spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T04:30:25.618Z","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T04:30:25.623Z","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3"}4{"parentUuid":null,"isSidechain":false,"promptId":"928f8c2b-79cf-4ec9-8a61-d59b8c4f450e","type":"user","message":{"role":"user","content":"Begin"},"uuid":"da864b61-65a8-41d8-9f43-6fddf954d531","timestamp":"2026-10-30T04:30:25.627Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","shapeHash":"c4a9886b5d404a87039300a171d4a2ccda220b548475a5284c56dab1eb465ef2","timestamp":"2026-10-30T04:30:25.640Z","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/RC0098\nDate: 2026-10-30\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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the user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3"}12{"parentUuid":"89f3dc7e-d967-40c1-b997-82faa8554896","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnxn7sHyvkPB7MPXXqL","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011B3HW4kZXKZDFUGz5TwkJX","name":"Bash","input":{"command":"cat TASK.md; 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The estimand for month t is g_t = 100 × (P_t / P_{t−12} − 1) in percent, where P is the index level taken from the most recent data vintage in which that month is available; the summary estimands are the mean of g_t over the window, the sub-period means for 2001-01 … 2013-12 and 2014-01 … 2026-09, a few named months, and the months of the maximum and minimum growth.\n\n## Inputs\n- `data/cpi_vintage_2016-07-15.csv` — observed data: CPIAUCSL as published by BLS/FRED in the ALFRED vintage of 2016-07-15, months 1947-01 to 2016-06 (834 rows). Columns `date` (ISO, first day of month), `cpi` (index, 1982–84 = 100). Source, retrieval date, license and SHA-256 in `data/README.md`.\n- `data/cpi_vintage_2026-10-28.csv` — observed data: CPIAUCSL in the ALFRED vintage of 2026-10-28, extract restricted to months 2014-01 to 2026-09 (153 rows). Same columns. An empty `cpi` cell means that the source reports no value for that month.\n- `data/cpi_assembled.csv` — a single file assembled from the two extracts above (columns `date`, `cpi`, `vintage`), intended to cover 1947-01 to 2026-09. It was assembled by hand and may contain assembly errors; the two vintage files are the authoritative inputs.\n\n## Information-availability rules\n- For each month, the index level to use is the value from the most recent vintage in which that month is available (the 2026-10-28 vintage where it covers the month, the 2016-07-15 vintage otherwise).\n- Growth rates must only be computed from index levels that exist in the sources; do not interpolate or impute missing index levels. Months for which g_t cannot be computed are reported as missing and counted.\n- Use only the files under `data/`.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F1-conf-01\" |\n| `first_growth_month` | string | YYYY-MM | first month of the window with a growth rate (expected \"2001-01\" if computable) |\n| `last_month` | string | YYYY-MM | last month of the window for which an index level exists in the sources |\n| `n_months_growth` | integer | months | months in the window with a computable g_t |\n| `n_months_missing_growth` | integer | months | months in the window without a computable g_t |\n| `n_analysed` | integer | months | same as `n_months_growth` |\n| `mean_growth_12m` | number | percent | mean of g_t over the window |\n| `mean_growth_12m_2001_2013` | number | percent | baseline: mean of g_t over 2001-01 … 2013-12 |\n| `mean_growth_12m_2014_2026` | number | percent | mean of g_t over 2014-01 … 2026-09 |\n| `growth_2014_06`, `growth_2016_07`, `growth_2020_05`, `growth_2022_06`, `growth_2026_09` | number | percent | g_t for those months |\n| `max_growth_12m`, `max_growth_month` | number, string | percent, YYYY-MM | maximum g_t in the window and its month |\n| `min_growth_12m`, `min_growth_month` | number, string | percent, YYYY-MM | minimum g_t in the window and its month |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | every data problem detected and how it was handled (empty list if none) |\n| `n_input_rows` | object | rows | {file name: number of data rows read (excluding the header)} for every file under `data/` that was read |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/analytic.csv` — one row per month of the window with a computable growth rate, columns `date` (YYYY-MM-01), `cpi`, `cpi_lag12`, `vintage_used`, `growth_12m`; exactly `n_analysed` rows.\n- `out/exclusions.csv` — the exclusion ledger with columns `step`, `rule`, `n_before`, `n_after`, `n_removed`, `rationale`; one row per step from the rows you start from to the `n_analysed` rows of `out/analytic.csv` (`n_before − n_removed = n_after` on every row; each step's `n_before` equals the previous step's `n_after`).\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/analytic.csv` and `out/exclusions.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 32\ndrwxr-xr-x   6 simon-pierreboucher  staff   192 oct.  29 23:41 .\ndrwxr-xr-x  67 simon-pierreboucher  staff  2144 oct.  30 00:30 ..\n-rw-r--r--   1 simon-pierreboucher  staff   731 oct.  29 23:41 bundle.json\ndrwxr-xr-x   6 simon-pierreboucher  staff   192 oct.  29 23:31 data\n-rw-r--r--   1 simon-pierreboucher  staff  1980 oct.  29 23:31 gate_spec.json\n-rw-r--r--   1 simon-pierreboucher  staff  4969 oct.  29 23:31 TASK.md\ndata:\ncpi_assembled.csv\ncpi_vintage_2016-07-15.csv\ncpi_vintage_2026-10-28.csv\nREADME.md","is_error":false}]},"uuid":"d1424f78-0d98-4782-a533-1375ca99f752","timestamp":"2026-10-30T04:30:27.868Z","toolUseResult":{"stdout":"# F1-conf-01 — 12-month CPI inflation from two data vintages\n\n## Scientific question\nCompute the 12-month growth rate of the U.S. consumer price index (CPIAUCSL, seasonally adjusted) for every month from 2001-01 through 2026-09 and summarise it. The estimand for month t is g_t = 100 × (P_t / P_{t−12} − 1) in percent, where P is the index level taken from the most recent data vintage in which that month is available; the summary estimands are the mean of g_t over the window, the sub-period means for 2001-01 … 2013-12 and 2014-01 … 2026-09, a few named months, and the months of the maximum and minimum growth.\n\n## Inputs\n- `data/cpi_vintage_2016-07-15.csv` — observed data: CPIAUCSL as published by BLS/FRED in the ALFRED vintage of 2016-07-15, months 1947-01 to 2016-06 (834 rows). Columns `date` (ISO, first day of month), `cpi` (index, 1982–84 = 100). Source, retrieval date, license and SHA-256 in `data/README.md`.\n- `data/cpi_vintage_2026-10-28.csv` — observed data: CPIAUCSL in the ALFRED vintage of 2026-10-28, extract restricted to months 2014-01 to 2026-09 (153 rows). Same columns. An empty `cpi` cell means that the source reports no value for that month.\n- `data/cpi_assembled.csv` — a single file assembled from the two extracts above (columns `date`, `cpi`, `vintage`), intended to cover 1947-01 to 2026-09. It was assembled by hand and may contain assembly errors; the two vintage files are the authoritative inputs.\n\n## Information-availability rules\n- For each month, the index level to use is the value from the most recent vintage in which that month is available (the 2026-10-28 vintage where it covers the month, the 2016-07-15 vintage otherwise).\n- Growth rates must only be computed from index levels that exist in the sources; do not interpolate or impute missing index levels. Months for which g_t cannot be computed are reported as missing and counted.\n- Use only the files under `data/`.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F1-conf-01\" |\n| `first_growth_month` | string | YYYY-MM | first month of the window with a growth rate (expected \"2001-01\" if computable) |\n| `last_month` | string | YYYY-MM | last month of the window for which an index level exists in the sources |\n| `n_months_growth` | integer | months | months in the window with a computable g_t |\n| `n_months_missing_growth` | integer | months | months in the window without a computable g_t |\n| `n_analysed` | integer | months | same as `n_months_growth` |\n| `mean_growth_12m` | number | percent | mean of g_t over the window |\n| `mean_growth_12m_2001_2013` | number | percent | baseline: mean of g_t over 2001-01 … 2013-12 |\n| `mean_growth_12m_2014_2026` | number | percent | mean of g_t over 2014-01 … 2026-09 |\n| `growth_2014_06`, `growth_2016_07`, `growth_2020_05`, `growth_2022_06`, `growth_2026_09` | number | percent | g_t for those months |\n| `max_growth_12m`, `max_growth_month` | number, string | percent, YYYY-MM | maximum g_t in the window and its month |\n| `min_growth_12m`, `min_growth_month` | number, string | percent, YYYY-MM | minimum g_t in the window and its month |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | every data problem detected and how it was handled (empty list if none) |\n| `n_input_rows` | object | rows | {file name: number of data rows read (excluding the header)} for every file under `data/` that was read |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/analytic.csv` — one row per month of the window with a computable growth rate, columns `date` (YYYY-MM-01), `cpi`, `cpi_lag12`, `vintage_used`, `growth_12m`; exactly `n_analysed` rows.\n- `out/exclusions.csv` — the exclusion ledger with columns `step`, `rule`, `n_before`, `n_after`, `n_removed`, `rationale`; one row per step from the rows you start from to the `n_analysed` rows of `out/analytic.csv` (`n_before − n_removed = n_after` on every row; each step's `n_before` equals the previous step's `n_after`).\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/analytic.csv` and `out/exclusions.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 32\ndrwxr-xr-x   6 simon-pierreboucher  staff   192 oct.  29 23:41 .\ndrwxr-xr-x  67 simon-pierreboucher  staff  2144 oct.  30 00:30 ..\n-rw-r--r--   1 simon-pierreboucher  staff   731 oct.  29 23:41 bundle.json\ndrwxr-xr-x   6 simon-pierreboucher  staff   192 oct.  29 23:31 data\n-rw-r--r--   1 simon-pierreboucher  staff  1980 oct.  29 23:31 gate_spec.json\n-rw-r--r--   1 simon-pierreboucher  staff  4969 oct.  29 23:31 TASK.md\ndata:\ncpi_assembled.csv\ncpi_vintage_2016-07-15.csv\ncpi_vintage_2026-10-28.csv\nREADME.md","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"d9da86a7-bde2-4673-bc26-34fa52ce35bb","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}14{"type":"api-request-blob","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","hash":"687952b383dc249da8fe30f6e061f96f7c6f051944d87e1d27c88ef0209ac385","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_011B3HW4kZXKZDFUGz5TwkJX","name":"Bash","input":{"command":"cat TASK.md; ls -la; ls -R data out 2>/dev/null | head -50"}}]}}15{"type":"api-request","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","id":"4eb4ef0c-1d8f-4a06-a704-5be84ad75aa3","timestamp":"2026-10-30T04:30:27.873Z","version":"2.1.314","querySource":"sdk","shapeHash":"c4a9886b5d404a87039300a171d4a2ccda220b548475a5284c56dab1eb465ef2","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":"d28b78d0-a469-4a7e-85ed-7c50da8511d9","keep":2,"tail":["687952b383dc249da8fe30f6e061f96f7c6f051944d87e1d27c88ef0209ac385","06f16f0b5b180136217139eb81dfc60eba38733ada67cad6c4295a9470e1e773"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"7106d31e5ad9617c3a0e04122ee7a9008b4e456aebdd6bf04243a3f504e69e6e"}16{"parentUuid":"d1424f78-0d98-4782-a533-1375ca99f752","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnxvDX4u9LDLQ5h5tsW","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01L6b492sygquMEtfhpqdJVe","name":"Bash","input":{"command":"cat data/README.md; cat gate_spec.json bundle.json; cd data; shasum -a 256 *; wc -l *; head -3 *.csv; grep -n ',$\\|,,' *.csv | head; tail -15 cpi_vintage_2026-10-28.csv"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2335,"cache_read_input_tokens":4107,"output_tokens":139,"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":2335},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXnxushFWfEwPvtBwuL4","requestRef":"4eb4ef0c-1d8f-4a06-a704-5be84ad75aa3","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"de8570b3-c2c6-4190-bb1a-a5aa699d1334","timestamp":"2026-10-30T04:30:29.761Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}17{"parentUuid":"de8570b3-c2c6-4190-bb1a-a5aa699d1334","isSidechain":false,"promptId":"928f8c2b-79cf-4ec9-8a61-d59b8c4f450e","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01L6b492sygquMEtfhpqdJVe","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}\n{\n  \"task_id\": \"F1-conf-01\",\n  \"family\": \"F1\",\n  \"split\": \"conf\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:41:11.620523+00:00\",\n  \"files\": {\n    \"TASK.md\": \"a1445e54705eb87a7873503e75c51e4f37ad8594693f10839f681fb9238ca91a\",\n    \"data/README.md\": \"ac136bff13bbdb5afac60e0404212ea1f73f992bd5cca1c1e7a6599df78f8965\",\n    \"data/cpi_assembled.csv\": \"ff732b201a525d6fb171d4e8b2d470f986863c1547a1928e42bb197c0777d44d\",\n    \"data/cpi_vintage_2016-07-15.csv\": \"281fbcdd6a6eeb3e9cb7c1f20476e6bd1f7b925bf140606fe46538fe83f2c83e\",\n    \"data/cpi_vintage_2026-10-28.csv\": \"8220d5b4d70ca32fc2c7b1975651c515967db8a87053cf725159c63ed274cfba\",\n    \"gate_spec.json\": \"2435fd30a9f36563592bbfefe3c379975747affc40c42d521c3227215aaf7619\"\n  }\n}ff732b201a525d6fb171d4e8b2d470f986863c1547a1928e42bb197c0777d44d  cpi_assembled.csv\n281fbcdd6a6eeb3e9cb7c1f20476e6bd1f7b925bf140606fe46538fe83f2c83e  cpi_vintage_2016-07-15.csv\n8220d5b4d70ca32fc2c7b1975651c515967db8a87053cf725159c63ed274cfba  cpi_vintage_2026-10-28.csv\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      11 README.md\n    1958 total\n==> cpi_assembled.csv <==\ndate,cpi,vintage\n1947-01-01,21.480,2016-07-15\n1947-02-01,21.620,2016-07-15\n\n==> cpi_vintage_2016-07-15.csv <==\ndate,cpi\n1947-01-01,21.480\n1947-02-01,21.620\n\n==> cpi_vintage_2026-10-28.csv <==\ndate,cpi\n2014-01-01,235.288\n2014-02-01,235.547\ncpi_assembled.csv:947:2025-11-01,,2026-10-28\n2025-07-01,322.169\n2025-08-01,323.291\n2025-09-01,324.245\n2025-10-01,\n2025-11-01,325.063\n2025-12-01,326.031\n2026-01-01,326.588\n2026-02-01,327.460\n2026-03-01,330.293\n2026-04-01,332.407\n2026-05-01,333.979\n2026-06-01,332.568\n2026-07-01,332.813\n2026-08-01,334.131\n2026-09-01,336.298","is_error":false}]},"uuid":"2083207c-a090-4679-8125-de6cc7ff9253","timestamp":"2026-10-30T04:30:29.816Z","toolUseResult":{"stdout":"# Data provenance — F1-conf-01\n\nAll three files concern the same series: **CPIAUCSL**, Consumer Price Index for All Urban Consumers: All Items in U.S. City Average, seasonally adjusted, index 1982–1984 = 100, monthly, produced by the U.S. Bureau of Labor Statistics and distributed by the Federal Reserve Bank of St. Louis (FRED / ALFRED). Terms: FRED/ALFRED terms of use (BLS data are in the public domain; attribution to BLS and FRED requested). Observed data, not simulated.\n\n| file | content | source URL | retrieved (UTC) | SHA-256 of the raw download |\n|---|---|---|---|---|\n| `cpi_vintage_2016-07-15.csv` | vintage of 2016-07-15 (values as published on that date), months 1947-01 … 2016-06, 834 rows | https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=CPIAUCSL&vintage_date=2016-07-15 | 2026-10-30T03:22Z | 74c7c4e0e3fa26059421d9620abe12d57df9a7786012acf39d694766cb854f6d |\n| `cpi_vintage_2026-10-28.csv` | vintage of 2026-10-28 (latest), extract restricted to months 2014-01 … 2026-09, 153 rows | https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=CPIAUCSL&vintage_date=2026-10-28&cosd=2014-01-01 | 2026-10-30T03:24Z | 480c9f9931a48e3194b73a8f6b65b325a51e185232456c1222a260dde64f9730 |\n| `cpi_assembled.csv` | a single monthly file assembled by the task author from the two extracts above, intended to cover 1947-01 … 2026-09 with a `vintage` column recording which extract each value came from | assembled from the two files above | 2026-10-30 | — |\n\nColumns of the vintage files: `date` (first day of the month, ISO), `cpi` (index level, 3 decimals). The assembled file adds `vintage` (the vintage date of the extract the value was taken from). The raw downloads were reformatted (two columns, ISO dates) without changing any value. An empty `cpi` cell means the source publishes no value for that month. The assembled file was produced by hand and may contain assembly errors.