spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T04:46:16.670Z","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T04:46:16.675Z","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2"}4{"parentUuid":null,"isSidechain":false,"promptId":"4c063876-53fe-4181-ad58-eb5eab9fc815","type":"user","message":{"role":"user","content":"Begin"},"uuid":"c4924fd0-fe96-460a-b14f-b0a504e0011e","timestamp":"2026-10-30T04:46:16.678Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0160","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","shapeHash":"2dd02160d273680ea9621f287d43802879b3bf673adec2f3bf71bba355d178ed","timestamp":"2026-10-30T04:46:16.687Z","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/RC0160\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/RC0160","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2"}12{"parentUuid":"60b710fa-a748-4e2b-85fd-bd1da6714786","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpArxWuSEFghCdHvDjc","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":2603,"cache_read_input_tokens":1504,"output_tokens":96,"output_tokens_details":{"thinking_tokens":22},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2603},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":862,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXpArgu3eB7uD1geXS4f","requestRef":"d729c0cf-1fb8-4b2e-a9a1-ba55dcc85166","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a9d9ad84-1695-46e7-bbbb-14455fef891a","timestamp":"2026-10-30T04:46:18.062Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0160","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"a9d9ad84-1695-46e7-bbbb-14455fef891a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpArxWuSEFghCdHvDjc","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015etDXGEdq725uLuw21iNyt","name":"Bash","input":{"command":"cat TASK.md; 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The estimand is the coefficient on `leverage` in the linear-probability model default_next = a + b1·leverage + b2·cash_ratio + b3·log_sales + e estimated by OLS on the firm-years whose outcome is observed, with standard errors clustered by firm. The analytic dataset must contain only predictors whose values were known at the origin year.\n\n## Inputs\n- `data/firm_panel.csv` — simulated data (synthetic firm-year panel; not observed data), 300 firms, years 2016–2022, one intended row per firm-year. Columns `firm_id`, `year`, `leverage`, `cash_ratio`, `log_sales`, `rating_2025`, `default_next`; definitions, units and the date at which each field becomes known are in `data/README.md` (codebook). Empty cells mean the value is not observed. The extract may contain assembly errors.\n\n## Information-availability rules\n- A variable may enter the model for origin year t only if its value would have been known at the end of year t (see the codebook's last column). Any variable that does not satisfy this rule must be excluded from the analytic dataset and listed in `variables_excluded`.\n- Firm-years whose outcome `default_next` is not observed cannot be used for estimation and must be listed in the exclusion ledger; do not impute outcomes.\n- Use only `data/firm_panel.csv`.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F1-conf-03\" |\n| `n_analysed` | integer | firm-years | observations in the estimation sample |\n| `n_firms` | integer | firms | distinct firms in the estimation sample |\n| `predictors_used` | array of strings | — | column names entered as regressors (excluding the intercept) |\n| `variables_excluded` | array of {\"variable\",\"reason\"} | — | columns of the file deliberately not used as predictors and why |\n| `beta_leverage` | number | probability points | OLS coefficient on `leverage` |\n| `se_leverage_cluster` | number | — | standard error of `beta_leverage` clustered by `firm_id` |\n| `r2` | number | — | in-sample R² of the model |\n| `default_rate` | number | — | baseline: share of firm-years with default_next = 1 in the estimation sample |\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 | {\"firm_panel.csv\": number of data rows read (excluding the header)} |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/analytic.csv` — the estimation sample: one row per firm-year used, columns `firm_id`, `year`, the predictors used and `default_next`; exactly `n_analysed` rows and no column that violates the information rule.\n- `out/exclusions.csv` — the exclusion ledger with columns `step`, `rule`, `n_before`, `n_after`, `n_removed`, `rationale`; one row per step from the number of rows read to `n_analysed` (`n_before − n_removed = n_after` on every row; each step's `n_before` equals the previous step's `n_after`). Column removals may be listed with `n_removed = 0`.\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  96 simon-pierreboucher  staff  3072 oct.  30 00:46 ..\n-rw-r--r--   1 simon-pierreboucher  staff   514 oct.  29 23:41 bundle.json\ndrwxr-xr-x   4 simon-pierreboucher  staff   128 oct.  29 23:38 data\n-rw-r--r--   1 simon-pierreboucher  staff  1756 oct.  29 23:38 gate_spec.json\n-rw-r--r--   1 simon-pierreboucher  staff  4218 oct.  29 23:38 TASK.md\ndata:\ntotal 160\ndrwxr-xr-x  4 simon-pierreboucher  staff    128 oct.  29 23:38 .\ndrwxr-xr-x  6 simon-pierreboucher  staff    192 oct.  29 23:41 ..\n-rw-r--r--  1 simon-pierreboucher  staff  73866 oct.  29 23:37 firm_panel.csv\n-rw-r--r--  1 simon-pierreboucher  staff   1279 oct.  29 23:38 README.md","is_error":true,"tool_use_id":"toolu_015etDXGEdq725uLuw21iNyt"}]},"uuid":"e3832513-e441-4fda-8ac6-bb05e12d295e","timestamp":"2026-10-30T04:46:18.148Z","toolUseResult":"Error: Exit code 1\n# F1-conf-03 — Information-respecting analytic dataset for next-year default\n\n## Scientific question\nEstimate the association between a firm's leverage at the end of year t and its default in year t+1, using only information that was available at the end of year t. The estimand is the coefficient on `leverage` in the linear-probability model default_next = a + b1·leverage + b2·cash_ratio + b3·log_sales + e estimated by OLS on the firm-years whose outcome is observed, with standard errors clustered by firm. The analytic dataset must contain only predictors whose values were known at the origin year.