),d.date.min(),d.date.max())\n dd=pd.to_datetime(d.date); print(' gaps', (dd.diff().dt.days>31).sum(), d.dtypes.to_dict())\nprint(c.vintage.value_counts())\nprint(c[c.date.duplicated(keep=False)])\nm=a.merge(b,on='date');m['d']=m.cpi_y-m.cpi_x;print(m.describe())\n# compare assembled to truth\nt=pd.concat([a[a.date<'2014-01-01'].assign(v='2016'),b.assign(v='2026')])\nx=c.merge(t,on='date',how='outer',indicator=True);print(x._merge.value_counts());x=x[x._merge=='both'];print(x[(x.cpi_x-x.cpi_y).abs()>1e-9])\nprint(a.cpi.describe())\n\"","description":"Audit data files"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2175,"cache_read_input_tokens":4315,"output_tokens":742,"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":2175},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXnwono4xcUsN3Tbpfau","requestRef":"72cac84f-9104-4c3a-bdd9-e6c0a92ab649","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"262f8dad-1fa4-47f4-b944-b1b4c4383925","timestamp":"2026-10-30T04:30:19.479Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100/data","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}22{"parentUuid":"262f8dad-1fa4-47f4-b944-b1b4c4383925","isSidechain":false,"promptId":"8f565a57-70f1-433b-b044-ef7f6d25465c","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01D9QHFRx9dgoURxq8mLBPq1","type":"tool_result","content":"(eval):cd:1: no such file or directory: data\ndate,cpi,vintage\n1947-01-01,21.480,2016-07-15\n1947-02-01,21.620,2016-07-15\na (834, 2) 0 0 1947-01-01 2016-06-01\n gaps 0 {'date': dtype('O'), 'cpi': dtype('float64')}\nb (153, 2) 0 1 2014-01-01 2026-09-01\n gaps 0 {'date': dtype('O'), 'cpi': dtype('float64')}\nc (957, 3) 0 1 1947-01-01 2026-10-01\n gaps 1 {'date': dtype('O'), 'cpi': dtype('float64'), 'vintage': dtype('O')}\nvintage\n2016-07-15 834\n2026-10-28 123\nName: count, dtype: int64\nEmpty DataFrame\nColumns: [date, cpi, vintage]\nIndex: []\n cpi_x cpi_y d\ncount 30.000000 30.000000 30.000000\nmean 237.215967 237.214667 -0.001300\nstd 1.173005 1.223574 0.223576\nmin 234.954000 234.747000 -0.455000\n25% 236.471750 236.306000 -0.210750\n50% 237.342000 237.445000 0.017000\n75% 237.873500 237.754000 0.156750\nmax 239.927000 240.222000 0.330000\n_merge\nboth 956\nleft_only 1\nright_only 1\nName: count, dtype: int64\n date cpi_x vintage cpi_y v _merge\n804 2014-01-01 235.436 2016-07-15 235.288 2026 both\n805 2014-02-01 235.621 2016-07-15 235.547 2026 both\n806 2014-03-01 235.897 2016-07-15 236.028 2026 both\n807 2014-04-01 236.495 2016-07-15 236.468 2026 both\n808 2014-05-01 236.803 2016-07-15 236.918 2026 both\n.. ... ... ... ... ... ...\n952 2026-05-01 332.407 2026-10-28 333.979 2026 both\n953 2026-06-01 333.979 2026-10-28 332.568 2026 both\n954 2026-07-01 332.568 2026-10-28 332.813 2026 both\n955 2026-08-01 332.813 2026-10-28 334.131 2026 both\n956 2026-09-01 334.131 2026-10-28 336.298 2026 both\n\n[150 rows x 6 columns]\ncount 834.000000\nmean 103.095929\nstd 72.925102\nmin 21.480000\n25% 30.987500\n50% 93.250000\n75% 164.700000\nmax 239.927000\nName: cpi, dtype: float64","is_error":false}]},"uuid":"dfbaca27-1b9e-4380-a18c-a709db4a24a8","timestamp":"2026-10-30T04:30:19.703Z","toolUseResult":{"stdout":"(eval):cd:1: no such file or directory: data\ndate,cpi,vintage\n1947-01-01,21.480,2016-07-15\n1947-02-01,21.620,2016-07-15\na (834, 2) 0 0 1947-01-01 2016-06-01\n gaps 0 {'date': dtype('O'), 'cpi': dtype('float64')}\nb (153, 2) 0 1 2014-01-01 2026-09-01\n gaps 0 {'date': dtype('O'), 'cpi': dtype('float64')}\nc (957, 3) 0 1 1947-01-01 2026-10-01\n gaps 1 {'date': dtype('O'), 'cpi': dtype('float64'), 'vintage': dtype('O')}\nvintage\n2016-07-15 834\n2026-10-28 123\nName: count, dtype: int64\nEmpty DataFrame\nColumns: [date, cpi, vintage]\nIndex: []\n cpi_x cpi_y d\ncount 30.000000 30.000000 30.000000\nmean 237.215967 237.214667 -0.001300\nstd 1.173005 1.223574 0.223576\nmin 234.954000 234.747000 -0.455000\n25% 236.471750 236.306000 -0.210750\n50% 237.342000 237.445000 0.017000\n75% 237.873500 237.754000 0.156750\nmax 239.927000 240.222000 0.330000\n_merge\nboth 956\nleft_only 1\nright_only 1\nName: count, dtype: int64\n date cpi_x vintage cpi_y v _merge\n804 2014-01-01 235.436 2016-07-15 235.288 2026 both\n805 2014-02-01 235.621 2016-07-15 235.547 2026 both\n806 2014-03-01 235.897 2016-07-15 236.028 2026 both\n807 2014-04-01 236.495 2016-07-15 236.468 2026 both\n808 2014-05-01 236.803 2016-07-15 236.918 2026 both\n.. ... ... ... ... ... ...\n952 2026-05-01 332.407 2026-10-28 333.979 2026 both\n953 2026-06-01 333.979 2026-10-28 332.568 2026 both\n954 2026-07-01 332.568 2026-10-28 332.813 2026 both\n955 2026-08-01 332.813 2026-10-28 334.131 2026 both\n956 2026-09-01 334.131 2026-10-28 336.298 2026 both\n\n[150 rows x 6 columns]\ncount 834.000000\nmean 103.095929\nstd 72.925102\nmin 21.480000\n25% 30.987500\n50% 93.250000\n75% 164.700000\nmax 239.927000\nName: cpi, dtype: float64","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"262f8dad-1fa4-47f4-b944-b1b4c4383925","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100/data","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}23{"type":"api-request-blob","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","hash":"a94b115037840b59d7313b0c5d79b8863ab2ba3b22240a9b79cc4042b87f5605","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_011rLND39H1hKT43SB3yTetm","name":"Bash","input":{"command":"cat data/README.md; cat gate_spec.json | head -50; cd data; sha256sum * 2>/dev/null || shasum -a 256 *; wc -l *.csv; cat cpi_vintage_2026-10-28.csv; head -3 cpi_vintage_2016-07-15.csv; tail -3 cpi_vintage_2016-07-15.csv","description":"Inspect data files and readme"}},{"type":"tool_use","id":"toolu_01D9QHFRx9dgoURxq8mLBPq1","name":"Bash","input":{"command":"cd data; head -3 cpi_assembled.csv; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\na=pd.read_csv('cpi_vintage_2016-07-15.csv');b=pd.read_csv('cpi_vintage_2026-10-28.csv');c=pd.read_csv('cpi_assembled.csv')\nfor n,d in [('a',a),('b',b),('c',c)]:\n print(n,d.shape,d.date.duplicated().sum(),d.cpi.isna().sum(),d.date.min(),d.date.max())\n dd=pd.to_datetime(d.date); print(' gaps', (dd.diff().dt.days>31).sum(), d.dtypes.to_dict())\nprint(c.vintage.value_counts())\nprint(c[c.date.duplicated(keep=False)])\nm=a.merge(b,on='date');m['d']=m.cpi_y-m.cpi_x;print(m.describe())\n# compare assembled to truth\nt=pd.concat([a[a.date<'2014-01-01'].assign(v='2016'),b.assign(v='2026')])\nx=c.merge(t,on='date',how='outer',indicator=True);print(x._merge.value_counts());x=x[x._merge=='both'];print(x[(x.cpi_x-x.cpi_y).abs()>1e-9])\nprint(a.cpi.describe())\n\"","description":"Audit data files"}}]}}24{"type":"api-request-blob","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","hash":"e48c27db1ba50252f568946cb5798df8273ef681a394195d130529c19ebd1371","message":{"role":"user","content":[{"tool_use_id":"toolu_011rLND39H1hKT43SB3yTetm","type":"tool_result","content":"# 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}\nff732b201a525d6fb171d4e8b2d470f986863c1547a1928e42bb197c0777d44d cpi_assembled.csv\n281fbcdd6a6eeb3e9cb7c1f20476e6bd1f7b925bf140606fe46538fe83f2c83e cpi_vintage_2016-07-15.csv\n8220d5b4d70ca32fc2c7b1975651c515967db8a87053cf725159c63ed274cfba cpi_vintage_2026-10-28.csv\nac136bff13bbdb5afac60e0404212ea1f73f992bd5cca1c1e7a6599df78f8965 README.md\n 958 cpi_assembled.csv\n 835 cpi_vintage_2016-07-15.csv\n 154 cpi_vintage_2026-10-28.csv\n 1947 total\ndate,cpi\n2014-01-01,235.288\n2014-02-01,235.547\n2014-03-01,236.028\n2014-04-01,236.468\n2014-05-01,236.918\n2014-06-01,237.231\n2014-07-01,237.498\n2014-08-01,237.460\n2014-09-01,237.477\n2014-10-01,237.430\n2014-11-01,236.983\n2014-12-01,236.252\n2015-01-01,234.747\n2015-02-01,235.342\n2015-03-01,235.976\n2015-04-01,236.222\n2015-05-01,237.001\n2015-06-01,237.657\n2015-07-01,238.034\n2015-08-01,238.033\n2015-09-01,237.498\n2015-10-01,237.733\n2015-11-01,238.017\n2015-12-01,237.761\n2016-01-01,237.652\n2016-02-01,237.336\n2016-03-01,238.080\n2016-04-01,238.992\n2016-05-01,239.557\n2016-06-01,240.222\n2016-07-01,240.101\n2016-08-01,240.545\n2016-09-01,241.176\n2016-10-01,241.741\n2016-11-01,242.026\n2016-12-01,242.637\n2017-01-01,243.618\n2017-02-01,244.006\n2017-03-01,243.892\n2017-04-01,244.193\n2017-05-01,244.004\n2017-06-01,244.163\n2017-07-01,244.243\n2017-08-01,245.183\n2017-09-01,246.435\n2017-10-01,246.626\n2017-11-01,247.284\n2017-12-01,247.805\n2018-01-01,248.859\n2018-02-01,249.529\n2018-03-01,249.577\n2018-04-01,250.227\n2018-05-01,250.792\n2018-06-01,251.018\n2018-07-01,251.214\n2018-08-01,251.663\n2018-09-01,252.182\n2018-10-01,252.772\n2018-11-01,252.594\n2018-12-01,252.767\n2019-01-01,252.561\n2019-02-01,253.319\n2019-03-01,254.277\n2019-04-01,255.233\n2019-05-01,255.296\n2019-06-01,255.213\n2019-07-01,255.802\n2019-08-01,256.036\n2019-09-01,256.430\n2019-10-01,257.155\n2019-11-01,257.879\n2019-12-01,258.630\n2020-01-01,259.127\n2020-02-01,259.250\n2020-03-01,258.076\n2020-04-01,256.032\n2020-05-01,255.802\n2020-06-01,257.042\n2020-07-01,258.352\n2020-08-01,259.316\n2020-09-01,259.997\n2020-10-01,260.319\n2020-11-01,260.911\n2020-12-01,262.045\n2021-01-01,262.687\n2021-02-01,263.579\n2021-03-01,264.961\n2021-04-01,266.614\n2021-05-01,268.383\n2021-06-01,270.654\n2021-07-01,271.903\n2021-08-01,272.676\n2021-09-01,273.910\n2021-10-01,276.550\n2021-11-01,278.919\n2021-12-01,280.845\n2022-01-01,282.543\n2022-02-01,284.500\n2022-03-01,287.674\n2022-04-01,288.561\n2022-05-01,291.298\n2022-06-01,294.957\n2022-07-01,294.913\n2022-08-01,295.097\n2022-09-01,296.349\n2022-10-01,298.007\n2022-11-01,298.786\n2022-12-01,298.832\n2023-01-01,300.420\n2023-02-01,301.450\n2023-03-01,301.821\n2023-04-01,302.845\n2023-05-01,303.334\n2023-06-01,304.014\n2023-07-01,304.609\n2023-08-01,306.082\n2023-09-01,307.276\n2023-10-01,307.696\n2023-11-01,308.148\n2023-12-01,308.741\n2024-01-01,309.698\n2024-02-01,310.967\n2024-03-01,312.345\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\ndate,cpi\n1947-01-01,21.480\n1947-02-01,21.620\n2016-04-01,238.890\n2016-05-01,239.410\n2016-06-01,239.927","is_error":false},{"tool_use_id":"toolu_01D9QHFRx9dgoURxq8mLBPq1","type":"tool_result","content":"(eval):cd:1: no such file or directory: data\ndate,cpi,vintage\n1947-01-01,21.480,2016-07-15\n1947-02-01,21.620,2016-07-15\na (834, 2) 0 0 1947-01-01 2016-06-01\n gaps 0 {'date': dtype('O'), 'cpi': dtype('float64')}\nb (153, 2) 0 1 2014-01-01 2026-09-01\n gaps 0 {'date': dtype('O'), 'cpi': dtype('float64')}\nc (957, 3) 0 1 1947-01-01 2026-10-01\n gaps 1 {'date': dtype('O'), 'cpi': dtype('float64'), 'vintage': dtype('O')}\nvintage\n2016-07-15 834\n2026-10-28 123\nName: count, dtype: int64\nEmpty DataFrame\nColumns: [date, cpi, vintage]\nIndex: []\n cpi_x cpi_y d\ncount 30.000000 30.000000 30.000000\nmean 237.215967 237.214667 -0.001300\nstd 1.173005 1.223574 0.223576\nmin 234.954000 234.747000 -0.455000\n25% 236.471750 236.306000 -0.210750\n50% 237.342000 237.445000 0.017000\n75% 237.873500 237.754000 0.156750\nmax 239.927000 240.222000 0.330000\n_merge\nboth 956\nleft_only 1\nright_only 1\nName: count, dtype: int64\n date cpi_x vintage cpi_y v _merge\n804 2014-01-01 235.436 2016-07-15 235.288 2026 both\n805 2014-02-01 235.621 2016-07-15 235.547 2026 both\n806 2014-03-01 235.897 2016-07-15 236.028 2026 both\n807 2014-04-01 236.495 2016-07-15 236.468 2026 both\n808 2014-05-01 236.803 2016-07-15 236.918 2026 both\n.. ... ... ... ... ... ...\n952 2026-05-01 332.407 2026-10-28 333.979 2026 both\n953 2026-06-01 333.979 2026-10-28 332.568 2026 both\n954 2026-07-01 332.568 2026-10-28 332.813 2026 both\n955 2026-08-01 332.813 2026-10-28 334.131 2026 both\n956 2026-09-01 334.131 2026-10-28 336.298 2026 both\n\n[150 rows x 6 columns]\ncount 834.000000\nmean 103.095929\nstd 72.925102\nmin 21.480000\n25% 30.987500\n50% 93.250000\n75% 164.700000\nmax 239.927000\nName: cpi, dtype: float64","is_error":false}]}}25{"type":"api-request","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","id":"6fa8c87e-fac6-4f92-bd11-594ba37eb3d9","timestamp":"2026-10-30T04:30:19.706Z","version":"2.1.314","querySource":"sdk","shapeHash":"feb5c91d53bbada2e1da572f056120317de4cfdbadce4316c811c4f5cc3165bd","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":"72cac84f-9104-4c3a-bdd9-e6c0a92ab649","keep":4,"tail":["a94b115037840b59d7313b0c5d79b8863ab2ba3b22240a9b79cc4042b87f5605","e48c27db1ba50252f568946cb5798df8273ef681a394195d130529c19ebd1371"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",1],"value":{"type":"ephemeral"}}],"requestDigest":"0b2ec37d10f4e56bb47a408fd4938e28b2028371b6a9cabd346fe8072116d78a"}26{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"dfbaca27-1b9e-4380-a18c-a709db4a24a8","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56"}27{"type":"atis-latch","atis":"","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56"}28{"parentUuid":"dfbaca27-1b9e-4380-a18c-a709db4a24a8","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnxKM3jYfBXn4VRxcv3","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":5423,"cache_read_input_tokens":6490,"output_tokens":726,"output_tokens_details":{"thinking_tokens":511},"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":5423},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":5179,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXnxK5fyssW3Vw5qgT8r","requestRef":"6fa8c87e-fac6-4f92-bd11-594ba37eb3d9","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"edf8e621-fd31-4b85-b879-062242160fe5","timestamp":"2026-10-30T04:30:26.237Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100/data","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"edf8e621-fd31-4b85-b879-062242160fe5","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnxKM3jYfBXn4VRxcv3","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01S8QYSH7XRhJEjmvygxXcrZ","name":"Bash","input":{"command":"~/wp29/venv/bin/python -c \"\nimport pandas as pd\nc=pd.read_csv('data/cpi_assembled.csv');d=pd.to_datetime(c.date);i=(d.diff().dt.days>31);print(c[i|i.shift(-1,fill_value=False)]);print(c.tail(14));print(c[c.cpi.isna()]);print(c.iloc[830:840])\"","description":"Locate assembled-file errors"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5423,"cache_read_input_tokens":6490,"output_tokens":726,"output_tokens_details":{"thinking_tokens":511},"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":5423},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXnxK5fyssW3Vw5qgT8r","requestRef":"6fa8c87e-fac6-4f92-bd11-594ba37eb3d9","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e910beb1-fd5a-43e4-9acc-8292ac3952d5","timestamp":"2026-10-30T04:30:27.776Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100/data","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}30{"parentUuid":"e910beb1-fd5a-43e4-9acc-8292ac3952d5","isSidechain":false,"promptId":"8f565a57-70f1-433b-b044-ef7f6d25465c","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nTraceback (most recent call last):\n File \"<string>\", line 3, in <module>\n c=pd.read_csv('data/cpi_assembled.csv');d=pd.to_datetime(c.date);i=(d.diff().dt.days>31);print(c[i|i.shift(-1,fill_value=False)]);print(c.tail(14));print(c[c.cpi.isna()]);print(c.iloc[830:840])\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n return _read(filepath_or_buffer, kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n parser = TextFileReader(filepath_or_buffer, **kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n self._engine = self._make_engine(f, self.engine)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n self.handles = get_handle(\n ~~~~~~~~~~^\n f,\n ^^\n ...<6 lines>...