\n{\n  \"task_id\": \"F1-conf-01\",\n  \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/analytic.csv\", \"out/exclusions.csv\"],\n  \"results_keys\": {\n    \"task_id\": {\"type\": \"string\", \"nullable\": false},\n    \"first_growth_month\": {\"type\": \"string\", \"nullable\": false},\n    \"last_month\": {\"type\": \"string\", \"nullable\": false},\n    \"n_months_growth\": {\"type\": \"integer\", \"nullable\": false},\n    \"n_months_missing_growth\": {\"type\": \"integer\", \"nullable\": false},\n    \"n_analysed\": {\"type\": \"integer\", \"nullable\": false},\n    \"mean_growth_12m\": {\"type\": \"number\", \"nullable\": false},\n    \"mean_growth_12m_2001_2013\": {\"type\": \"number\", \"nullable\": false},\n    \"mean_growth_12m_2014_2026\": {\"type\": \"number\", \"nullable\": false},\n    \"growth_2014_06\": {\"type\": \"number\", \"nullable\": true},\n    \"growth_2016_07\": {\"type\": \"number\", \"nullable\": true},\n    \"growth_2020_05\": {\"type\": \"number\", \"nullable\": true},\n    \"growth_2022_06\": {\"type\": \"number\", \"nullable\": true},\n    \"growth_2026_09\": {\"type\": \"number\", \"nullable\": true},\n    \"max_growth_12m\": {\"type\": \"number\", \"nullable\": false},\n    \"max_growth_month\": {\"type\": \"string\", \"nullable\": false},\n    \"min_growth_12m\": {\"type\": \"number\", \"nullable\": false},\n    \"min_growth_month\": {\"type\": \"string\", \"nullable\": false},\n    \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n    \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n    \"data_issues\": {\"type\": \"array\", \"nullable\": false},\n    \"n_input_rows\": {\"type\": \"object\", \"nullable\": false},\n    \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n  },\n  \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n  \"family_checks\": [\"exclusion_ledger_sums\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\"],\n  \"ci_pairs\": [],\n  \"positive_keys\": [],\n  \"chronology_pairs\": [],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 1e-6\n}\n{\n  \"task_id\": \"F1-conf-01\",\n  \"family\": \"F1\",\n  \"split\": \"conf\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:41:11.620523+00:00\",\n  \"files\": {\n    \"TASK.md\": \"a1445e54705eb87a7873503e75c51e4f37ad8594693f10839f681fb9238ca91a\",\n    \"data/README.md\": \"ac136bff13bbdb5afac60e0404212ea1f73f992bd5cca1c1e7a6599df78f8965\",\n    \"data/cpi_assembled.csv\": \"ff732b201a525d6fb171d4e8b2d470f986863c1547a1928e42bb197c0777d44d\",\n    \"data/cpi_vintage_2016-07-15.csv\": \"281fbcdd6a6eeb3e9cb7c1f20476e6bd1f7b925bf140606fe46538fe83f2c83e\",\n    \"data/cpi_vintage_2026-10-28.csv\": \"8220d5b4d70ca32fc2c7b1975651c515967db8a87053cf725159c63ed274cfba\",\n    \"gate_spec.json\": \"2435fd30a9f36563592bbfefe3c379975747affc40c42d521c3227215aaf7619\"\n  }\n}ff732b201a525d6fb171d4e8b2d470f986863c1547a1928e42bb197c0777d44d  cpi_assembled.csv\n281fbcdd6a6eeb3e9cb7c1f20476e6bd1f7b925bf140606fe46538fe83f2c83e  cpi_vintage_2016-07-15.csv\n8220d5b4d70ca32fc2c7b1975651c515967db8a87053cf725159c63ed274cfba  cpi_vintage_2026-10-28.csv\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      11 README.md\n    1958 total\n==> cpi_assembled.csv <==\ndate,cpi,vintage\n1947-01-01,21.480,2016-07-15\n1947-02-01,21.620,2016-07-15\n\n==> cpi_vintage_2016-07-15.csv <==\ndate,cpi\n1947-01-01,21.480\n1947-02-01,21.620\n\n==> cpi_vintage_2026-10-28.csv <==\ndate,cpi\n2014-01-01,235.288\n2014-02-01,235.547\ncpi_assembled.csv:947:2025-11-01,,2026-10-28\n2025-07-01,322.169\n2025-08-01,323.291\n2025-09-01,324.245\n2025-10-01,\n2025-11-01,325.063\n2025-12-01,326.031\n2026-01-01,326.588\n2026-02-01,327.460\n2026-03-01,330.293\n2026-04-01,332.407\n2026-05-01,333.979\n2026-06-01,332.568\n2026-07-01,332.813\n2026-08-01,334.131\n2026-09-01,336.298","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"de8570b3-c2c6-4190-bb1a-a5aa699d1334","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098/data","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}18{"type":"api-request-blob","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","hash":"8930f28524211c31316013e662a8b0648c0d4c0a87b2eff31978033f2d4e4122","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01L6b492sygquMEtfhpqdJVe","name":"Bash","input":{"command":"cat data/README.md; cat gate_spec.json bundle.json; cd data; shasum -a 256 *; wc -l *; head -3 *.csv; grep -n ',$\\|,,' *.csv | head; tail -15 cpi_vintage_2026-10-28.csv"}}]}}19{"type":"api-request","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","id":"e69eceb7-4010-472e-93f8-fb7621c6ba1a","timestamp":"2026-10-30T04:30:29.820Z","version":"2.1.314","querySource":"sdk","shapeHash":"c4a9886b5d404a87039300a171d4a2ccda220b548475a5284c56dab1eb465ef2","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":"4eb4ef0c-1d8f-4a06-a704-5be84ad75aa3","keep":4,"tail":["8930f28524211c31316013e662a8b0648c0d4c0a87b2eff31978033f2d4e4122","6a65f38578852e529f942145f8b2fd6f3b1d7fe38cf0b563941d485b219acd0e"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"d190eb418e731020782f40d51d4fee912dbc76734f9cbde12e1293e41aae45df"}20{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"2083207c-a090-4679-8125-de6cc7ff9253","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3"}21{"type":"atis-latch","atis":"","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3"}22{"parentUuid":"2083207c-a090-4679-8125-de6cc7ff9253","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXny4VMKMpWP2xjwGkQj","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":2888,"cache_read_input_tokens":6442,"output_tokens":1025,"output_tokens_details":{"thinking_tokens":297},"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":2888},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":2768,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXny4DVFM9XSGyjs3CnS","requestRef":"e69eceb7-4010-472e-93f8-fb7621c6ba1a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5760dcd4-db68-4512-b1aa-3a36885d0046","timestamp":"2026-10-30T04:30:33.895Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098/data","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}23{"parentUuid":"5760dcd4-db68-4512-b1aa-3a36885d0046","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXny4VMKMpWP2xjwGkQj","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01NGgAf8XTbi2MQiFwqkAvnA","name":"Bash","input":{"command":"cd data; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\no=pd.read_csv('cpi_vintage_2016-07-15.csv');n=pd.read_csv('cpi_vintage_2026-10-28.csv');a=pd.read_csv('cpi_assembled.csv')\nfor name,d in [('old',o),('new',n),('asm',a)]:\n  d['date']=pd.to_datetime(d['date'])\n  print(name,d.shape,d.date.min(),d.date.max(),d.cpi.isna().sum(),d.date.duplicated().sum())\n  r=pd.date_range(d.date.min(),d.date.max(),freq='MS'); print(' missing dates',sorted(set(r)-set(d.date))[:10], 'sorted',d.date.is_monotonic_increasing)\n  print(' min/max',d.cpi.min(),d.cpi.max(), 'pct jumps',(d.cpi.pct_change().abs()>0.03).sum())\nprint(a.vintage.value_counts())\nprint(a[a.date.duplicated(keep=False)])\nm=pd.merge(o,n,on='date',suffixes=('_o','_n'));print(len(m),(m.cpi_o-m.cpi_n).describe())\nref=pd.concat([o[o.date<'2014-01-01'].assign(v='2016-07-15'),n.assign(v='2026-10-28')]).rename(columns={'v':'vintage'})\nref['vintage']=ref.vintage\nc=pd.merge(a,ref,on='date',how='outer',suffixes=('_a','_r'),indicator=True)\nprint(c._merge.value_counts())\nbad=c[(c._merge!='both')|((c.cpi_a-c.cpi_r).abs()>1e-9)|(c.cpi_a.isna()!=c.cpi_r.isna())|(c.vintage_a.astype(str)!=c.vintage_r.astype(str))]\nprint(bad.to_string())\n\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2888,"cache_read_input_tokens":6442,"output_tokens":1025,"output_tokens_details":{"thinking_tokens":297},"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":2888},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXny4DVFM9XSGyjs3CnS","requestRef":"e69eceb7-4010-472e-93f8-fb7621c6ba1a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"496c4c70-d294-41d2-b12e-2a73d76276c6","timestamp":"2026-10-30T04:30:39.494Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098/data","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"496c4c70-d294-41d2-b12e-2a73d76276c6","isSidechain":false,"promptId":"928f8c2b-79cf-4ec9-8a61-d59b8c4f450e","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NGgAf8XTbi2MQiFwqkAvnA","type":"tool_result","content":"(eval):cd:1: no such file or directory: data\n<string>:8: FutureWarning: The default fill_method='pad' in Series.pct_change is deprecated and will be removed in a future version. Either fill in any non-leading NA values prior to calling pct_change or specify 'fill_method=None' to not fill NA values.\n<string>:8: FutureWarning: The default fill_method='pad' in Series.pct_change is deprecated and will be removed in a future version. Either fill in any non-leading NA values prior to calling pct_change or specify 'fill_method=None' to not fill NA values.\nold (834, 2) 1947-01-01 00:00:00 2016-06-01 00:00:00 0 0\n missing dates [] sorted True\n min/max 21.48 239.927 pct jumps 0\nnew (153, 2) 2014-01-01 00:00:00 2026-09-01 00:00:00 1 0\n missing dates [] sorted True\n min/max 234.747 336.298 pct jumps 0\nasm (957, 3) 1947-01-01 00:00:00 2026-10-01 00:00:00 1 0\n missing dates [Timestamp('2016-07-01 00:00:00')] sorted True\n min/max 21.48 336.298 pct jumps 0\nvintage\n2016-07-15    834\n2026-10-28    123\nName: count, dtype: int64\nEmpty DataFrame\nColumns: [date, cpi, vintage]\nIndex: []\n30 count    30.000000\nmean      0.001300\nstd       0.223576\nmin      -0.330000\n25%      -0.156750\n50%      -0.017000\n75%       0.210750\nmax       0.455000\ndtype: float64\n_merge\nboth          956\nleft_only       1\nright_only      1\nName: count, dtype: int64\n          date    cpi_a   vintage_a    cpi_r   vintage_r      _merge\n804 2014-01-01  235.436  2016-07-15  235.288  2026-10-28        both\n805 2014-02-01  235.621  2016-07-15  235.547  2026-10-28        both\n806 2014-03-01  235.897  2016-07-15  236.028  2026-10-28        both\n807 2014-04-01  236.495  2016-07-15  236.468  2026-10-28        both\n808 2014-05-01  236.803  2016-07-15  236.918  2026-10-28        both\n809 2014-06-01  237.016  2016-07-15  237.231  2026-10-28        both\n810 2014-07-01  237.259  2016-07-15  237.498  2026-10-28        both\n811 2014-08-01  237.163  2016-07-15  237.460  2026-10-28        both\n812 2014-09-01  237.510  2016-07-15  237.477  2026-10-28        both\n813 2014-10-01  237.651  2016-07-15  237.430  2026-10-28        both\n814 2014-11-01  237.261  2016-07-15  236.983  2026-10-28        both\n815 2014-12-01  236.464  2016-07-15  236.252  2026-10-28        both\n816 2015-01-01  234.954  2016-07-15  234.747  2026-10-28        both\n817 2015-02-01  235.415  2016-07-15  235.342  2026-10-28        both\n818 2015-03-01  235.859  2016-07-15  235.976  2026-10-28        both\n819 2015-04-01  236.197  2016-07-15  236.222  2026-10-28        both\n820 2015-05-01  236.876  2016-07-15  237.001  2026-10-28        both\n821 2015-06-01  237.423  2016-07-15  237.657  2026-10-28        both\n822 2015-07-01  237.734  2016-07-15  238.034  2026-10-28        both\n823 2015-08-01  237.703  2016-07-15  238.033  2026-10-28        both\n824 2015-09-01  237.489  2016-07-15  237.498  2026-10-28        both\n825 2015-10-01  237.949  2016-07-15  237.733  2026-10-28        both\n826 2015-11-01  238.302  2016-07-15  238.017  2026-10-28        both\n827 2015-12-01  238.041  2016-07-15  237.761  2026-10-28        both\n828 2016-01-01  238.107  2016-07-15  237.652  2026-10-28        both\n829 2016-02-01  237.707  2016-07-15  237.336  2026-10-28        both\n830 2016-03-01  237.920  2016-07-15  238.080  2026-10-28        both\n831 2016-04-01  238.890  2016-07-15  238.992  2026-10-28        both\n832 2016-05-01  239.410  2016-07-15  239.557  2026-10-28        both\n833 2016-06-01  239.927  2016-07-15  240.222  2026-10-28        both\n834 2016-07-01      NaN         NaN  240.101  2026-10-28  right_only\n835 2016-08-01  240.101  2026-10-28  240.545  2026-10-28        both\n836 2016-09-01  240.545  2026-10-28  241.176  2026-10-28        both\n837 2016-10-01  241.176  2026-10-28  241.741  2026-10-28        both\n838 2016-11-01  241.741  2026-10-28  242.026  2026-10-28        both\n839 2016-12-01  242.026  2026-10-28  242.637  2026-10-28        both\n840 2017-01-01  242.637  2026-10-28  243.618  2026-10-28        both\n841 2017-02-01  243.618  2026-10-28  244.006  2026-10-28        both\n842 2017-03-01  244.006  2026-10-28  243.892  2026-10-28        both\n843 2017-04-01  243.892  2026-10-28  244.193  2026-10-28        both\n844 2017-05-01  244.193  2026-10-28  244.004  2026-10-28        both\n845 2017-06-01  244.004  2026-10-28  244.163  2026-10-28        both\n846 2017-07-01  244.163  2026-10-28  244.243  2026-10-28        both\n847 2017-08-01  244.243  2026-10-28  245.183  2026-10-28        both\n848 2017-09-01  245.183  2026-10-28  246.435  2026-10-28        both\n849 2017-10-01  246.435  2026-10-28  246.626  2026-10-28        both\n850 2017-11-01  246.626  2026-10-28  247.284  2026-10-28        both\n851 2017-12-01  247.284  2026-10-28  247.805  2026-10-28        both\n852 2018-01-01  247.805  2026-10-28  248.859  2026-10-28        both\n853 2018-02-01  248.859  2026-10-28  249.529  2026-10-28        both\n854 2018-03-01  249.529  2026-10-28  249.577  2026-10-28        both\n855 2018-04-01  249.577  2026-10-28  250.227  2026-10-28        both\n856 2018-05-01  250.227  2026-10-28  250.792  2026-10-28        both\n857 2018-06-01  250.792  2026-10-28  251.018  2026-10-28        both\n858 2018-07-01  251.018  2026-10-28  251.214  2026-10-28        both\n859 2018-08-01  251.214  2026-10-28  251.663  2026-10-28        both\n860 2018-09-01  251.663  2026-10-28  252.182  2026-10-28        both\n861 2018-10-01  252.182  2026-10-28  252.772  2026-10-28        both\n862 2018-11-01  252.772  2026-10-28  252.594  2026-10-28        both\n863 2018-12-01  252.594  2026-10-28  252.767  2026-10-28        both\n864 2019-01-01  252.767  2026-10-28  252.561  2026-10-28        both\n865 2019-02-01  252.561  2026-10-28  253.319  2026-10-28        both\n866 2019-03-01  253.319  2026-10-28  254.277  2026-10-28        both\n867 2019-04-01  254.277  2026-10-28  255.233  2026-10-28        both\n868 2019-05-01  255.233  2026-10-28  255.296  2026-10-28        both\n869 2019-06-01  255.296  2026-10-28  255.213  2026-10-28        both\n870 2019-07-01  255.213  2026-10-28  255.802  2026-10-28        both\n871 2019-08-01  255.802  2026-10-28  256.036  2026-10-28        both\n872 2019-09-01  256.036  2026-10-28  256.430  2026-10-28        both\n873 2019-10-01  256.430  2026-10-28  257.155  2026-10-28        both\n874 2019-11-01  257.155  2026-10-28  257.879  2026-10-28        both\n875 2019-12-01  257.879  2026-10-28  258.630  2026-10-28        both\n876 2020-01-01  258.630  2026-10-28  259.127  2026-10-28        both\n877 2020-02-01  259.127  2026-10-28  259.250  2026-10-28        both\n878 2020-03-01  259.250  2026-10-28  258.076  2026-10-28        both\n879 2020-04-01  258.076  2026-10-28  256.032  2026-10-28        both\n880 2020-05-01  256.032  2026-10-28  255.802  2026-10-28        both\n881 2020-06-01  255.802  2026-10-28  257.042  2026-10-28        both\n882 2020-07-01  257.042  2026-10-28  258.352  2026-10-28        both\n883 2020-08-01  258.352  2026-10-28  259.316  2026-10-28        both\n884 2020-09-01  259.316  2026-10-28  259.997  2026-10-28        both\n885 2020-10-01  259.997  2026-10-28  260.319  2026-10-28        both\n886 2020-11-01  260.319  2026-10-28  260.911  2026-10-28        both\n887 2020-12-01  260.911  2026-10-28  262.045  2026-10-28        both\n888 2021-01-01  262.045  2026-10-28  262.687  2026-10-28        both\n889 2021-02-01  262.687  2026-10-28  263.579  2026-10-28        both\n890 2021-03-01  263.579  2026-10-28  264.961  2026-10-28        both\n891 2021-04-01  264.961  2026-10-28  266.614  2026-10-28        both\n892 2021-05-01  266.614  2026-10-28  268.383  2026-10-28        both\n893 2021-06-01  268.383  2026-10-28  270.654  2026-10-28        both\n894 2021-07-01  270.654  2026-10-28  271.903  2026-10-28        both\n895 2021-08-01  271.903  2026-10-28  272.676  2026-10-28        both\n896 2021-09-01  272.676  2026-10-28  273.910  2026-10-28        both\n897 2021-10-01  273.910  2026-10-28  276.550  2026-10-28        both\n898 2021-11-01  276.550  2026-10-28  278.919  2026-10-28        both\n899 2021-12-01  278.919  2026-10-28  280.845  2026-10-28        both\n900 2022-01-01  280.845  2026-10-28  282.543  2026-10-28        both\n901 2022-02-01  282.543  2026-10-28  284.500  2026-10-28        both\n902 2022-03-01  284.500  2026-10-28  287.674  2026-10-28        both\n903 2022-04-01  287.674  2026-10-28  288.561  2026-10-28        both\n904 2022-05-01  288.561  2026-10-28  291.298  2026-10-28        both\n905 2022-06-01  291.298  2026-10-28  294.957  2026-10-28        both\n906 2022-07-01  294.957  2026-10-28  294.913  2026-10-28        both\n907 2022-08-01  294.913  2026-10-28  295.097  2026-10-28        both\n908 2022-09-01  295.097  2026-10-28  296.349  2026-10-28        both\n909 2022-10-01  296.349  2026-10-28  298.007  2026-10-28        both\n910 2022-11-01  298.007  2026-10-28  298.786  2026-10-28        both\n911 2022-12-01  298.786  2026-10-28  298.832  2026-10-28        both\n912 2023-01-01  298.832  2026-10-28  300.420  2026-10-28        both\n913 2023-02-01  300.420  2026-10-28  301.450  2026-10-28        both\n914 2023-03-01  301.450  2026-10-28  301.821  2026-10-28        both\n915 2023-04-01  301.821  2026-10-28  302.845  2026-10-28        both\n916 2023-05-01  302.845  2026-10-28  303.334  2026-10-28        both\n917 2023-06-01  303.334  2026-10-28  304.014  2026-10-28        both\n918 2023-07-01  304.014  2026-10-28  304.609  2026-10-28        both\n919 2023-08-01  304.609  2026-10-28  306.082  2026-10-28        both\n920 2023-09-01  306.082  2026-10-28  307.276  2026-10-28        both\n921 2023-10-01  307.276  2026-10-28  307.696  2026-10-28        both\n922 2023-11-01  307.696  2026-10-28  308.148  2026-10-28        both\n923 2023-12-01  308.148  2026-10-28  308.741  2026-10-28        both\n924 2024-01-01  308.741  2026-10-28  309.698  2026-10-28        both\n925 2024-02-01  309.698  2026-10-28  310.967  2026-10-28        both\n926 2024-03-01  310.967  2026-10-28  312.345  2026-10-28        both\n927 2024-04-01  312.345  2026-10-28  313.023  2026-10-28        both\n928 2024-05-01  313.023  2026-10-28  313.175  2026-10-28        both\n929 2024-06-01  313.175  2026-10-28  313.044  2026-10-28        both\n930 2024-07-01  313.044  2026-10-28  313.569  2026-10-28        both\n931 2024-08-01  313.569  2026-10-28  314.062  2026-10-28        both\n932 2024-09-01  314.062  2026-10-28  314.732  2026-10-28        both\n933 2024-10-01  314.732  2026-10-28  315.631  2026-10-28        both\n934 2024-11-01  315.631  2026-10-28  316.528  2026-10-28        both\n935 2024-12-01  316.528  2026-10-28  317.604  2026-10-28        both\n936 2025-01-01  317.604  2026-10-28  318.961  2026-10-28        both\n937 2025-02-01  318.961  2026-10-28  319.679  2026-10-28        both\n938 2025-03-01  319.679  2026-10-28  319.785  2026-10-28        both\n939 2025-04-01  319.785  2026-10-28  320.302  2026-10-28        both\n940 2025-05-01  320.302  2026-10-28  320.620  2026-10-28        both\n941 2025-06-01  320.620  2026-10-28  321.435  2026-10-28        both\n942 2025-07-01  321.435  2026-10-28  322.169  2026-10-28        both\n943 2025-08-01  322.169  2026-10-28  323.291  2026-10-28        both\n944 2025-09-01  323.291  2026-10-28  324.245  2026-10-28        both\n945 2025-10-01  324.245  2026-10-28      NaN  2026-10-28        both\n946 2025-11-01      NaN  2026-10-28  325.063  2026-10-28        both\n947 2025-12-01  325.063  2026-10-28  326.031  2026-10-28        both\n948 2026-01-01  326.031  2026-10-28  326.588  2026-10-28        both\n949 2026-02-01  326.588  2026-10-28  327.460  2026-10-28        both\n950 2026-03-01  327.460  2026-10-28  330.293  2026-10-28        both\n951 2026-04-01  330.293  2026-10-28  332.407  2026-10-28        both\n952 2026-05-01  332.407  2026-10-28  333.979  2026-10-28        both\n953 2026-06-01  333.979  2026-10-28  332.568  2026-10-28        both\n954 2026-07-01  332.568  2026-10-28  332.813  2026-10-28        both\n955 2026-08-01  332.813  2026-10-28  334.131  2026-10-28        both\n956 2026-09-01  334.131  2026-10-28  336.298  2026-10-28        both\n957 2026-10-01  336.298  2026-10-28      NaN         NaN   left_only","is_error":false}]},"uuid":"39d8f8b8-191a-4b9a-bfa7-ac38dd4b5134","timestamp":"2026-10-30T04:30:39.792Z","toolUseResult":{"stdout":"(eval):cd:1: no such file or directory: data\n<string>:8: FutureWarning: The default fill_method='pad' in Series.pct_change is deprecated and will be removed in a future version. Either fill in any non-leading NA values prior to calling pct_change or specify 'fill_method=None' to not fill NA values.