\n\n## Inputs\n- `data/firm_panel.csv` — simulated data (synthetic firm-year panel; not observed data), 300 firms, years 2016–2022, one intended row per firm-year. Columns `firm_id`, `year`, `leverage`, `cash_ratio`, `log_sales`, `rating_2025`, `default_next`; definitions, units and the date at which each field becomes known are in `data/README.md` (codebook). Empty cells mean the value is not observed. The extract may contain assembly errors.\n\n## Information-availability rules\n- A variable may enter the model for origin year t only if its value would have been known at the end of year t (see the codebook's last column). Any variable that does not satisfy this rule must be excluded from the analytic dataset and listed in `variables_excluded`.\n- Firm-years whose outcome `default_next` is not observed cannot be used for estimation and must be listed in the exclusion ledger; do not impute outcomes.\n- Use only `data/firm_panel.csv`.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F1-conf-03\" |\n| `n_analysed` | integer | firm-years | observations in the estimation sample |\n| `n_firms` | integer | firms | distinct firms in the estimation sample |\n| `predictors_used` | array of strings | — | column names entered as regressors (excluding the intercept) |\n| `variables_excluded` | array of {\"variable\",\"reason\"} | — | columns of the file deliberately not used as predictors and why |\n| `beta_leverage` | number | probability points | OLS coefficient on `leverage` |\n| `se_leverage_cluster` | number | — | standard error of `beta_leverage` clustered by `firm_id` |\n| `r2` | number | — | in-sample R² of the model |\n| `default_rate` | number | — | baseline: share of firm-years with default_next = 1 in the estimation sample |\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 | {\"firm_panel.csv\": number of data rows read (excluding the header)} |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/analytic.csv` — the estimation sample: one row per firm-year used, columns `firm_id`, `year`, the predictors used and `default_next`; exactly `n_analysed` rows and no column that violates the information rule.\n- `out/exclusions.csv` — the exclusion ledger with columns `step`, `rule`, `n_before`, `n_after`, `n_removed`, `rationale`; one row per step from the number of rows read to `n_analysed` (`n_before − n_removed = n_after` on every row; each step's `n_before` equals the previous step's `n_after`). Column removals may be listed with `n_removed = 0`.\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). 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it is not observed data). One intended row per firm and year, years 2016–2022, 300 firms. The extract was assembled from the firm master file and the annual accounts and may contain assembly errors.\n\n| column | type | definition | when the value becomes known |\n|---|---|---|---|\n| `firm_id` | string | firm identifier | at origin |\n| `year` | integer | fiscal year t (the origin year) | at origin |\n| `leverage` | number | total debt / total assets at the end of year t | end of year t |\n| `cash_ratio` | number | cash and equivalents / total assets at the end of year t | end of year t |\n| `log_sales` | number | natural log of sales in year t (thousands USD) | end of year t |\n| `rating_2025` | string (A/B/C/D) | credit rating assigned to the firm in the 2025 portfolio review; the review used the firm's complete realised default history through 2022 and its average leverage over the sample; one value per firm, constant across years (final-vintage field) | 2025 |\n| `default_next` | integer 0/1 | 1 if the firm defaulted in year t+1, 0 otherwise; empty when the outcome year had not yet been observed at the data cut (end of 2022) | end of year t+1 |\n{\n  \"task_id\": \"F1-conf-03\",\n  \"required_files\": [\n    \"out/results.json\",\n    \"out/report.md\",\n    \"out/analysis.py\",\n    \"out/claims.json\",\n    \"out/analytic.csv\",\n    \"out/exclusions.csv\"\n  ],\n  \"results_keys\": {\n    \"task_id\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"n_analysed\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"n_firms\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"predictors_used\": {\n      \"type\": \"array\",\n      \"nullable\": false\n    },\n    \"variables_excluded\": {\n      \"type\": \"array\",\n      \"nullable\": false\n    },\n    \"beta_leverage\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"se_leverage_cluster\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"r2\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"default_rate\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"feasibility\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"feasibility_reason\": {\n      \"type\": \"string\",\n      \"nullable\": true\n    },\n    \"data_issues\": {\n      \"type\": \"array\",\n      \"nullable\": false\n    },\n    \"n_input_rows\": {\n      \"type\": \"object\",\n      \"nullable\": false\n    },\n    \"random_seed\": {\n      \"type\": \"integer\",\n      \"nullable\": true\n    }\n  },\n  \"report_sections\": [\n    \"Question\",\n    \"Data and exclusions\",\n    \"Method\",\n    \"Results\",\n    \"Uncertainty\",\n    \"Limitations and open issues\"\n  ],\n  \"family_checks\": [\n    \"exclusion_ledger_sums\",\n    \"claims_reference_existing_keys\",\n    \"phase_ledger_consistent\",\n    \"positive_se\"\n  ],\n  \"ci_pairs\": [],\n  \"positive_keys\": [\n    \"se_leverage_cluster\"\n  ],\n  \"chronology_pairs\": [],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 1e-06\n}{\n  \"task_id\": \"F1-conf-03\",\n  \"family\": \"F1\",\n  \"split\": \"conf\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:41:11.660683+00:00\",\n  \"files\": {\n    \"TASK.md\": \"89b3ae62efc5d41942ef1484b256066f58b5e6075d63edcbbc69f53e76cdd438\",\n    \"data/README.md\": \"26573e982ab6ca9e7f90d2b89bd2878aa6a670f90da21496bff787b59216b9a2\",\n    \"data/firm_panel.csv\": \"a1f1af88b1ab05900155991f2468cce09695af978923e5bc3ef9c28d45683b6f\",\n    \"gate_spec.json\": \"485525fd3cc180616a452c24f04aec384c433768a96eef644650ff8d5bfcc5d4\"\n  }\n}firm_id,year,leverage,cash_ratio,log_sales,rating_2025,default_next\nF042,2022,0.5244,0.0709,6.0889,D,\nF188,2016,0.568,0.102,6.8038,D,0\nF186,2017,0.3798,0.1008,5.7041,D,1\nF105,2020,0.229,0.1368,4.5621,A,0\na1f1af88b1ab05900155991f2468cce09695af978923e5bc3ef9c28d45683b6f  data/firm_panel.csv\n26573e982ab6ca9e7f90d2b89bd2878aa6a670f90da21496bff787b59216b9a2  data/README.md","is_error":false}]},"uuid":"832ba662-3a52-4991-b35c-0f040c53e9d6","timestamp":"2026-10-30T04:46:19.755Z","toolUseResult":{"stdout":"# Codebook — F1-conf-03 (`firm_panel.csv`)\n\nSimulated data (a synthetic firm-year panel generated for this exercise; it is not observed data). One intended row per firm and year, years 2016–2022, 300 firms. The extract was assembled from the firm master file and the annual accounts and may contain assembly errors.