\n storage_options=self.options.get(\"storage_options\", None),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n handle = open(\n handle,\n ...<3 lines>...\n newline=\"\",\n )\nFileNotFoundError: [Errno 2] No such file or directory: 'data/cpi_assembled.csv'","is_error":true,"tool_use_id":"toolu_01S8QYSH7XRhJEjmvygxXcrZ"}]},"uuid":"7d92742a-8562-4248-a10e-181852df7dbf","timestamp":"2026-10-30T04:30:28.001Z","toolUseResult":"Error: Exit code 1\nTraceback (most recent call last):\n File \"<string>\", line 3, in <module>\n c=pd.read_csv('data/cpi_assembled.csv');d=pd.to_datetime(c.date);i=(d.diff().dt.days>31);print(c[i|i.shift(-1,fill_value=False)]);print(c.tail(14));print(c[c.cpi.isna()]);print(c.iloc[830:840])\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n return _read(filepath_or_buffer, kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n parser = TextFileReader(filepath_or_buffer, **kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n self._engine = self._make_engine(f, self.engine)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n self.handles = get_handle(\n ~~~~~~~~~~^\n f,\n ^^\n ...<6 lines>...\n storage_options=self.options.get(\"storage_options\", None),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n handle = open(\n handle,\n ...<3 lines>...\n newline=\"\",\n )\nFileNotFoundError: [Errno 2] No such file or directory: 'data/cpi_assembled.csv'","sandboxed":false,"sourceToolAssistantUUID":"e910beb1-fd5a-43e4-9acc-8292ac3952d5","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100/data","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}31{"type":"api-request-blob","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","hash":"c60e9bbbb89f898041305b9d5fef6cea00a4b4cf4afd69fe6ba012968a31cb5f","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSixAKEAgTGAI4AUIIdGhpbmtpbmcSDFvfBaLjvjUy3YowuRoMTvucbAC8GGHz5pxLIjBdBRQ9MSoXFW8P8Mf8jR1fBa60slIT33Mdqzw4kszylLqX3md1I55j0etes0Iu7KwqqA9pUJxYM//UcGn5LpD70U9ItTQg/OS+9asWizZhRHbBlPt2dS0isOXqNZm1gmc029EPQjqBSYx4xOQdwAh1EqEk+Q8YqmKoBaRiFTHTtry0TWEptnVwV/4PrRz+7ue17slkA4Xhcf1VpAqN9euCA2YWjW2O/oX/ygNDUkUTzGKX/B61QY1hPIZna9gxcnIeyhjOLP7loQlK9FNreKL01JGT7tAmS4tRymWEchgz4QaxdL2zo/3BnnFtqEnk+K5Ms1ebs0A+UuE+mxRfkrCVrfg6R/YnZC+5c6GIngov2syQra1Nap8lyDtukd6Kgc0D5FGVxkiP2lg0ii/JzBBn16aIQDw9y7tKYBNdulqyj9PVHjrdOlmvkKVPiAV6dF5pGHPbie6fkVHK93D41LEcQCXGyiNNFjqEC5Mzt6+he1BYsEzn9tYM+ZewsDPwz2Nxlif1paKz6kddCxJJVY/pqtx5QWdhPwQ5LdtX6oS2DiVVMHsnLqpj3c3pGRt6or35t7+oWda5djy86kX9wP40mxPgKo9G/xGaYjh/CQMqCKzBbyyqvJdr/6T+CwUbYeSYjszjvgVAGBGeh6no127bAGnlsBmm8eWGGs+YYs9gUZgDkF8QRe4Cq5cP5bry/wCgSPi1reVFeSGOcsJpmhxE0pGlKN7IwEIC7dIG2LY4GUJ5rMWlgXQIh9SEjz6L5/VhtJ5/B6w4pdIdFfdwhS5WiRxe76v8r9PZanUJJd03V1Gz+sjLaQLQSnWA7I4SR1Y/u+YKnAayaq3Bxy0qEpvjirMvJJD7+kzpY2fhTqlOU1LtSCBSpRIsL1uPHfcsOfVrbVNshxA/IR3nYq9b/NwWzTW+cZiEDOunSK4ym+PUecf6oybvSZlxBATo+uDQaYauD1fNKjXsnJnKJgyDonTjtSSrkV6TQfQqrjd3DxfCR1VUnQggx5/RKwyL5s65wwvK7/MX8QG8gUTFNjlC5LVrBuMmDnSJCU1z1GVDULtSXLuAI0t5M6N8dRVCLfZZC58Cf03C5ag4LtRSyRRJAzkveJFb5Z0WGjQDpnxdH+64N5agpYXXk7z3hisD+Dla9ZzNRjeHLBiVufUhXTf7SgnDkE8XFFshtTyhBAUFgeSavDdecu78tph4dluorr632mR79B7tbQSa7Mg7/p3FNc+uZVKYRMYmyAW+pEGneLuid/VaiB+v3lxYp71AHAPsZ8ESWVuH2/xePRT4iBuDGO6FNNN0b/s8cMxAlboQi3LQ4oZdb5U9AR2lp92xF1IGsL/qAsV0rI/d1f0me6ffrJalOuwIGhszuvjOSKgengadrkn5BZMO/Q8CCV8mWRYFGzfNUuAe+7kz4807TfulsdpIzjREx7d6fe4Dgq3udFJvHKahhubJN3p7NRSkoFwvx1j+bFU9D8o0l7U31hNnrn/6coRVURZ4aoORVX2H9d3iTmPkiqavun7VHm8Vk8U3ddcZrhKFFG1Pial5O4I3kJA846bt7/mj/wj6f3JnQn45RYrQbuL5zBx+KYfOdyl1Po4ik5jTEVVBBiqJ47dKZONe8yFq+zo6yHF0F2jhyWYtYjnQ5q65ZuE65fWyl3ak/xgEufS7pajshEbnxfZYgRmIcHkmq4kRzcE/XQqrbyUAic9Un0ib5+A57C6no6Ak4xABbXfolxXKTWYHbQd+gj7Jj02XnErq7S6/C3UhDJFFftUzjnqTAyl49HWFqcI/84s7i4Xfc574P4o/iP5cT4u+INUSYc67Z0o0B1vAX0ymu3uyKtKTOOhDU7EbtW+8r0FD3BsbVeGVwAmcIMHXuGTWzxVpFckN4Skq2qcDZvpTF+DoGnYSCkTr4NCu6X0Thjs7NSlgx9suXJGKcDfOF+k5On1IH4vJsnm2XC8Eo0hMZgZN32D9XJcW1/Jfehf+mlo80pZHdfq2aUj/J1481/fHxTrfxcyxpMFgxzjrgRIq7nfcLwkhFYPJyXHVhRnV4sCTH2VPb+/OIpXTLsy8WDvQM+iWBxuvfb/k6+gvl7SrQcM2OGqmXcTw88k9fq3MHbCdgez8fu3gyAwz1n6wX8M3p7dxDnAV5678nxCXeO0PEc3nav8NEAklyuHJJylcbzdfraegfzhrf+EIY/4a/44P8PHv+27MeH/jkOsCT0AUQOJ7mXyKx1Vi7zmsFLJGu/8Ru8Vok5hcHRKMHBa4cns8UAAhB69sJ8lEaiwt5/q0NmvDojhj0sdT2t0K3KvgambVf2LTQPeHGaqlY7bIDN4/tzem67NiK9j0ufIAyEtiO3v7nXCkW4forIXbHS9OcnW00nc47QSB9u9qe1cf6Y+lVM+baS6iamZk7KOusfBzQL2oNrLCY2fxJZ/wGtKSbRSZtjNwxnAHddGP0iuDgdWLoLl52fbF9sl7jteme4z3bhVqLfs89BryNVfliY2tDsmulFsO0X7XA419ywCfa2nhb2S6V0stkLM6cPtXHeko32TKk2dUKP4gS2cdIGlQ+R+EqmaAmjmlfqCp44e8qsXb/M6a+eGGhhnlEFgaX0JvqtmT1Yelxd4lfjxZiI/56TRnRNHsfRVPmesaWzf0E+ve/DkDTbLGRMvxVxiTHVI5VIKg2RRcookqnbfrGAE="},{"type":"tool_use","id":"toolu_01S8QYSH7XRhJEjmvygxXcrZ","name":"Bash","input":{"command":"~/wp29/venv/bin/python -c \"\nimport pandas as pd\nc=pd.read_csv('data/cpi_assembled.csv');d=pd.to_datetime(c.date);i=(d.diff().dt.days>31);print(c[i|i.shift(-1,fill_value=False)]);print(c.tail(14));print(c[c.cpi.isna()]);print(c.iloc[830:840])\"","description":"Locate assembled-file errors"}}]}}32{"type":"api-request","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","id":"14b39d14-e17c-43ca-a3a2-7a9d6c014aac","timestamp":"2026-10-30T04:30:28.004Z","version":"2.1.314","querySource":"sdk","shapeHash":"feb5c91d53bbada2e1da572f056120317de4cfdbadce4316c811c4f5cc3165bd","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":"6fa8c87e-fac6-4f92-bd11-594ba37eb3d9","keep":6,"tail":["c60e9bbbb89f898041305b9d5fef6cea00a4b4cf4afd69fe6ba012968a31cb5f","96b0642d21e4854e22f1f45e58b49b642802cdfb4a4824b634ab2d24154d8b1c"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"9a6bdceb274dd6f797a224c760df7e5b052fa49a7c61047de1d43d766b6f39bc"}33{"parentUuid":"7d92742a-8562-4248-a10e-181852df7dbf","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnxvpEda5K2a4fgzNFC","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01GgeoW5Eb9LLUQEwap6djsr","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0100; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nc=pd.read_csv('data/cpi_assembled.csv');d=pd.to_datetime(c.date);i=(d.diff().dt.days>31);print(c[i|i.shift(-1,fill_value=False)]);print(c.tail(14));print(c[c.cpi.isna()]);print(c.iloc[830:840])\"","description":"Locate assembled-file errors"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1502,"cache_read_input_tokens":11913,"output_tokens":243,"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":1502},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXnxvZbzFbTuiG4wzZ6N","requestRef":"14b39d14-e17c-43ca-a3a2-7a9d6c014aac","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"36b2ccad-c942-4765-a5f5-4f744d8c5aa3","timestamp":"2026-10-30T04:30:30.530Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100/data","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}34{"parentUuid":"36b2ccad-c942-4765-a5f5-4f744d8c5aa3","isSidechain":false,"promptId":"8f565a57-70f1-433b-b044-ef7f6d25465c","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GgeoW5Eb9LLUQEwap6djsr","type":"tool_result","content":" date cpi vintage\n833 2016-06-01 239.927 2016-07-15\n834 2016-08-01 240.101 2026-10-28\n date cpi vintage\n943 2025-09-01 323.291 2026-10-28\n944 2025-10-01 324.245 2026-10-28\n945 2025-11-01 NaN 2026-10-28\n946 2025-12-01 325.063 2026-10-28\n947 2026-01-01 326.031 2026-10-28\n948 2026-02-01 326.588 2026-10-28\n949 2026-03-01 327.460 2026-10-28\n950 2026-04-01 330.293 2026-10-28\n951 2026-05-01 332.407 2026-10-28\n952 2026-06-01 333.979 2026-10-28\n953 2026-07-01 332.568 2026-10-28\n954 2026-08-01 332.813 2026-10-28\n955 2026-09-01 334.131 2026-10-28\n956 2026-10-01 336.298 2026-10-28\n date cpi vintage\n945 2025-11-01 NaN 2026-10-28\n date cpi vintage\n830 2016-03-01 237.920 2016-07-15\n831 2016-04-01 238.890 2016-07-15\n832 2016-05-01 239.410 2016-07-15\n833 2016-06-01 239.927 2016-07-15\n834 2016-08-01 240.101 2026-10-28\n835 2016-09-01 240.545 2026-10-28\n836 2016-10-01 241.176 2026-10-28\n837 2016-11-01 241.741 2026-10-28\n838 2016-12-01 242.026 2026-10-28\n839 2017-01-01 242.637 2026-10-28","is_error":false}]},"uuid":"9b10756b-82c1-4460-a475-cc4db31e98b3","timestamp":"2026-10-30T04:30:30.755Z","toolUseResult":{"stdout":" date cpi vintage\n833 2016-06-01 239.927 2016-07-15\n834 2016-08-01 240.101 2026-10-28\n date cpi vintage\n943 2025-09-01 323.291 2026-10-28\n944 2025-10-01 324.245 2026-10-28\n945 2025-11-01 NaN 2026-10-28\n946 2025-12-01 325.063 2026-10-28\n947 2026-01-01 326.031 2026-10-28\n948 2026-02-01 326.588 2026-10-28\n949 2026-03-01 327.460 2026-10-28\n950 2026-04-01 330.293 2026-10-28\n951 2026-05-01 332.407 2026-10-28\n952 2026-06-01 333.979 2026-10-28\n953 2026-07-01 332.568 2026-10-28\n954 2026-08-01 332.813 2026-10-28\n955 2026-09-01 334.131 2026-10-28\n956 2026-10-01 336.298 2026-10-28\n date cpi vintage\n945 2025-11-01 NaN 2026-10-28\n date cpi vintage\n830 2016-03-01 237.920 2016-07-15\n831 2016-04-01 238.890 2016-07-15\n832 2016-05-01 239.410 2016-07-15\n833 2016-06-01 239.927 2016-07-15\n834 2016-08-01 240.101 2026-10-28\n835 2016-09-01 240.545 2026-10-28\n836 2016-10-01 241.176 2026-10-28\n837 2016-11-01 241.741 2026-10-28\n838 2016-12-01 242.026 2026-10-28\n839 2017-01-01 242.637 2026-10-28","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"36b2ccad-c942-4765-a5f5-4f744d8c5aa3","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}35{"type":"api-request-blob","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","hash":"e552b866fda27f5aa1f272f0afb96260d7246f01b5744c9335f9023ac9086c75","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01GgeoW5Eb9LLUQEwap6djsr","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0100; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nc=pd.read_csv('data/cpi_assembled.csv');d=pd.to_datetime(c.date);i=(d.diff().dt.days>31);print(c[i|i.shift(-1,fill_value=False)]);print(c.tail(14));print(c[c.cpi.isna()]);print(c.iloc[830:840])\"","description":"Locate assembled-file errors"}}]}}36{"type":"api-request","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","id":"d7daa568-1a8f-492d-8226-943ff329eb30","timestamp":"2026-10-30T04:30:30.757Z","version":"2.1.314","querySource":"sdk","shapeHash":"feb5c91d53bbada2e1da572f056120317de4cfdbadce4316c811c4f5cc3165bd","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":"14b39d14-e17c-43ca-a3a2-7a9d6c014aac","keep":8,"tail":["e552b866fda27f5aa1f272f0afb96260d7246f01b5744c9335f9023ac9086c75","261ab535914be61e8920bdf2b86de73f72592b352c3678169ce4272815057974"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"3f5b2fa041b0774de1dffca6cb1c32f5148e458114dbd8eda1cdcbfdea0326b4"}37{"parentUuid":"9b10756b-82c1-4460-a475-cc4db31e98b3","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXny8cuNWRA7hdXBbv1f","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":831,"cache_read_input_tokens":13415,"output_tokens":882,"output_tokens_details":{"thinking_tokens":124},"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":831},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":806,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXny8M2SS9ZTbQJ5kNu1","requestRef":"d7daa568-1a8f-492d-8226-943ff329eb30","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0faee8e3-b429-4b9b-a0fb-4aa80fbe2335","timestamp":"2026-10-30T04:30:32.802Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}38{"parentUuid":"0faee8e3-b429-4b9b-a0fb-4aa80fbe2335","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXny8cuNWRA7hdXBbv1f","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_019oK5yHLAtFsgZiaTHtAPgi","name":"Bash","input":{"command":"mkdir -p out/state && cd out && cat > state/contract.md <<'EOF'\n# Contract — F1-conf-01\n**Question.** 12-month growth of CPIAUCSL (SA) for each month 2001-01..2026-09, g_t = 100(P_t/P_{t-12} - 1), 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 otherwise). Lags for 2001-01..2014-12 therefore come from 2000-01..2013-12 in the 2016 vintage.\n**Estimands.** g_t per month; mean over window; sub-period means 2001-01..2013-12 and 2014-01..2026-09; g at 2014-06, 2016-07, 2020-05, 2022-06, 2026-09; max/min and months. Meaningful under: the vintage-splice rule (mixing vintages across a ratio at the 2014 boundary is accepted by the rule), no imputation.\n**Outputs.** out/results.json (keys listed in TASK.md), 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**Information rules.** Most-recent-vintage per month; no interpolation/imputation; missing g_t counted; only data/ files.\n**Invalidity.** Using assembled file values where they conflict with vintage files; imputing missing months; using older vintage where newer covers the month.\nEOF\ncat > state/log.md <<'EOF'\n# Log\n- 2026-10-30 P0: read TASK.md, wrote contract. P0 passed.\n- 2026-10-30 P1: audited data. Decision: cpi_assembled.csv is read for diagnostics only (it has assembly errors); vintage files are authoritative. 2025-10 missing in 2026 vintage -> g_2025-10 missing, not imputed.\nEOF","description":"Write contract and log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":831,"cache_read_input_tokens":13415,"output_tokens":882,"output_tokens_details":{"thinking_tokens":124},"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":831},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXny8M2SS9ZTbQJ5kNu1","requestRef":"d7daa568-1a8f-492d-8226-943ff329eb30","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2e9ada0c-e11f-4860-be20-240ce8077236","timestamp":"2026-10-30T04:30:38.910Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}39{"parentUuid":"2e9ada0c-e11f-4860-be20-240ce8077236","isSidechain":false,"promptId":"8f565a57-70f1-433b-b044-ef7f6d25465c","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019oK5yHLAtFsgZiaTHtAPgi","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"7f436849-5176-412a-a891-5acf63b7e24f","timestamp":"2026-10-30T04:30:38.960Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"2e9ada0c-e11f-4860-be20-240ce8077236","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100/out","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}40{"type":"api-request-blob","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","hash":"2c316ee9b040629ce279cc348f82a50253d184ff10d90d42248d8300dd62061e","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_019oK5yHLAtFsgZiaTHtAPgi","name":"Bash","input":{"command":"mkdir -p out/state && cd out && cat > state/contract.md <<'EOF'\n# Contract — F1-conf-01\n**Question.** 12-month growth of CPIAUCSL (SA) for each month 2001-01..2026-09, g_t = 100(P_t/P_{t-12} - 1), 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 otherwise). Lags for 2001-01..2014-12 therefore come from 2000-01..2013-12 in the 2016 vintage.\n**Estimands.** g_t per month; mean over window; sub-period means 2001-01..2013-12 and 2014-01..2026-09; g at 2014-06, 2016-07, 2020-05, 2022-06, 2026-09; max/min and months. Meaningful under: the vintage-splice rule (mixing vintages across a ratio at the 2014 boundary is accepted by the rule), no imputation.\n**Outputs.** out/results.json (keys listed in TASK.md), 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**Information rules.** Most-recent-vintage per month; no interpolation/imputation; missing g_t counted; only data/ files.\n**Invalidity.** Using assembled file values where they conflict with vintage files; imputing missing months; using older vintage where newer covers the month.\nEOF\ncat > state/log.md <<'EOF'\n# Log\n- 2026-10-30 P0: read TASK.md, wrote contract. P0 passed.\n- 2026-10-30 P1: audited data. Decision: cpi_assembled.csv is read for diagnostics only (it has assembly errors); vintage files are authoritative. 2025-10 missing in 2026 vintage -> g_2025-10 missing, not imputed.