\n<string>:8: FutureWarning: The default fill_method='pad' in Series.pct_change is deprecated and will be removed in a future version. Either fill in any non-leading NA values prior to calling pct_change or specify 'fill_method=None' to not fill NA values.\nold (834, 2) 1947-01-01 00:00:00 2016-06-01 00:00:00 0 0\n missing dates [] sorted True\n min/max 21.48 239.927 pct jumps 0\nnew (153, 2) 2014-01-01 00:00:00 2026-09-01 00:00:00 1 0\n missing dates [] sorted True\n min/max 234.747 336.298 pct jumps 0\nasm (957, 3) 1947-01-01 00:00:00 2026-10-01 00:00:00 1 0\n missing dates [Timestamp('2016-07-01 00:00:00')] sorted True\n min/max 21.48 336.298 pct jumps 0\nvintage\n2016-07-15    834\n2026-10-28    123\nName: count, dtype: int64\nEmpty DataFrame\nColumns: [date, cpi, vintage]\nIndex: []\n30 count    30.000000\nmean      0.001300\nstd       0.223576\nmin      -0.330000\n25%      -0.156750\n50%      -0.017000\n75%       0.210750\nmax       0.455000\ndtype: float64\n_merge\nboth          956\nleft_only       1\nright_only      1\nName: count, dtype: int64\n          date    cpi_a   vintage_a    cpi_r   vintage_r      _merge\n804 2014-01-01  235.436  2016-07-15  235.288  2026-10-28        both\n805 2014-02-01  235.621  2016-07-15  235.547  2026-10-28        both\n806 2014-03-01  235.897  2016-07-15  236.028  2026-10-28        both\n807 2014-04-01  236.495  2016-07-15  236.468  2026-10-28        both\n808 2014-05-01  236.803  2016-07-15  236.918  2026-10-28        both\n809 2014-06-01  237.016  2016-07-15  237.231  2026-10-28        both\n810 2014-07-01  237.259  2016-07-15  237.498  2026-10-28        both\n811 2014-08-01  237.163  2016-07-15  237.460  2026-10-28        both\n812 2014-09-01  237.510  2016-07-15  237.477  2026-10-28        both\n813 2014-10-01  237.651  2016-07-15  237.430  2026-10-28        both\n814 2014-11-01  237.261  2016-07-15  236.983  2026-10-28        both\n815 2014-12-01  236.464  2016-07-15  236.252  2026-10-28        both\n816 2015-01-01  234.954  2016-07-15  234.747  2026-10-28        both\n817 2015-02-01  235.415  2016-07-15  235.342  2026-10-28        both\n818 2015-03-01  235.859  2016-07-15  235.976  2026-10-28        both\n819 2015-04-01  236.197  2016-07-15  236.222  2026-10-28        both\n820 2015-05-01  236.876  2016-07-15  237.001  2026-10-28        both\n821 2015-06-01  237.423  2016-07-15  237.657  2026-10-28        both\n822 2015-07-01  237.734  2016-07-15  238.034  2026-10-28        both\n823 2015-08-01  237.703  2016-07-15  238.033  2026-10-28        both\n824 2015-09-01  237.489  2016-07-15  237.498  2026-10-28        both\n825 2015-10-01  237.949  2016-07-15  237.733  2026-10-28        both\n826 2015-11-01  238.302  2016-07-15  238.017  2026-10-28        both\n827 2015-12-01  238.041  2016-07-15  237.761  2026-10-28        both\n828 2016-01-01  238.107  2016-07-15  237.652  2026-10-28        both\n829 2016-02-01  237.707  2016-07-15  237.336  2026-10-28        both\n830 2016-03-01  237.920  2016-07-15  238.080  2026-10-28        both\n831 2016-04-01  238.890  2016-07-15  238.992  2026-10-28        both\n832 2016-05-01  239.410  2016-07-15  239.557  2026-10-28        both\n833 2016-06-01  239.927  2016-07-15  240.222  2026-10-28        both\n834 2016-07-01      NaN         NaN  240.101  2026-10-28  right_only\n835 2016-08-01  240.101  2026-10-28  240.545  2026-10-28        both\n836 2016-09-01  240.545  2026-10-28  241.176  2026-10-28        both\n837 2016-10-01  241.176  2026-10-28  241.741  2026-10-28        both\n838 2016-11-01  241.741  2026-10-28  242.026  2026-10-28        both\n839 2016-12-01  242.026  2026-10-28  242.637  2026-10-28        both\n840 2017-01-01  242.637  2026-10-28  243.618  2026-10-28        both\n841 2017-02-01  243.618  2026-10-28  244.006  2026-10-28        both\n842 2017-03-01  244.006  2026-10-28  243.892  2026-10-28        both\n843 2017-04-01  243.892  2026-10-28  244.193  2026-10-28        both\n844 2017-05-01  244.193  2026-10-28  244.004  2026-10-28        both\n845 2017-06-01  244.004  2026-10-28  244.163  2026-10-28        both\n846 2017-07-01  244.163  2026-10-28  244.243  2026-10-28        both\n847 2017-08-01  244.243  2026-10-28  245.183  2026-10-28        both\n848 2017-09-01  245.183  2026-10-28  246.435  2026-10-28        both\n849 2017-10-01  246.435  2026-10-28  246.626  2026-10-28        both\n850 2017-11-01  246.626  2026-10-28  247.284  2026-10-28        both\n851 2017-12-01  247.284  2026-10-28  247.805  2026-10-28        both\n852 2018-01-01  247.805  2026-10-28  248.859  2026-10-28        both\n853 2018-02-01  248.859  2026-10-28  249.529  2026-10-28        both\n854 2018-03-01  249.529  2026-10-28  249.577  2026-10-28        both\n855 2018-04-01  249.577  2026-10-28  250.227  2026-10-28        both\n856 2018-05-01  250.227  2026-10-28  250.792  2026-10-28        both\n857 2018-06-01  250.792  2026-10-28  251.018  2026-10-28        both\n858 2018-07-01  251.018  2026-10-28  251.214  2026-10-28        both\n859 2018-08-01  251.214  2026-10-28  251.663  2026-10-28        both\n860 2018-09-01  251.663  2026-10-28  252.182  2026-10-28        both\n861 2018-10-01  252.182  2026-10-28  252.772  2026-10-28        both\n862 2018-11-01  252.772  2026-10-28  252.594  2026-10-28        both\n863 2018-12-01  252.594  2026-10-28  252.767  2026-10-28        both\n864 2019-01-01  252.767  2026-10-28  252.561  2026-10-28        both\n865 2019-02-01  252.561  2026-10-28  253.319  2026-10-28        both\n866 2019-03-01  253.319  2026-10-28  254.277  2026-10-28        both\n867 2019-04-01  254.277  2026-10-28  255.233  2026-10-28        both\n868 2019-05-01  255.233  2026-10-28  255.296  2026-10-28        both\n869 2019-06-01  255.296  2026-10-28  255.213  2026-10-28        both\n870 2019-07-01  255.213  2026-10-28  255.802  2026-10-28        both\n871 2019-08-01  255.802  2026-10-28  256.036  2026-10-28        both\n872 2019-09-01  256.036  2026-10-28  256.430  2026-10-28        both\n873 2019-10-01  256.430  2026-10-28  257.155  2026-10-28        both\n874 2019-11-01  257.155  2026-10-28  257.879  2026-10-28        both\n875 2019-12-01  257.879  2026-10-28  258.630  2026-10-28        both\n876 2020-01-01  258.630  2026-10-28  259.127  2026-10-28        both\n877 2020-02-01  259.127  2026-10-28  259.250  2026-10-28        both\n878 2020-03-01  259.250  2026-10-28  258.076  2026-10-28        both\n879 2020-04-01  258.076  2026-10-28  256.032  2026-10-28        both\n880 2020-05-01  256.032  2026-10-28  255.802  2026-10-28        both\n881 2020-06-01  255.802  2026-10-28  257.042  2026-10-28        both\n882 2020-07-01  257.042  2026-10-28  258.352  2026-10-28        both\n883 2020-08-01  258.352  2026-10-28  259.316  2026-10-28        both\n884 2020-09-01  259.316  2026-10-28  259.997  2026-10-28        both\n885 2020-10-01  259.997  2026-10-28  260.319  2026-10-28        both\n886 2020-11-01  260.319  2026-10-28  260.911  2026-10-28        both\n887 2020-12-01  260.911  2026-10-28  262.045  2026-10-28        both\n888 2021-01-01  262.045  2026-10-28  262.687  2026-10-28        both\n889 2021-02-01  262.687  2026-10-28  263.579  2026-10-28        both\n890 2021-03-01  263.579  2026-10-28  264.961  2026-10-28        both\n891 2021-04-01  264.961  2026-10-28  266.614  2026-10-28        both\n892 2021-05-01  266.614  2026-10-28  268.383  2026-10-28        both\n893 2021-06-01  268.383  2026-10-28  270.654  2026-10-28        both\n894 2021-07-01  270.654  2026-10-28  271.903  2026-10-28        both\n895 2021-08-01  271.903  2026-10-28  272.676  2026-10-28        both\n896 2021-09-01  272.676  2026-10-28  273.910  2026-10-28        both\n897 2021-10-01  273.910  2026-10-28  276.550  2026-10-28        both\n898 2021-11-01  276.550  2026-10-28  278.919  2026-10-28        both\n899 2021-12-01  278.919  2026-10-28  280.845  2026-10-28        both\n900 2022-01-01  280.845  2026-10-28  282.543  2026-10-28        both\n901 2022-02-01  282.543  2026-10-28  284.500  2026-10-28        both\n902 2022-03-01  284.500  2026-10-28  287.674  2026-10-28        both\n903 2022-04-01  287.674  2026-10-28  288.561  2026-10-28        both\n904 2022-05-01  288.561  2026-10-28  291.298  2026-10-28        both\n905 2022-06-01  291.298  2026-10-28  294.957  2026-10-28        both\n906 2022-07-01  294.957  2026-10-28  294.913  2026-10-28        both\n907 2022-08-01  294.913  2026-10-28  295.097  2026-10-28        both\n908 2022-09-01  295.097  2026-10-28  296.349  2026-10-28        both\n909 2022-10-01  296.349  2026-10-28  298.007  2026-10-28        both\n910 2022-11-01  298.007  2026-10-28  298.786  2026-10-28        both\n911 2022-12-01  298.786  2026-10-28  298.832  2026-10-28        both\n912 2023-01-01  298.832  2026-10-28  300.420  2026-10-28        both\n913 2023-02-01  300.420  2026-10-28  301.450  2026-10-28        both\n914 2023-03-01  301.450  2026-10-28  301.821  2026-10-28        both\n915 2023-04-01  301.821  2026-10-28  302.845  2026-10-28        both\n916 2023-05-01  302.845  2026-10-28  303.334  2026-10-28        both\n917 2023-06-01  303.334  2026-10-28  304.014  2026-10-28        both\n918 2023-07-01  304.014  2026-10-28  304.609  2026-10-28        both\n919 2023-08-01  304.609  2026-10-28  306.082  2026-10-28        both\n920 2023-09-01  306.082  2026-10-28  307.276  2026-10-28        both\n921 2023-10-01  307.276  2026-10-28  307.696  2026-10-28        both\n922 2023-11-01  307.696  2026-10-28  308.148  2026-10-28        both\n923 2023-12-01  308.148  2026-10-28  308.741  2026-10-28        both\n924 2024-01-01  308.741  2026-10-28  309.698  2026-10-28        both\n925 2024-02-01  309.698  2026-10-28  310.967  2026-10-28        both\n926 2024-03-01  310.967  2026-10-28  312.345  2026-10-28        both\n927 2024-04-01  312.345  2026-10-28  313.023  2026-10-28        both\n928 2024-05-01  313.023  2026-10-28  313.175  2026-10-28        both\n929 2024-06-01  313.175  2026-10-28  313.044  2026-10-28        both\n930 2024-07-01  313.044  2026-10-28  313.569  2026-10-28        both\n931 2024-08-01  313.569  2026-10-28  314.062  2026-10-28        both\n932 2024-09-01  314.062  2026-10-28  314.732  2026-10-28        both\n933 2024-10-01  314.732  2026-10-28  315.631  2026-10-28        both\n934 2024-11-01  315.631  2026-10-28  316.528  2026-10-28        both\n935 2024-12-01  316.528  2026-10-28  317.604  2026-10-28        both\n936 2025-01-01  317.604  2026-10-28  318.961  2026-10-28        both\n937 2025-02-01  318.961  2026-10-28  319.679  2026-10-28        both\n938 2025-03-01  319.679  2026-10-28  319.785  2026-10-28        both\n939 2025-04-01  319.785  2026-10-28  320.302  2026-10-28        both\n940 2025-05-01  320.302  2026-10-28  320.620  2026-10-28        both\n941 2025-06-01  320.620  2026-10-28  321.435  2026-10-28        both\n942 2025-07-01  321.435  2026-10-28  322.169  2026-10-28        both\n943 2025-08-01  322.169  2026-10-28  323.291  2026-10-28        both\n944 2025-09-01  323.291  2026-10-28  324.245  2026-10-28        both\n945 2025-10-01  324.245  2026-10-28      NaN  2026-10-28        both\n946 2025-11-01      NaN  2026-10-28  325.063  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data; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\no=pd.read_csv('cpi_vintage_2016-07-15.csv');n=pd.read_csv('cpi_vintage_2026-10-28.csv');a=pd.read_csv('cpi_assembled.csv')\nfor name,d in [('old',o),('new',n),('asm',a)]:\n  d['date']=pd.to_datetime(d['date'])\n  print(name,d.shape,d.date.min(),d.date.max(),d.cpi.isna().sum(),d.date.duplicated().sum())\n  r=pd.date_range(d.date.min(),d.date.max(),freq='MS'); print(' missing dates',sorted(set(r)-set(d.date))[:10], 'sorted',d.date.is_monotonic_increasing)\n  print(' min/max',d.cpi.min(),d.cpi.max(), 'pct jumps',(d.cpi.pct_change().abs()>0.03).sum())\nprint(a.vintage.value_counts())\nprint(a[a.date.duplicated(keep=False)])\nm=pd.merge(o,n,on='date',suffixes=('_o','_n'));print(len(m),(m.cpi_o-m.cpi_n).describe())\nref=pd.concat([o[o.date<'2014-01-01'].assign(v='2016-07-15'),n.assign(v='2026-10-28')]).rename(columns={'v':'vintage'})\nref['vintage']=ref.vintage\nc=pd.merge(a,ref,on='date',how='outer',suffixes=('_a','_r'),indicator=True)\nprint(c._merge.value_counts())\nbad=c[(c._merge!='both')|((c.cpi_a-c.cpi_r).abs()>1e-9)|(c.cpi_a.isna()!=c.cpi_r.isna())|(c.vintage_a.astype(str)!=c.vintage_r.astype(str))]\nprint(bad.to_string())\n\""}}]}}26{"type":"api-request","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","id":"acc2c71e-519c-4f04-815a-2091b6bcd8f4","timestamp":"2026-10-30T04:30:39.795Z","version":"2.1.314","querySource":"sdk","shapeHash":"c4a9886b5d404a87039300a171d4a2ccda220b548475a5284c56dab1eb465ef2","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":"e69eceb7-4010-472e-93f8-fb7621c6ba1a","keep":6,"tail":["51c5ee1881b0baa33905900337289e16d9c126baaad980afb855d996e146f693","e03a0ebfcfa378159d98448a8cb9b60766a90c9524305f54d6a23de8123f7d20"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"a9ba73aa22cb5ced81a3f9ca750fc7ad010553160b2b97700c06b62c92776a01"}27{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"39d8f8b8-191a-4b9a-bfa7-ac38dd4b5134","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3"}28{"type":"atis-latch","atis":"","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3"}29{"parentUuid":"39d8f8b8-191a-4b9a-bfa7-ac38dd4b5134","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnyoAQDx8E9LoNBTUeA","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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-p out/state && cat > out/analysis.py <<'EOF'\n\"\"\"F1-conf-01: 12-month CPI inflation from two ALFRED vintages.\nReads only data/; writes out/results.json, out/analytic.csv, out/exclusions.csv.\nDeterministic; no randomness used (random_seed = null).\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nD = os.path.join(ROOT, \"data\"); O = os.path.join(ROOT, \"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\ndef load(f):\n    d = pd.read_csv(os.path.join(D, f), dtype={\"cpi\": float})\n    d[\"date\"] = pd.to_datetime(d[\"date\"])\n    return d\n\nold, new, asm = load(F_OLD), load(F_NEW), load(F_ASM)\nfor d in (old, new):\n    assert not d[\"date\"].duplicated().any()\n    assert d[\"date\"].is_monotonic_increasing\nissues = []\n\n# ---- Splice: most recent vintage wins wherever it covers the month (row present) ----\nstack = pd.concat([old.assign(vintage=V_OLD), new.assign(vintage=V_NEW)], ignore_index=True)\nn_stack = len(stack)\ncovered_new = set(new[\"date\"])\nspliced = stack[~((stack[\"vintage\"] == V_OLD) & stack[\"date\"].isin(covered_new))]\nspliced = spliced.sort_values(\"date\").set_index(\"date\")\nn_spliced = len(spliced)\nfull_idx = pd.date_range(spliced.index.min(), spliced.index.max(), freq=\"MS\")\nassert len(full_idx) == n_spliced, \"gap in calendar after splice\"\n\nmissing_lvls = spliced.index[spliced[\"cpi\"].isna()]\nfor m in missing_lvls:\n    issues.append({\"issue\": f\"Vintage {spliced.loc[m,'vintage']} reports no CPI value for {m:%Y-%m} (empty cell).\",\n                   \"handling\": \"Not imputed; g_t for that month (and for month+12 if inside the window) reported missing and counted.\"})\n\n# ---- Growth rates (no interpolation) ----\ns = spliced.reindex(full_idx)\nlag = s[\"cpi\"].shift(12)            # calendar-exact lag (index has no gaps)\ng = 100.0 * (s[\"cpi\"] / lag - 1.0)\nwin = s.loc[W0:W1].copy()\nwin[\"cpi_lag12\"] = lag.loc[W0:W1]; win[\"growth_12m\"] = g.loc[W0:W1]\nn_window = len(win)\nok = win[\"growth_12m\"].notna()\nan = win[ok]\nn_an = len(an)\n\n# cross-vintage revision size in the overlap (diagnostic for the splice)\nov = pd.merge(old, new, on=\"date\", suffixes=(\"_old\", \"_new\")).dropna()\nrev = (ov[\"cpi_new\"] - ov[\"cpi_old\"])\nissues.append({\"issue\": f\"The two vintages overlap for {len(ov)} months ({ov.date.min():%Y-%m}..