\n\n| column | type | definition | when the value becomes known |\n|---|---|---|---|\n| `firm_id` | string | firm identifier | at origin |\n| `year` | integer | fiscal year t (the origin year) | at origin |\n| `leverage` | number | total debt / total assets at the end of year t | end of year t |\n| `cash_ratio` | number | cash and equivalents / total assets at the end of year t | end of year t |\n| `log_sales` | number | natural log of sales in year t (thousands USD) | end of year t |\n| `rating_2025` | string (A/B/C/D) | credit rating assigned to the firm in the 2025 portfolio review; the review used the firm's complete realised default history through 2022 and its average leverage over the sample; one value per firm, constant across years (final-vintage field) | 2025 |\n| `default_next` | integer 0/1 | 1 if the firm defaulted in year t+1, 0 otherwise; empty when the outcome year had not yet been observed at the data cut (end of 2022) | end of year t+1 |\n{\n  \"task_id\": \"F1-conf-03\",\n  \"required_files\": [\n    \"out/results.json\",\n    \"out/report.md\",\n    \"out/analysis.py\",\n    \"out/claims.json\",\n    \"out/analytic.csv\",\n    \"out/exclusions.csv\"\n  ],\n  \"results_keys\": {\n    \"task_id\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"n_analysed\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"n_firms\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"predictors_used\": {\n      \"type\": \"array\",\n      \"nullable\": false\n    },\n    \"variables_excluded\": {\n      \"type\": \"array\",\n      \"nullable\": false\n    },\n    \"beta_leverage\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"se_leverage_cluster\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"r2\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"default_rate\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"feasibility\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"feasibility_reason\": {\n      \"type\": \"string\",\n      \"nullable\": true\n    },\n    \"data_issues\": {\n      \"type\": \"array\",\n      \"nullable\": false\n    },\n    \"n_input_rows\": {\n      \"type\": \"object\",\n      \"nullable\": false\n    },\n    \"random_seed\": {\n      \"type\": \"integer\",\n      \"nullable\": true\n    }\n  },\n  \"report_sections\": [\n    \"Question\",\n    \"Data and exclusions\",\n    \"Method\",\n    \"Results\",\n    \"Uncertainty\",\n    \"Limitations and open issues\"\n  ],\n  \"family_checks\": [\n    \"exclusion_ledger_sums\",\n    \"claims_reference_existing_keys\",\n    \"phase_ledger_consistent\",\n    \"positive_se\"\n  ],\n  \"ci_pairs\": [],\n  \"positive_keys\": [\n    \"se_leverage_cluster\"\n  ],\n  \"chronology_pairs\": [],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 1e-06\n}{\n  \"task_id\": \"F1-conf-03\",\n  \"family\": \"F1\",\n  \"split\": \"conf\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:41:11.660683+00:00\",\n  \"files\": {\n    \"TASK.md\": \"89b3ae62efc5d41942ef1484b256066f58b5e6075d63edcbbc69f53e76cdd438\",\n    \"data/README.md\": \"26573e982ab6ca9e7f90d2b89bd2878aa6a670f90da21496bff787b59216b9a2\",\n    \"data/firm_panel.csv\": \"a1f1af88b1ab05900155991f2468cce09695af978923e5bc3ef9c28d45683b6f\",\n    \"gate_spec.json\": \"485525fd3cc180616a452c24f04aec384c433768a96eef644650ff8d5bfcc5d4\"\n  }\n}firm_id,year,leverage,cash_ratio,log_sales,rating_2025,default_next\nF042,2022,0.5244,0.0709,6.0889,D,\nF188,2016,0.568,0.102,6.8038,D,0\nF186,2017,0.3798,0.1008,5.7041,D,1\nF105,2020,0.229,0.1368,4.5621,A,0\na1f1af88b1ab05900155991f2468cce09695af978923e5bc3ef9c28d45683b6f  data/firm_panel.csv\n26573e982ab6ca9e7f90d2b89bd2878aa6a670f90da21496bff787b59216b9a2  data/README.md","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"29a402ba-f933-40ea-82ba-8b99f9902c38","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0160","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}21{"parentUuid":"832ba662-3a52-4991-b35c-0f040c53e9d6","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpAyALUy8Zct3DQ4j2a","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01JJDSgcHbuaQ4cPjGphZT7s","name":"Bash","input":{"command":"~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/firm_panel.csv');print(d.shape);print(d.dtypes);print(d.describe(include='all').T);print(d.isna().sum())\nprint('fulldup',d.duplicated().sum(),'keydup',d.duplicated(['firm_id','year'],keep=False).sum())\nk=d[d.duplicated(['firm_id','year'],keep=False)].sort_values(['firm_id','year']);print(k.head(20))\nprint(d.year.value_counts().sort_index());print(d.default_next.value_counts(dropna=False))\nprint(d.groupby('year').default_next.apply(lambda s:s.isna().sum()))\nprint(d.firm_id.nunique())\nfor c in ['leverage','cash_ratio','log_sales']: print(c,(d[c]<0).sum(),(d[c]>1).sum())\n\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2074,"cache_read_input_tokens":4107,"output_tokens":457,"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":2074},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpAxtiRQQHNcqRnvo8n","requestRef":"46ec87d3-142e-4b73-bc10-2e97648658fe","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"85cd7711-704e-4d81-bdba-bf3ed26e9cd4","timestamp":"2026-10-30T04:46:22.040Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0160","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}22{"parentUuid":"85cd7711-704e-4d81-bdba-bf3ed26e9cd4","isSidechain":false,"promptId":"4c063876-53fe-4181-ad58-eb5eab9fc815","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JJDSgcHbuaQ4cPjGphZT7s","type":"tool_result","content":"(2140, 7)\nfirm_id          object\nyear              int64\nleverage        float64\ncash_ratio      float64\nlog_sales       float64\nrating_2025      object\ndefault_next    float64\ndtype: object\n               count unique   top  freq  ...       