\nEOF","description":"Write contract and log"}}]}}41{"type":"api-request","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","id":"f59c4052-7582-4d96-a9c6-8cebc648acd0","timestamp":"2026-10-30T04:30:38.964Z","version":"2.1.314","querySource":"sdk","shapeHash":"feb5c91d53bbada2e1da572f056120317de4cfdbadce4316c811c4f5cc3165bd","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":"d7daa568-1a8f-492d-8226-943ff329eb30","keep":10,"tail":["2c316ee9b040629ce279cc348f82a50253d184ff10d90d42248d8300dd62061e","d4592d296ecc88c8570d7142e80df2a45e176789187b39acc6f228636d4fdd5e"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"44a942a9446e1485947627f13da571037d26083695f9196315c807c4e7c34fbb"}42{"parentUuid":"7f436849-5176-412a-a891-5acf63b7e24f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnyjgYcQvc1NyrBs1W8","type":"message","role":"assistant","content":[{"type":"text","text":"Now the analysis script."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":897,"cache_read_input_tokens":14246,"output_tokens":3547,"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":897},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXnyjQvDV9533Q9it48g","requestRef":"f59c4052-7582-4d96-a9c6-8cebc648acd0","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"cd6e1abb-7b84-45e6-be85-e7594a511561","timestamp":"2026-10-30T04:30:40.006Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100/out","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}43{"parentUuid":"cd6e1abb-7b84-45e6-be85-e7594a511561","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnyjgYcQvc1NyrBs1W8","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012F9e8egChXtsFUuntzcdBz","name":"Bash","input":{"command":"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, os\nimport numpy as np\nimport pandas as pd\n\nD, O = \"data\", \"out\"\nF_OLD, F_NEW, F_ASM = \"cpi_vintage_2016-07-15.csv\", \"cpi_vintage_2026-10-28.csv\", \"cpi_assembled.csv\"\nV_OLD, V_NEW = \"2016-07-15\", \"2026-10-28\"\nW0, W1 = pd.Timestamp(\"2001-01-01\"), pd.Timestamp(\"2026-09-01\")\n\nold = pd.read_csv(os.path.join(D, F_OLD), parse_dates=[\"date\"])\nnew = pd.read_csv(os.path.join(D, F_NEW), parse_dates=[\"date\"])\nasm = pd.read_csv(os.path.join(D, F_ASM), parse_dates=[\"date\"])\nn_input_rows = {F_OLD: len(old), F_NEW: len(new), F_ASM: len(asm)}\nissues = []\n\nfor name, df in [(F_OLD, old), (F_NEW, new)]:\n assert not df.date.duplicated().any(), name\n full = pd.date_range(df.date.min(), df.date.max(), freq=\"MS\")\n assert len(full) == len(df), f\"gap in {name}\"\n for d in df.loc[df.cpi.isna(), \"date\"]:\n issues.append({\"issue\": f\"{name}: no index value reported for {d:%Y-%m} (empty cpi cell)\",\n \"handling\": \"Month treated as unavailable in that vintage; the older vintage does not cover it, so P is missing. \"\n \"Not interpolated; g_t for this month (and for t+12 if inside the window) reported as missing.\"})\n\n# Splice: most recent vintage in which the month is available (non-empty value).\no = old.dropna(subset=[\"cpi\"]).assign(vintage_used=V_OLD).set_index(\"date\")\nn = new.dropna(subset=[\"cpi\"]).assign(vintage_used=V_NEW).set_index(\"date\")\nlev = pd.concat([n, o[~o.index.isin(n.index)]]).sort_index()\n# months covered by the new vintage's date range but with empty cells are NOT backfilled from old (old does not cover them anyway)\nov = old.merge(new, on=\"date\", suffixes=(\"_old\", \"_new\")).dropna()\nissues.append({\"issue\": f\"Vintages overlap for {ov.date.min():%Y-%m}..{ov.date.max():%Y-%m} ({len(ov)} months) with differing levels \"\n f\"(max abs diff {np.abs(ov.cpi_new-ov.cpi_old).max():.3f} index points; seasonal-factor/revision differences)\",\n \"handling\": \"2026-10-28 vintage used for all overlap months per the most-recent-vintage rule; growth for 2014-01..2014-12 \"\n \"uses a 2026-vintage numerator and a 2016-vintage (2013) denominator, as the rule implies.\"})\n\n# Diagnose the hand-assembled file against the authoritative splice\na = asm.set_index(\"date\")\nfull_rng = pd.date_range(\"1947-01-01\", \"2026-09-01\", freq=\"MS\")\nmissing_in_asm = [d for d in full_rng if d not in a.index]\nextra_in_asm = [d for d in a.index if d not in full_rng]\nif missing_in_asm:\n issues.append({\"issue\": f\"{F_ASM}: months absent: {', '.join(f'{d:%Y-%m}' for d in missing_in_asm)}\",\n \"handling\": \"Assembled file not used for estimation; levels taken from the two vintage files.\"})\nif extra_in_asm:\n issues.append({\"issue\": f\"{F_ASM}: months beyond the sources' coverage: {', '.join(f'{d:%Y-%m}' for d in extra_in_asm)} \"\n \"(values appear shifted one month later from 2016-07 onward)\",\n \"handling\": \"Assembled file not used for estimation.\"})\ncmp_ = a.join(lev[[\"cpi\", \"vintage_used\"]], how=\"inner\", rsuffix=\"_auth\")\nvint_wrong = (cmp_.vintage != cmp_.vintage_used).sum()\nval_wrong = (~np.isclose(cmp_.cpi, cmp_.cpi_auth) | (cmp_.cpi.isna() != cmp_.cpi_auth.isna())).sum()\nissues.append({\"issue\": f\"{F_ASM}: of {len(cmp_)} months shared with the authoritative splice, {val_wrong} have a different level and \"\n f\"{vint_wrong} a different vintage label (2014-01..2016-06 taken from the older vintage; 2016-07 dropped and later values mis-dated)\",\n \"handling\": \"Assembled file read for diagnostics only; all estimates use the vintage files directly.\"})\n\n# Growth rates\nlev = lev.reindex(pd.date_range(lev.index.min(), lev.index.max(), freq=\"MS\"))\nlag = lev.cpi.shift(12)\ng = 100 * (lev.cpi / lag - 1)\nwin = pd.date_range(W0, W1, freq=\"MS\")\ntab = pd.DataFrame({\"cpi\": lev.cpi, \"cpi_lag12\": lag, \"vintage_used\": lev.vintage_used, \"growth_12m\": g}).reindex(win)\nok = tab.growth_12m.notna()\nanalytic = tab[ok].copy()\nmissing_months = [f\"{d:%Y-%m}\" for d in tab.index[~ok]]\n\nexcl = [\n dict(step=1, rule=\"Window 2001-01..2026-09 (all calendar months)\", n_before=len(win), n_after=len(win), n_removed=0,\n rationale=\"Start from every month of the target window\"),\n dict(step=2, rule=\"Drop months with no index level P_t in the most recent covering vintage\",\n n_before=len(win), n_after=int(tab.cpi.notna().sum()), n_removed=int(tab.cpi.isna().sum()),\n rationale=\"No imputation allowed: \" + \", \".join(f\"{d:%Y-%m}\" for d in tab.index[tab.cpi.isna()])),\n]\nn2 = int(tab.cpi.notna().sum())\nexcl.append(dict(step=3, rule=\"Drop months with no index level P_{t-12}\", n_before=n2, n_after=int(ok.sum()),\n n_removed=n2 - int(ok.sum()),\n rationale=\"No imputation allowed: \" + (\", \".join(f\"{d:%Y-%m}\" for d in tab.index[tab.cpi.notna() & tab.cpi_lag12.isna()]) or \"none\")))\npd.DataFrame(excl).to_csv(os.path.join(O, \"exclusions.csv\"), index=False)\n\nout_a = analytic.reset_index().rename(columns={\"index\": \"date\"})\nout_a[\"date\"] = out_a.date.dt.strftime(\"%Y-%m-%d\")\nout_a.to_csv(os.path.join(O, \"analytic.csv\"), index=False, float_format=\"%.10g\")\n\ngs = analytic.growth_12m\ndef gm(m):\n v = tab.growth_12m.get(pd.Timestamp(m + \"-01\"))\n return None if v is None or pd.isna(v) else round(float(v), 10)\nr = lambda x: round(float(x), 10)\nlast_month = lev.cpi.loc[:W1].dropna().index.max()\nres = {\n \"task_id\": \"F1-conf-01\",\n \"first_growth_month\": f\"{gs.index.min():%Y-%m}\",\n \"last_month\": f\"{last_month:%Y-%m}\",\n \"n_months_growth\": int(ok.sum()),\n \"n_months_missing_growth\": int((~ok).sum()),\n \"n_analysed\": int(ok.sum()),\n \"mean_growth_12m\": r(gs.mean()),\n \"mean_growth_12m_2001_2013\": r(gs.loc[:\"2013-12-01\"].mean()),\n \"mean_growth_12m_2014_2026\": r(gs.loc[\"2014-01-01\":].mean()),\n \"growth_2014_06\": gm(\"2014-06\"), \"growth_2016_07\": gm(\"2016-07\"), \"growth_2020_05\": gm(\"2020-05\"),\n \"growth_2022_06\": gm(\"2022-06\"), \"growth_2026_09\": gm(\"2026-09\"),\n \"max_growth_12m\": r(gs.max()), \"max_growth_month\": f\"{gs.idxmax():%Y-%m}\",\n \"min_growth_12m\": r(gs.min()), \"min_growth_month\": f\"{gs.idxmin():%Y-%m}\",\n \"feasibility\": \"complete\" if ok.all() else \"partial\",\n \"feasibility_reason\": None if ok.all() else\n f\"g_t not computable for {len(missing_months)} month(s) in the window ({', '.join(missing_months)}) because the source reports no index level; \"\n \"these months are excluded from all means and extremes. All summary estimands were computed.\",\n \"data_issues\": issues,\n \"n_input_rows\": n_input_rows,\n \"random_seed\": None,\n \"n_months_2001_2013\": int(gs.loc[:\"2013-12-01\"].size),\n \"n_months_2014_2026\": int(gs.loc[\"2014-01-01\":].size),\n \"missing_growth_months\": missing_months,\n}\nwith open(os.path.join(O, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps({k: v for k, v in res.items() if k not in (\"data_issues\",)}, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && head -3 out/analytic.csv && wc -l out/analytic.csv && grep -A3 '\"issue\"' out/results.json | head -40","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":897,"cache_read_input_tokens":14246,"output_tokens":3547,"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":897},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXnyjQvDV9533Q9it48g","requestRef":"f59c4052-7582-4d96-a9c6-8cebc648acd0","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"699ad3ff-8239-4ab9-a1d8-c589a76b597b","timestamp":"2026-10-30T04:31:07.507Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100/out","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}44{"parentUuid":"699ad3ff-8239-4ab9-a1d8-c589a76b597b","isSidechain":false,"promptId":"8f565a57-70f1-433b-b044-ef7f6d25465c","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 2\n(eval):1: no such file or directory: out/analysis.py\n/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/simon-pierreboucher/wp29/runs/RC0100/out/out/analysis.py': [Errno 2] No such file or directory","is_error":true,"tool_use_id":"toolu_012F9e8egChXtsFUuntzcdBz"}]},"uuid":"4f20db00-9b06-4c34-8a29-411dcb7257da","timestamp":"2026-10-30T04:31:07.574Z","toolUseResult":"Error: Exit code 2\n(eval):1: no such file or directory: out/analysis.py\n/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/simon-pierreboucher/wp29/runs/RC0100/out/out/analysis.py': [Errno 2] No such file or directory","sandboxed":false,"sourceToolAssistantUUID":"699ad3ff-8239-4ab9-a1d8-c589a76b597b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100/out","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}45{"type":"api-request-blob","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","hash":"829f208d30b92fb245bdcaa02cb4f46b570a9421de2445d2865183bcd2e64c24","message":{"role":"assistant","content":[{"type":"text","text":"Now the analysis script."},{"type":"tool_use","id":"toolu_012F9e8egChXtsFUuntzcdBz","name":"Bash","input":{"command":"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, os\nimport numpy as np\nimport pandas as pd\n\nD, O = \"data\", \"out\"\nF_OLD, F_NEW, F_ASM = \"cpi_vintage_2016-07-15.csv\", \"cpi_vintage_2026-10-28.csv\", \"cpi_assembled.csv\"\nV_OLD, V_NEW = \"2016-07-15\", \"2026-10-28\"\nW0, W1 = pd.Timestamp(\"2001-01-01\"), pd.Timestamp(\"2026-09-01\")\n\nold = pd.read_csv(os.path.join(D, F_OLD), parse_dates=[\"date\"])\nnew = pd.read_csv(os.path.join(D, F_NEW), parse_dates=[\"date\"])\nasm = pd.read_csv(os.path.join(D, F_ASM), parse_dates=[\"date\"])\nn_input_rows = {F_OLD: len(old), F_NEW: len(new), F_ASM: len(asm)}\nissues = []\n\nfor name, df in [(F_OLD, old), (F_NEW, new)]:\n assert not df.date.duplicated().any(), name\n full = pd.date_range(df.date.min(), df.date.max(), freq=\"MS\")\n assert len(full) == len(df), f\"gap in {name}\"\n for d in df.loc[df.cpi.isna(), \"date\"]:\n issues.append({\"issue\": f\"{name}: no index value reported for {d:%Y-%m} (empty cpi cell)\",\n \"handling\": \"Month treated as unavailable in that vintage; the older vintage does not cover it, so P is missing. \"\n \"Not interpolated; g_t for this month (and for t+12 if inside the window) reported as missing.\"})\n\n# Splice: most recent vintage in which the month is available (non-empty value).\no = old.dropna(subset=[\"cpi\"]).assign(vintage_used=V_OLD).set_index(\"date\")\nn = new.dropna(subset=[\"cpi\"]).assign(vintage_used=V_NEW).set_index(\"date\")\nlev = pd.concat([n, o[~o.index.isin(n.index)]]).sort_index()\n# months covered by the new vintage's date range but with empty cells are NOT backfilled from old (old does not cover them anyway)\nov = old.merge(new, on=\"date\", suffixes=(\"_old\", \"_new\")).dropna()\nissues.append({\"issue\": f\"Vintages overlap for {ov.date.min():%Y-%m}..{ov.date.max():%Y-%m} ({len(ov)} months) with differing levels \"\n f\"(max abs diff {np.abs(ov.cpi_new-ov.cpi_old).max():.3f} index points; seasonal-factor/revision differences)\",\n \"handling\": \"2026-10-28 vintage used for all overlap months per the most-recent-vintage rule; growth for 2014-01..2014-12 \"\n \"uses a 2026-vintage numerator and a 2016-vintage (2013) denominator, as the rule implies.\"})\n\n# Diagnose the hand-assembled file against the authoritative splice\na = asm.set_index(\"date\")\nfull_rng = pd.date_range(\"1947-01-01\", \"2026-09-01\", freq=\"MS\")\nmissing_in_asm = [d for d in full_rng if d not in a.index]\nextra_in_asm = [d for d in a.index if d not in full_rng]\nif missing_in_asm:\n issues.append({\"issue\": f\"{F_ASM}: months absent: {', '.join(f'{d:%Y-%m}' for d in missing_in_asm)}\",\n \"handling\": \"Assembled file not used for estimation; levels taken from the two vintage files.\"})\nif extra_in_asm:\n issues.append({\"issue\": f\"{F_ASM}: months beyond the sources' coverage: {', '.join(f'{d:%Y-%m}' for d in extra_in_asm)} \"\n \"(values appear shifted one month later from 2016-07 onward)\",\n \"handling\": \"Assembled file not used for estimation.\"})\ncmp_ = a.join(lev[[\"cpi\", \"vintage_used\"]], how=\"inner\", rsuffix=\"_auth\")\nvint_wrong = (cmp_.vintage != cmp_.vintage_used).sum()\nval_wrong = (~np.isclose(cmp_.cpi, cmp_.cpi_auth) | (cmp_.cpi.isna() != cmp_.cpi_auth.isna())).sum()\nissues.append({\"issue\": f\"{F_ASM}: of {len(cmp_)} months shared with the authoritative splice, {val_wrong} have a different level and \"\n f\"{vint_wrong} a different vintage label (2014-01..2016-06 taken from the older vintage; 2016-07 dropped and later values mis-dated)\",\n \"handling\": \"Assembled file read for diagnostics only; all estimates use the vintage files directly.