{ov.date.max():%Y-%m}) with different values \"\n                        f\"(revisions of seasonal factors; new-old ranges {rev.min():.3f}..{rev.max():.3f} index points).\",\n               \"handling\": \"Per the availability rule the 2026-10-28 value is used for 2014-01 onward; 2016-07-15 values only before 2014-01. \"\n                           \"Hence g_t for 2014-01..2014-12 divides a 2026-vintage level by a 2016-vintage level (mixed-vintage ratio); noted as a limitation.\"})\n\n# ---- Audit of the hand-assembled file (not used for estimation) ----\nref = s[[\"cpi\", \"vintage\"]].reset_index().rename(columns={\"index\": \"date\"})\nc = pd.merge(asm, ref, on=\"date\", how=\"outer\", suffixes=(\"_asm\", \"_ref\"), indicator=True)\nonly_asm = c[c[\"_merge\"] == \"left_only\"][\"date\"]\nonly_ref = c[c[\"_merge\"] == \"right_only\"][\"date\"]\nboth = c[c[\"_merge\"] == \"both\"]\nvaldiff = both[~(np.isclose(both.cpi_asm, both.cpi_ref, atol=1e-9) | (both.cpi_asm.isna() & both.cpi_ref.isna()))]\noldused = both[(both.vintage_asm.astype(str) == V_OLD) & (both.vintage_ref == V_NEW)]\n# detect one-month shift: asm value at t equals reference value at t-1\nsh = pd.merge(asm, ref.assign(date=ref[\"date\"] + pd.offsets.MonthBegin(1)), on=\"date\", suffixes=(\"_asm\", \"_prev\"))\nsh = sh[sh.date > pd.Timestamp(\"2016-07-01\")]\nn_shift = int((np.isclose(sh.cpi_asm, sh.cpi_prev, atol=1e-9) | (sh.cpi_asm.isna() & sh.cpi_prev.isna())).sum())\nissues.append({\"issue\": f\"cpi_assembled.csv uses 2016-07-15 vintage values for {len(oldused)} months \"\n                        f\"({oldused.date.min():%Y-%m}..{oldused.date.max():%Y-%m}) that the 2026-10-28 vintage covers, violating the most-recent-vintage rule.\",\n               \"handling\": \"Assembled file not used for estimation; series re-built from the two vintage files.\"})\nissues.append({\"issue\": f\"cpi_assembled.csv is missing month(s) {', '.join(f'{d:%Y-%m}' for d in only_ref)} and contains month(s) \"\n                        f\"{', '.join(f'{d:%Y-%m}' for d in only_asm)} not present in any source; from 2016-08 onward {n_shift} of {len(sh)} values \"\n                        f\"equal the source value of the previous month (one-month date shift). In total {len(valdiff)} common months have values differing from the authoritative splice.\",\n               \"handling\": \"Assembled file treated as unreliable and used only for this audit; all estimates come from the vintage files.\"})\nissues.append({\"issue\": \"SHA-256 values in data/README.md refer to the raw downloads, not to the reformatted CSV files in data/, so they cannot be used to verify the files.\",\n               \"handling\": \"Recorded the SHA-256 of the files as received in out/state/data_audit.md; no further action.\"})\n\n# ---- Outputs ----\nym = lambda t: f\"{t:%Y-%m}\"\ndef gm(t):\n    t = pd.Timestamp(t); v = win.loc[t, \"growth_12m\"]\n    return None if pd.isna(v) else float(v)\ngg = an[\"growth_12m\"]\nmissing_months = [ym(t) for t in win.index[~ok]]\nres = {\n    \"task_id\": \"F1-conf-01\",\n    \"first_growth_month\": ym(an.index.min()),\n    \"last_month\": ym(s.index[s[\"cpi\"].notna() & (s.index <= W1)].max()),\n    \"n_months_growth\": int(n_an),\n    \"n_months_missing_growth\": int(n_window - n_an),\n    \"n_analysed\": int(n_an),\n    \"mean_growth_12m\": float(gg.mean()),\n    \"mean_growth_12m_2001_2013\": float(gg.loc[\"2001-01-01\":\"2013-12-01\"].mean()),\n    \"mean_growth_12m_2014_2026\": float(gg.loc[\"2014-01-01\":\"2026-09-01\"].mean()),\n    \"growth_2014_06\": gm(\"2014-06-01\"), \"growth_2016_07\": gm(\"2016-07-01\"),\n    \"growth_2020_05\": gm(\"2020-05-01\"), \"growth_2022_06\": gm(\"2022-06-01\"),\n    \"growth_2026_09\": gm(\"2026-09-01\"),\n    \"max_growth_12m\": float(gg.max()), \"max_growth_month\": ym(gg.idxmax()),\n    \"min_growth_12m\": float(gg.min()), \"min_growth_month\": ym(gg.idxmin()),\n    \"feasibility\": \"complete\" if n_an == n_window else \"partial\",\n    \"feasibility_reason\": None if n_an == n_window else\n        (f\"{n_window - n_an} of {n_window} window months have no computable 12-month growth rate ({', '.join(missing_months)}) because the \"\n         \"source reports no index level; per the task rules they are not imputed. All window means are over the remaining months; all named months are computable.\"),\n    \"data_issues\": issues,\n    \"n_input_rows\": {F_OLD: int(len(old)), F_NEW: int(len(new)), F_ASM: int(len(asm))},\n    \"random_seed\": None,\n    # auxiliary (not required) keys\n    \"n_months_window\": int(n_window),\n    \"missing_growth_months\": missing_months,\n    \"n_months_growth_2001_2013\": int(gg.loc[\"2001-01-01\":\"2013-12-01\"].size),\n    \"n_months_growth_2014_2026\": int(gg.loc[\"2014-01-01\":\"2026-09-01\"].size),\n    \"sd_growth_12m\": float(gg.std(ddof=1)),\n    \"max_abs_vintage_revision_overlap\": float(rev.abs().max()),\n}\nout = an.reset_index().rename(columns={\"index\": \"date\"})\nout[\"date\"] = out[\"date\"].dt.strftime(\"%Y-%m-01\")\nout = out.rename(columns={\"vintage\": \"vintage_used\"})[[\"date\", \"cpi\", \"cpi_lag12\", \"vintage_used\", \"growth_12m\"]]\nout.to_csv(os.path.join(O, \"analytic.csv\"), index=False, float_format=\"%.10g\")\n\nn_before_win = n_spliced\nex = [\n    (\"1\", \"Drop 2016-07-15-vintage rows for months covered by the 2026-10-28 vintage\", n_stack, n_spliced,\n     \"Information rule: use the most recent vintage covering the month\"),\n    (\"2\", f\"Keep months in window {ym(W0)}..{ym(W1)}\", n_spliced, n_window,\n     \"Earlier months only serve as 12-month lags\"),\n    (\"3\", \"Drop window months without a computable g_t (P_t or P_t-12 missing)\", n_window, n_an,\n     \"No interpolation/imputation allowed; missing: \" + \";\".join(missing_months)),\n]\nexd = pd.DataFrame(ex, columns=[\"step\", \"rule\", \"n_before\", \"n_after\"])\nexd[\"n_removed\"] = exd.n_before - exd.n_after\nexd[\"rationale\"] = [e[4] for e in ex]\nexd.to_csv(os.path.join(O, \"exclusions.csv\"), index=False)\n\nwith open(os.path.join(O, \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2)\nprint(f\"n_analysed={n_an} of {n_window}; mean={res['mean_growth_12m']:.4f}; \"\n      f\"2001-13={res['mean_growth_12m_2001_2013']:.4f}; 2014-26={res['mean_growth_12m_2014_2026']:.4f}\")\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/exclusions.csv && head -3 out/analytic.csv && grep -n \"2025-1\\|2014-0[12]\" out/analytic.csv"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":6737,"cache_read_input_tokens":9330,"output_tokens":4795,"output_tokens_details":{"thinking_tokens":723},"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":6737},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXnynsmzKtPkjWqmm8RE","requestRef":"acc2c71e-519c-4f04-815a-2091b6bcd8f4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3621ac7a-6875-42df-9b0a-26f42dbc67f5","timestamp":"2026-10-30T04:31:20.543Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098/data","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}31{"parentUuid":"3621ac7a-6875-42df-9b0a-26f42dbc67f5","isSidechain":false,"promptId":"928f8c2b-79cf-4ec9-8a61-d59b8c4f450e","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KbstxJfMQGsoN2WTzZk928","type":"tool_result","content":"Traceback (most recent call last):\n  File \"/Users/simon-pierreboucher/wp29/runs/RC0098/data/out/analysis.py\", line 19, in <module>\n    old, new, asm = load(F_OLD), load(F_NEW), load(F_ASM)\n                    ~~~~^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/runs/RC0098/data/out/analysis.py\", line 15, in load\n    d = pd.read_csv(os.path.join(D, f), dtype={\"cpi\": float})\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: '/Users/simon-pierreboucher/wp29/runs/RC0098/data/data/cpi_vintage_2016-07-15.csv'","is_error":false}]},"uuid":"888a74b1-1978-4d05-9a26-7dfc22e040eb","timestamp":"2026-10-30T04:31:20.818Z","toolUseResult":{"stdout":"Traceback (most recent call last):\n  File \"/Users/simon-pierreboucher/wp29/runs/RC0098/data/out/analysis.py\", line 19, in <module>\n    old, new, asm = load(F_OLD), load(F_NEW), load(F_ASM)\n                    ~~~~^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/runs/RC0098/data/out/analysis.py\", line 15, in load\n    d = pd.read_csv(os.path.join(D, f), dtype={\"cpi\": float})\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: '/Users/simon-pierreboucher/wp29/runs/RC0098/data/data/cpi_vintage_2016-07-15.csv'","stderr":"","interrupted":false,"isImage":false,"returnCodeInterpretation":"No matches found","noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"3621ac7a-6875-42df-9b0a-26f42dbc67f5","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098/data","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}32{"type":"api-request-blob","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","hash":"bbb429e4b2b089dfd36b7db333b93917fbfc0878b6085003e33febd8f8c89959","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01KbstxJfMQGsoN2WTzZk928","name":"Bash","input":{"command":"mkdir -p out/state && cat > out/analysis.py <<'EOF'\n\"\"\"F1-conf-01: 12-month CPI inflation from two ALFRED vintages.\nReads only data/; writes out/results.json, out/analytic.csv, out/exclusions.csv.\nDeterministic; no randomness used (random_seed = null).\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nD = os.path.join(ROOT, \"data\"); O = os.path.join(ROOT, \"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\ndef load(f):\n    d = pd.read_csv(os.path.join(D, f), dtype={\"cpi\": float})\n    d[\"date\"] = pd.to_datetime(d[\"date\"])\n    return d\n\nold, new, asm = load(F_OLD), load(F_NEW), load(F_ASM)\nfor d in (old, new):\n    assert not d[\"date\"].duplicated().any()\n    assert d[\"date\"].is_monotonic_increasing\nissues = []\n\n# ---- Splice: most recent vintage wins wherever it covers the month (row present) ----\nstack = pd.concat([old.assign(vintage=V_OLD), new.assign(vintage=V_NEW)], ignore_index=True)\nn_stack = len(stack)\ncovered_new = set(new[\"date\"])\nspliced = stack[~((stack[\"vintage\"] == V_OLD) & stack[\"date\"].isin(covered_new))]\nspliced = spliced.sort_values(\"date\").set_index(\"date\")\nn_spliced = len(spliced)\nfull_idx = pd.date_range(spliced.index.min(), spliced.index.max(), freq=\"MS\")\nassert len(full_idx) == n_spliced, \"gap in calendar after splice\"\n\nmissing_lvls = spliced.index[spliced[\"cpi\"].isna()]\nfor m in missing_lvls:\n    issues.append({\"issue\": f\"Vintage {spliced.loc[m,'vintage']} reports no CPI value for {m:%Y-%m} (empty cell).\",\n                   \"handling\": \"Not imputed; g_t for that month (and for month+12 if inside the window) reported missing and counted.\"})\n\n# ---- Growth rates (no interpolation) ----\ns = spliced.reindex(full_idx)\nlag = s[\"cpi\"].shift(12)            # calendar-exact lag (index has no gaps)\ng = 100.0 * (s[\"cpi\"] / lag - 1.0)\nwin = s.loc[W0:W1].copy()\nwin[\"cpi_lag12\"] = lag.loc[W0:W1]; win[\"growth_12m\"] = g.loc[W0:W1]\nn_window = len(win)\nok = win[\"growth_12m\"].notna()\nan = win[ok]\nn_an = len(an)\n\n# cross-vintage revision size in the overlap (diagnostic for the splice)\nov = pd.merge(old, new, on=\"date\", suffixes=(\"_old\", \"_new\")).dropna()\nrev = (ov[\"cpi_new\"] - ov[\"cpi_old\"])\nissues.append({\"issue\": f\"The two vintages overlap for {len(ov)} months ({ov.date.min():%Y-%m}..{ov.date.max():%Y-%m}) with different values \"\n                        f\"(revisions of seasonal factors; new-old ranges {rev.min():.3f}..{rev.max():.3f} index points).\",\n               \"handling\": \"Per the availability rule the 2026-10-28 value is used for 2014-01 onward; 2016-07-15 values only before 2014-01. \"\n                           \"Hence g_t for 2014-01..2014-12 divides a 2026-vintage level by a 2016-vintage level (mixed-vintage ratio); noted as a limitation.\"})\n\n# ---- Audit of the hand-assembled file (not used for estimation) ----\nref = s[[\"cpi\", \"vintage\"]].reset_index().rename(columns={\"index\": \"date\"})\nc = pd.merge(asm, ref, on=\"date\", how=\"outer\", suffixes=(\"_asm\", \"_ref\"), indicator=True)\nonly_asm = c[c[\"_merge\"] == \"left_only\"][\"date\"]\nonly_ref = c[c[\"_merge\"] == \"right_only\"][\"date\"]\nboth = c[c[\"_merge\"] == \"both\"]\nvaldiff = both[~(np.isclose(both.cpi_asm, both.cpi_ref, atol=1e-9) | (both.cpi_asm.isna() & both.cpi_ref.isna()))]\noldused = both[(both.vintage_asm.astype(str) == V_OLD) & (both.vintage_ref == V_NEW)]\n# detect one-month shift: asm value at t equals reference value at t-1\nsh = pd.merge(asm, ref.assign(date=ref[\"date\"] + pd.offsets.MonthBegin(1)), on=\"date\", suffixes=(\"_asm\", \"_prev\"))\nsh = sh[sh.date > pd.Timestamp(\"2016-07-01\")]\nn_shift = int((np.isclose(sh.cpi_asm, sh.cpi_prev, atol=1e-9) | (sh.cpi_asm.isna() & sh.cpi_prev.isna())).sum())\nissues.append({\"issue\": f\"cpi_assembled.csv uses 2016-07-15 vintage values for {len(oldused)} months \"\n                        f\"({oldused.date.min():%Y-%m}..{oldused.date.max():%Y-%m}) that the 2026-10-28 vintage covers, violating the most-recent-vintage rule.\",\n               \"handling\": \"Assembled file not used for estimation; series re-built from the two vintage files.\"})\nissues.append({\"issue\": f\"cpi_assembled.csv is missing month(s) {', '.join(f'{d:%Y-%m}' for d in only_ref)} and contains month(s) \"\n                        f\"{', '.join(f'{d:%Y-%m}' for d in only_asm)} not present in any source; from 2016-08 onward {n_shift} of {len(sh)} values \"\n                        f\"equal the source value of the previous month (one-month date shift). In total {len(valdiff)} common months have values differing from the authoritative splice.\",\n               \"handling\": \"Assembled file treated as unreliable and used only for this audit; all estimates come from the vintage files.\"})\nissues.append({\"issue\": \"SHA-256 values in data/README.md refer to the raw downloads, not to the reformatted CSV files in data/, so they cannot be used to verify the files.\",\n               \"handling\": \"Recorded the SHA-256 of the files as received in out/state/data_audit.md; no further action.\"})\n\n# ---- Outputs ----\nym = lambda t: f\"{t:%Y-%m}\"\ndef gm(t):\n    t = pd.Timestamp(t); v = win.loc[t, \"growth_12m\"]\n    return None if pd.isna(v) else float(v)\ngg = an[\"growth_12m\"]\nmissing_months = [ym(t) for t in win.index[~ok]]\nres = {\n    \"task_id\": \"F1-conf-01\",\n    \"first_growth_month\": ym(an.index.min()),\n    \"last_month\": ym(s.index[s[\"cpi\"].notna() & (s.index <= W1)].max()),\n    \"n_months_growth\": int(n_an),\n    \"n_months_missing_growth\": int(n_window - n_an),\n    \"n_analysed\": int(n_an),\n    \"mean_growth_12m\": float(gg.mean()),\n    \"mean_growth_12m_2001_2013\": float(gg.loc[\"2001-01-01\":\"2013-12-01\"].mean()),\n    \"mean_growth_12m_2014_2026\": float(gg.loc[\"2014-01-01\":\"2026-09-01\"].mean()),\n    \"growth_2014_06\": gm(\"2014-06-01\"), \"growth_2016_07\": gm(\"2016-07-01\"),\n    \"growth_2020_05\": gm(\"2020-05-01\"), \"growth_2022_06\": gm(\"2022-06-01\"),\n    \"growth_2026_09\": gm(\"2026-09-01\"),\n    \"max_growth_12m\": float(gg.max()), \"max_growth_month\": ym(gg.idxmax()),\n    \"min_growth_12m\": float(gg.min()), \"min_growth_month\": ym(gg.idxmin()),\n    \"feasibility\": \"complete\" if n_an == n_window else \"partial\",\n    \"feasibility_reason\": None if n_an == n_window else\n        (f\"{n_window - n_an} of {n_window} window months have no computable 12-month growth rate ({', '.join(missing_months)}) because the \"\n         \"source reports no index level; per the task rules they are not imputed. All window means are over the remaining months; all named months are computable.\"),\n    \"data_issues\": issues,\n    \"n_input_rows\": {F_OLD: int(len(old)), F_NEW: int(len(new)), F_ASM: int(len(asm))},\n    \"random_seed\": None,\n    # auxiliary (not required) keys\n    \"n_months_window\": int(n_window),\n    \"missing_growth_months\": missing_months,\n    \"n_months_growth_2001_2013\": int(gg.loc[\"2001-01-01\":\"2013-12-01\"].size),\n    \"n_months_growth_2014_2026\": int(gg.loc[\"2014-01-01\":\"2026-09-01\"].size),\n    \"sd_growth_12m\": float(gg.std(ddof=1)),\n    \"max_abs_vintage_revision_overlap\": float(rev.abs().max()),\n}\nout = an.reset_index().rename(columns={\"index\": \"date\"})\nout[\"date\"] = out[\"date\"].dt.strftime(\"%Y-%m-01\")\nout = out.rename(columns={\"vintage\": \"vintage_used\"})[[\"date\", \"cpi\", \"cpi_lag12\", \"vintage_used\", \"growth_12m\"]]\nout.to_csv(os.path.join(O, \"analytic.csv\"), index=False, float_format=\"%.10g\")\n\nn_before_win = n_spliced\nex = [\n    (\"1\", \"Drop 2016-07-15-vintage rows for months covered by the 2026-10-28 vintage\", n_stack, n_spliced,\n     \"Information rule: use the most recent vintage covering the month\"),\n    (\"2\", f\"Keep months in window {ym(W0)}..