25%      50%       75%     max\nfirm_id         2140    300  F042     8  ...       NaN      NaN       NaN     NaN\nyear          2140.0    NaN   NaN   NaN  ...    2017.0   2019.0    2021.0  2022.0\nleverage      2140.0    NaN   NaN   NaN  ...     0.235   0.3829    0.5458    0.95\ncash_ratio    2140.0    NaN   NaN   NaN  ...  0.104575  0.17855  0.265875  0.7406\nlog_sales     2140.0    NaN   NaN   NaN  ...  4.331375   4.9998  5.652925  8.8786\nrating_2025     2140      4     D  1146  ...       NaN      NaN       NaN     NaN\ndefault_next  1833.0    NaN   NaN   NaN  ...       0.0      0.0       0.0     1.0\n\n[7 rows x 11 columns]\nfirm_id           0\nyear              0\nleverage          0\ncash_ratio        0\nlog_sales         0\nrating_2025       0\ndefault_next    307\ndtype: int64\nfulldup 40 keydup 80\n     firm_id  year  leverage  cash_ratio  log_sales rating_2025  default_next\n812     F015  2016    0.4195      0.0748     3.1490           D           1.0\n1439    F015  2016    0.4195      0.0748     3.1490           D           1.0\n804     F017  2019    0.5908      0.3866     6.5654           C           0.0\n978     F017  2019    0.5908      0.3866     6.5654           C           0.0\n336     F024  2022    0.4509      0.2859     4.3565           D           NaN\n1058    F024  2022    0.4509      0.2859     4.3565           D           NaN\n102     F025  2018    0.2454      0.2691     4.3037           D           0.0\n1682    F025  2018    0.2454      0.2691     4.3037           D           0.0\n82      F040  2018    0.6918      0.2139     4.8758           D           1.0\n237     F040  2018    0.6918      0.2139     4.8758           D           1.0\n0       F042  2022    0.5244      0.0709     6.0889           D           NaN\n15      F042  2022    0.5244      0.0709     6.0889           D           NaN\n4       F062  2020    0.2496      0.2919     6.3207           B           0.0\n1176    F062  2020    0.2496      0.2919     6.3207           B           0.0\n752     F064  2018    0.4409      0.2196     5.6493           D           0.0\n1384    F064  2018    0.4409      0.2196     5.6493           D           0.0\n127     F065  2018    0.8433      0.6880     5.8157           D           0.0\n2072    F065  2018    0.8433      0.6880     5.8157           D           0.0\n50      F066  2021    0.4655      0.1123     3.2088           D           1.0\n1560    F066  2021    0.4655      0.1123     3.2088           D           1.0\nyear\n2016    305\n2017    301\n2018    309\n2019    305\n2020    306\n2021    307\n2022    307\nName: count, dtype: int64\ndefault_next\n0.0    1609\nNaN     307\n1.0     224\nName: count, dtype: int64\nyear\n2016      0\n2017      0\n2018      0\n2019      0\n2020      0\n2021      0\n2022    307\nName: default_next, dtype: int64\n300\nleverage 0 0\ncash_ratio 0 0\nlog_sales 0 2140","is_error":false}]},"uuid":"2b6a3cde-82c7-41e7-971a-ef7de5de824a","timestamp":"2026-10-30T04:46:22.267Z","toolUseResult":{"stdout":"(2140, 7)\nfirm_id          object\nyear              int64\nleverage        float64\ncash_ratio      float64\nlog_sales       float64\nrating_2025      object\ndefault_next    float64\ndtype: object\n               count unique   top  freq  ...       25%      50%       75%     max\nfirm_id         2140    300  F042     8  ...       NaN      NaN       NaN     NaN\nyear          2140.0    NaN   NaN   NaN  ...    2017.0   2019.0    2021.0  2022.0\nleverage      2140.0    NaN   NaN   NaN  ...     0.235   0.3829    0.5458    0.95\ncash_ratio    2140.0    NaN   NaN   NaN  ...  0.104575  0.17855  0.265875  0.7406\nlog_sales     2140.0    NaN   NaN   NaN  ...  4.331375   4.9998  5.652925  8.8786\nrating_2025     2140      4     D  1146  ...       NaN      NaN       NaN     NaN\ndefault_next  1833.0    NaN   NaN   NaN  ...       0.0      0.0       0.0     1.0\n\n[7 rows x 11 columns]\nfirm_id           0\nyear              0\nleverage          0\ncash_ratio        0\nlog_sales         0\nrating_2025       0\ndefault_next    307\ndtype: int64\nfulldup 40 keydup 80\n     firm_id  year  leverage  cash_ratio  log_sales rating_2025  default_next\n812     F015  2016    0.4195      0.0748     3.1490           D           1.0\n1439    F015  2016    0.4195      0.0748     3.1490           D           1.0\n804     F017  2019    0.5908      0.3866     6.5654           C           0.0\n978     F017  2019    0.5908      0.3866     6.5654           C           0.0\n336     F024  2022    0.4509      0.2859     4.3565           D           NaN\n1058    F024  2022    0.4509      0.2859     4.3565           D           NaN\n102     F025  2018    0.2454      0.2691     4.3037           D           0.0\n1682    F025  2018    0.2454      0.2691     4.3037           D           0.0\n82      F040  2018    0.6918      0.2139     4.8758           D           1.0\n237     F040  2018    0.6918      0.2139     4.8758           D           1.0\n0       F042  2022    0.5244      0.0709     6.0889           D           NaN\n15      F042  2022    0.5244      0.0709     6.0889           D           NaN\n4       F062  2020    0.2496      0.2919     6.3207           B           0.0\n1176    F062  2020    0.2496      0.2919     6.3207           B           0.0\n752     F064  2018    0.4409      0.2196     5.6493           D           0.0\n1384    F064  2018    0.4409      0.2196     5.6493           D           0.0\n127     F065  2018    0.8433      0.6880     5.8157           D           0.0\n2072    F065  2018    0.8433      0.6880     5.8157           D           0.0\n50      F066  2021    0.4655      0.1123     3.2088           D           1.0\n1560    F066  2021    0.4655      0.1123     3.2088           D           1.0\nyear\n2016    305\n2017    301\n2018    309\n2019    305\n2020    306\n2021    307\n2022    307\nName: count, dtype: int64\ndefault_next\n0.0    1609\nNaN     307\n1.0     224\nName: count, dtype: int64\nyear\n2016      