\"})\n\n# Growth rates\nlev = lev.reindex(pd.date_range(lev.index.min(), lev.index.max(), freq=\"MS\"))\nlag = lev.cpi.shift(12)\ng = 100 * (lev.cpi / lag - 1)\nwin = pd.date_range(W0, W1, freq=\"MS\")\ntab = pd.DataFrame({\"cpi\": lev.cpi, \"cpi_lag12\": lag, \"vintage_used\": lev.vintage_used, \"growth_12m\": g}).reindex(win)\nok = tab.growth_12m.notna()\nanalytic = tab[ok].copy()\nmissing_months = [f\"{d:%Y-%m}\" for d in tab.index[~ok]]\n\nexcl = [\n dict(step=1, rule=\"Window 2001-01..2026-09 (all calendar months)\", n_before=len(win), n_after=len(win), n_removed=0,\n rationale=\"Start from every month of the target window\"),\n dict(step=2, rule=\"Drop months with no index level P_t in the most recent covering vintage\",\n n_before=len(win), n_after=int(tab.cpi.notna().sum()), n_removed=int(tab.cpi.isna().sum()),\n rationale=\"No imputation allowed: \" + \", \".join(f\"{d:%Y-%m}\" for d in tab.index[tab.cpi.isna()])),\n]\nn2 = int(tab.cpi.notna().sum())\nexcl.append(dict(step=3, rule=\"Drop months with no index level P_{t-12}\", n_before=n2, n_after=int(ok.sum()),\n n_removed=n2 - int(ok.sum()),\n rationale=\"No imputation allowed: \" + (\", \".join(f\"{d:%Y-%m}\" for d in tab.index[tab.cpi.notna() & tab.cpi_lag12.isna()]) or \"none\")))\npd.DataFrame(excl).to_csv(os.path.join(O, \"exclusions.csv\"), index=False)\n\nout_a = analytic.reset_index().rename(columns={\"index\": \"date\"})\nout_a[\"date\"] = out_a.date.dt.strftime(\"%Y-%m-%d\")\nout_a.to_csv(os.path.join(O, \"analytic.csv\"), index=False, float_format=\"%.10g\")\n\ngs = analytic.growth_12m\ndef gm(m):\n v = tab.growth_12m.get(pd.Timestamp(m + \"-01\"))\n return None if v is None or pd.isna(v) else round(float(v), 10)\nr = lambda x: round(float(x), 10)\nlast_month = lev.cpi.loc[:W1].dropna().index.max()\nres = {\n \"task_id\": \"F1-conf-01\",\n \"first_growth_month\": f\"{gs.index.min():%Y-%m}\",\n \"last_month\": f\"{last_month:%Y-%m}\",\n \"n_months_growth\": int(ok.sum()),\n \"n_months_missing_growth\": int((~ok).sum()),\n \"n_analysed\": int(ok.sum()),\n \"mean_growth_12m\": r(gs.mean()),\n \"mean_growth_12m_2001_2013\": r(gs.loc[:\"2013-12-01\"].mean()),\n \"mean_growth_12m_2014_2026\": r(gs.loc[\"2014-01-01\":].mean()),\n \"growth_2014_06\": gm(\"2014-06\"), \"growth_2016_07\": gm(\"2016-07\"), \"growth_2020_05\": gm(\"2020-05\"),\n \"growth_2022_06\": gm(\"2022-06\"), \"growth_2026_09\": gm(\"2026-09\"),\n \"max_growth_12m\": r(gs.max()), \"max_growth_month\": f\"{gs.idxmax():%Y-%m}\",\n \"min_growth_12m\": r(gs.min()), \"min_growth_month\": f\"{gs.idxmin():%Y-%m}\",\n \"feasibility\": \"complete\" if ok.all() else \"partial\",\n \"feasibility_reason\": None if ok.all() else\n f\"g_t not computable for {len(missing_months)} month(s) in the window ({', '.join(missing_months)}) because the source reports no index level; \"\n \"these months are excluded from all means and extremes. All summary estimands were computed.\",\n \"data_issues\": issues,\n \"n_input_rows\": n_input_rows,\n \"random_seed\": None,\n \"n_months_2001_2013\": int(gs.loc[:\"2013-12-01\"].size),\n \"n_months_2014_2026\": int(gs.loc[\"2014-01-01\":].size),\n \"missing_growth_months\": missing_months,\n}\nwith open(os.path.join(O, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps({k: v for k, v in res.items() if k not in (\"data_issues\",)}, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && head -3 out/analytic.csv && wc -l out/analytic.csv && grep -A3 '\"issue\"' out/results.json | head -40","description":"Write and run analysis script"}}]}}46{"type":"api-request","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","id":"a011adfe-0250-4f7a-b5e2-f255365c4b52","timestamp":"2026-10-30T04:31:07.577Z","version":"2.1.314","querySource":"sdk","shapeHash":"feb5c91d53bbada2e1da572f056120317de4cfdbadce4316c811c4f5cc3165bd","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":"f59c4052-7582-4d96-a9c6-8cebc648acd0","keep":12,"tail":["829f208d30b92fb245bdcaa02cb4f46b570a9421de2445d2865183bcd2e64c24","65e641eb8fa905b2d4f434bb3dd8863283357e68e3f6165343dcb238543f3d3a"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"1fa838a2c397a1e680fc38c0eb1ac6df82e4b3d50cc66344f5dc2259e2f5055d"}47{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"4f20db00-9b06-4c34-8a29-411dcb7257da","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56"}48{"type":"atis-latch","atis":"","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56"}49{"parentUuid":"4f20db00-9b06-4c34-8a29-411dcb7257da","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo1r5pUQAPY1C7tqyss","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":3707,"cache_read_input_tokens":15143,"output_tokens":201,"output_tokens_details":{"thinking_tokens":63},"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":3707},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":624,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXo1qohY9G6Ta5B8e3wv","requestRef":"a011adfe-0250-4f7a-b5e2-f255365c4b52","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8146c094-629a-462b-8878-5f33339e5c6d","timestamp":"2026-10-30T04:31:13.532Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100/out","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}50{"parentUuid":"8146c094-629a-462b-8878-5f33339e5c6d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo1r5pUQAPY1C7tqyss","type":"message","role":"assistant","content":[{"type":"text","text":"The shell was still in `out/`, so I'll rerun from the workspace root."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3707,"cache_read_input_tokens":15143,"output_tokens":201,"output_tokens_details":{"thinking_tokens":63},"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":3707},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXo1qohY9G6Ta5B8e3wv","requestRef":"a011adfe-0250-4f7a-b5e2-f255365c4b52","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"56afeada-03ae-4ac0-b918-ee779e9fac45","timestamp":"2026-10-30T04:31:13.534Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100/out","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}51{"parentUuid":"56afeada-03ae-4ac0-b918-ee779e9fac45","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo1r5pUQAPY1C7tqyss","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011RjBKDnv6d6M8fJmrKvnC9","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0100 && pwd && ls out out/state","description":"Return to workspace root"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3707,"cache_read_input_tokens":15143,"output_tokens":201,"output_tokens_details":{"thinking_tokens":63},"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":3707},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXo1qohY9G6Ta5B8e3wv","requestRef":"a011adfe-0250-4f7a-b5e2-f255365c4b52","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"594c54eb-bda4-4328-9d13-377ced767528","timestamp":"2026-10-30T04:31:14.192Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100/out","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}52{"parentUuid":"594c54eb-bda4-4328-9d13-377ced767528","isSidechain":false,"promptId":"8f565a57-70f1-433b-b044-ef7f6d25465c","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011RjBKDnv6d6M8fJmrKvnC9","type":"tool_result","content":"/Users/simon-pierreboucher/wp29/runs/RC0100\nout:\nstate\n\nout/state:\ncontract.md\nlog.md","is_error":false}]},"uuid":"76836468-e731-44b6-a301-95c8d7e51841","timestamp":"2026-10-30T04:31:14.235Z","toolUseResult":{"stdout":"/Users/simon-pierreboucher/wp29/runs/RC0100\nout:\nstate\n\nout/state:\ncontract.md\nlog.md","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"594c54eb-bda4-4328-9d13-377ced767528","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}53{"type":"api-request-blob","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","hash":"16175433d0b4822fa6a2550c041f41a6a723fb869d64720f02927296c369470c","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"The shell was still in `out/`, so I'll rerun from the workspace root."},{"type":"tool_use","id":"toolu_011RjBKDnv6d6M8fJmrKvnC9","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0100 && pwd && ls out out/state","description":"Return to workspace root"}}]}}54{"type":"api-request","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","id":"3d1bbbf9-9d2b-4b16-b230-d792f6e6bdb5","timestamp":"2026-10-30T04:31:14.240Z","version":"2.1.314","querySource":"sdk","shapeHash":"feb5c91d53bbada2e1da572f056120317de4cfdbadce4316c811c4f5cc3165bd","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":"a011adfe-0250-4f7a-b5e2-f255365c4b52","keep":14,"tail":["16175433d0b4822fa6a2550c041f41a6a723fb869d64720f02927296c369470c","1626400d27e4c36f1066d11be4b522d184fba72d3b0acbb9af237ca87f13a234"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"7b20db9fb615999538c6ca35246ba0182acc1bb4ceacded2374c2cb237bfce6d"}55{"parentUuid":"76836468-e731-44b6-a301-95c8d7e51841","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo2LendkpGReMwhtFdc","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":250,"cache_read_input_tokens":18850,"output_tokens":3492,"output_tokens_details":{"thinking_tokens":18},"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":250},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":327,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXo2LJwjeCUZ6FefHtGU","requestRef":"3d1bbbf9-9d2b-4b16-b230-d792f6e6bdb5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c56ca070-4915-474c-8be5-12fb3bdbe133","timestamp":"2026-10-30T04:31:15.718Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}56{"parentUuid":"c56ca070-4915-474c-8be5-12fb3bdbe133","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo2LendkpGReMwhtFdc","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01HK35sn25nBqQ6ogb7NJ7zc","name":"Bash","input":{"command":"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, os\nimport numpy as np\nimport pandas as pd\n\nD, O = \"data\", \"out\"\nF_OLD, F_NEW, F_ASM = \"cpi_vintage_2016-07-15.csv\", \"cpi_vintage_2026-10-28.csv\", \"cpi_assembled.csv\"\nV_OLD, V_NEW = \"2016-07-15\", \"2026-10-28\"\nW0, W1 = pd.Timestamp(\"2001-01-01\"), pd.Timestamp(\"2026-09-01\")\n\nold = pd.read_csv(os.path.join(D, F_OLD), parse_dates=[\"date\"])\nnew = pd.read_csv(os.path.join(D, F_NEW), parse_dates=[\"date\"])\nasm = pd.read_csv(os.path.join(D, F_ASM), parse_dates=[\"date\"])\nn_input_rows = {F_OLD: len(old), F_NEW: len(new), F_ASM: len(asm)}\nissues = []\n\nfor name, df in [(F_OLD, old), (F_NEW, new)]:\n assert not df.date.duplicated().any(), name\n full = pd.date_range(df.date.min(), df.date.max(), freq=\"MS\")\n assert len(full) == len(df), f\"gap in {name}\"\n for d in df.loc[df.cpi.isna(), \"date\"]:\n issues.append({\"issue\": f\"{name}: no index value reported for {d:%Y-%m} (empty cpi cell)\",\n \"handling\": \"Month treated as unavailable in that vintage; the older vintage does not cover it, so P is missing. \"\n \"Not interpolated; g_t for this month (and for t+12 if inside the window) reported as missing.\"})\n\n# Splice: most recent vintage in which the month is available (non-empty value).\no = old.dropna(subset=[\"cpi\"]).assign(vintage_used=V_OLD).set_index(\"date\")\nn = new.dropna(subset=[\"cpi\"]).assign(vintage_used=V_NEW).set_index(\"date\")\nlev = pd.concat([n, o[~o.index.isin(n.index)]]).sort_index()\nov = old.merge(new, on=\"date\", suffixes=(\"_old\", \"_new\")).dropna()\nissues.append({\"issue\": f\"Vintages overlap for {ov.date.min():%Y-%m}..{ov.date.max():%Y-%m} ({len(ov)} months) with differing levels \"\n f\"(max abs diff {np.abs(ov.cpi_new-ov.cpi_old).max():.3f} index points; seasonal-factor/revision differences)\",\n \"handling\": \"2026-10-28 vintage used for all overlap months per the most-recent-vintage rule; growth for 2014-01..2014-12 \"\n \"uses a 2026-vintage numerator and a 2016-vintage (2013) denominator, as the rule implies.\"})\n\n# Diagnose the hand-assembled file against the authoritative splice\na = asm.set_index(\"date\")\nfull_rng = pd.date_range(\"1947-01-01\", \"2026-09-01\", freq=\"MS\")\nmissing_in_asm = [d for d in full_rng if d not in a.index]\nextra_in_asm = [d for d in a.index if d not in full_rng]\nif missing_in_asm:\n issues.append({\"issue\": f\"{F_ASM}: months absent: {', '.join(f'{d:%Y-%m}' for d in missing_in_asm)}\",\n \"handling\": \"Assembled file not used for estimation; levels taken from the two vintage files.\"})\nif extra_in_asm:\n issues.append({\"issue\": f\"{F_ASM}: months beyond the sources' coverage: {', '.join(f'{d:%Y-%m}' for d in extra_in_asm)} \"\n \"(values appear shifted one month later from 2016-07 onward)\",\n \"handling\": \"Assembled file not used for estimation.\"})\ncmp_ = a.join(lev[[\"cpi\", \"vintage_used\"]], how=\"inner\", rsuffix=\"_auth\")\nvint_wrong = int((cmp_.vintage != cmp_.vintage_used).sum())\nval_wrong = int((~np.isclose(cmp_.cpi, cmp_.cpi_auth, equal_nan=True)).sum())\nissues.append({\"issue\": f\"{F_ASM}: of {len(cmp_)} months shared with the authoritative splice, {val_wrong} have a different level and \"\n f\"{vint_wrong} a different vintage label (2014-01..2016-06 taken from the older vintage; 2016-07 dropped and later values mis-dated)\",\n \"handling\": \"Assembled file read for diagnostics only; all estimates use the vintage files directly.\"})\n\n# Growth rates\nlev = lev.reindex(pd.date_range(lev.index.min(), lev.index.max(), freq=\"MS\"))\nlag = lev.cpi.shift(12)\ng = 100 * (lev.cpi / lag - 1)\nwin = pd.date_range(W0, W1, freq=\"MS\")\ntab = pd.DataFrame({\"cpi\": lev.cpi, \"cpi_lag12\": lag, \"vintage_used\": lev.vintage_used, \"growth_12m\": g}).reindex(win)\nok = tab.growth_12m.notna()\nanalytic = tab[ok].copy()\nmissing_months = [f\"{d:%Y-%m}\" for d in tab.index[~ok]]\n\nn2 = int(tab.cpi.notna().sum())\nexcl = [\n dict(step=1, rule=\"Window 2001-01..2026-09 (all calendar months)\", n_before=len(win), n_after=len(win), n_removed=0,\n rationale=\"Start from every month of the target window\"),\n dict(step=2, rule=\"Drop months with no index level P_t in the most recent covering vintage\",\n n_before=len(win), n_after=n2, n_removed=len(win) - n2,\n rationale=\"No imputation allowed: \" + (\", \".join(f\"{d:%Y-%m}\" for d in tab.index[tab.cpi.isna()]) or \"none\")),\n dict(step=3, rule=\"Drop months with no index level P_{t-12}\", n_before=n2, n_after=int(ok.sum()),\n n_removed=n2 - int(ok.sum()),\n rationale=\"No imputation allowed: \" + (\", \".join(f\"{d:%Y-%m}\" for d in tab.index[tab.cpi.notna() & tab.cpi_lag12.isna()]) or \"none\")),\n]\npd.DataFrame(excl).to_csv(os.path.join(O, \"exclusions.csv\"), index=False)\n\nout_a = analytic.reset_index().rename(columns={\"index\": \"date\"})\nout_a[\"date\"] = out_a.date.dt.strftime(\"%Y-%m-%d\")\nout_a.to_csv(os.path.join(O, \"analytic.csv\"), index=False, float_format=\"%.10g\")\n\ngs = analytic.growth_12m\ndef gm(m):\n v = tab.growth_12m.get(pd.Timestamp(m + \"-01\"))\n return None if v is None or pd.isna(v) else round(float(v), 10)\nr = lambda x: round(float(x), 10)\nlast_month = lev.cpi.loc[:W1].dropna().index.max()\nres = {\n \"task_id\": \"F1-conf-01\",\n \"first_growth_month\": f\"{gs.index.min():%Y-%m}\",\n \"last_month\": f\"{last_month:%Y-%m}\",\n \"n_months_growth\": int(ok.sum()),\n \"n_months_missing_growth\": int((~ok).sum()),\n \"n_analysed\": int(ok.sum()),\n \"mean_growth_12m\": r(gs.mean()),\n \"mean_growth_12m_2001_2013\": r(gs.loc[:\"2013-12-01\"].mean()),\n \"mean_growth_12m_2014_2026\": r(gs.loc[\"2014-01-01\":].mean()),\n \"growth_2014_06\": gm(\"2014-06\"), \"growth_2016_07\": gm(\"2016-07\"), \"growth_2020_05\": gm(\"2020-05\"),\n \"growth_2022_06\": gm(\"2022-06\"), \"growth_2026_09\": gm(\"2026-09\"),\n \"max_growth_12m\": r(gs.max()), \"max_growth_month\": f\"{gs.idxmax():%Y-%m}\",\n \"min_growth_12m\": r(gs.min()), \"min_growth_month\": f\"{gs.idxmin():%Y-%m}\",\n \"feasibility\": \"complete\" if ok.all() else \"partial\",\n \"feasibility_reason\": None if ok.all() else\n f\"g_t not computable for {len(missing_months)} month(s) in the window ({', '.join(missing_months)}) because the source reports no index level; \"\n \"these months are excluded from all means and extremes. All summary estimands were computed.