{ym(W1)}\", n_spliced, n_window,\n     \"Earlier months only serve as 12-month lags\"),\n    (\"3\", \"Drop window months without a computable g_t (P_t or P_t-12 missing)\", n_window, n_an,\n     \"No interpolation/imputation allowed; missing: \" + \";\".join(missing_months)),\n]\nexd = pd.DataFrame(ex, columns=[\"step\", \"rule\", \"n_before\", \"n_after\"])\nexd[\"n_removed\"] = exd.n_before - exd.n_after\nexd[\"rationale\"] = [e[4] for e in ex]\nexd.to_csv(os.path.join(O, \"exclusions.csv\"), index=False)\n\nwith open(os.path.join(O, \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2)\nprint(f\"n_analysed={n_an} of {n_window}; mean={res['mean_growth_12m']:.4f}; \"\n      f\"2001-13={res['mean_growth_12m_2001_2013']:.4f}; 2014-26={res['mean_growth_12m_2014_2026']:.4f}\")\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/exclusions.csv && head -3 out/analytic.csv && grep -n \"2025-1\\|2014-0[12]\" out/analytic.csv"}}]}}33{"type":"api-request","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","id":"ab1178fc-75a3-4d2b-b0c0-69e13d6d57ac","timestamp":"2026-10-30T04:31:20.822Z","version":"2.1.314","querySource":"sdk","shapeHash":"c4a9886b5d404a87039300a171d4a2ccda220b548475a5284c56dab1eb465ef2","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":"acc2c71e-519c-4f04-815a-2091b6bcd8f4","keep":8,"tail":["bbb429e4b2b089dfd36b7db333b93917fbfc0878b6085003e33febd8f8c89959","f27ece78d2987a6dd1ba7f1fee38f96c326f51e4ad535a9e4617b59aa27554de"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"d62a16448f80a99d4b19aad9cdb8df8ca768036ff4f27448c161e3673a289401"}34{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"888a74b1-1978-4d05-9a26-7dfc22e040eb","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3"}35{"type":"atis-latch","atis":"","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3"}36{"parentUuid":"888a74b1-1978-4d05-9a26-7dfc22e040eb","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo2pUMPNv6ACMWHPrP7","type":"message","role":"assistant","content":[{"type":"text","text":"The shell was in data/ (earlier cd persisted). Moving it to the right place."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5637,"cache_read_input_tokens":16067,"output_tokens":236,"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":5637},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXo2pHhERHg6aSdg2D33","requestRef":"ab1178fc-75a3-4d2b-b0c0-69e13d6d57ac","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"13b3c241-1bcc-4c92-96c4-bd3300d35127","timestamp":"2026-10-30T04:31:25.709Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098/data","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}37{"parentUuid":"13b3c241-1bcc-4c92-96c4-bd3300d35127","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo2pUMPNv6ACMWHPrP7","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Sjwb4MRpr3o8vV214qNN5C","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0098 && mkdir -p out/state && mv data/out/analysis.py out/ && rmdir data/out/state data/out && ls data && shasum -a 256 data/* && ~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/exclusions.csv && head -3 out/analytic.csv && grep -n \"2025-1\\|2014-0[12]\" out/analytic.csv"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5637,"cache_read_input_tokens":16067,"output_tokens":236,"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":5637},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXo2pHhERHg6aSdg2D33","requestRef":"ab1178fc-75a3-4d2b-b0c0-69e13d6d57ac","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"dd342c7e-d853-419e-a239-91efd920c8cf","timestamp":"2026-10-30T04:31:26.996Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098/data","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}38{"parentUuid":"dd342c7e-d853-419e-a239-91efd920c8cf","isSidechain":false,"promptId":"928f8c2b-79cf-4ec9-8a61-d59b8c4f450e","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Sjwb4MRpr3o8vV214qNN5C","type":"tool_result","content":"cpi_assembled.csv\ncpi_vintage_2016-07-15.csv\ncpi_vintage_2026-10-28.csv\nREADME.md\nff732b201a525d6fb171d4e8b2d470f986863c1547a1928e42bb197c0777d44d  data/cpi_assembled.csv\n281fbcdd6a6eeb3e9cb7c1f20476e6bd1f7b925bf140606fe46538fe83f2c83e  data/cpi_vintage_2016-07-15.csv\n8220d5b4d70ca32fc2c7b1975651c515967db8a87053cf725159c63ed274cfba  data/cpi_vintage_2026-10-28.csv\nac136bff13bbdb5afac60e0404212ea1f73f992bd5cca1c1e7a6599df78f8965  data/README.md\nTraceback (most recent call last):\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/internals/construction.py\", line 939, in _finalize_columns_and_data\n    columns = _validate_or_indexify_columns(contents, columns)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/internals/construction.py\", line 986, in _validate_or_indexify_columns\n    raise AssertionError(\n    ...<2 lines>...\n    )\nAssertionError: 4 columns passed, passed data had 5 columns\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"/Users/simon-pierreboucher/wp29/runs/RC0098/out/analysis.py\", line 132, in <module>\n    exd = pd.DataFrame(ex, columns=[\"step\", \"rule\", \"n_before\", \"n_after\"])\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/frame.py\", line 855, in __init__\n    arrays, columns, index = nested_data_to_arrays(\n                             ~~~~~~~~~~~~~~~~~~~~~^\n        # error: Argument 3 to \"nested_data_to_arrays\" has incompatible\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n    ...<4 lines>...\n        dtype,\n        ^^^^^^\n    )\n    ^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/internals/construction.py\", line 520, in nested_data_to_arrays\n    arrays, columns = to_arrays(data, columns, dtype=dtype)\n                      ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/internals/construction.py\", line 845, in to_arrays\n    content, columns = _finalize_columns_and_data(arr, columns, dtype)\n                       ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/internals/construction.py\", line 942, in _finalize_columns_and_data\n    raise ValueError(err) from err\nValueError: 4 columns passed, passed data had 5 columns","is_error":false}]},"uuid":"0db3c7db-4216-47a0-9679-2fd2b9d59667","timestamp":"2026-10-30T04:31:27.325Z","toolUseResult":{"stdout":"cpi_assembled.csv\ncpi_vintage_2016-07-15.csv\ncpi_vintage_2026-10-28.csv\nREADME.md\nff732b201a525d6fb171d4e8b2d470f986863c1547a1928e42bb197c0777d44d  data/cpi_assembled.csv\n281fbcdd6a6eeb3e9cb7c1f20476e6bd1f7b925bf140606fe46538fe83f2c83e  data/cpi_vintage_2016-07-15.csv\n8220d5b4d70ca32fc2c7b1975651c515967db8a87053cf725159c63ed274cfba  data/cpi_vintage_2026-10-28.csv\nac136bff13bbdb5afac60e0404212ea1f73f992bd5cca1c1e7a6599df78f8965  data/README.md\nTraceback (most recent call last):\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/internals/construction.py\", line 939, in _finalize_columns_and_data\n    columns = _validate_or_indexify_columns(contents, columns)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/internals/construction.py\", line 986, in _validate_or_indexify_columns\n    raise AssertionError(\n    ...<2 lines>...\n    )\nAssertionError: 4 columns passed, passed data had 5 columns\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"/Users/simon-pierreboucher/wp29/runs/RC0098/out/analysis.py\", line 132, in <module>\n    exd = pd.DataFrame(ex, columns=[\"step\", \"rule\", \"n_before\", \"n_after\"])\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/frame.py\", line 855, in __init__\n    arrays, columns, index = nested_data_to_arrays(\n                             ~~~~~~~~~~~~~~~~~~~~~^\n        # error: Argument 3 to \"nested_data_to_arrays\" has incompatible\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n    ...<4 lines>...\n        dtype,\n        ^^^^^^\n    )\n    ^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/internals/construction.py\", line 520, in nested_data_to_arrays\n    arrays, columns = to_arrays(data, columns, dtype=dtype)\n                      ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/internals/construction.py\", line 845, in to_arrays\n    content, columns = _finalize_columns_and_data(arr, columns, dtype)\n                       ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/internals/construction.py\", line 942, in _finalize_columns_and_data\n    raise ValueError(err) from err\nValueError: 4 columns passed, passed data had 5 columns","stderr":"","interrupted":false,"isImage":false,"returnCodeInterpretation":"No matches found","noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"dd342c7e-d853-419e-a239-91efd920c8cf","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098/data","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}39{"type":"api-request-blob","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","hash":"3dd1dba67fc827502cb61354ba31f856d031433722014b2144adee54fa5ccd32","message":{"role":"assistant","content":[{"type":"text","text":"The shell was in data/ (earlier cd persisted). Moving it to the right place."},{"type":"tool_use","id":"toolu_01Sjwb4MRpr3o8vV214qNN5C","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0098 && mkdir -p out/state && mv data/out/analysis.py out/ && rmdir data/out/state data/out && ls data && shasum -a 256 data/* && ~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/exclusions.csv && head -3 out/analytic.csv && grep -n \"2025-1\\|2014-0[12]\" out/analytic.csv"}}]}}40{"type":"api-request","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","id":"4994f417-a2e4-4c42-a34c-f7955186e5d8","timestamp":"2026-10-30T04:31:27.328Z","version":"2.1.314","querySource":"sdk","shapeHash":"c4a9886b5d404a87039300a171d4a2ccda220b548475a5284c56dab1eb465ef2","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":"ab1178fc-75a3-4d2b-b0c0-69e13d6d57ac","keep":10,"tail":["3dd1dba67fc827502cb61354ba31f856d031433722014b2144adee54fa5ccd32","d49d018966b636c04a64e00b4a6dac16c520ad63fcd97b00b2fb3956c8f2d550"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"d672cbccfbe533c062c7df22a57f4aa6a1ccbe73d595d5884b29740406a697c1"}41{"parentUuid":"0db3c7db-4216-47a0-9679-2fd2b9d59667","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo3JLf3quLQDPoodCMx","type":"message","role":"assistant","content":[{"type":"text","text":"Data hashes unchanged. Fixing the ledger construction."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1474,"cache_read_input_tokens":21704,"output_tokens":443,"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":1474},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXo3J52mpMYwce9c1ws5","requestRef":"4994f417-a2e4-4c42-a34c-f7955186e5d8","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"62da8de7-c0b8-4420-b889-22f2738a064f","timestamp":"2026-10-30T04:31:28.693Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098/data","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}42{"parentUuid":"62da8de7-c0b8-4420-b889-22f2738a064f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo3JLf3quLQDPoodCMx","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01JXWDfT5PRvuEVC7kMBpuDs","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0098 && ~/wp29/venv/bin/python - <<'EOF'\np='out/analysis.py';s=open(p).read()\ns=s.replace('''exd = pd.DataFrame(ex, columns=[\"step\", \"rule\", \"n_before\", \"n_after\"])\nexd[\"n_removed\"] = exd.n_before - exd.n_after\nexd[\"rationale\"] = [e[4] for e in ex]''','''exd = pd.DataFrame(ex, columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"rationale\"])\nexd[\"n_removed\"] = exd.n_before - exd.n_after\nexd = exd[[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]]''')\nopen(p,'w').write(s)\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/exclusions.csv && head -3 out/analytic.csv && grep -n \"2025-1\\|2014-0[12]\" out/analytic.csv; wc -l out/analytic.csv"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1474,"cache_read_input_tokens":21704,"output_tokens":443,"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":1474},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXo3J52mpMYwce9c1ws5","requestRef":"4994f417-a2e4-4c42-a34c-f7955186e5d8","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e651099b-5b8d-4270-8e78-cf5beea964af","timestamp":"2026-10-30T04:31:31.235Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098/data","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}43{"parentUuid":"e651099b-5b8d-4270-8e78-cf5beea964af","isSidechain":false,"promptId":"928f8c2b-79cf-4ec9-8a61-d59b8c4f450e","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JXWDfT5PRvuEVC7kMBpuDs","type":"tool_result","content":"n_analysed=308 of 309; mean=2.5741; 2001-13=2.3612; 2014-26=2.7926\n{\n  \"task_id\": \"F1-conf-01\",\n  \"first_growth_month\": \"2001-01\",\n  \"last_month\": \"2026-09\",\n  \"n_months_growth\": 308,\n  \"n_months_missing_growth\": 1,\n  \"n_analysed\": 308,\n  \"mean_growth_12m\": 2.5741201900729784,\n  \"mean_growth_12m_2001_2013\": 2.3612153974326064,\n  \"mean_growth_12m_2014_2026\": 2.7926277404144115,\n  \"growth_2014_06\": 2.0976342438575157,\n  \"growth_2016_07\": 0.8683633430518256,\n  \"growth_2020_05\": 0.1982013035848551,\n  \"growth_2022_06\": 8.979361103105798,\n  \"growth_2026_09\": 3.7172508442689933,\n  \"max_growth_12m\": 8.979361103105798,\n  \"max_growth_month\": \"2022-06\",\n  \"min_growth_12m\": -1.9587610037622771,\n  \"min_growth_month\": \"2009-07\",\n  \"feasibility\": \"partial\",\n  \"feasibility_reason\": \"1 of 309 window months have no computable 12-month growth rate (2025-10) because the source reports no index level; per the task rules they are not imputed. All window means are over the remaining months; all named months are computable.\",\n  \"data_issues\": [\n    {\n      \"issue\": \"Vintage 2026-10-28 reports no CPI value for 2025-10 (empty cell).\",\n      \"handling\": \"Not imputed; g_t for that month (and for month+12 if inside the window) reported missing and counted.\"\n    },\n    {\n      \"issue\": \"The two vintages overlap for 30 months (2014-01..2016-06) with different values (revisions of seasonal factors; new-old ranges -0.455..0.330 index points).\",\n      \"handling\": \"Per the availability rule the 2026-10-28 value is used for 2014-01 onward; 2016-07-15 values only before 2014-01. Hence g_t for 2014-01..2014-12 divides a 2026-vintage level by a 2016-vintage level (mixed-vintage ratio); noted as a limitation.\"\n    },\n    {\n      \"issue\": \"cpi_assembled.csv uses 2016-07-15 vintage values for 30 months (2014-01..2016-06) that the 2026-10-28 vintage covers, violating the most-recent-vintage rule.\",\n      \"handling\": \"Assembled file not used for estimation; series re-built from the two vintage files.\"\n    },\n    {\n      \"issue\": \"cpi_assembled.csv is missing month(s) 2016-07 and contains month(s) 2026-10 not present in any source; from 2016-08 onward 123 of 123 values equal the source value of the previous month (one-month date shift). In total 152 common months have values differing from the authoritative splice.\",\n      \"handling\": \"Assembled file treated as unreliable and used only for this audit; all estimates come from the vintage files.\"\n    },\n    {\n      \"issue\": \"SHA-256 values in data/README.md refer to the raw downloads, not to the reformatted CSV files in data/, so they cannot be used to verify the files.\",\n      \"handling\": \"Recorded the SHA-256 of the files as received in out/state/data_audit.md; no further action.\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"cpi_vintage_2016-07-15.csv\": 834,\n    \"cpi_vintage_2026-10-28.csv\": 153,\n    \"cpi_assembled.csv\": 957\n  },\n  \"random_seed\": null,\n  \"n_months_window\": 309,\n  \"missing_growth_months\": [\n    \"2025-10\"\n  ],\n  \"n_months_growth_2001_2013\": 156,\n  \"n_months_growth_2014_2026\": 152,\n  \"sd_growth_12m\": 1.7407883258999681,\n  \"max_abs_vintage_revision_overlap\": 0.4550000000000125\n}step,rule,n_before,n_after,n_removed,rationale\n1,Drop 2016-07-15-vintage rows for months covered by the 2026-10-28 vintage,987,957,30,Information rule: use the most recent vintage covering the month\n2,Keep months in window 2001-01..2026-09,957,309,648,Earlier months only serve as 12-month lags\n3,Drop window months without a computable g_t (P_t or P_t-12 missing),309,308,1,No interpolation/imputation allowed; missing: 2025-10\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\n158:2014-01-01,235.288,231.641,2026-10-28,1.574419036\n159:2014-02-01,235.547,233.005,2026-10-28,1.090963713\n299:2025-11-01,325.063,316.528,2026-10-28,2.696443916\n300:2025-12-01,326.031,317.604,2026-10-28,2.653304115\n     309 out/analytic.csv","is_error":false}]},"uuid":"f5e3dc0e-030c-4054-8d27-829f9b695365","timestamp":"2026-10-30T04:31:31.554Z","toolUseResult":{"stdout":"n_analysed=308 of 309; mean=2.5741; 2001-13=2.3612; 2014-26=2.7926\n{\n  \"task_id\": \"F1-conf-01\",\n  \"first_growth_month\": \"2001-01\",\n  \"last_month\": \"2026-09\",\n  \"n_months_growth\": 308,\n  \"n_months_missing_growth\": 1,\n  \"n_analysed\": 308,\n  \"mean_growth_12m\": 2.5741201900729784,\n  \"mean_growth_12m_2001_2013\": 2.3612153974326064,\n  \"mean_growth_12m_2014_2026\": 2.7926277404144115,\n  \"growth_2014_06\": 2.0976342438575157,\n  \"growth_2016_07\": 0.8683633430518256,\n  \"growth_2020_05\": 0.1982013035848551,\n  \"growth_2022_06\": 8.979361103105798,\n  \"growth_2026_09\": 3.7172508442689933,\n  \"max_growth_12m\": 8.979361103105798,\n  \"max_growth_month\": \"2022-06\",\n  \"min_growth_12m\": -1.9587610037622771,\n  \"min_growth_month\": \"2009-07\",\n  \"feasibility\": \"partial\",\n  \"feasibility_reason\": \"1 of 309 window months have no computable 12-month growth rate (2025-10) because the source reports no index level; per the task rules they are not imputed. All window means are over the remaining months; all named months are computable.\",\n  \"data_issues\": [\n    {\n      \"issue\": \"Vintage 2026-10-28 reports no CPI value for 2025-10 (empty cell).\",\n      \"handling\": \"Not imputed; g_t for that month (and for month+12 if inside the window) reported missing and counted.\"\n    },\n    {\n      \"issue\": \"The two vintages overlap for 30 months (2014-01..2016-06) with different values (revisions of seasonal factors; new-old ranges -0.455..0.330 index points).\",\n      \"handling\": \"Per the availability rule the 2026-10-28 value is used for 2014-01 onward; 2016-07-15 values only before 2014-01. Hence g_t for 2014-01..2014-12 divides a 2026-vintage level by a 2016-vintage level (mixed-vintage ratio); noted as a limitation.