0\n2017      0\n2018      0\n2019      0\n2020      0\n2021      0\n2022    307\nName: default_next, dtype: int64\n300\nleverage 0 0\ncash_ratio 0 0\nlog_sales 0 2140","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"85cd7711-704e-4d81-bdba-bf3ed26e9cd4","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0160","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}23{"type":"api-request-blob","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","hash":"4288f583dc57f7164dcb6fcea10e435be0366f7e472450896ba7b6bb077b2d4b","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01CRSWXXUhrGWbwK4YsyNZGh","name":"Bash","input":{"command":"cat data/README.md; cat gate_spec.json; cat bundle.json; head -5 data/firm_panel.csv; shasum -a 256 data/*"}},{"type":"tool_use","id":"toolu_01JJDSgcHbuaQ4cPjGphZT7s","name":"Bash","input":{"command":"~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/firm_panel.csv');print(d.shape);print(d.dtypes);print(d.describe(include='all').T);print(d.isna().sum())\nprint('fulldup',d.duplicated().sum(),'keydup',d.duplicated(['firm_id','year'],keep=False).sum())\nk=d[d.duplicated(['firm_id','year'],keep=False)].sort_values(['firm_id','year']);print(k.head(20))\nprint(d.year.value_counts().sort_index());print(d.default_next.value_counts(dropna=False))\nprint(d.groupby('year').default_next.apply(lambda s:s.isna().sum()))\nprint(d.firm_id.nunique())\nfor c in ['leverage','cash_ratio','log_sales']: print(c,(d[c]<0).sum(),(d[c]>1).sum())\n\""}}]}}24{"type":"api-request-blob","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","hash":"1eb5cdbde428811d5f860c72d3c39aceda431522b412483f50c19d12ca905d3c","message":{"role":"user","content":[{"tool_use_id":"toolu_01CRSWXXUhrGWbwK4YsyNZGh","type":"tool_result","content":"# Codebook — F1-conf-03 (`firm_panel.csv`)\n\nSimulated data (a synthetic firm-year panel generated for this exercise; it is not observed data). One intended row per firm and year, years 2016–2022, 300 firms. The extract was assembled from the firm master file and the annual accounts and may contain assembly errors.\n\n| column | type | definition | when the value becomes known |\n|---|---|---|---|\n| `firm_id` | string | firm identifier | at origin |\n| `year` | integer | fiscal year t (the origin year) | at origin |\n| `leverage` | number | total debt / total assets at the end of year t | end of year t |\n| `cash_ratio` | number | cash and equivalents / total assets at the end of year t | end of year t |\n| `log_sales` | number | natural log of sales in year t (thousands USD) | end of year t |\n| `rating_2025` | string (A/B/C/D) | credit rating assigned to the firm in the 2025 portfolio review; the review used the firm's complete realised default history through 2022 and its average leverage over the sample; one value per firm, constant across years (final-vintage field) | 2025 |\n| `default_next` | integer 0/1 | 1 if the firm defaulted in year t+1, 0 otherwise; empty when the outcome year had not yet been observed at the data cut (end of 2022) | end of year t+1 |\n{\n  \"task_id\": \"F1-conf-03\",\n  \"required_files\": [\n    \"out/results.json\",\n    \"out/report.md\",\n    \"out/analysis.py\",\n    \"out/claims.json\",\n    \"out/analytic.csv\",\n    \"out/exclusions.csv\"\n  ],\n  \"results_keys\": {\n    \"task_id\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"n_analysed\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"n_firms\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"predictors_used\": {\n      \"type\": \"array\",\n      \"nullable\": false\n    },\n    \"variables_excluded\": {\n      \"type\": \"array\",\n      \"nullable\": false\n    },\n    \"beta_leverage\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"se_leverage_cluster\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"r2\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"default_rate\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"feasibility\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"feasibility_reason\": {\n      \"type\": \"string\",\n      \"nullable\": true\n    },\n    \"data_issues\": {\n      \"type\": \"array\",\n      \"nullable\": false\n    },\n    \"n_input_rows\": {\n      \"type\": \"object\",\n      \"nullable\": false\n    },\n    \"random_seed\": {\n      \"type\": \"integer\",\n      \"nullable\": true\n    }\n  },\n  \"report_sections\": [\n    \"Question\",\n    \"Data and exclusions\",\n    \"Method\",\n    \"Results\",\n    \"Uncertainty\",\n    \"Limitations and open issues\"\n  ],\n  \"family_checks\": [\n    \"exclusion_ledger_sums\",\n    \"claims_reference_existing_keys\",\n    \"phase_ledger_consistent\",\n    \"positive_se\"\n  ],\n  \"ci_pairs\": [],\n  \"positive_keys\": [\n    \"se_leverage_cluster\"\n  ],\n  \"chronology_pairs\": [],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 1e-06\n}{\n  \"task_id\": \"F1-conf-03\",\n  \"family\": \"F1\",\n  \"split\": \"conf\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:41:11.660683+00:00\",\n  \"files\": {\n    \"TASK.md\": \"89b3ae62efc5d41942ef1484b256066f58b5e6075d63edcbbc69f53e76cdd438\",\n    \"data/README.md\": \"26573e982ab6ca9e7f90d2b89bd2878aa6a670f90da21496bff787b59216b9a2\",\n    \"data/firm_panel.csv\": \"a1f1af88b1ab05900155991f2468cce09695af978923e5bc3ef9c28d45683b6f\",\n    \"gate_spec.json\": \"485525fd3cc180616a452c24f04aec384c433768a96eef644650ff8d5bfcc5d4\"\n  }\n}firm_id,year,leverage,cash_ratio,log_sales,rating_2025,default_next\nF042,2022,0.5244,0.0709,6.0889,D,\nF188,2016,0.568,0.102,6.8038,D,0\nF186,2017,0.3798,0.1008,5.7041,D,1\nF105,2020,0.229,0.1368,4.5621,A,0\na1f1af88b1ab05900155991f2468cce09695af978923e5bc3ef9c28d45683b6f  data/firm_panel.csv\n26573e982ab6ca9e7f90d2b89bd2878aa6a670f90da21496bff787b59216b9a2  data/README.md","is_error":false},{"tool_use_id":"toolu_01JJDSgcHbuaQ4cPjGphZT7s","type":"tool_result","content":"(2140, 7)\nfirm_id          object\nyear              int64\nleverage        float64\ncash_ratio      float64\nlog_sales       float64\nrating_2025      object\ndefault_next    float64\ndtype: object\n               count unique   top  freq  ...       25%      50%       75%     max\nfirm_id         2140    300  F042     8  ...       