\",\n \"data_issues\": issues,\n \"n_input_rows\": n_input_rows,\n \"random_seed\": None,\n \"n_months_2001_2013\": int(gs.loc[:\"2013-12-01\"].size),\n \"n_months_2014_2026\": int(gs.loc[\"2014-01-01\":].size),\n \"missing_growth_months\": missing_months,\n}\nwith open(os.path.join(O, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps({k: v for k, v in res.items() if k != \"data_issues\"}, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && head -3 out/analytic.csv && wc -l out/analytic.csv && grep '\"issue\"' out/results.json","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":250,"cache_read_input_tokens":18850,"output_tokens":3492,"output_tokens_details":{"thinking_tokens":18},"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":250},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXo2LJwjeCUZ6FefHtGU","requestRef":"3d1bbbf9-9d2b-4b16-b230-d792f6e6bdb5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8d9c9c69-97bd-42a8-9568-5a880072e422","timestamp":"2026-10-30T04:31:37.153Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}57{"parentUuid":"8d9c9c69-97bd-42a8-9568-5a880072e422","isSidechain":false,"promptId":"8f565a57-70f1-433b-b044-ef7f6d25465c","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HK35sn25nBqQ6ogb7NJ7zc","type":"tool_result","content":"{\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) in the window (2025-10) because the source reports no index level; these months are excluded from all means and extremes. All summary estimands were computed.\",\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 \"n_months_2001_2013\": 156,\n \"n_months_2014_2026\": 152,\n \"missing_growth_months\": [\n \"2025-10\"\n ]\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1,Window 2001-01..2026-09 (all calendar months),309,309,0,Start from every month of the target window\n2,Drop months with no index level P_t in the most recent covering vintage,309,308,1,No imputation allowed: 2025-10\n3,Drop months with no index level P_{t-12},308,308,0,No imputation allowed: none\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\n \"issue\": \"cpi_vintage_2026-10-28.csv: no index value reported for 2025-10 (empty cpi cell)\",\n \"issue\": \"Vintages overlap for 2014-01..2016-06 (30 months) with differing levels (max abs diff 0.455 index points; seasonal-factor/revision differences)\",\n \"issue\": \"cpi_assembled.csv: months absent: 2016-07\",\n \"issue\": \"cpi_assembled.csv: months beyond the sources' coverage: 2026-10 (values appear shifted one month later from 2016-07 onward)\",\n \"issue\": \"cpi_assembled.csv: of 955 months shared with the authoritative splice, 151 have a different level and 30 a different vintage label (2014-01..2016-06 taken from the older vintage; 2016-07 dropped and later values mis-dated)\",","is_error":false}]},"uuid":"41c74583-e07e-4ef3-addd-cfb4def82006","timestamp":"2026-10-30T04:31:37.401Z","toolUseResult":{"stdout":"{\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) in the window (2025-10) because the source reports no index level; these months are excluded from all means and extremes. All summary estimands were computed.\",\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 \"n_months_2001_2013\": 156,\n \"n_months_2014_2026\": 152,\n \"missing_growth_months\": [\n \"2025-10\"\n ]\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1,Window 2001-01..2026-09 (all calendar months),309,309,0,Start from every month of the target window\n2,Drop months with no index level P_t in the most recent covering vintage,309,308,1,No imputation allowed: 2025-10\n3,Drop months with no index level P_{t-12},308,308,0,No imputation allowed: none\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\n \"issue\": \"cpi_vintage_2026-10-28.csv: no index value reported for 2025-10 (empty cpi cell)\",\n \"issue\": \"Vintages overlap for 2014-01..2016-06 (30 months) with differing levels (max abs diff 0.455 index points; seasonal-factor/revision differences)\",\n \"issue\": \"cpi_assembled.csv: months absent: 2016-07\",\n \"issue\": \"cpi_assembled.csv: months beyond the sources' coverage: 2026-10 (values appear shifted one month later from 2016-07 onward)\",\n \"issue\": \"cpi_assembled.csv: of 955 months shared with the authoritative splice, 151 have a different level and 30 a different vintage label (2014-01..2016-06 taken from the older vintage; 2016-07 dropped and later values mis-dated)\",","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"8d9c9c69-97bd-42a8-9568-5a880072e422","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}58{"type":"api-request-blob","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","hash":"ed9dee9b0f7b9b43b64a94cad6c263d7b27470b7058ab0e7fad4178fba0c4814","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSqwYKEAgTGAI4AUIIdGhpbmtpbmcSDA1Hujbmj863ld42axoM9FZ0RRG4aUOLdXPpIjAW0ke72jvE7TFcZogYD6vjuQy1hdVqiFOpu9j+30dfHSByw2flIBF/RYGOfgTH6/EqyAW242VBOFPxqtKD5XJWGdeGThoIRGpYDb0NZCQE+98RaxYnpNlOBM/x4lV48fMr9Km1O5/qYz5Ju9cq0rWMNAXemVjD9fCRui8S0ee3fr7WsjAvrAxgzpYv5xKR1G2VIi+2Xyl7GFLIHehpvNh+LzZ7MOsedDDYwo/Q4jph7dvTdpTRf7EBaGttYeB0umgfgBuOYko8D9T0AgC2jmJTF2d20w4lSjF/QpfXfKidnPuC45DLjbVFhkkDS4kpeWXSbhYnuHaQlGUJqHuyR03COlCt2+C8pNb7dJz1IjhDGKPJ5pn5XYdWV9XpIAXyQgUljWjpPacIkGGVE+Z1H1LsLVPARz9huogqI6YPFrH0jxQ3IDuc6bph+APaeHTwabgzeW8S5y30DElbwIZqzpIIvJcuTBmOLIQkz/ZvCc/7pEr3VR0h+SO2x7wMX5zdAKepKNjQBsJwg7CUJdYIpSY6kwaQDGxQiTYBHBjxTG+iWjmD+KZJDwVQknxNShgmKNgS4HocdQkNNj7qBgAGSqvGgOjNYoGNa/45QPltxHF0qA0O5tzt5td+B8wVoM0wYOADdZsk5cWIvGneAis7V9fB/VxlZPbNFNT8QFZg2mMYDWLNst3BbPykwfpvLojdYwSBmmTYVL9KAs8vzcYLMLq0pF2wJrzUsjnIgrSCM+Sqr+FZMxPPwY0AWmhkms2ICwZTOCUBsBNkpnRq0QCzdq02gBuXFhvrkslCmGxh1v+BUq/m6y9wsyBRWIGk5L5wkOlapo1XbtgizVxOtWSTCTHCIt9HGwQf2yJ57as+K2j5UIflOUR13Jx1Ov1u4xHh08STgFtecwttIYhdaeomT0MjFmTnxGaGsF8In3MLCY1D5mkV0YMFICyf1qAwXZMXWZH2xj8AwpiI24mHRvop+q7m8Dbwf7Sy4qpXykZ2gAsq0IbW0j0EV4BRqIyDGAE="},{"type":"tool_use","id":"toolu_01HK35sn25nBqQ6ogb7NJ7zc","name":"Bash","input":{"command":"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, os\nimport numpy as np\nimport pandas as pd\n\nD, O = \"data\", \"out\"\nF_OLD, F_NEW, F_ASM = \"cpi_vintage_2016-07-15.csv\", \"cpi_vintage_2026-10-28.csv\", \"cpi_assembled.csv\"\nV_OLD, V_NEW = \"2016-07-15\", \"2026-10-28\"\nW0, W1 = pd.Timestamp(\"2001-01-01\"), pd.Timestamp(\"2026-09-01\")\n\nold = pd.read_csv(os.path.join(D, F_OLD), parse_dates=[\"date\"])\nnew = pd.read_csv(os.path.join(D, F_NEW), parse_dates=[\"date\"])\nasm = pd.read_csv(os.path.join(D, F_ASM), parse_dates=[\"date\"])\nn_input_rows = {F_OLD: len(old), F_NEW: len(new), F_ASM: len(asm)}\nissues = []\n\nfor name, df in [(F_OLD, old), (F_NEW, new)]:\n assert not df.date.duplicated().any(), name\n full = pd.date_range(df.date.min(), df.date.max(), freq=\"MS\")\n assert len(full) == len(df), f\"gap in {name}\"\n for d in df.loc[df.cpi.isna(), \"date\"]:\n issues.append({\"issue\": f\"{name}: no index value reported for {d:%Y-%m} (empty cpi cell)\",\n \"handling\": \"Month treated as unavailable in that vintage; the older vintage does not cover it, so P is missing. \"\n \"Not interpolated; g_t for this month (and for t+12 if inside the window) reported as missing.\"})\n\n# Splice: most recent vintage in which the month is available (non-empty value).\no = old.dropna(subset=[\"cpi\"]).assign(vintage_used=V_OLD).set_index(\"date\")\nn = new.dropna(subset=[\"cpi\"]).assign(vintage_used=V_NEW).set_index(\"date\")\nlev = pd.concat([n, o[~o.index.isin(n.index)]]).sort_index()\nov = old.merge(new, on=\"date\", suffixes=(\"_old\", \"_new\")).dropna()\nissues.append({\"issue\": f\"Vintages overlap for {ov.date.min():%Y-%m}..{ov.date.max():%Y-%m} ({len(ov)} months) with differing levels \"\n f\"(max abs diff {np.abs(ov.cpi_new-ov.cpi_old).max():.3f} index points; seasonal-factor/revision differences)\",\n \"handling\": \"2026-10-28 vintage used for all overlap months per the most-recent-vintage rule; growth for 2014-01..2014-12 \"\n \"uses a 2026-vintage numerator and a 2016-vintage (2013) denominator, as the rule implies.\"})\n\n# Diagnose the hand-assembled file against the authoritative splice\na = asm.set_index(\"date\")\nfull_rng = pd.date_range(\"1947-01-01\", \"2026-09-01\", freq=\"MS\")\nmissing_in_asm = [d for d in full_rng if d not in a.index]\nextra_in_asm = [d for d in a.index if d not in full_rng]\nif missing_in_asm:\n issues.append({\"issue\": f\"{F_ASM}: months absent: {', '.join(f'{d:%Y-%m}' for d in missing_in_asm)}\",\n \"handling\": \"Assembled file not used for estimation; levels taken from the two vintage files.\"})\nif extra_in_asm:\n issues.append({\"issue\": f\"{F_ASM}: months beyond the sources' coverage: {', '.join(f'{d:%Y-%m}' for d in extra_in_asm)} \"\n \"(values appear shifted one month later from 2016-07 onward)\",\n \"handling\": \"Assembled file not used for estimation.\"})\ncmp_ = a.join(lev[[\"cpi\", \"vintage_used\"]], how=\"inner\", rsuffix=\"_auth\")\nvint_wrong = int((cmp_.vintage != cmp_.vintage_used).sum())\nval_wrong = int((~np.isclose(cmp_.cpi, cmp_.cpi_auth, equal_nan=True)).sum())\nissues.append({\"issue\": f\"{F_ASM}: of {len(cmp_)} months shared with the authoritative splice, {val_wrong} have a different level and \"\n f\"{vint_wrong} a different vintage label (2014-01..2016-06 taken from the older vintage; 2016-07 dropped and later values mis-dated)\",\n \"handling\": \"Assembled file read for diagnostics only; all estimates use the vintage files directly.\"})\n\n# Growth rates\nlev = lev.reindex(pd.date_range(lev.index.min(), lev.index.max(), freq=\"MS\"))\nlag = lev.cpi.shift(12)\ng = 100 * (lev.cpi / lag - 1)\nwin = pd.date_range(W0, W1, freq=\"MS\")\ntab = pd.DataFrame({\"cpi\": lev.cpi, \"cpi_lag12\": lag, \"vintage_used\": lev.vintage_used, \"growth_12m\": g}).reindex(win)\nok = tab.growth_12m.notna()\nanalytic = tab[ok].copy()\nmissing_months = [f\"{d:%Y-%m}\" for d in tab.index[~ok]]\n\nn2 = int(tab.cpi.notna().sum())\nexcl = [\n dict(step=1, rule=\"Window 2001-01..2026-09 (all calendar months)\", n_before=len(win), n_after=len(win), n_removed=0,\n rationale=\"Start from every month of the target window\"),\n dict(step=2, rule=\"Drop months with no index level P_t in the most recent covering vintage\",\n n_before=len(win), n_after=n2, n_removed=len(win) - n2,\n rationale=\"No imputation allowed: \" + (\", \".join(f\"{d:%Y-%m}\" for d in tab.index[tab.cpi.isna()]) or \"none\")),\n dict(step=3, rule=\"Drop months with no index level P_{t-12}\", n_before=n2, n_after=int(ok.sum()),\n n_removed=n2 - int(ok.sum()),\n rationale=\"No imputation allowed: \" + (\", \".join(f\"{d:%Y-%m}\" for d in tab.index[tab.cpi.notna() & tab.cpi_lag12.isna()]) or \"none\")),\n]\npd.DataFrame(excl).to_csv(os.path.join(O, \"exclusions.csv\"), index=False)\n\nout_a = analytic.reset_index().rename(columns={\"index\": \"date\"})\nout_a[\"date\"] = out_a.date.dt.strftime(\"%Y-%m-%d\")\nout_a.to_csv(os.path.join(O, \"analytic.csv\"), index=False, float_format=\"%.10g\")\n\ngs = analytic.growth_12m\ndef gm(m):\n v = tab.growth_12m.get(pd.Timestamp(m + \"-01\"))\n return None if v is None or pd.isna(v) else round(float(v), 10)\nr = lambda x: round(float(x), 10)\nlast_month = lev.cpi.loc[:W1].dropna().index.max()\nres = {\n \"task_id\": \"F1-conf-01\",\n \"first_growth_month\": f\"{gs.index.min():%Y-%m}\",\n \"last_month\": f\"{last_month:%Y-%m}\",\n \"n_months_growth\": int(ok.sum()),\n \"n_months_missing_growth\": int((~ok).sum()),\n \"n_analysed\": int(ok.sum()),\n \"mean_growth_12m\": r(gs.mean()),\n \"mean_growth_12m_2001_2013\": r(gs.loc[:\"2013-12-01\"].mean()),\n \"mean_growth_12m_2014_2026\": r(gs.loc[\"2014-01-01\":].mean()),\n \"growth_2014_06\": gm(\"2014-06\"), \"growth_2016_07\": gm(\"2016-07\"), \"growth_2020_05\": gm(\"2020-05\"),\n \"growth_2022_06\": gm(\"2022-06\"), \"growth_2026_09\": gm(\"2026-09\"),\n \"max_growth_12m\": r(gs.max()), \"max_growth_month\": f\"{gs.idxmax():%Y-%m}\",\n \"min_growth_12m\": r(gs.min()), \"min_growth_month\": f\"{gs.idxmin():%Y-%m}\",\n \"feasibility\": \"complete\" if ok.all() else \"partial\",\n \"feasibility_reason\": None if ok.all() else\n f\"g_t not computable for {len(missing_months)} month(s) in the window ({', '.join(missing_months)}) because the source reports no index level; \"\n \"these months are excluded from all means and extremes. All summary estimands were computed.\",\n \"data_issues\": issues,\n \"n_input_rows\": n_input_rows,\n \"random_seed\": None,\n \"n_months_2001_2013\": int(gs.loc[:\"2013-12-01\"].size),\n \"n_months_2014_2026\": int(gs.loc[\"2014-01-01\":].size),\n \"missing_growth_months\": missing_months,\n}\nwith open(os.path.join(O, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps({k: v for k, v in res.items() if k != \"data_issues\"}, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && head -3 out/analytic.csv && wc -l out/analytic.csv && grep '\"issue\"' out/results.json","description":"Write and run analysis script"}}]}}59{"type":"api-request","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","id":"a6c778f4-8c66-47dd-b0d7-78e9f715fbe8","timestamp":"2026-10-30T04:31:37.404Z","version":"2.1.314","querySource":"sdk","shapeHash":"feb5c91d53bbada2e1da572f056120317de4cfdbadce4316c811c4f5cc3165bd","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":"3d1bbbf9-9d2b-4b16-b230-d792f6e6bdb5","keep":16,"tail":["ed9dee9b0f7b9b43b64a94cad6c263d7b27470b7058ab0e7fad4178fba0c4814","7c36e29e326f2fb03872c261a5ca38e21bdab6480e7a86856c0987ca1158214a"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",17,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"35d92f48de8d999848a07e7ad70553e7006f40953efd49635c05a4c129a42bba"}60{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"41c74583-e07e-4ef3-addd-cfb4def82006","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56"}61{"type":"atis-latch","atis":"","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56"}62{"parentUuid":"41c74583-e07e-4ef3-addd-cfb4def82006","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo43ZC26xisk7hd9hiC","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSpQoKEAgTGAI4AUIIdGhpbmtpbmcSDDzLy39tLOc2+iT3bxoMvpfjFwOGUCgP02IHIjBGr2ARXenuSsFDsXBgGXhnjJ8hvc/C9LT32S6iGZSyIO8g9SvLYDX+bs3pZI9yhG4qwgnc0T5hNK7rFbplb3iR/EyBTBPIUOgZyzyAGqAQG2QOvNXwG/D4/3vDWBpz18ucLoYe8e95NMUIUw8ZJwNV5tM9R0+wYnyJNSRnN6QQ/+eR4bp3WO2woHAiL6iR+ewWI9lgQikdtWJMptCn9KcDivQs8e/h519tDkxmKWVb2hqqLoSzmcca3lYSwsWiKEsidBp+JQh268yaRiaCMUa7h8JqvuscuXn/di1ktUAbsu2brpbjYRuEtwcaqM9PIralr/vXtNDB3hs/iwCILAoVyM44sCMY9mPK50c3+062S0G/f272MR4czOHRQjDceKH7fDxZXWmf10ivupzNkIc9LgnrSkaVCEEpMQZZnAXcdnJqGmQ4Ez0pIXZtMgCTX+fJkP8c6BnRd5b8bH6RnklJD8P3sQZnGsep+o77fBELFvj1/pnEyGy1igwRNFxEGOfwxdy2WSqU9DDJHlBn5n6TXaVGshpOSqA6oTmI7qu3M5eH4ZFRRXbnV8vVSAlswtK4yzdXk2dGJVFO8LVSwEO6vMuE3A3UTHYBnDB3gXd+VbJBRqq7JfND15sj9NMBoa/cIwS1BtvFwpTXqGde8QtLZG5pUW81G0BqraaVNgjpXBlWzFanGgj8WOrkjuMtCj4gS/V2EUSqtqXtsnkK+g7K7h+IJk8l3H+coF3TuQoKotMl25v4MQ0oPhwOEs4ddKtzqvdYkMtm39U5b/HOEX9wD1LwSsVjCGxPK36fb1cxCNrtC9TEnGtriX0WsUWfS3n7OX2NB22v8Q9rMTRM2xpd5hAiMb2odqedZxlJTuT1mcSiGKW0ks2XnaalMDKT2W83S929DHHB6NYH03hrvVYT1iVVmJFRSuw3W+tN99DyNTZYvMGHsZO3bfvThg3oVrmnmd8TSMo4uMqhAMRJNOjyFJASPW+5Tr65CuseYhhCDRw0l95VDOF7K/nJR4WbQ22RPd7doOM2vRILWt6QSx66xOGimODvX9fQt8KphX6nFi2N+4Iz7CLEo8/4e8HxM1FjBD9MEgRBcXg/DzXZC//Azj50T3ik5bsSYGYVKI9q3EkfpcD02xECvtHKSAFxt/Cjc7jsTx7h5pfGcMk1mEn2UDfmVOyKb24ZaFY//pPaU70XJsVTf3H71yX1MFuQ2qFvZPY4tXQrgIWna2FPMSQkIVzTTFyLmUEs56aeXqnLSaoQRKr1Wj2gZVPWVOdM9Qw1zjLk3j8EB2ofSw+eqttx22GoUJPUFng7O6lDANA4IMn92SzsTWS10fhhS6JKiIRudinxbM2RHpGxeIu3s+0xUoA4PL/GkTXxIafTf3u2g+mrW07q8yetBCFTd36+sQig9GAR8G0jDPq/1rNXiZMCxFjnXmNTwjc2i8YJOt/t751cBpD6FMdniAvwALFjIeJ+ADyNPASw30DKTtN3O0KJXG56drDdj0a2vAUDZ7GvGDOGs+m9NZ6b9RaFPsbUfUdjOb9l6BCN72syRJewPNNiDgIVGoycjOSFv0OnyBGMSq7x4vFtTvd5Tjfkd7sEhUIzPSZsRuAA+2J05kK9gv52njBXOT++W54YoBPTntGJ/spN/KF5mf6RxngihVpuZH7eaMRKiyyALWMgdeDudk0Ff7xxtYEYAQ=="}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4532,"cache_read_input_tokens":19100,"output_tokens":1088,"output_tokens_details":{"thinking_tokens":193},"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":4532},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1731,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXo43J4dQPQQg6nPX2XY","requestRef":"a6c778f4-8c66-47dd-b0d7-78e9f715fbe8","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4feaf69a-0231-40f1-91d8-7ad5b1eaeb5c","timestamp":"2026-10-30T04:31:40.669Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}63{"parentUuid":"4feaf69a-0231-40f1-91d8-7ad5b1eaeb5c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo43ZC26xisk7hd9hiC","type":"message","role":"assistant","content":[{"type":"text","text":"Now verification: clean re-run and independent consistency checks."