\"\n    },\n    {\n      \"issue\": \"cpi_assembled.csv uses 2016-07-15 vintage values for 30 months (2014-01..2016-06) that the 2026-10-28 vintage covers, violating the most-recent-vintage rule.\",\n      \"handling\": \"Assembled file not used for estimation; series re-built from the two vintage files.\"\n    },\n    {\n      \"issue\": \"cpi_assembled.csv is missing month(s) 2016-07 and contains month(s) 2026-10 not present in any source; from 2016-08 onward 123 of 123 values equal the source value of the previous month (one-month date shift). In total 152 common months have values differing from the authoritative splice.\",\n      \"handling\": \"Assembled file treated as unreliable and used only for this audit; all estimates come from the vintage files.\"\n    },\n    {\n      \"issue\": \"SHA-256 values in data/README.md refer to the raw downloads, not to the reformatted CSV files in data/, so they cannot be used to verify the files.\",\n      \"handling\": \"Recorded the SHA-256 of the files as received in out/state/data_audit.md; no further action.\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"cpi_vintage_2016-07-15.csv\": 834,\n    \"cpi_vintage_2026-10-28.csv\": 153,\n    \"cpi_assembled.csv\": 957\n  },\n  \"random_seed\": null,\n  \"n_months_window\": 309,\n  \"missing_growth_months\": [\n    \"2025-10\"\n  ],\n  \"n_months_growth_2001_2013\": 156,\n  \"n_months_growth_2014_2026\": 152,\n  \"sd_growth_12m\": 1.7407883258999681,\n  \"max_abs_vintage_revision_overlap\": 0.4550000000000125\n}step,rule,n_before,n_after,n_removed,rationale\n1,Drop 2016-07-15-vintage rows for months covered by the 2026-10-28 vintage,987,957,30,Information rule: use the most recent vintage covering the month\n2,Keep months in window 2001-01..2026-09,957,309,648,Earlier months only serve as 12-month lags\n3,Drop window months without a computable g_t (P_t or P_t-12 missing),309,308,1,No interpolation/imputation allowed; missing: 2025-10\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\n158:2014-01-01,235.288,231.641,2026-10-28,1.574419036\n159:2014-02-01,235.547,233.005,2026-10-28,1.090963713\n299:2025-11-01,325.063,316.528,2026-10-28,2.696443916\n300:2025-12-01,326.031,317.604,2026-10-28,2.653304115\n     309 out/analytic.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"e651099b-5b8d-4270-8e78-cf5beea964af","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}44{"type":"api-request-blob","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","hash":"d46dc953ba16d85fa7d2f21bd11b8c4f7a71f76fc27ee8990049d175bd5864ea","message":{"role":"assistant","content":[{"type":"text","text":"Data hashes unchanged. Fixing the ledger construction."},{"type":"tool_use","id":"toolu_01JXWDfT5PRvuEVC7kMBpuDs","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0098 && ~/wp29/venv/bin/python - <<'EOF'\np='out/analysis.py';s=open(p).read()\ns=s.replace('''exd = pd.DataFrame(ex, columns=[\"step\", \"rule\", \"n_before\", \"n_after\"])\nexd[\"n_removed\"] = exd.n_before - exd.n_after\nexd[\"rationale\"] = [e[4] for e in ex]''','''exd = pd.DataFrame(ex, columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"rationale\"])\nexd[\"n_removed\"] = exd.n_before - exd.n_after\nexd = exd[[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]]''')\nopen(p,'w').write(s)\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/exclusions.csv && head -3 out/analytic.csv && grep -n \"2025-1\\|2014-0[12]\" out/analytic.csv; wc -l 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of 309; mean=2.5741; 2001-13=2.3612; 2014-26=2.7926\n  \"sens_mean_growth_201401_201606_old_vintage\": 0.9069521485742471,\n  \"sens_mean_growth_201401_201606_rule\": 0.9032230102017299,\n  \"sens_max_abs_diff_growth_201401_201606\": 0.12733173561301214","is_error":false}]},"uuid":"1a16cf15-7862-4f7a-b05d-f381dc19df9a","timestamp":"2026-10-30T04:31:39.255Z","toolUseResult":{"stdout":"n_analysed=308 of 309; mean=2.5741; 2001-13=2.3612; 2014-26=2.7926\n  \"sens_mean_growth_201401_201606_old_vintage\": 0.9069521485742471,\n  \"sens_mean_growth_201401_201606_rule\": 0.9032230102017299,\n  \"sens_max_abs_diff_growth_201401_201606\": 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out/results.json"}}]}}50{"type":"api-request","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","id":"4db3a11d-7241-432f-a4d4-1a8563c330df","timestamp":"2026-10-30T04:31:39.258Z","version":"2.1.314","querySource":"sdk","shapeHash":"c4a9886b5d404a87039300a171d4a2ccda220b548475a5284c56dab1eb465ef2","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":"b39947a6-2e34-4ff2-8aa3-8aa083399626","keep":14,"tail":["188f4cd97412dc5194a6d8c57cb02e57f8f9ed5585aaba4fafd33008fb3ed051","e0e110bca0875f9719661544668de7ba2db8fbe9f4dac3d4f87be7de2a1524b6"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"b7624f843956e50d3c033d12ee4665b4ebcfbfd31a8b5a895bc04f8f66dd6507"}51{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"1a16cf15-7862-4f7a-b05d-f381dc19df9a","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3"}52{"type":"atis-latch","atis":"","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3"}53{"parentUuid":"1a16cf15-7862-4f7a-b05d-f381dc19df9a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo4BHXbScdvMEqR96Cd","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":941,"cache_read_input_tokens":25353,"output_tokens":1050,"output_tokens_details":{"thinking_tokens":56},"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":941},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":742,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXo4B67xy9DbHWD3fDzb","requestRef":"4db3a11d-7241-432f-a4d4-1a8563c330df","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"48de6c0f-358c-428f-9d72-57bb6a0f146d","timestamp":"2026-10-30T04:31:43.531Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}54{"parentUuid":"48de6c0f-358c-428f-9d72-57bb6a0f146d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo4BHXbScdvMEqR96Cd","type":"message","role":"assistant","content":[{"type":"text","text":"Now the state files and verification checks."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":941,"cache_read_input_tokens":25353,"output_tokens":1050,"output_tokens_details":{"thinking_tokens":56},"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":941},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXo4B67xy9DbHWD3fDzb","requestRef":"4db3a11d-7241-432f-a4d4-1a8563c330df","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"69621461-0e80-42a5-bb9e-0edabfb0210a","timestamp":"2026-10-30T04:31:43.531Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}55{"parentUuid":"69621461-0e80-42a5-bb9e-0edabfb0210a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo4BHXbScdvMEqR96Cd","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01RePz8Q7JtQCNCNiSD77SvR","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json && rm out/results.json out/analytic.csv out/exclusions.csv && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp out/results.json /tmp/r1.json && echo IDENTICAL && ~/wp29/venv/bin/python - <<'EOF'\nimport json,pandas as pd,numpy as np\nr=json.load(open('out/results.json'));a=pd.read_csv('out/analytic.csv');e=pd.read_csv('out/exclusions.csv')\nc=[]\nc.append((\"analytic rows == n_analysed\",len(a)==r['n_analysed']))\nc.append((\"n_growth+n_missing == 309 window months\",r['n_months_growth']+r['n_months_missing_growth']==309))\nc.append((\"ledger n_before-n_removed==n_after\",all(e.n_before-e.n_removed==e.n_after)))\nc.append((\"ledger chain\",all(e.n_before.values[1:]==e.n_after.values[:-1])))\nc.append((\"ledger start 834+153, end n_analysed\",e.n_before.iloc[0]==987 and e.n_after.iloc[-1]==r['n_analysed']))\nc.append((\"growth recomputed from analytic cols\",np.allclose(100*(a.cpi/a.cpi_lag12-1),a.growth_12m,atol=1e-7)))\nc.append((\"vintage_used: 2016 vintage before 2014, 2026 after\",(a[a.date<'2014-01-01'].vintage_used.astype(str)=='2016-07-15').all() and (a[a.date>='2014-01-01'].vintage_used.astype(str)=='2026-10-28').all()))\nc.append((\"mean consistent with subperiod weighted mean\",abs((156*r['mean_growth_12m_2001_2013']+152*r['mean_growth_12m_2014_2026'])/308-r['mean_growth_12m'])<1e-9))\nc.append((\"max/min within analytic and named month match\",abs(a.growth_12m.max()-r['max_growth_12m'])<1e-9 and r['growth_2022_06']==r['max_growth_12m']))\nc.append((\"no 2025-10 row; window dates within 2001-01..2026-09\",('2025-10-01' not in set(a.date)) and a.date.min()=='2001-01-01' and a.date.max()=='2026-09-01'))\nc.append((\"magnitudes plausible (|g|<15%)\",a.growth_12m.abs().max()<15))\nfor k,v in c: print(f\"- [{'PASS' if v else 'FAIL'}] {k}\")\nEOF"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":941,"cache_read_input_tokens":25353,"output_tokens":1050,"output_tokens_details":{"thinking_tokens":56},"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":941},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXo4B67xy9DbHWD3fDzb","requestRef":"4db3a11d-7241-432f-a4d4-1a8563c330df","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"97a3f30f-4f56-49db-a557-816f424649f9","timestamp":"2026-10-30T04:31:50.458Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}56{"parentUuid":"97a3f30f-4f56-49db-a557-816f424649f9","isSidechain":false,"promptId":"928f8c2b-79cf-4ec9-8a61-d59b8c4f450e","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RePz8Q7JtQCNCNiSD77SvR","type":"tool_result","content":"IDENTICAL\n- [PASS] analytic rows == n_analysed\n- [PASS] n_growth+n_missing == 309 window months\n- [PASS] ledger n_before-n_removed==n_after\n- [PASS] ledger chain\n- [PASS] ledger start 834+153, end n_analysed\n- [PASS] growth recomputed from analytic cols\n- [PASS] vintage_used: 2016 vintage before 2014, 2026 after\n- [PASS] mean consistent with subperiod weighted mean\n- [PASS] max/min within analytic and named month match\n- [PASS] no 2025-10 row; window dates within 2001-01..2026-09\n- [PASS] magnitudes plausible (|g|<15%)","is_error":false}]},"uuid":"0c22974f-da88-4657-8a19-f10b22e3018e","timestamp":"2026-10-30T04:31:51.005Z","toolUseResult":{"stdout":"IDENTICAL\n- [PASS] analytic rows == n_analysed\n- [PASS] n_growth+n_missing == 309 window months\n- [PASS] ledger n_before-n_removed==n_after\n- [PASS] ledger chain\n- [PASS] ledger start 834+153, end n_analysed\n- [PASS] growth recomputed from analytic cols\n- [PASS] vintage_used: 2016 vintage before 2014, 2026 after\n- [PASS] mean consistent with subperiod weighted mean\n- [PASS] max/min within analytic and named month match\n- [PASS] no 2025-10 row; window dates within 2001-01..2026-09\n- [PASS] magnitudes plausible (|g|<15%)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"97a3f30f-4f56-49db-a557-816f424649f9","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}57{"type":"api-request-blob","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","hash":"79b52ee9f8927b6a99b072d0c53d25a778a9c18be94f265f4b1be18f36e97fea","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"Now the state files and verification checks."},{"type":"tool_use","id":"toolu_01RePz8Q7JtQCNCNiSD77SvR","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json && rm out/results.json out/analytic.csv out/exclusions.csv && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp out/results.json /tmp/r1.json && echo IDENTICAL && ~/wp29/venv/bin/python - <<'EOF'\nimport json,pandas as pd,numpy as np\nr=json.load(open('out/results.json'));a=pd.read_csv('out/analytic.csv');e=pd.read_csv('out/exclusions.csv')\nc=[]\nc.append((\"analytic rows == n_analysed\",len(a)==r['n_analysed']))\nc.append((\"n_growth+n_missing == 309 window months\",r['n_months_growth']+r['n_months_missing_growth']==309))\nc.append((\"ledger n_before-n_removed==n_after\",all(e.n_before-e.n_removed==e.n_after)))\nc.append((\"ledger chain\",all(e.n_before.values[1:]==e.n_after.values[:-1])))\nc.append((\"ledger start 834+153, end n_analysed\",e.n_before.iloc[0]==987 and e.n_after.iloc[-1]==r['n_analysed']))\nc.append((\"growth recomputed from analytic cols\",np.allclose(100*(a.cpi/a.cpi_lag12-1),a.growth_12m,atol=1e-7)))\nc.append((\"vintage_used: 2016 vintage before 2014, 2026 after\",(a[a.date<'2014-01-01'].vintage_used.astype(str)=='2016-07-15').all() and (a[a.date>='2014-01-01'].vintage_used.astype(str)=='2026-10-28').all()))\nc.append((\"mean consistent with subperiod weighted mean\",abs((156*r['mean_growth_12m_2001_2013']+152*r['mean_growth_12m_2014_2026'])/308-r['mean_growth_12m'])<1e-9))\nc.append((\"max/min within analytic and named month match\",abs(a.growth_12m.max()-r['max_growth_12m'])<1e-9 and r['growth_2022_06']==r['max_growth_12m']))\nc.append((\"no 2025-10 row; window dates within 2001-01..2026-09\",('2025-10-01' not in set(a.date)) and a.date.min()=='2001-01-01' and a.date.max()=='2026-09-01'))\nc.append((\"magnitudes plausible (|g|<15%)\",a.growth_12m.abs().max()<15))\nfor k,v in c: print(f\"- [{'PASS' if v else 'FAIL'}] {k}\")\nEOF"}}]}}58{"type":"api-request","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","id":"2c744084-a4fe-4656-ae65-ddccc43a91a5","timestamp":"2026-10-30T04:31:51.008Z","version":"2.1.314","querySource":"sdk","shapeHash":"c4a9886b5d404a87039300a171d4a2ccda220b548475a5284c56dab1eb465ef2","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":"4db3a11d-7241-432f-a4d4-1a8563c330df","keep":16,"tail":["79b52ee9f8927b6a99b072d0c53d25a778a9c18be94f265f4b1be18f36e97fea","bec9705e5dd8b60013c4a77895870de9f29da9336ee1f4a31158235d5ed4655d"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",17,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"64057b294225f85d4e7ac37a06dc144dca0aaff5814d89a592defef054a5bce9"}59{"parentUuid":"0c22974f-da88-4657-8a19-f10b22e3018e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo53mcqa64XCoPPYs53","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":1271,"cache_read_input_tokens":26294,"output_tokens":2021,"output_tokens_details":{"thinking_tokens":32},"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":1271},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":240,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXo53Kq1CKP6ZszKA5Ns","requestRef":"2c744084-a4fe-4656-ae65-ddccc43a91a5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"80004061-876d-48e7-bd66-8b7d952ef93c","timestamp":"2026-10-30T04:31:52.613Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}60{"parentUuid":"80004061-876d-48e7-bd66-8b7d952ef93c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo53mcqa64XCoPPYs53","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01UMzJWQxgRrkF3jcibLZVmm","name":"Bash","input":{"command":"cd out/state && cat > contract.md <<'EOF'\n# Contract — F1-conf-01\n**Question.** 12-month growth of CPIAUCSL (SA) for each month 2001-01..2026-09 and summaries.\n**Estimands.** g_t = 100(P_t/P_{t-12} − 1), P from the most recent vintage covering the month (2026-10-28 vintage for 2014-01..2026-09, 2016-07-15 vintage before). Summaries: mean over window, 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 two vintages share the same base (1982–84=100) so ratios across the 2014 splice are comparable (small seasonal-factor revision inconsistency accepted).\n**Outputs.** out/results.json (keys per TASK.md table), out/analytic.csv (date,cpi,cpi_lag12,vintage_used,growth_12m; n_analysed rows), out/exclusions.csv (chained ledger), out/report.md (6 sections), out/analysis.py, out/claims.json.\n**Availability rules.** Most recent vintage per month; no interpolation/imputation; missing g_t counted; only data/.\n**Invalidating conditions.** Using the hand-assembled file uncritically; imputing missing levels; using the old vintage where the new covers the month.\nEOF\ncat > data_audit.md <<'EOF'\n# Data audit\n| file | SHA-256 (as received) | rows | cols | range | missing cpi | dup dates |\n|---|---|---|---|---|---|---|\n| cpi_vintage_2016-07-15.csv | 281fbcdd6a6eeb3e9cb7c1f20476e6bd1f7b925bf140606fe46538fe83f2c83e | 834 | 2 (date,cpi) | 1947-01..2016-06, no calendar gaps | 0 | 0 |\n| cpi_vintage_2026-10-28.csv | 8220d5b4d70ca32fc2c7b1975651c515967db8a87053cf725159c63ed274cfba | 153 | 2 | 2014-01..2026-09, no calendar gaps | 1 (2025-10) | 0 |\n| cpi_assembled.csv | ff732b201a525d6fb171d4e8b2d470f986863c1547a1928e42bb197c0777d44d | 957 | 3 (date,cpi,vintage) | 1947-01..2026-10, 2016-07 absent | 1 (labelled 2025-11) | 0 |\n| README.md | ac136bff13bbdb5afac60e0404212ea1f73f992bd5cca1c1e7a6599df78f8965 | – | – | – | – | – |\n\nUnits: index 1982–84=100, monthly, SA. No month-to-month change >3% in any file.\nREADME SHA-256 values refer to raw downloads (differ from the files here) — cannot verify.\n\n**Anomalies**\n- 2026 vintage: 2025-10 empty (source publishes no value). Not imputed → g_2025-10 missing (g_2026-10 would also be missing but is outside window).\n- Vintage overlap 2014-01..2016-06 (30 months): values differ by −0.455..+0.330 index points (revisions).\n- Assembled file: (i) uses 2016 vintage for 2014-01..2016-06 (violates rule); (ii) 2016-07 missing; (iii) from 2016-08 every value is the source value of the previous month (one-month shift), so the empty cell appears at 2025-11 and a spurious 2026-10 row exists. **Decision:** assembled file is not used for estimation; read only for this audit (its row count is reported in n_input_rows).\nEOF\ncat > checks.md <<'EOF'\n# Verification checks (P4)\n- [PASS] Clean re-run (deleted results.json/analytic.csv/exclusions.csv, re-ran) → results.json byte-identical.