NaN      NaN       NaN     NaN\nyear          2140.0    NaN   NaN   NaN  ...    2017.0   2019.0    2021.0  2022.0\nleverage      2140.0    NaN   NaN   NaN  ...     0.235   0.3829    0.5458    0.95\ncash_ratio    2140.0    NaN   NaN   NaN  ...  0.104575  0.17855  0.265875  0.7406\nlog_sales     2140.0    NaN   NaN   NaN  ...  4.331375   4.9998  5.652925  8.8786\nrating_2025     2140      4     D  1146  ...       NaN      NaN       NaN     NaN\ndefault_next  1833.0    NaN   NaN   NaN  ...       0.0      0.0       0.0     1.0\n\n[7 rows x 11 columns]\nfirm_id           0\nyear              0\nleverage          0\ncash_ratio        0\nlog_sales         0\nrating_2025       0\ndefault_next    307\ndtype: int64\nfulldup 40 keydup 80\n     firm_id  year  leverage  cash_ratio  log_sales rating_2025  default_next\n812     F015  2016    0.4195      0.0748     3.1490           D           1.0\n1439    F015  2016    0.4195      0.0748     3.1490           D           1.0\n804     F017  2019    0.5908      0.3866     6.5654           C           0.0\n978     F017  2019    0.5908      0.3866     6.5654           C           0.0\n336     F024  2022    0.4509      0.2859     4.3565           D           NaN\n1058    F024  2022    0.4509      0.2859     4.3565           D           NaN\n102     F025  2018    0.2454      0.2691     4.3037           D           0.0\n1682    F025  2018    0.2454      0.2691     4.3037           D           0.0\n82      F040  2018    0.6918      0.2139     4.8758           D           1.0\n237     F040  2018    0.6918      0.2139     4.8758           D           1.0\n0       F042  2022    0.5244      0.0709     6.0889           D           NaN\n15      F042  2022    0.5244      0.0709     6.0889           D           NaN\n4       F062  2020    0.2496      0.2919     6.3207           B           0.0\n1176    F062  2020    0.2496      0.2919     6.3207           B           0.0\n752     F064  2018    0.4409      0.2196     5.6493           D           0.0\n1384    F064  2018    0.4409      0.2196     5.6493           D           0.0\n127     F065  2018    0.8433      0.6880     5.8157           D           0.0\n2072    F065  2018    0.8433      0.6880     5.8157           D           0.0\n50      F066  2021    0.4655      0.1123     3.2088           D           1.0\n1560    F066  2021    0.4655      0.1123     3.2088           D           1.0\nyear\n2016    305\n2017    301\n2018    309\n2019    305\n2020    306\n2021    307\n2022    307\nName: count, dtype: int64\ndefault_next\n0.0    1609\nNaN     307\n1.0     224\nName: count, dtype: int64\nyear\n2016      0\n2017      0\n2018      0\n2019      0\n2020      0\n2021      0\n2022    307\nName: default_next, dtype: int64\n300\nleverage 0 0\ncash_ratio 0 0\nlog_sales 0 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year\n7    300\nName: count, dtype: int64 rating_2025\n1    300\nName: count, dtype: int64\n1 0.02 0.0046 1.2882\n(1800, 7) True\ndefault_next  0.0  1.0\nrating_2025           \nA             426    0\nB             252    0\nC             162    0\nD             742  218","is_error":false}]},"uuid":"719b6bdc-56ff-418a-a549-e05f1c87c336","timestamp":"2026-10-30T04:46:26.624Z","toolUseResult":{"stdout":"2100 year\n7    300\nName: count, dtype: int64 rating_2025\n1    300\nName: count, dtype: int64\n1 0.02 0.0046 1.2882\n(1800, 7) True\ndefault_next  0.0  1.0\nrating_2025           \nA             426    0\nB             252    0\nC             162    0\nD             742  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d.firm_id.str.match(r'^F\\d{3}$').all())\nprint(pd.crosstab(d.rating_2025,d.default_next))\n\""}}]}}32{"type":"api-request","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","id":"229e0975-ba3e-4072-994f-0b19a3c41d30","timestamp":"2026-10-30T04:46:26.626Z","version":"2.1.314","querySource":"sdk","shapeHash":"2dd02160d273680ea9621f287d43802879b3bf673adec2f3bf71bba355d178ed","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":"30da6331-4b7f-4b51-8c36-0b621871882f","keep":6,"tail":["30cb88575da5e64aea93b805a9fae4d03d4aebe1da8b323fbf3e20dda89ce455","188c3a3e63a4ba33db98640c57c10db99978bc423694fa690906abea074fe652"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"3e2c0767425269d778fe1ddb81aaf9e1e2e658d21fb836885bd883ae5f24f35b"}33{"parentUuid":"719b6bdc-56ff-418a-a549-e05f1c87c336","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpBbPRB5RsospVTxGxP","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":515,"cache_read_input_tokens":9655,"output_tokens":1254,"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":515},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":612,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXpBb7JJF9tZoFJgctpX","requestRef":"229e0975-ba3e-4072-994f-0b19a3c41d30","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a78d6171-823a-401b-a2b9-6ee78d2f6044","timestamp":"2026-10-30T04:46:28.143Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0160","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}34{"parentUuid":"a78d6171-823a-401b-a2b9-6ee78d2f6044","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpBbPRB5RsospVTxGxP","type":"message","role":"assistant","content":[{"type":"text","text":"Data is clean apart from 40 exact duplicate rows; `rating_2025` is a look-ahead field (perfectly separates defaults). Writing state files and the analysis script."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":515,"cache_read_input_tokens":9655,"output_tokens":1254,"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":515},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpBb7JJF9tZoFJgctpX","requestRef":"229e0975-ba3e-4072-994f-0b19a3c41d30","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"90d912a8-4fef-456f-becb-799b44035322","timestamp":"2026-10-30T04:46:28.146Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0160","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}35{"parentUuid":"90d912a8-4fef-456f-becb-799b44035322","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpBbPRB5RsospVTxGxP","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_019aTfpXe7D4vdcSTAQnHbTo","name":"Bash","input":{"command":"mkdir -p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract — F1-conf-03\n**Question:** association between firm leverage at end of year t and default in t+1, using only info available at end of t.