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4532,"cache_read_input_tokens":19100,"output_tokens":1088,"output_tokens_details":{"thinking_tokens":193},"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":4532},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXo43J4dQPQQg6nPX2XY","requestRef":"a6c778f4-8c66-47dd-b0d7-78e9f715fbe8","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"62cbd318-ee08-4a79-95bc-03e3540d6ca8","timestamp":"2026-10-30T04:31:40.673Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}64{"parentUuid":"62cbd318-ee08-4a79-95bc-03e3540d6ca8","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo43ZC26xisk7hd9hiC","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01T2znoyk1vfhWEKCDccM8NS","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; shasum -a 256 out/results.json out/analytic.csv out/exclusions.csv; rm out/results.json out/analytic.csv out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; shasum -a 256 out/results.json out/analytic.csv out/exclusions.csv; cmp /tmp/r1.json out/results.json && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd, json, numpy as np\nr=json.load(open('out/results.json')); a=pd.read_csv('out/analytic.csv')\no=pd.read_csv('data/cpi_vintage_2016-07-15.csv',index_col=0); n=pd.read_csv('data/cpi_vintage_2026-10-28.csv',index_col=0)\ndef P(d): return n.cpi[d] if d in n.index else o.cpi[d]\n# independent spot checks\nfor m,lag in [('2014-06','2013-06'),('2016-07','2015-07'),('2020-05','2019-05'),('2022-06','2021-06'),('2026-09','2025-09'),('2009-07','2008-07')]:\n print(m, 100*(P(m+'-01')/P(lag+'-01')-1))\nprint('rows',len(a)==r['n_analysed'], r['n_months_growth']+r['n_months_missing_growth']==309)\nprint('submeans weighted', (r['mean_growth_12m_2001_2013']*156+r['mean_growth_12m_2014_2026']*152)/308, r['mean_growth_12m'])\nprint('recompute', np.allclose(100*(a.cpi/a.cpi_lag12-1),a.growth_12m), a.growth_12m.mean())\nprint('vintage', a[a.date>='2014-01-01'].vintage_used.unique(), a[a.date<'2014-01-01'].vintage_used.unique())\nprint('max/min', a.growth_12m.max(), a.loc[a.growth_12m.idxmax(),'date'], a.growth_12m.min(), a.loc[a.growth_12m.idxmin(),'date'])\nprint('2025-10 absent', '2025-10-01' not in set(a.date), '2026-10-01' not in set(a.date))\nEOF","description":"Reproducibility and consistency checks"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4532,"cache_read_input_tokens":19100,"output_tokens":1088,"output_tokens_details":{"thinking_tokens":193},"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":4532},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXo43J4dQPQQg6nPX2XY","requestRef":"a6c778f4-8c66-47dd-b0d7-78e9f715fbe8","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f8545905-5022-42e4-a847-e76bd62177a4","timestamp":"2026-10-30T04:31:46.879Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}65{"parentUuid":"f8545905-5022-42e4-a847-e76bd62177a4","isSidechain":false,"promptId":"8f565a57-70f1-433b-b044-ef7f6d25465c","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01T2znoyk1vfhWEKCDccM8NS","type":"tool_result","content":"78303c9f5d7510180e14abd970c4002328b1c31c26182b982eb2d26150099e05 out/results.json\na5ec3bfeeec38023b2b0a5bf4e5335250285154861ed0b70154f77622879dde8 out/analytic.csv\n7a3ea6b24780578efc56b6b1deb2468d3f0553af379291a57fd4914f768a87f4 out/exclusions.csv\n78303c9f5d7510180e14abd970c4002328b1c31c26182b982eb2d26150099e05 out/results.json\na5ec3bfeeec38023b2b0a5bf4e5335250285154861ed0b70154f77622879dde8 out/analytic.csv\n7a3ea6b24780578efc56b6b1deb2468d3f0553af379291a57fd4914f768a87f4 out/exclusions.csv\nIDENTICAL\n2014-06 2.0976342438575157\n2016-07 0.8683633430518256\n2020-05 0.1982013035848551\n2022-06 8.979361103105798\n2026-09 3.7172508442689933\n2009-07 -1.9587610037622771\nrows True True\nsubmeans weighted 2.5741201900493507 2.5741201901\nrecompute True 2.574120190074143\nvintage ['2026-10-28'] ['2016-07-15']\nmax/min 8.979361103 2022-06-01 -1.958761004 2009-07-01\n2025-10 absent True True","is_error":false}]},"uuid":"df2e4c2e-2141-4a0c-b3b3-0b935c2081e4","timestamp":"2026-10-30T04:31:47.312Z","toolUseResult":{"stdout":"78303c9f5d7510180e14abd970c4002328b1c31c26182b982eb2d26150099e05 out/results.json\na5ec3bfeeec38023b2b0a5bf4e5335250285154861ed0b70154f77622879dde8 out/analytic.csv\n7a3ea6b24780578efc56b6b1deb2468d3f0553af379291a57fd4914f768a87f4 out/exclusions.csv\n78303c9f5d7510180e14abd970c4002328b1c31c26182b982eb2d26150099e05 out/results.json\na5ec3bfeeec38023b2b0a5bf4e5335250285154861ed0b70154f77622879dde8 out/analytic.csv\n7a3ea6b24780578efc56b6b1deb2468d3f0553af379291a57fd4914f768a87f4 out/exclusions.csv\nIDENTICAL\n2014-06 2.0976342438575157\n2016-07 0.8683633430518256\n2020-05 0.1982013035848551\n2022-06 8.979361103105798\n2026-09 3.7172508442689933\n2009-07 -1.9587610037622771\nrows True True\nsubmeans weighted 2.5741201900493507 2.5741201901\nrecompute True 2.574120190074143\nvintage ['2026-10-28'] ['2016-07-15']\nmax/min 8.979361103 2022-06-01 -1.958761004 2009-07-01\n2025-10 absent True True","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"f8545905-5022-42e4-a847-e76bd62177a4","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}66{"type":"api-request-blob","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","hash":"21d4b2a5123bde777b553de6e101c78edc5af716c55f651a9976d11704743b81","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"Now verification: clean re-run and independent consistency checks."},{"type":"tool_use","id":"toolu_01T2znoyk1vfhWEKCDccM8NS","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; shasum -a 256 out/results.json out/analytic.csv out/exclusions.csv; rm out/results.json out/analytic.csv out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; shasum -a 256 out/results.json out/analytic.csv out/exclusions.csv; cmp /tmp/r1.json out/results.json && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd, json, numpy as np\nr=json.load(open('out/results.json')); a=pd.read_csv('out/analytic.csv')\no=pd.read_csv('data/cpi_vintage_2016-07-15.csv',index_col=0); n=pd.read_csv('data/cpi_vintage_2026-10-28.csv',index_col=0)\ndef P(d): return n.cpi[d] if d in n.index else o.cpi[d]\n# independent spot checks\nfor m,lag in [('2014-06','2013-06'),('2016-07','2015-07'),('2020-05','2019-05'),('2022-06','2021-06'),('2026-09','2025-09'),('2009-07','2008-07')]:\n print(m, 100*(P(m+'-01')/P(lag+'-01')-1))\nprint('rows',len(a)==r['n_analysed'], r['n_months_growth']+r['n_months_missing_growth']==309)\nprint('submeans weighted', (r['mean_growth_12m_2001_2013']*156+r['mean_growth_12m_2014_2026']*152)/308, r['mean_growth_12m'])\nprint('recompute', np.allclose(100*(a.cpi/a.cpi_lag12-1),a.growth_12m), a.growth_12m.mean())\nprint('vintage', a[a.date>='2014-01-01'].vintage_used.unique(), a[a.date<'2014-01-01'].vintage_used.unique())\nprint('max/min', a.growth_12m.max(), a.loc[a.growth_12m.idxmax(),'date'], a.growth_12m.min(), a.loc[a.growth_12m.idxmin(),'date'])\nprint('2025-10 absent', '2025-10-01' not in set(a.date), '2026-10-01' not in set(a.date))\nEOF","description":"Reproducibility and consistency checks"}}]}}67{"type":"api-request","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","id":"84547891-5fc6-4745-b176-cb8a0dc1ac61","timestamp":"2026-10-30T04:31:47.315Z","version":"2.1.314","querySource":"sdk","shapeHash":"feb5c91d53bbada2e1da572f056120317de4cfdbadce4316c811c4f5cc3165bd","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":"a6c778f4-8c66-47dd-b0d7-78e9f715fbe8","keep":18,"tail":["21d4b2a5123bde777b553de6e101c78edc5af716c55f651a9976d11704743b81","a817d52d8d7935e43630a1866bc8542e59b9962b0e861a2195aacd35af4c0287"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"09b7e1faf4ed6efa91387fb42ac1ebff63b8816f23faf43062cf96166299d2d4"}68{"parentUuid":"df2e4c2e-2141-4a0c-b3b3-0b935c2081e4","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo4mv8WgxcUNfYNLqAA","type":"message","role":"assistant","content":[{"type":"text","text":"Now the state files, then the gate."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1583,"cache_read_input_tokens":23632,"output_tokens":2091,"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":1583},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXo4meznEaWb7mYUfp6G","requestRef":"84547891-5fc6-4745-b176-cb8a0dc1ac61","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5d8647a8-ba9b-4acd-b55c-e22d3df7a8a4","timestamp":"2026-10-30T04:31:48.526Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}69{"parentUuid":"5d8647a8-ba9b-4acd-b55c-e22d3df7a8a4","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo4mv8WgxcUNfYNLqAA","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01PmeBrHmG3PdgAjtLFEHgCa","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n| file | SHA-256 (as found in data/) | rows | cols | date range | missing cpi | duplicate dates | gaps |\n|---|---|---|---|---|---|---|---|\n| cpi_vintage_2016-07-15.csv | 281fbcdd6a6eeb3e9cb7c1f20476e6bd1f7b925bf140606fe46538fe83f2c83e | 834 | 2 (date, cpi) | 1947-01..2016-06 | 0 | 0 | none |\n| cpi_vintage_2026-10-28.csv | 8220d5b4d70ca32fc2c7b1975651c515967db8a87053cf725159c63ed274cfba | 153 | 2 (date, cpi) | 2014-01..2026-09 | 1 (2025-10) | 0 | none |\n| cpi_assembled.csv | ff732b201a525d6fb171d4e8b2d470f986863c1547a1928e42bb197c0777d44d | 957 | 3 (date, cpi, vintage) | 1947-01..2026-10 | 1 (labelled 2025-11) | 0 | 2016-07 missing |\n| README.md | ac136bff13bbdb5afac60e0404212ea1f73f992bd5cca1c1e7a6599df78f8965 | — | — | — | — | — | — |\n\nNote: README SHA-256 values refer to the raw downloads before reformatting, so they do not match the reformatted CSVs here (expected per README).\n\nUnits: index 1982–84=100, SA, monthly, first-of-month dates. 2016 vintage cpi range 21.48–239.927.\n\nAnomalies:\n- 2026 vintage: empty cpi for 2025-10 (source reports no value). Older vintage does not cover it -> P_2025-10 missing; g_2025-10 missing. g_2026-10 would also need it but lies outside the window.\n- Overlap 2014-01..2016-06 (30 months): levels differ by up to 0.455 points (revisions/seasonal factors). Rule: use 2026 vintage.\n- cpi_assembled.csv errors: overlap months use the OLD vintage (30 months mislabelled vs. rule); 2016-07 omitted; from 2016-08 onward every value is shifted one month later (e.g. labelled 2016-08 = true 2016-07 value 240.101; labelled 2026-10 = true 2026-09 value 336.298); hence the missing value appears as 2025-11. 151 of 955 shared months have a wrong level.\nDecision: the assembled file is read for diagnostics only (counted in n_input_rows); all estimates come from the two vintage files. No input violates the availability rules beyond these assembly errors.\nEOF\ncat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n1. Clean re-run: deleted results.json, analytic.csv, exclusions.csv; re-ran analysis.py; SHA-256 identical (results.json 78303c9f…). PASS\n2. Independent spot recomputation of g for 2014-06, 2016-07, 2020-05, 2022-06, 2026-09, 2009-07 directly from vintage files: matches results to 1e-10. PASS\n3. n_months_growth + n_months_missing_growth = 309 (window months); analytic.csv rows = n_analysed = 308. PASS\n4. Exclusion ledger: 309 -> 309 -> 308 -> 308, each n_before − n_removed = n_after, chain consistent. PASS\n5. Weighted sub-period means (156, 152 months) reproduce the overall mean 2.5741201901. PASS\n6. growth_12m in analytic.csv equals 100(cpi/cpi_lag12 − 1) on every row. PASS\n7. vintage_used = 2026-10-28 for all months ≥2014-01, 2016-07-15 before (information rule). PASS\n8. max/min and their months match analytic.csv. 2025-10 absent (no imputation); no month beyond 2026-09. PASS\n9. Magnitudes: overall mean 2.57% close to 2001–2013 baseline 2.36%; extremes (−1.96% 2009-07, 8.98% 2022-06) plausible for US CPI. PASS\nNo uncertainty intervals are produced (deterministic arithmetic on published data), so no CI checks apply.\nEOF\necho \"- 2026-10-30 P2: baseline mean_growth_12m_2001_2013 = 2.3612 computed. P3: analysis.py written and run. P4: clean re-run identical; checks in checks.md all pass.\" >> out/state/log.md\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np={\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read; contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256, rows, schema, ranges, missing, duplicates recorded in data_audit.md\",\"assembled-file errors identified\",\"exclusions.csv chain sums verified\"],\"notes\":\"assembled file used for diagnostics only\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"baseline mean_growth_12m_2001_2013 computed with same rule\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py runs from root, reads only data/, no randomness\"],\"notes\":\"\"},\n\"P4\":{\"status\":\"running\",\"checks\":[\"clean re-run: identical SHA-256 of all outputs\",\"9 consistency checks pass (checks.md)\"],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\njson.dump(p,open('out/state/phases.json','w'),indent=2)\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts","description":"Write audit, checks, phases; run gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1583,"cache_read_input_tokens":23632,"output_tokens":2091,"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":1583},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXo4meznEaWb7mYUfp6G","requestRef":"84547891-5fc6-4745-b176-cb8a0dc1ac61","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e0a08a4f-d9f5-43dd-babd-c15e68a5b033","timestamp":"2026-10-30T04:32:06.485Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}70{"parentUuid":"e0a08a4f-d9f5-43dd-babd-c15e68a5b033","isSidechain":false,"promptId":"8f565a57-70f1-433b-b044-ef7f6d25465c","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PmeBrHmG3PdgAjtLFEHgCa","type":"tool_result","content":"PGAR 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 0.2s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0100/out/state/gate_receipts/20261030T043206.762299Z.json\n20261030T043206.762299Z.json","is_error":false}]},"uuid":"7036bb80-c027-4964-a93a-5f8a3197177d","timestamp":"2026-10-30T04:32:06.769Z","toolUseResult":{"stdout":"PGAR 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 0.2s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0100/out/state/gate_receipts/20261030T043206.762299Z.json\n20261030T043206.762299Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"e0a08a4f-d9f5-43dd-babd-c15e68a5b033","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}71{"type":"api-request-blob","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","hash":"5a352c84e2dc2955e48b82d643923b63096b71be8b3b72e84f2de2fc818940ab","message":{"role":"assistant","content":[{"type":"text","text":"Now the state files, then the gate."