\n- [PASS] analytic rows == n_analysed (308)\n- [PASS] n_growth + n_missing == 309 window months\n- [PASS] ledger n_before − n_removed == n_after on each row\n- [PASS] ledger chained; starts at 834+153=987 stacked rows, ends at n_analysed\n- [PASS] growth_12m recomputed from cpi/cpi_lag12 columns\n- [PASS] vintage_used = 2016-07-15 before 2014-01, 2026-10-28 from 2014-01\n- [PASS] overall mean equals count-weighted mean of sub-period means (156, 152 months)\n- [PASS] max equals analytic max and equals growth_2022_06\n- [PASS] no 2025-10 row; analytic dates within 2001-01..2026-09\n- [PASS] magnitudes plausible (|g| < 15%); window mean 2.57% vs baseline 2001–13 mean 2.36% (same order)\n- No CIs/SEs: estimands are deterministic transforms of published data (no sampling uncertainty).\nEOF\ncat > log.md <<'EOF'\n# Log\n- 2026-10-30 P0: read TASK.md, wrote contract.\n- 2026-10-30 P1: audited data; found assembled-file errors (old vintage in overlap, missing 2016-07, one-month shift) and 2025-10 gap in source. Decision: build series from vintage files only.\n- 2026-10-30 P2/P3: analysis.py written; baseline = 2001–2013 mean (task-named key). Feasibility \"partial\" (1 window month missing g).\n- 2026-10-30 P4: clean rerun identical; consistency checks pass. Gate run.\nEOF\nls"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1271,"cache_read_input_tokens":26294,"output_tokens":2021,"output_tokens_details":{"thinking_tokens":32},"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":1271},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXo53Kq1CKP6ZszKA5Ns","requestRef":"2c744084-a4fe-4656-ae65-ddccc43a91a5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"858a90d6-5193-4e00-868b-35f6d980e39f","timestamp":"2026-10-30T04:32:10.654Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}61{"parentUuid":"858a90d6-5193-4e00-868b-35f6d980e39f","isSidechain":false,"promptId":"928f8c2b-79cf-4ec9-8a61-d59b8c4f450e","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01UMzJWQxgRrkF3jcibLZVmm","type":"tool_result","content":"checks.md\ncontract.md\ndata_audit.md\nlog.md","is_error":false}]},"uuid":"d3325fe5-7a45-4e36-b217-8fc6c1bc46ca","timestamp":"2026-10-30T04:32:10.687Z","toolUseResult":{"stdout":"checks.md\ncontract.md\ndata_audit.md\nlog.md","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"858a90d6-5193-4e00-868b-35f6d980e39f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098/out/state","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}62{"type":"api-request-blob","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","hash":"6701e8e2677d26f5412eefec91907abca1b1d74bf48dd9c323893e277a44a558","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01UMzJWQxgRrkF3jcibLZVmm","name":"Bash","input":{"command":"cd out/state && cat > contract.md <<'EOF'\n# Contract — F1-conf-01\n**Question.** 12-month growth of CPIAUCSL (SA) for each month 2001-01..2026-09 and summaries.\n**Estimands.** g_t = 100(P_t/P_{t-12} − 1), P from the most recent vintage covering the month (2026-10-28 vintage for 2014-01..2026-09, 2016-07-15 vintage before). Summaries: mean over window, 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 two vintages share the same base (1982–84=100) so ratios across the 2014 splice are comparable (small seasonal-factor revision inconsistency accepted).\n**Outputs.** out/results.json (keys per TASK.md table), out/analytic.csv (date,cpi,cpi_lag12,vintage_used,growth_12m; n_analysed rows), out/exclusions.csv (chained ledger), out/report.md (6 sections), out/analysis.py, out/claims.json.\n**Availability rules.** Most recent vintage per month; no interpolation/imputation; missing g_t counted; only data/.\n**Invalidating conditions.** Using the hand-assembled file uncritically; imputing missing levels; using the old vintage where the new covers the month.\nEOF\ncat > data_audit.md <<'EOF'\n# Data audit\n| file | SHA-256 (as received) | rows | cols | range | missing cpi | dup dates |\n|---|---|---|---|---|---|---|\n| cpi_vintage_2016-07-15.csv | 281fbcdd6a6eeb3e9cb7c1f20476e6bd1f7b925bf140606fe46538fe83f2c83e | 834 | 2 (date,cpi) | 1947-01..2016-06, no calendar gaps | 0 | 0 |\n| cpi_vintage_2026-10-28.csv | 8220d5b4d70ca32fc2c7b1975651c515967db8a87053cf725159c63ed274cfba | 153 | 2 | 2014-01..2026-09, no calendar gaps | 1 (2025-10) | 0 |\n| cpi_assembled.csv | ff732b201a525d6fb171d4e8b2d470f986863c1547a1928e42bb197c0777d44d | 957 | 3 (date,cpi,vintage) | 1947-01..2026-10, 2016-07 absent | 1 (labelled 2025-11) | 0 |\n| README.md | ac136bff13bbdb5afac60e0404212ea1f73f992bd5cca1c1e7a6599df78f8965 | – | – | – | – | – |\n\nUnits: index 1982–84=100, monthly, SA. No month-to-month change >3% in any file.\nREADME SHA-256 values refer to raw downloads (differ from the files here) — cannot verify.\n\n**Anomalies**\n- 2026 vintage: 2025-10 empty (source publishes no value). Not imputed → g_2025-10 missing (g_2026-10 would also be missing but is outside window).\n- Vintage overlap 2014-01..2016-06 (30 months): values differ by −0.455..+0.330 index points (revisions).\n- Assembled file: (i) uses 2016 vintage for 2014-01..2016-06 (violates rule); (ii) 2016-07 missing; (iii) from 2016-08 every value is the source value of the previous month (one-month shift), so the empty cell appears at 2025-11 and a spurious 2026-10 row exists. **Decision:** assembled file is not used for estimation; read only for this audit (its row count is reported in n_input_rows).\nEOF\ncat > checks.md <<'EOF'\n# Verification checks (P4)\n- [PASS] Clean re-run (deleted results.json/analytic.csv/exclusions.csv, re-ran) → results.json byte-identical.\n- [PASS] analytic rows == n_analysed (308)\n- [PASS] n_growth + n_missing == 309 window months\n- [PASS] ledger n_before − n_removed == n_after on each row\n- [PASS] ledger chained; starts at 834+153=987 stacked rows, ends at n_analysed\n- [PASS] growth_12m recomputed from cpi/cpi_lag12 columns\n- [PASS] vintage_used = 2016-07-15 before 2014-01, 2026-10-28 from 2014-01\n- [PASS] overall mean equals count-weighted mean of sub-period means (156, 152 months)\n- [PASS] max equals analytic max and equals growth_2022_06\n- [PASS] no 2025-10 row; analytic dates within 2001-01..2026-09\n- [PASS] magnitudes plausible (|g| < 15%); window mean 2.57% vs baseline 2001–13 mean 2.36% (same order)\n- No CIs/SEs: estimands are deterministic transforms of published data (no sampling uncertainty).\nEOF\ncat > log.md <<'EOF'\n# Log\n- 2026-10-30 P0: read TASK.md, wrote contract.\n- 2026-10-30 P1: audited data; found assembled-file errors (old vintage in overlap, missing 2016-07, one-month shift) and 2025-10 gap in source. Decision: build series from vintage files only.\n- 2026-10-30 P2/P3: analysis.py written; baseline = 2001–2013 mean (task-named key). Feasibility \"partial\" (1 window month missing g).\n- 2026-10-30 P4: clean rerun identical; consistency checks pass. Gate run.\nEOF\nls"}}]}}63{"type":"api-request","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","id":"3270be20-4b19-4b17-a443-3413e8e02814","timestamp":"2026-10-30T04:32:10.691Z","version":"2.1.314","querySource":"sdk","shapeHash":"c4a9886b5d404a87039300a171d4a2ccda220b548475a5284c56dab1eb465ef2","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":"2c744084-a4fe-4656-ae65-ddccc43a91a5","keep":18,"tail":["6701e8e2677d26f5412eefec91907abca1b1d74bf48dd9c323893e277a44a558","655971262079037004c81994234c96232090979554be014e1cdcb9d449988f0e"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"49652d4f2ceb2a6411da6d577e049dfdaa1155c1f83aa76d36d781b4fca5a406"}64{"parentUuid":"d3325fe5-7a45-4e36-b217-8fc6c1bc46ca","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo6Vn4qu4CEDgqjuFwK","type":"message","role":"assistant","content":[{"type":"text","text":"Now report, claims, phases, then gate."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2046,"cache_read_input_tokens":27565,"output_tokens":2974,"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":2046},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXo6VWSjf6dTuhpaMCSi","requestRef":"3270be20-4b19-4b17-a443-3413e8e02814","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"12ed9c1a-57e0-4fe8-9d85-16145174d40b","timestamp":"2026-10-30T04:32:12.011Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098/out/state","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}65{"parentUuid":"12ed9c1a-57e0-4fe8-9d85-16145174d40b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo6Vn4qu4CEDgqjuFwK","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01EoQX8Bgqk2uL2xsNaKyM7h","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0098/out && cat > report.md <<'EOF'\n# F1-conf-01 — 12-month CPI inflation from two data vintages\n\n## Question\n12-month growth rate g_t = 100 × (P_t/P_{t−12} − 1) of CPIAUCSL (seasonally adjusted) for each month 2001-01 … 2026-09, with P_t taken from the most recent ALFRED vintage covering the month, and summary statistics of g_t.\n\n## Data and exclusions\nInputs (rows read, `n_input_rows`): 2016-07-15 vintage (834 rows, 1947-01…2016-06), 2026-10-28 vintage (153 rows, 2014-01…2026-09), and the hand-assembled file (957 rows), which was read **only for auditing**. The audit (`data_issues`) found that the assembled file uses the old vintage for 2014-01…2016-06 (violating the most-recent-vintage rule), omits 2016-07 and from 2016-08 onward is shifted by one month (each value belongs to the previous month, producing a spurious 2026-10 row). It was therefore not used for estimation. The series was re-built from the two vintage files.\n\nThe 2026-10-28 vintage reports no value for 2025-10 (empty cell). This level was not imputed.\n\nExclusion ledger (`out/exclusions.csv`): 987 stacked vintage rows → drop 30 superseded 2016-vintage rows (2014-01…2016-06) → 957 unique months → keep the 309 window months → drop 1 month without a computable g_t (2025-10) → 308 analysed months (`n_analysed`, `n_months_missing_growth`, `missing_growth_months`).\n\n## Method\nSplice: 2016-07-15 vintage levels for months before 2014-01, 2026-10-28 vintage levels from 2014-01 to 2026-09. The calendar has no gaps, so g_t is computed with an exact 12-month lag, and a month is missing whenever P_t or P_{t−12} is missing. No randomness is involved (`random_seed` = null). Baseline: the unconditional mean over 2001-01…2013-12 (`mean_growth_12m_2001_2013`), which uses only the 2016 vintage.\n\n## Results\n- Coverage: the first growth month is 2001-01 (`first_growth_month`) and the last month with a level is 2026-09 (`last_month`). 308 of 309 months have a growth rate (`n_months_growth`, `n_months_missing_growth`). `feasibility` = \"partial\" because 2025-10 has no growth rate.\n- The mean of g_t over the window is 2.574% (`mean_growth_12m`). The 2001–2013 mean is 2.361% (`mean_growth_12m_2001_2013`, 156 months) and the 2014–2026 mean is 2.793% (`mean_growth_12m_2014_2026`, 152 months).\n- Named months: 2014-06 2.098% (`growth_2014_06`); 2016-07 0.868% (`growth_2016_07`); 2020-05 0.198% (`growth_2020_05`); 2022-06 8.979% (`growth_2022_06`); 2026-09 3.717% (`growth_2026_09`).\n- The maximum is 8.979% in 2022-06 (`max_growth_12m`, `max_growth_month`). The minimum is −1.959% in 2009-07 (`min_growth_12m`, `min_growth_month`).\n\n## Uncertainty\nThese quantities are fixed transformations of published index levels, so there is no sampling uncertainty and no confidence intervals are reported. The dispersion of g_t over the window is an SD of 1.741 pp (`sd_growth_12m`). The main uncertainty is which vintage to use. In the 30 overlapping months the two vintages differ by up to 0.455 index points (`max_abs_vintage_revision_overlap`). For 2014-01…2016-06, growth computed from the 2016 vintage alone has a mean of 0.907% (`sens_mean_growth_201401_201606_old_vintage`), against 0.903% under the rule (`sens_mean_growth_201401_201606_rule`). Individual months differ by at most 0.127 pp (`sens_max_abs_diff_growth_201401_201606`).\n\n## Limitations and open issues\n- 2025-10 has no growth rate because the source publishes no level for it. All window and 2014–2026 means therefore cover 308 and 152 months, not 309 and 153.\n- For 2014-01…2014-12, g_t divides a 2026-vintage level by a 2016-vintage level. This is what the availability rule requires, but it mixes seasonal-factor vintages. `vintage_used` in analytic.csv refers to P_t only.\n- The hand-assembled file is unreliable (see `data_issues`) and was not used.\n- The SHA-256 values in data/README.md describe the raw downloads, not the reformatted files, so the files could not be verified against them.\nEOF\ncat > claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"308 of the 309 window months have a computable 12-month growth rate; 2025-10 is missing because the source reports no index level, so feasibility is partial.\",\"results_keys\":[\"n_months_growth\",\"n_months_missing_growth\",\"n_analysed\",\"feasibility\",\"feasibility_reason\"],\"status\":\"supported\",\"caveat\":\"Not imputed by rule.\"},\n {\"id\":\"C2\",\"text\":\"Mean 12-month CPI inflation over 2001-01..2026-09 is about 2.57%, 2.36% in 2001-2013 and 2.79% in 2014-2026.\",\"results_keys\":[\"mean_growth_12m\",\"mean_growth_12m_2001_2013\",\"mean_growth_12m_2014_2026\"],\"status\":\"supported\",\"caveat\":\"Means exclude 2025-10.\"},\n {\"id\":\"C3\",\"text\":\"The maximum is 8.98% in 2022-06 and the minimum is -1.96% in 2009-07.\",\"results_keys\":[\"max_growth_12m\",\"max_growth_month\",\"min_growth_12m\",\"min_growth_month\",\"growth_2022_06\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C4\",\"text\":\"Named-month growth rates: 2014-06 2.10%, 2016-07 0.87%, 2020-05 0.20%, 2026-09 3.72%.\",\"results_keys\":[\"growth_2014_06\",\"growth_2016_07\",\"growth_2020_05\",\"growth_2026_09\"],\"status\":\"supported\",\"caveat\":\"2014-06 mixes vintages (lag from the 2016 vintage).\"},\n {\"id\":\"C5\",\"text\":\"The hand-assembled file contains assembly errors (old vintage in the overlap, a missing month, a one-month shift) and was not used for estimation.\",\"results_keys\":[\"data_issues\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C6\",\"text\":\"The choice of vintage in the overlap changes 2014-01..2016-06 growth rates by at most about 0.13 pp.\",\"results_keys\":[\"sens_max_abs_diff_growth_201401_201606\",\"sens_mean_growth_201401_201606_old_vintage\",\"sens_mean_growth_201401_201606_rule\",\"max_abs_vintage_revision_overlap\"],\"status\":\"supported\",\"caveat\":\"Sensitivity only; not a required estimand.\"}\n]\nEOF\ncat > state/phases.json <<'EOF'\n{\n \"P0\":{\"status\":\"passed\",\"checks\":[\"contract.md written covering question, estimands, outputs, rules, invalidating conditions\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256, rows, ranges, missing, duplicates recorded in data_audit.md\",\"assembled file compared with splice: errors found, excluded from estimation\",\"exclusion ledger written by analysis.py\"],\"notes\":\"\"},\n \"P2\":{\"status\":\"passed\",\"checks\":[\"baseline mean_growth_12m_2001_2013 computed = 2.3612\"],\"notes\":\"\"},\n \"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, writes results.json, analytic.csv, exclusions.csv; no RNG\"],\"notes\":\"\"},\n \"P4\":{\"status\":\"running\",\"checks\":[\"clean rerun byte-identical\",\"11 consistency checks PASS (checks.md)\"],\"notes\":\"\"},\n \"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}\n}\nEOF\ncd .. && ~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2046,"cache_read_input_tokens":27565,"output_tokens":2974,"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":2046},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXo6VWSjf6dTuhpaMCSi","requestRef":"3270be20-4b19-4b17-a443-3413e8e02814","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e1c0e289-8942-4712-8f6d-4417af8a4fe9","timestamp":"2026-10-30T04:32:34.268Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098/out/state","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}66{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"e1c0e289-8942-4712-8f6d-4417af8a4fe9","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3"}67{"type":"atis-latch","atis":"","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3"}68{"parentUuid":"e1c0e289-8942-4712-8f6d-4417af8a4fe9","isSidechain":false,"promptId":"928f8c2b-79cf-4ec9-8a61-d59b8c4f450e","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01EoQX8Bgqk2uL2xsNaKyM7h","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — FAILED (1 of 40 checks failed)\n  [ok] file:out/results.json: present\n  [ok] file:out/report.md: present\n  [ok] file:out/analysis.py: present\n  [ok] file:out/claims.json: present\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  [ok] section:Question: present\n  [ok] section:Data and exclusions: present\n  [ok] section:Method: present\n  [ok] section:Results: present\n  [ok] section:Uncertainty: present\n  [ok] section:Limitations and open issues: present\n  [ok] claims_json: 6 claims, all keys resolve\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] exclusion_ledger: 3 steps consistent\n  [ok] reproducibility: regenerated in 0.3s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0098/out/state/gate_receipts/20261030T043234.593864Z.json\n20261030T043234.593864Z.json","is_error":false}]},"uuid":"7601b21c-d3b4-489c-9ac3-e7a638dd83dc","timestamp":"2026-10-30T04:32:34.606Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — FAILED (1 of 40 checks failed)\n  [ok] file:out/results.json: present\n  [ok] file:out/report.md: present\n  [ok] file:out/analysis.py: present\n  [ok] file:out/claims.json: present\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  [ok] section:Question: present\n  [ok] section:Data and exclusions: present\n  [ok] section:Method: present\n  [ok] section:Results: present\n  [ok] section:Uncertainty: present\n  [ok] section:Limitations and open issues: present\n  [ok] claims_json: 6 claims, all keys resolve\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] exclusion_ledger: 3 steps consistent\n  [ok] reproducibility: regenerated in 0.3s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0098/out/state/gate_receipts/20261030T043234.593864Z.json\n20261030T043234.593864Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"e1c0e289-8942-4712-8f6d-4417af8a4fe9","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}69{"type":"api-request-blob","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","hash":"1298fcb1d1ff450f95491ea351d4e358a738e67e13406c1249e372f8981e3047","message":{"role":"assistant","content":[{"type":"text","text":"Now report, claims, phases, then gate."