\n**Estimand:** b1 in LPM default_next ~ 1 + leverage + cash_ratio + log_sales, OLS on firm-years with observed outcome, SE clustered by firm_id. Meaningful as a conditional association (not causal) assuming the rows are correct firm-year records and linear approximation.\n**Outputs:** out/results.json (task_id, n_analysed, n_firms, predictors_used, variables_excluded, beta_leverage, se_leverage_cluster, r2, default_rate, feasibility, feasibility_reason, data_issues, n_input_rows, random_seed); out/analytic.csv; out/exclusions.csv; out/report.md (6 sections); out/analysis.py; out/claims.json.\n**Information rules:** predictors only if known at end of t (codebook); rating_2025 known 2025 and built from realised defaults -> must be excluded. Rows with missing default_next excluded and ledgered, no imputation. Only data/firm_panel.csv.\n**Invalidity:** using rating_2025; imputing outcomes; counting duplicated rows twice; no observed outcomes.\nEOF\ncat > log.md <<'EOF'\n# Log\n- 2026-10-30 P0: contract written.\n- 2026-10-30 P1: audit done; 40 exact duplicate rows found (2140->2100 = 300x7); rating_2025 is look-ahead (2025, uses realised defaults) -> excluded; 300 rows (all 2022) have missing outcome -> excluded. Decision: drop exact duplicates keeping first.\nEOF\ncat > data_audit.md <<'EOF'\n# Data audit\n- data/firm_panel.csv sha256 a1f1af88b1ab05900155991f2468cce09695af978923e5bc3ef9c28d45683b6f; 2140 data rows, 7 columns (firm_id str, year int, leverage, cash_ratio, log_sales float, rating_2025 str A-D, default_next 0/1).\n- data/README.md sha256 26573e982ab6ca9e7f90d2b89bd2878aa6a670f90da21496bff787b59216b9a2 (codebook).\n- Years 2016–2022; 300 firms (IDs F###).\n- Duplicates: 40 rows are exact duplicates of another row; all (firm_id, year) key duplicates are exact duplicates (80 rows involved). After dropping: 2100 rows = 300 firms × 7 years, balanced.\n- Missing: only default_next, 307 raw rows (300 after dedupe), all in year 2022 (outcome year 2023 not observed at the 2022 data cut).\n- Ranges: leverage 0.02–0.95, cash_ratio 0.0046–0.74, log_sales 1.29–8.88; no negatives, no impossible values.\n- rating_2025 constant within firm; known only in 2025 and built from realised defaults through 2022: violates information rule. Crosstab: all 218 defaults (deduped, observed) are in rating D; A/B/C have zero defaults → clear leakage. Excluded from predictors and from analytic.csv.\nEOF"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":515,"cache_read_input_tokens":9655,"output_tokens":1254,"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":515},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXpBb7JJF9tZoFJgctpX","requestRef":"229e0975-ba3e-4072-994f-0b19a3c41d30","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0aebf53a-b1da-42b1-93cf-3e1357792f63","timestamp":"2026-10-30T04:46:38.868Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0160","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}36{"parentUuid":"0aebf53a-b1da-42b1-93cf-3e1357792f63","isSidechain":false,"promptId":"4c063876-53fe-4181-ad58-eb5eab9fc815","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019aTfpXe7D4vdcSTAQnHbTo","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"a72431cb-b061-4a9f-8b33-c2c25b1b198f","timestamp":"2026-10-30T04:46:38.903Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"0aebf53a-b1da-42b1-93cf-3e1357792f63","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0160/out/state","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}37{"type":"api-request-blob","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","hash":"79e174e92e9eaf396bd1fffb6088cc7d30ea430ebfca32f65ae4ae5afe7128f6","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"Data is clean apart from 40 exact duplicate rows; `rating_2025` is a look-ahead field (perfectly separates defaults). Writing state files and the analysis script."},{"type":"tool_use","id":"toolu_019aTfpXe7D4vdcSTAQnHbTo","name":"Bash","input":{"command":"mkdir -p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract — F1-conf-03\n**Question:** association between firm leverage at end of year t and default in t+1, using only info available at end of t.\n**Estimand:** b1 in LPM default_next ~ 1 + leverage + cash_ratio + log_sales, OLS on firm-years with observed outcome, SE clustered by firm_id. Meaningful as a conditional association (not causal) assuming the rows are correct firm-year records and linear approximation.\n**Outputs:** out/results.json (task_id, n_analysed, n_firms, predictors_used, variables_excluded, beta_leverage, se_leverage_cluster, r2, default_rate, feasibility, feasibility_reason, data_issues, n_input_rows, random_seed); out/analytic.csv; out/exclusions.csv; out/report.md (6 sections); out/analysis.py; out/claims.json.\n**Information rules:** predictors only if known at end of t (codebook); rating_2025 known 2025 and built from realised defaults -> must be excluded. Rows with missing default_next excluded and ledgered, no imputation. Only data/firm_panel.csv.\n**Invalidity:** using rating_2025; imputing outcomes; counting duplicated rows twice; no observed outcomes.\nEOF\ncat > log.md <<'EOF'\n# Log\n- 2026-10-30 P0: contract written.\n- 2026-10-30 P1: audit done; 40 exact duplicate rows found (2140->2100 = 300x7); rating_2025 is look-ahead (2025, uses realised defaults) -> excluded; 300 rows (all 2022) have missing outcome -> excluded. Decision: drop exact duplicates keeping first.\nEOF\ncat > data_audit.md <<'EOF'\n# Data audit\n- data/firm_panel.csv sha256 a1f1af88b1ab05900155991f2468cce09695af978923e5bc3ef9c28d45683b6f; 2140 data rows, 7 columns (firm_id str, year int, leverage, cash_ratio, log_sales float, rating_2025 str A-D, default_next 0/1).\n- data/README.md sha256 26573e982ab6ca9e7f90d2b89bd2878aa6a670f90da21496bff787b59216b9a2 (codebook).\n- Years 2016–2022; 300 firms (IDs F###).\n- Duplicates: 40 rows are exact duplicates of another row; all (firm_id, year) key duplicates are exact duplicates (80 rows involved). After dropping: 2100 rows = 300 firms × 7 years, balanced.