},{"type":"tool_use","id":"toolu_01PmeBrHmG3PdgAjtLFEHgCa","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n| file | SHA-256 (as found in data/) | rows | cols | date range | missing cpi | duplicate dates | gaps |\n|---|---|---|---|---|---|---|---|\n| cpi_vintage_2016-07-15.csv | 281fbcdd6a6eeb3e9cb7c1f20476e6bd1f7b925bf140606fe46538fe83f2c83e | 834 | 2 (date, cpi) | 1947-01..2016-06 | 0 | 0 | none |\n| cpi_vintage_2026-10-28.csv | 8220d5b4d70ca32fc2c7b1975651c515967db8a87053cf725159c63ed274cfba | 153 | 2 (date, cpi) | 2014-01..2026-09 | 1 (2025-10) | 0 | none |\n| cpi_assembled.csv | ff732b201a525d6fb171d4e8b2d470f986863c1547a1928e42bb197c0777d44d | 957 | 3 (date, cpi, vintage) | 1947-01..2026-10 | 1 (labelled 2025-11) | 0 | 2016-07 missing |\n| README.md | ac136bff13bbdb5afac60e0404212ea1f73f992bd5cca1c1e7a6599df78f8965 | — | — | — | — | — | — |\n\nNote: README SHA-256 values refer to the raw downloads before reformatting, so they do not match the reformatted CSVs here (expected per README).\n\nUnits: index 1982–84=100, SA, monthly, first-of-month dates. 2016 vintage cpi range 21.48–239.927.\n\nAnomalies:\n- 2026 vintage: empty cpi for 2025-10 (source reports no value). Older vintage does not cover it -> P_2025-10 missing; g_2025-10 missing. g_2026-10 would also need it but lies outside the window.\n- Overlap 2014-01..2016-06 (30 months): levels differ by up to 0.455 points (revisions/seasonal factors). Rule: use 2026 vintage.\n- cpi_assembled.csv errors: overlap months use the OLD vintage (30 months mislabelled vs. rule); 2016-07 omitted; from 2016-08 onward every value is shifted one month later (e.g. labelled 2016-08 = true 2016-07 value 240.101; labelled 2026-10 = true 2026-09 value 336.298); hence the missing value appears as 2025-11. 151 of 955 shared months have a wrong level.\nDecision: the assembled file is read for diagnostics only (counted in n_input_rows); all estimates come from the two vintage files. No input violates the availability rules beyond these assembly errors.\nEOF\ncat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n1. Clean re-run: deleted results.json, analytic.csv, exclusions.csv; re-ran analysis.py; SHA-256 identical (results.json 78303c9f…). PASS\n2. Independent spot recomputation of g for 2014-06, 2016-07, 2020-05, 2022-06, 2026-09, 2009-07 directly from vintage files: matches results to 1e-10. PASS\n3. n_months_growth + n_months_missing_growth = 309 (window months); analytic.csv rows = n_analysed = 308. PASS\n4. Exclusion ledger: 309 -> 309 -> 308 -> 308, each n_before − n_removed = n_after, chain consistent. PASS\n5. Weighted sub-period means (156, 152 months) reproduce the overall mean 2.5741201901. PASS\n6. growth_12m in analytic.csv equals 100(cpi/cpi_lag12 − 1) on every row. PASS\n7. vintage_used = 2026-10-28 for all months ≥2014-01, 2016-07-15 before (information rule). PASS\n8. max/min and their months match analytic.csv. 2025-10 absent (no imputation); no month beyond 2026-09. PASS\n9. Magnitudes: overall mean 2.57% close to 2001–2013 baseline 2.36%; extremes (−1.96% 2009-07, 8.98% 2022-06) plausible for US CPI. PASS\nNo uncertainty intervals are produced (deterministic arithmetic on published data), so no CI checks apply.\nEOF\necho \"- 2026-10-30 P2: baseline mean_growth_12m_2001_2013 = 2.3612 computed. P3: analysis.py written and run. P4: clean re-run identical; checks in checks.md all pass.\" >> out/state/log.md\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np={\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read; contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256, rows, schema, ranges, missing, duplicates recorded in data_audit.md\",\"assembled-file errors identified\",\"exclusions.csv chain sums verified\"],\"notes\":\"assembled file used for diagnostics only\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"baseline mean_growth_12m_2001_2013 computed with same rule\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py runs from root, reads only data/, no randomness\"],\"notes\":\"\"},\n\"P4\":{\"status\":\"running\",\"checks\":[\"clean re-run: identical SHA-256 of all outputs\",\"9 consistency checks pass (checks.md)\"],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\njson.dump(p,open('out/state/phases.json','w'),indent=2)\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts","description":"Write audit, checks, phases; run gate"}}]}}72{"type":"api-request","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","id":"6a040fb3-b093-4390-bdb1-b59a63cab790","timestamp":"2026-10-30T04:32:06.772Z","version":"2.1.314","querySource":"sdk","shapeHash":"feb5c91d53bbada2e1da572f056120317de4cfdbadce4316c811c4f5cc3165bd","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":"84547891-5fc6-4745-b176-cb8a0dc1ac61","keep":20,"tail":["5a352c84e2dc2955e48b82d643923b63096b71be8b3b72e84f2de2fc818940ab","82677671b2f623d7b7a2516a762c9fd1adb23c25e3f0f661f2a37d9a9f73e8d6"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",21,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"83eb3e54d5bee2d16cf2679641a9fbe0f40698cd3efda868208df5759776bb43"}73{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"7036bb80-c027-4964-a93a-5f8a3197177d","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56"}74{"type":"atis-latch","atis":"","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56"}75{"parentUuid":"7036bb80-c027-4964-a93a-5f8a3197177d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo6D8xaovWAEzvkT2cm","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":2955,"cache_read_input_tokens":25215,"output_tokens":3230,"output_tokens_details":{"thinking_tokens":52},"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":2955},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":891,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXo6Cr5z1uVJ1hTK1LT6","requestRef":"6a040fb3-b093-4390-bdb1-b59a63cab790","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"302bb790-5126-4523-8bbf-0322b59cb020","timestamp":"2026-10-30T04:32:09.124Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}76{"parentUuid":"302bb790-5126-4523-8bbf-0322b59cb020","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo6D8xaovWAEzvkT2cm","type":"message","role":"assistant","content":[{"type":"text","text":"The P4 gate run fails only on the P5 deliverables, which don't exist yet; the contract and reproducibility checks pass. Next I'll write the report and claims."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2955,"cache_read_input_tokens":25215,"output_tokens":3230,"output_tokens_details":{"thinking_tokens":52},"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":2955},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXo6Cr5z1uVJ1hTK1LT6","requestRef":"6a040fb3-b093-4390-bdb1-b59a63cab790","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2e1379f1-1189-40d7-94d6-68807dbca5d6","timestamp":"2026-10-30T04:32:09.126Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}77{"parentUuid":"2e1379f1-1189-40d7-94d6-68807dbca5d6","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo6D8xaovWAEzvkT2cm","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01BmZXpdVRTEyCAmtUEH5uU7","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F1-conf-01 — 12-month CPI inflation from two data vintages\n\n## Question\nWhat was the 12-month growth rate g_t = 100 × (P_t / P_{t−12} − 1) of CPIAUCSL (seasonally adjusted) for each month from 2001-01 to 2026-09? P_t comes from the most recent vintage that covers month t: the 2026-10-28 vintage for 2014-01 … 2026-09, and the 2016-07-15 vintage for earlier months. The report also gives the mean over the window, the means for 2001–2013 and 2014–2026, five named months, and the extremes.\n\n## Data and exclusions\n- Inputs (`n_input_rows`): `cpi_vintage_2016-07-15.csv` has 834 rows covering 1947-01 … 2016-06. `cpi_vintage_2026-10-28.csv` has 153 rows covering 2014-01 … 2026-09, and its 2025-10 value is empty. `cpi_assembled.csv` has 957 rows. Hashes and the full audit are in `out/state/data_audit.md`.\n- **The hand-assembled file contains errors, so I did not use it for estimation** (`data_issues`):\n - For 2014-01 … 2016-06 it takes values from the *older* vintage, which contradicts the most-recent-vintage rule.\n - It omits 2016-07.\n - From 2016-08 onward every value sits one month later than its true month. As a result it contains a spurious 2026-10 row and shows the missing value at 2025-11 instead of 2025-10.\n - 151 of the 955 months it shares with the correct splice have a wrong level.\n\n I read it only to diagnose these problems. All levels come directly from the two vintage files.\n- The two vintages overlap for 2014-01 … 2016-06, and their levels differ by up to 0.455 index points. I used the 2026 vintage throughout the overlap (`data_issues`).\n- Exclusion ledger (`out/exclusions.csv`): the window has 309 months. One month is dropped because P_t is missing (2025-10). No month lacks P_{t−12}. That leaves `n_analysed` = 308 months, with `n_months_missing_growth` = 1 (`missing_growth_months` = [\"2025-10\"]).\n\n## Method\n1. Splice the two vintages month by month, taking the non-empty value from the most recent vintage that covers the month. I did no interpolation or imputation.\n2. Compute g_t = 100 × (P_t / P_{t−12} − 1) wherever both levels exist.\n3. Because of the splice rule, g_t for 2014-01 … 2014-12 divides a 2026-vintage level by a 2016-vintage (2013) level.\n4. Compute the means and extremes over the computable months only.\n\nThe whole procedure is deterministic, uses no random seed (`random_seed` = null), and is implemented in `out/analysis.py`.\n\n## Results\n- The first growth month is 2001-01 (`first_growth_month`). The last month with a level is 2026-09 (`last_month`). g_t can be computed for 308 months (`n_months_growth`).\n- Mean g_t over 2001-01 … 2026-09: **2.574%** (`mean_growth_12m`).\n- Baseline mean for 2001-01 … 2013-12 (156 months): **2.361%** (`mean_growth_12m_2001_2013`, `n_months_2001_2013`).\n- Mean for 2014-01 … 2026-09 (152 months): **2.793%** (`mean_growth_12m_2014_2026`, `n_months_2014_2026`).\n- Named months:\n - 2014-06: 2.098% (`growth_2014_06`)\n - 2016-07: 0.868% (`growth_2016_07`)\n - 2020-05: 0.198% (`growth_2020_05`)\n - 2022-06: 8.979% (`growth_2022_06`)\n - 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 is `partial` (`feasibility`, `feasibility_reason`). Every summary estimand was computed, but g_t for 2025-10 cannot be computed because the source has no index level for that month.\n\n## Uncertainty\nThese numbers are exact arithmetic on published index levels, so they have no sampling uncertainty and I report no confidence intervals. The main source of variation is data revision. Where the vintages overlap, levels differ by up to 0.455 index points (`data_issues`), so growth rates for 2014–2016 would change somewhat with a different vintage choice. I did not quantify that sensitivity because the task fixes the vintage rule. The 2001–2013 figures reflect the 2016 vintage and could differ in the current vintage, which the task does not provide.\n\n## Limitations and open issues\n- The 2025-10 level is missing, so g_t for that month is missing. It is left out of the overall mean and the 2014–2026 mean, and is not imputed (`n_months_missing_growth`).\n- The 2014 growth rates mix two vintages (2026 numerator, 2013 denominator from the 2016 vintage). The information rule requires this, but it can introduce a small seasonal-revision artefact.\n- `cpi_assembled.csv` was not used because of the assembly errors listed above. Anyone using it would get mis-dated values from 2016-08 onward.\n- The README's SHA-256 values refer to the raw downloads before reformatting, so I could not check them against the CSVs provided.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Mean 12-month CPI growth over 2001-01..2026-09 (308 computable months) is 2.574%.\",\"results_keys\":[\"mean_growth_12m\",\"n_months_growth\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Excludes 2025-10 (no index level).\"},\n {\"id\":\"C2\",\"text\":\"Mean growth was 2.361% in 2001-2013 (baseline) and 2.793% in 2014-01..2026-09.\",\"results_keys\":[\"mean_growth_12m_2001_2013\",\"mean_growth_12m_2014_2026\",\"n_months_2001_2013\",\"n_months_2014_2026\"],\"status\":\"supported\",\"caveat\":\"Sub-periods use different vintages; 2014-2026 mean excludes 2025-10.\"},\n {\"id\":\"C3\",\"text\":\"Maximum 12-month growth was 8.979% in 2022-06; minimum was -1.959% in 2009-07.\",\"results_keys\":[\"max_growth_12m\",\"max_growth_month\",\"min_growth_12m\",\"min_growth_month\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C4\",\"text\":\"Growth in named months: 2014-06 2.098%, 2016-07 0.868%, 2020-05 0.198%, 2022-06 8.979%, 2026-09 3.717%.\",\"results_keys\":[\"growth_2014_06\",\"growth_2016_07\",\"growth_2020_05\",\"growth_2022_06\",\"growth_2026_09\"],\"status\":\"supported\",\"caveat\":\"2014-06 mixes 2026-vintage numerator with 2016-vintage denominator per the vintage rule.\"},\n {\"id\":\"C5\",\"text\":\"g_t is not computable for one month (2025-10) because the source reports no index level; the task is therefore partially feasible, with all summary estimands computed.\",\"results_keys\":[\"n_months_missing_growth\",\"missing_growth_months\",\"feasibility\",\"feasibility_reason\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C6\",\"text\":\"cpi_assembled.csv contains assembly errors (old-vintage values in the overlap, missing 2016-07, one-month shift from 2016-08) and was not used for estimation.\",\"results_keys\":[\"data_issues\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np[\"P4\"][\"status\"]=\"passed\"; p[\"P4\"][\"checks\"].append(\"gate run 20261030T043206.762299Z: all contract/reproducibility checks ok; failures only for P5 deliverables (report, claims) and non-terminal phases, expected at this stage\")\np[\"P5\"]={\"status\":\"passed\",\"checks\":[\"report.md written with 6 sections citing results keys\",\"claims.json written (6 claims)\",\"final gate run: see receipt\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=2)\nEOF\necho \"- 2026-10-30 P4 passed (gate receipt 20261030T043206). P5: report and claims written; final gate run.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v '\\[ok\\]'","description":"Write report, claims; run final gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2955,"cache_read_input_tokens":25215,"output_tokens":3230,"output_tokens_details":{"thinking_tokens":52},"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":2955},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXo6Cr5z1uVJ1hTK1LT6","requestRef":"6a040fb3-b093-4390-bdb1-b59a63cab790","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6a3dea94-0be1-4244-b242-f8eeffacf2c6","timestamp":"2026-10-30T04:32:32.838Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}78{"parentUuid":"6a3dea94-0be1-4244-b242-f8eeffacf2c6","isSidechain":false,"promptId":"8f565a57-70f1-433b-b044-ef7f6d25465c","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BmZXpdVRTEyCAmtUEH5uU7","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 40 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0100/out/state/gate_receipts/20261030T043233.114528Z.json","is_error":false}]},"uuid":"7f14e451-1f83-4a96-969f-f51c7a808880","timestamp":"2026-10-30T04:32:33.120Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 40 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0100/out/state/gate_receipts/20261030T043233.114528Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"6a3dea94-0be1-4244-b242-f8eeffacf2c6","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}79{"type":"api-request-blob","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","hash":"713fcc43dec3b2cf22ee763c6f3908a06340b66cd2d6ee9d3c1ccbc783103bc6","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"The P4 gate run fails only on the P5 deliverables, which don't exist yet; the contract and reproducibility checks pass. Next I'll write the report and claims."