},{"type":"tool_use","id":"toolu_01EoQX8Bgqk2uL2xsNaKyM7h","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0098/out && cat > report.md <<'EOF'\n# F1-conf-01 — 12-month CPI inflation from two data vintages\n\n## Question\n12-month growth rate g_t = 100 × (P_t/P_{t−12} − 1) of CPIAUCSL (seasonally adjusted) for each month 2001-01 … 2026-09, with P_t taken from the most recent ALFRED vintage covering the month, and summary statistics of g_t.\n\n## Data and exclusions\nInputs (rows read, `n_input_rows`): 2016-07-15 vintage (834 rows, 1947-01…2016-06), 2026-10-28 vintage (153 rows, 2014-01…2026-09), and the hand-assembled file (957 rows), which was read **only for auditing**. The audit (`data_issues`) found that the assembled file uses the old vintage for 2014-01…2016-06 (violating the most-recent-vintage rule), omits 2016-07 and from 2016-08 onward is shifted by one month (each value belongs to the previous month, producing a spurious 2026-10 row). It was therefore not used for estimation. The series was re-built from the two vintage files.\n\nThe 2026-10-28 vintage reports no value for 2025-10 (empty cell). This level was not imputed.\n\nExclusion ledger (`out/exclusions.csv`): 987 stacked vintage rows → drop 30 superseded 2016-vintage rows (2014-01…2016-06) → 957 unique months → keep the 309 window months → drop 1 month without a computable g_t (2025-10) → 308 analysed months (`n_analysed`, `n_months_missing_growth`, `missing_growth_months`).\n\n## Method\nSplice: 2016-07-15 vintage levels for months before 2014-01, 2026-10-28 vintage levels from 2014-01 to 2026-09. The calendar has no gaps, so g_t is computed with an exact 12-month lag, and a month is missing whenever P_t or P_{t−12} is missing. No randomness is involved (`random_seed` = null). Baseline: the unconditional mean over 2001-01…2013-12 (`mean_growth_12m_2001_2013`), which uses only the 2016 vintage.\n\n## Results\n- Coverage: the first growth month is 2001-01 (`first_growth_month`) and the last month with a level is 2026-09 (`last_month`). 308 of 309 months have a growth rate (`n_months_growth`, `n_months_missing_growth`). `feasibility` = \"partial\" because 2025-10 has no growth rate.\n- The mean of g_t over the window is 2.574% (`mean_growth_12m`). The 2001–2013 mean is 2.361% (`mean_growth_12m_2001_2013`, 156 months) and the 2014–2026 mean is 2.793% (`mean_growth_12m_2014_2026`, 152 months).\n- Named months: 2014-06 2.098% (`growth_2014_06`); 2016-07 0.868% (`growth_2016_07`); 2020-05 0.198% (`growth_2020_05`); 2022-06 8.979% (`growth_2022_06`); 2026-09 3.717% (`growth_2026_09`).\n- The maximum is 8.979% in 2022-06 (`max_growth_12m`, `max_growth_month`). The minimum is −1.959% in 2009-07 (`min_growth_12m`, `min_growth_month`).\n\n## Uncertainty\nThese quantities are fixed transformations of published index levels, so there is no sampling uncertainty and no confidence intervals are reported. The dispersion of g_t over the window is an SD of 1.741 pp (`sd_growth_12m`). The main uncertainty is which vintage to use. In the 30 overlapping months the two vintages differ by up to 0.455 index points (`max_abs_vintage_revision_overlap`). For 2014-01…2016-06, growth computed from the 2016 vintage alone has a mean of 0.907% (`sens_mean_growth_201401_201606_old_vintage`), against 0.903% under the rule (`sens_mean_growth_201401_201606_rule`). Individual months differ by at most 0.127 pp (`sens_max_abs_diff_growth_201401_201606`).\n\n## Limitations and open issues\n- 2025-10 has no growth rate because the source publishes no level for it. All window and 2014–2026 means therefore cover 308 and 152 months, not 309 and 153.\n- For 2014-01…2014-12, g_t divides a 2026-vintage level by a 2016-vintage level. This is what the availability rule requires, but it mixes seasonal-factor vintages. `vintage_used` in analytic.csv refers to P_t only.\n- The hand-assembled file is unreliable (see `data_issues`) and was not used.\n- The SHA-256 values in data/README.md describe the raw downloads, not the reformatted files, so the files could not be verified against them.\nEOF\ncat > claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"308 of the 309 window months have a computable 12-month growth rate; 2025-10 is missing because the source reports no index level, so feasibility is partial.\",\"results_keys\":[\"n_months_growth\",\"n_months_missing_growth\",\"n_analysed\",\"feasibility\",\"feasibility_reason\"],\"status\":\"supported\",\"caveat\":\"Not imputed by rule.\"},\n {\"id\":\"C2\",\"text\":\"Mean 12-month CPI inflation over 2001-01..2026-09 is about 2.57%, 2.36% in 2001-2013 and 2.79% in 2014-2026.\",\"results_keys\":[\"mean_growth_12m\",\"mean_growth_12m_2001_2013\",\"mean_growth_12m_2014_2026\"],\"status\":\"supported\",\"caveat\":\"Means exclude 2025-10.\"},\n {\"id\":\"C3\",\"text\":\"The maximum is 8.98% in 2022-06 and the minimum is -1.96% in 2009-07.\",\"results_keys\":[\"max_growth_12m\",\"max_growth_month\",\"min_growth_12m\",\"min_growth_month\",\"growth_2022_06\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C4\",\"text\":\"Named-month growth rates: 2014-06 2.10%, 2016-07 0.87%, 2020-05 0.20%, 2026-09 3.72%.\",\"results_keys\":[\"growth_2014_06\",\"growth_2016_07\",\"growth_2020_05\",\"growth_2026_09\"],\"status\":\"supported\",\"caveat\":\"2014-06 mixes vintages (lag from the 2016 vintage).\"},\n {\"id\":\"C5\",\"text\":\"The hand-assembled file contains assembly errors (old vintage in the overlap, a missing month, a one-month shift) and was not used for estimation.\",\"results_keys\":[\"data_issues\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C6\",\"text\":\"The choice of vintage in the overlap changes 2014-01..2016-06 growth rates by at most about 0.13 pp.\",\"results_keys\":[\"sens_max_abs_diff_growth_201401_201606\",\"sens_mean_growth_201401_201606_old_vintage\",\"sens_mean_growth_201401_201606_rule\",\"max_abs_vintage_revision_overlap\"],\"status\":\"supported\",\"caveat\":\"Sensitivity only; not a required estimand.\"}\n]\nEOF\ncat > state/phases.json <<'EOF'\n{\n \"P0\":{\"status\":\"passed\",\"checks\":[\"contract.md written covering question, estimands, outputs, rules, invalidating conditions\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256, rows, ranges, missing, duplicates recorded in data_audit.md\",\"assembled file compared with splice: errors found, excluded from estimation\",\"exclusion ledger written by analysis.py\"],\"notes\":\"\"},\n \"P2\":{\"status\":\"passed\",\"checks\":[\"baseline mean_growth_12m_2001_2013 computed = 2.3612\"],\"notes\":\"\"},\n \"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, writes results.json, analytic.csv, exclusions.csv; no RNG\"],\"notes\":\"\"},\n \"P4\":{\"status\":\"running\",\"checks\":[\"clean rerun byte-identical\",\"11 consistency checks PASS (checks.md)\"],\"notes\":\"\"},\n \"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}\n}\nEOF\ncd .. && ~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; 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I'm setting their final statuses and re-running the gate."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3798,"cache_read_input_tokens":29611,"output_tokens":737,"output_tokens_details":{"thinking_tokens":44},"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":3798},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXo8Fkmsjomb8ZSc4y1a","requestRef":"541fab0d-64bb-4632-a873-5628ab5618f5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"30e7b1b7-48bb-4b63-abb1-6f542806c8bc","timestamp":"2026-10-30T04:32:36.749Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}73{"parentUuid":"30e7b1b7-48bb-4b63-abb1-6f542806c8bc","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo8G1uB9UxrqGN4gzGx","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018QsGsuVWnaAx3NuXv1FxSg","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np='out/state/phases.json';d=json.load(open(p))\nd['P4']={\"status\":\"passed\",\"checks\":[\"clean rerun byte-identical\",\"11 consistency checks PASS (checks.md)\",\"gate run 20261030T043234.593864Z: 39/40 ok; only failure = phase ledger non-terminal (P4/P5 in progress), contract/reproducibility checks ok\"],\"notes\":\"receipt out/state/gate_receipts/20261030T043234.593864Z.json\"}\nd['P5']={\"status\":\"passed\",\"checks\":[\"report.md with 6 sections citing results keys\",\"claims.json 6 claims, keys resolve (gate)\",\"final gate run recorded in log.md\"],\"notes\":\"\"}\njson.dump(d,open(p,'w'),indent=1)\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v \"\\[ok\\]\"; R=$(ls -t out/state/gate_receipts | head -1); echo \"- 2026-10-30 P4 passed (gate receipt 20261030T043234.593864Z, only phase-ledger failure). P5 passed; final gate receipt $R.\" >> out/state/log.md; ~/wp29/venv/bin/python - <<EOF\nimport json\np='out/state/phases.json';d=json.load(open(p))\nd['P5']['notes']=\"final gate receipt out/state/gate_receipts/$R\"\njson.dump(d,open(p,'w'),indent=1)\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | head -1"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3798,"cache_read_input_tokens":29611,"output_tokens":737,"output_tokens_details":{"thinking_tokens":44},"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":3798},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXo8Fkmsjomb8ZSc4y1a","requestRef":"541fab0d-64bb-4632-a873-5628ab5618f5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c43f28e2-eac4-41dc-8eb9-d7c5b4ff5a17","timestamp":"2026-10-30T04:32:41.240Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}74{"parentUuid":"c43f28e2-eac4-41dc-8eb9-d7c5b4ff5a17","isSidechain":false,"promptId":"928f8c2b-79cf-4ec9-8a61-d59b8c4f450e","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018QsGsuVWnaAx3NuXv1FxSg","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 40 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0098/out/state/gate_receipts/20261030T043241.580878Z.json\nPGAR GATE pgar_gate/1.0 — PASSED (0 of 40 checks failed)","is_error":false}]},"uuid":"31a0417b-4a89-4438-a5fc-070ea71bc532","timestamp":"2026-10-30T04:32:41.924Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 40 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0098/out/state/gate_receipts/20261030T043241.580878Z.json\nPGAR GATE pgar_gate/1.0 — PASSED (0 of 40 checks failed)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"c43f28e2-eac4-41dc-8eb9-d7c5b4ff5a17","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}75{"type":"api-request-blob","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","hash":"f0c44112ecc4df7fd557387b82c6c1419b0dd068c638fcb2c2d65e4ff028dc59","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"The only failure was the phase ledger, which is expected because P4 and P5 were still marked as in progress. I'm setting their final statuses and re-running the gate."},{"type":"tool_use","id":"toolu_018QsGsuVWnaAx3NuXv1FxSg","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np='out/state/phases.json';d=json.load(open(p))\nd['P4']={\"status\":\"passed\",\"checks\":[\"clean rerun byte-identical\",\"11 consistency checks PASS (checks.md)\",\"gate run 20261030T043234.593864Z: 39/40 ok; only failure = phase ledger non-terminal (P4/P5 in progress), contract/reproducibility checks ok\"],\"notes\":\"receipt out/state/gate_receipts/20261030T043234.593864Z.json\"}\nd['P5']={\"status\":\"passed\",\"checks\":[\"report.md with 6 sections citing results keys\",\"claims.json 6 claims, keys resolve (gate)\",\"final gate run recorded in log.md\"],\"notes\":\"\"}\njson.dump(d,open(p,'w'),indent=1)\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v \"\\[ok\\]\"; R=$(ls -t out/state/gate_receipts | head -1); echo \"- 2026-10-30 P4 passed (gate receipt 20261030T043234.593864Z, only phase-ledger failure). P5 passed; final gate receipt $R.\" >> out/state/log.md; ~/wp29/venv/bin/python - <<EOF\nimport json\np='out/state/phases.json';d=json.load(open(p))\nd['P5']['notes']=\"final gate receipt out/state/gate_receipts/$R\"\njson.dump(d,open(p,'w'),indent=1)\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | head -1"}}]}}76{"type":"api-request","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","id":"094bb05d-c42d-4c43-b534-fa5666a9e6bc","timestamp":"2026-10-30T04:32:41.927Z","version":"2.1.314","querySource":"sdk","shapeHash":"c4a9886b5d404a87039300a171d4a2ccda220b548475a5284c56dab1eb465ef2","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":"541fab0d-64bb-4632-a873-5628ab5618f5","keep":22,"tail":["f0c44112ecc4df7fd557387b82c6c1419b0dd068c638fcb2c2d65e4ff028dc59","c12b9f36c853f1923e622f053b4aa1c6f0a648c61cb3091316174e9fdb5aa0ce"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",23,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"7d444c712ee51ec46ecd9771acc161188c3b715eaa00d83e78da5c0132a41666"}77{"parentUuid":"31a0417b-4a89-4438-a5fc-070ea71bc532","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo8oM4RFVqk9LjsFkQS","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":838,"cache_read_input_tokens":33409,"output_tokens":881,"output_tokens_details":{"thinking_tokens":20},"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":838},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":6536,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXo8o2xbUUt8Np2XGEcc","requestRef":"094bb05d-c42d-4c43-b534-fa5666a9e6bc","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"abb6b91a-59a1-4e45-9b23-d22efd49915a","timestamp":"2026-10-30T04:32:49.651Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}78{"parentUuid":"abb6b91a-59a1-4e45-9b23-d22efd49915a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXo8oM4RFVqk9LjsFkQS","type":"message","role":"assistant","content":[{"type":"text","text":"I computed the 12-month CPI inflation series and its summaries. 308 of the 309 months from 2001-01 to 2026-09 have a growth rate, so the result is marked partial. The latest gate run passed (0 of 40 checks failed), and re-running `out/analysis.py` from a clean state reproduces identical results.\n\n| Quantity | Value |\n|---|---|\n| Mean over the window (308 months) | 2.574% |\n| Mean 2001–2013 (baseline, 156 months) | 2.361% |\n| Mean 2014–2026 (152 months) | 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% in 2022-06 |\n| Minimum | −1.959% in 2009-07 |\n\n**Why it's partial:** the 2026-10-28 data vintage has no value for 2025-10. The task forbids filling gaps, so that month has no growth rate. It is listed as missing, and the overall and 2014–2026 means cover the remaining months.\n\n**The hand-assembled file was not used.** I compared it against the two vintage files and found three errors:\n- For 2014-01 to 2016-06 it uses the older vintage's values, against the rule to use the newest vintage.\n- It leaves out 2016-07.\n- From 2016-08 onward every value belongs to the previous month, which also creates a false 2026-10 row.\n\nI rebuilt the series from the two vintage files, and the file's errors are listed under `data_issues` in `out/results.json`.\n\n**Other limitations:**\n- **Mixed vintages in 2014:** each 2014 growth rate divides a 2026-vintage level by a 2013 level from the 2016 vintage, as the rule requires. Over 2014-01 to 2016-06, using the 2016 vintage instead changes individual growth rates by at most 0.127 percentage points.\n- **Checksums:** the SHA-256 values in `data/README.md` describe the original downloads, not the reformatted files provided, so I couldn't verify the files against them.\n- **Uncertainty:** these numbers are direct calculations from published index levels, so there are no confidence intervals.\n\nFiles are in `out/`:\n- `results.json`\n- `analytic.csv`\n- `exclusions.csv`\n- `analysis.py`\n- `report.md`\n- `claims.json`\n- `state/` (contract, data audit, checks, log, phase ledger, gate receipts)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":838,"cache_read_input_tokens":33409,"output_tokens":881,"output_tokens_details":{"thinking_tokens":20},"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":838},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXo8o2xbUUt8Np2XGEcc","requestRef":"094bb05d-c42d-4c43-b534-fa5666a9e6bc","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2b27a0ae-ea17-4682-854a-26da0927515e","timestamp":"2026-10-30T04:32:49.976Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0098","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","version":"2.1.314","gitBranch":"HEAD"}79{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"2b27a0ae-ea17-4682-854a-26da0927515e","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3"}80{"type":"cost-state","sessionId":"77547acd-4efe-4406-81a5-ede7d693bdf3","totalCostUSD":0.5128518,"totalAPIDuration":141146,"totalAPIDurationWithoutRetries":141140,"totalToolDuration":3177,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":144509,"startTime":1793334625474,"modelUsage":{"claude-opus-5-5":{"inputTokens":26,"outputTokens":15206,"thinkingTokens":1451,"cacheReadInputTokens":224564,"cacheCreationInputTokens":32743,"cacheCreationEphemeral5mInputTokens":32743,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.5128518}},"hasUnknownModelCost":false}81