},{"type":"tool_use","id":"toolu_01BmZXpdVRTEyCAmtUEH5uU7","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F1-conf-01 — 12-month CPI inflation from two data vintages\n\n## Question\nWhat was the 12-month growth rate g_t = 100 × (P_t / P_{t−12} − 1) of CPIAUCSL (seasonally adjusted) for each month from 2001-01 to 2026-09? P_t comes from the most recent vintage that covers month t: the 2026-10-28 vintage for 2014-01 … 2026-09, and the 2016-07-15 vintage for earlier months. The report also gives the mean over the window, the means for 2001–2013 and 2014–2026, five named months, and the extremes.\n\n## Data and exclusions\n- Inputs (`n_input_rows`): `cpi_vintage_2016-07-15.csv` has 834 rows covering 1947-01 … 2016-06. `cpi_vintage_2026-10-28.csv` has 153 rows covering 2014-01 … 2026-09, and its 2025-10 value is empty. `cpi_assembled.csv` has 957 rows. Hashes and the full audit are in `out/state/data_audit.md`.\n- **The hand-assembled file contains errors, so I did not use it for estimation** (`data_issues`):\n - For 2014-01 … 2016-06 it takes values from the *older* vintage, which contradicts the most-recent-vintage rule.\n - It omits 2016-07.\n - From 2016-08 onward every value sits one month later than its true month. As a result it contains a spurious 2026-10 row and shows the missing value at 2025-11 instead of 2025-10.\n - 151 of the 955 months it shares with the correct splice have a wrong level.\n\n I read it only to diagnose these problems. All levels come directly from the two vintage files.\n- The two vintages overlap for 2014-01 … 2016-06, and their levels differ by up to 0.455 index points. I used the 2026 vintage throughout the overlap (`data_issues`).\n- Exclusion ledger (`out/exclusions.csv`): the window has 309 months. One month is dropped because P_t is missing (2025-10). No month lacks P_{t−12}. That leaves `n_analysed` = 308 months, with `n_months_missing_growth` = 1 (`missing_growth_months` = [\"2025-10\"]).\n\n## Method\n1. Splice the two vintages month by month, taking the non-empty value from the most recent vintage that covers the month. I did no interpolation or imputation.\n2. Compute g_t = 100 × (P_t / P_{t−12} − 1) wherever both levels exist.\n3. Because of the splice rule, g_t for 2014-01 … 2014-12 divides a 2026-vintage level by a 2016-vintage (2013) level.\n4. Compute the means and extremes over the computable months only.\n\nThe whole procedure is deterministic, uses no random seed (`random_seed` = null), and is implemented in `out/analysis.py`.\n\n## Results\n- The first growth month is 2001-01 (`first_growth_month`). The last month with a level is 2026-09 (`last_month`). g_t can be computed for 308 months (`n_months_growth`).\n- Mean g_t over 2001-01 … 2026-09: **2.574%** (`mean_growth_12m`).\n- Baseline mean for 2001-01 … 2013-12 (156 months): **2.361%** (`mean_growth_12m_2001_2013`, `n_months_2001_2013`).\n- Mean for 2014-01 … 2026-09 (152 months): **2.793%** (`mean_growth_12m_2014_2026`, `n_months_2014_2026`).\n- Named months:\n - 2014-06: 2.098% (`growth_2014_06`)\n - 2016-07: 0.868% (`growth_2016_07`)\n - 2020-05: 0.198% (`growth_2020_05`)\n - 2022-06: 8.979% (`growth_2022_06`)\n - 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 is `partial` (`feasibility`, `feasibility_reason`). Every summary estimand was computed, but g_t for 2025-10 cannot be computed because the source has no index level for that month.\n\n## Uncertainty\nThese numbers are exact arithmetic on published index levels, so they have no sampling uncertainty and I report no confidence intervals. The main source of variation is data revision. Where the vintages overlap, levels differ by up to 0.455 index points (`data_issues`), so growth rates for 2014–2016 would change somewhat with a different vintage choice. I did not quantify that sensitivity because the task fixes the vintage rule. The 2001–2013 figures reflect the 2016 vintage and could differ in the current vintage, which the task does not provide.\n\n## Limitations and open issues\n- The 2025-10 level is missing, so g_t for that month is missing. It is left out of the overall mean and the 2014–2026 mean, and is not imputed (`n_months_missing_growth`).\n- The 2014 growth rates mix two vintages (2026 numerator, 2013 denominator from the 2016 vintage). The information rule requires this, but it can introduce a small seasonal-revision artefact.\n- `cpi_assembled.csv` was not used because of the assembly errors listed above. Anyone using it would get mis-dated values from 2016-08 onward.\n- The README's SHA-256 values refer to the raw downloads before reformatting, so I could not check them against the CSVs provided.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Mean 12-month CPI growth over 2001-01..2026-09 (308 computable months) is 2.574%.\",\"results_keys\":[\"mean_growth_12m\",\"n_months_growth\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Excludes 2025-10 (no index level).\"},\n {\"id\":\"C2\",\"text\":\"Mean growth was 2.361% in 2001-2013 (baseline) and 2.793% in 2014-01..2026-09.\",\"results_keys\":[\"mean_growth_12m_2001_2013\",\"mean_growth_12m_2014_2026\",\"n_months_2001_2013\",\"n_months_2014_2026\"],\"status\":\"supported\",\"caveat\":\"Sub-periods use different vintages; 2014-2026 mean excludes 2025-10.\"},\n {\"id\":\"C3\",\"text\":\"Maximum 12-month growth was 8.979% in 2022-06; minimum was -1.959% in 2009-07.\",\"results_keys\":[\"max_growth_12m\",\"max_growth_month\",\"min_growth_12m\",\"min_growth_month\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C4\",\"text\":\"Growth in named months: 2014-06 2.098%, 2016-07 0.868%, 2020-05 0.198%, 2022-06 8.979%, 2026-09 3.717%.\",\"results_keys\":[\"growth_2014_06\",\"growth_2016_07\",\"growth_2020_05\",\"growth_2022_06\",\"growth_2026_09\"],\"status\":\"supported\",\"caveat\":\"2014-06 mixes 2026-vintage numerator with 2016-vintage denominator per the vintage rule.\"},\n {\"id\":\"C5\",\"text\":\"g_t is not computable for one month (2025-10) because the source reports no index level; the task is therefore partially feasible, with all summary estimands computed.\",\"results_keys\":[\"n_months_missing_growth\",\"missing_growth_months\",\"feasibility\",\"feasibility_reason\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C6\",\"text\":\"cpi_assembled.csv contains assembly errors (old-vintage values in the overlap, missing 2016-07, one-month shift from 2016-08) and was not used for estimation.\",\"results_keys\":[\"data_issues\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np[\"P4\"][\"status\"]=\"passed\"; p[\"P4\"][\"checks\"].append(\"gate run 20261030T043206.762299Z: all contract/reproducibility checks ok; failures only for P5 deliverables (report, claims) and non-terminal phases, expected at this stage\")\np[\"P5\"]={\"status\":\"passed\",\"checks\":[\"report.md written with 6 sections citing results keys\",\"claims.json written (6 claims)\",\"final gate run: see receipt\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=2)\nEOF\necho \"- 2026-10-30 P4 passed (gate receipt 20261030T043206). P5: report and claims written; final gate run.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v '\\[ok\\]'","description":"Write report, claims; run final gate"}}]}}80{"type":"api-request","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","id":"bedc8863-a4c5-4493-bff4-879c4d76cfa0","timestamp":"2026-10-30T04:32:33.123Z","version":"2.1.314","querySource":"sdk","shapeHash":"feb5c91d53bbada2e1da572f056120317de4cfdbadce4316c811c4f5cc3165bd","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":"6a040fb3-b093-4390-bdb1-b59a63cab790","keep":22,"tail":["713fcc43dec3b2cf22ee763c6f3908a06340b66cd2d6ee9d3c1ccbc783103bc6","bee8b05b4dbb95577c4d9f858772e442ed6c2d4979a79077aeec14cd76834c4e"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",23,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"5e951e8cd392bdc729588c20c55cd517d4aadd81f79ffb78156985e5ed547af9"}81{"parentUuid":"7f14e451-1f83-4a96-969f-f51c7a808880","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo89nMf5Pvz87mmPAn2","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01TE5bw3YbxZw5DNZAU3jLgK","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np[\"P5\"][\"checks\"][-1]=\"final gate run 20261030T043233.114528Z: PASSED (0 of 40 failed)\"\njson.dump(p,open('out/state/phases.json','w'),indent=2)\nEOF\necho \"- 2026-10-30 Final gate PASSED (receipt 20261030T043233). All phases passed.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | head -1","description":"Record final receipt and confirm gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3325,"cache_read_input_tokens":28170,"output_tokens":285,"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":3325},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXo89VVc5aoZTbWATvJL","requestRef":"bedc8863-a4c5-4493-bff4-879c4d76cfa0","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e98a513b-e9f5-469f-adaf-fd18bae624de","timestamp":"2026-10-30T04:32:36.001Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}82{"parentUuid":"e98a513b-e9f5-469f-adaf-fd18bae624de","isSidechain":false,"promptId":"8f565a57-70f1-433b-b044-ef7f6d25465c","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TE5bw3YbxZw5DNZAU3jLgK","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 40 checks failed)","is_error":false}]},"uuid":"028f34e5-97c5-4ab3-bef7-21378c166644","timestamp":"2026-10-30T04:32:36.258Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 40 checks failed)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"e98a513b-e9f5-469f-adaf-fd18bae624de","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}83{"type":"api-request-blob","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","hash":"9b3913421b90a915bf25220b80910fc1675cfbd7513769ef0a13e64f69f1e7db","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01TE5bw3YbxZw5DNZAU3jLgK","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np[\"P5\"][\"checks\"][-1]=\"final gate run 20261030T043233.114528Z: PASSED (0 of 40 failed)\"\njson.dump(p,open('out/state/phases.json','w'),indent=2)\nEOF\necho \"- 2026-10-30 Final gate PASSED (receipt 20261030T043233). All phases passed.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | head -1","description":"Record final receipt and confirm gate"}}]}}84{"type":"api-request","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","id":"3805fc7e-8c09-4265-9f08-4bea0024fcf5","timestamp":"2026-10-30T04:32:36.261Z","version":"2.1.314","querySource":"sdk","shapeHash":"feb5c91d53bbada2e1da572f056120317de4cfdbadce4316c811c4f5cc3165bd","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":"bedc8863-a4c5-4493-bff4-879c4d76cfa0","keep":24,"tail":["9b3913421b90a915bf25220b80910fc1675cfbd7513769ef0a13e64f69f1e7db","e3f91e2915575fe7a58325dd6caf76692f140740419dc7bd29fa9fcdffb2d8f1"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",25,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"80867608f50de448d7991c84a5fc05b79e55047b7669548d452cac50528ba721"}85{"parentUuid":"028f34e5-97c5-4ab3-bef7-21378c166644","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo8PDEchkyomQphwSy8","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":325,"cache_read_input_tokens":31495,"output_tokens":904,"output_tokens_details":{"thinking_tokens":18},"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":325},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":6842,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXo8NvsjHYYTDtntVuz3","requestRef":"3805fc7e-8c09-4265-9f08-4bea0024fcf5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f751ed3d-2ade-483d-a71f-dbe434004662","timestamp":"2026-10-30T04:32:44.431Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}86{"parentUuid":"f751ed3d-2ade-483d-a71f-dbe434004662","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo8PDEchkyomQphwSy8","type":"message","role":"assistant","content":[{"type":"text","text":"I've computed the 12-month CPI inflation series for 2001-01 to 2026-09 and all the requested summaries. It covers 308 of the 309 months; 2025-10 can't be computed. The final gate run passed (0 of 40 checks failed), and a clean re-run of `out/analysis.py` reproduced every output exactly.\n\n| Measure | Value |\n|---|---|\n| Mean, 2001-01 to 2026-09 | 2.574% |\n| Mean, 2001–2013 (baseline) | 2.361% |\n| Mean, 2014-01 to 2026-09 | 2.793% |\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| Maximum | 8.979% (2022-06) |\n| Minimum | −1.959% (2009-07) |\n\n- **One month missing:** the 2026-10-28 vintage has no value for 2025-10, and the older vintage doesn't cover that month. As the task requires, I didn't fill the gap, so that month has no growth rate and is left out of the means. I marked the result as \"partial\" for that reason; all the summary figures were still computed.\n- **The assembled file is unreliable:** I read `cpi_assembled.csv` only to check it and took every figure from the two vintage files instead. It has these errors:\n - For 2014-01 to 2016-06 it uses the older vintage, which breaks the use-the-latest-vintage rule.\n - It leaves out 2016-07.\n - From 2016-08 onward every value is filed one month too late, which also adds a 2026-10 row that the sources don't have.\n - 151 of the 955 months it shares with the vintage files have the wrong value.\n- **2014 mixes vintages:** growth rates for 2014 divide a 2026-vintage level by a 2013 level from the 2016 vintage. The vintage rule requires this, but it may cause small distortions from seasonal-adjustment revisions.\n- **No confidence intervals:** the figures are exact calculations on published data. The real source of variation is revisions: where the two vintages overlap, levels differ by up to 0.455 index points. I didn't measure how much a different vintage choice would change the results, because the task fixes the rule.\n- **README hashes not checked:** the README's checksums are for the raw downloads before reformatting, so I couldn't verify them against the CSV files provided.\n\nEverything is in `out/`; the report is `report.md`, and `analysis.py` regenerates `results.json`, `analytic.csv` and `exclusions.csv`."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":325,"cache_read_input_tokens":31495,"output_tokens":904,"output_tokens_details":{"thinking_tokens":18},"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":325},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXo8NvsjHYYTDtntVuz3","requestRef":"3805fc7e-8c09-4265-9f08-4bea0024fcf5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e3a3a09e-852d-4aea-8004-3b95748a3fd5","timestamp":"2026-10-30T04:32:45.153Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","version":"2.1.314","gitBranch":"HEAD"}87{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"e3a3a09e-852d-4aea-8004-3b95748a3fd5","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56"}88{"type":"atis-latch","atis":"","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56"}89{"type":"cost-state","sessionId":"79ec378c-6b7e-4bcb-af24-bbc5f6eb3a56","totalCostUSD":0.5456896,"totalAPIDuration":152480,"totalAPIDurationWithoutRetries":152476,"totalToolDuration":2204,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":154782,"startTime":1793334610398,"modelUsage":{"claude-opus-5-5":{"inputTokens":28,"outputTokens":17565,"thinkingTokens":998,"cacheReadInputTokens":213488,"cacheCreationInputTokens":30316,"cacheCreationEphemeral5mInputTokens":30316,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.5456896}},"hasUnknownModelCost":false}90