\n- Missing: only default_next, 307 raw rows (300 after dedupe), all in year 2022 (outcome year 2023 not observed at the 2022 data cut).\n- Ranges: leverage 0.02–0.95, cash_ratio 0.0046–0.74, log_sales 1.29–8.88; no negatives, no impossible values.\n- rating_2025 constant within firm; known only in 2025 and built from realised defaults through 2022: violates information rule. Crosstab: all 218 defaults (deduped, observed) are in rating D; A/B/C have zero defaults → clear leakage. Excluded from predictors and from analytic.csv.\nEOF"}}]}}38{"type":"api-request","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","id":"dfc8f94a-099d-4e5c-8ffb-d0660602d3f7","timestamp":"2026-10-30T04:46:38.906Z","version":"2.1.314","querySource":"sdk","shapeHash":"2dd02160d273680ea9621f287d43802879b3bf673adec2f3bf71bba355d178ed","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":"229e0975-ba3e-4072-994f-0b19a3c41d30","keep":8,"tail":["79e174e92e9eaf396bd1fffb6088cc7d30ea430ebfca32f65ae4ae5afe7128f6","19eccd7e234d9b1a57040532d9f7337d3a16d1f85ac8ce3ea4cf0e1395760f4c"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"330e717a23e1657c87cb835825102c05ab03f80c24aadbe2146549ad55d51dae"}39{"parentUuid":"a72431cb-b061-4a9f-8b33-c2c25b1b198f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpCVuYFshbPvici9ixa","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01CknpmQT6kh2q6YK1bzJprg","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F1-conf-03: LPM of next-year default on end-of-year-t predictors. Run from workspace root.\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nimport statsmodels.api as sm\n\nos.makedirs(\"out\", exist_ok=True)\nraw = pd.read_csv(\"data/firm_panel.csv\", dtype={\"firm_id\": str})\nn_raw = len(raw)\nPRED = [\"leverage\", \"cash_ratio\", \"log_sales\"]\nledger = []\ndef step(name, rule, before, after, why):\n    ledger.append(dict(step=name, rule=rule, n_before=before, n_after=after, n_removed=before - after, rationale=why))\n\nstep(\"0_read\", \"rows read from data/firm_panel.csv\", n_raw, n_raw, \"raw input\")\nn_exact_dup = int(raw.duplicated().sum())\nn_key_dup = int(raw.duplicated([\"firm_id\", \"year\"]).sum())\nd = raw.drop_duplicates().reset_index(drop=True)\nassert not d.duplicated([\"firm_id\", \"year\"]).any(), \"non-identical key duplicates present\"\nstep(\"1_dedupe\", \"drop exact duplicate rows (keep first)\", n_raw, len(d), \"assembly error: identical firm-year rows repeated\")\nstep(\"2_drop_rating_2025\", \"remove column rating_2025 (known 2025, uses realised defaults)\", len(d), len(d), \"violates information-availability rule; column removal only\")\nn_b = len(d)\nn_miss_by_year = d.loc[d.default_next.isna(), \"year\"].value_counts().to_dict()\nd = d[d.default_next.notna()].copy()\nstep(\"3_outcome_observed\", \"drop rows with missing default_next\", n_b, len(d), \"outcome year not observed at data cut (end 2022); no imputation\")\nn_b = len(d)\nd = d.dropna(subset=PRED)\nstep(\"4_complete_predictors\", \"drop rows with missing predictors\", n_b, len(d), \"listwise deletion (none expected)\")\n\nd[\"default_next\"] = d[\"default_next\"].astype(int)\nd = d.sort_values([\"firm_id\", \"year\"]).reset_index(drop=True)\nanalytic = d[[\"firm_id\", \"year\"] + PRED + [\"default_next\"]]\nanalytic.to_csv(\"out/analytic.csv\", index=False)\npd.DataFrame(ledger).to_csv(\"out/exclusions.csv\", index=False)\n\nX = sm.add_constant(analytic[PRED])\ngroups = pd.factorize(analytic[\"firm_id\"])[0]\nfit = sm.OLS(analytic[\"default_next\"], X).fit(cov_type=\"cluster\", cov_kwds={\"groups\": groups})\n\nres = {\n    \"task_id\": \"F1-conf-03\",\n    \"n_analysed\": int(len(analytic)),\n    \"n_firms\": int(analytic.firm_id.nunique()),\n    \"predictors_used\": PRED,\n    \"variables_excluded\": [\n        {\"variable\": \"rating_2025\", \"reason\": \"Assigned in the 2025 review using realised default history through 2022 and sample-average leverage; not known at end of year t (look-ahead leakage). In the data every observed default is in rating D.\"},\n        {\"variable\": \"firm_id\", \"reason\": \"identifier; used only for clustering, not as a predictor\"},\n        {\"variable\": \"year\", \"reason\": \"origin-year index; not part of the specified model\"},\n        {\"variable\": \"default_next\", \"reason\": \"outcome variable\"},\n    ],\n    \"beta_leverage\": float(fit.params[\"leverage\"]),\n    \"se_leverage_cluster\": float(fit.bse[\"leverage\"]),\n    \"r2\": float(fit.rsquared),\n    \"default_rate\": float(analytic.default_next.mean()),\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": [\n        {\"issue\": f\"{n_exact_dup} exact duplicate rows ({n_key_dup} duplicated firm_id-year keys, all identical copies)\", \"handling\": \"dropped duplicates keeping one copy; panel becomes 300 firms x 7 years\"},\n        {\"issue\": \"rating_2025 is a final-vintage field built from future defaults (information leakage)\", \"handling\": \"excluded from predictors and from out/analytic.csv\"},\n        {\"issue\": f\"default_next missing for {sum(n_miss_by_year.values())} deduplicated rows, by year: {{{', '.join(f'{int(k)}: {v}' for k, v in sorted(n_miss_by_year.items()))}}}\", \"handling\": \"excluded from estimation (outcome not observed at data cut); not imputed\"},\n    ],\n    \"n_input_rows\": {\"firm_panel.csv\": int(n_raw)},\n    \"random_seed\": None,\n    # supplementary\n    \"beta_cash_ratio\": float(fit.params[\"cash_ratio\"]),\n    \"beta_log_sales\": float(fit.params[\"log_sales\"]),\n    \"intercept\": float(fit.params[\"const\"]),\n    \"ci95_leverage_low\": float(fit.conf_int().loc[\"leverage\", 0]),\n    \"ci95_leverage_high\": float(fit.conf_int().loc[\"leverage\", 1]),\n    \"p_leverage_cluster\": float(fit.pvalues[\"leverage\"]),\n    \"se_leverage_classical\": float(sm.OLS(analytic[\"default_next\"], X).fit().bse[\"leverage\"]),\n    \"n_rows_after_dedupe\": int(n_raw - n_exact_dup),\n}\nwith open(\"out/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"n_analysed\", \"n_firms\", \"beta_leverage\", \"se_leverage_cluster\", \"r2\", \"default_rate\"]}, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && cat out/results.json | head -50"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1269,"cache_read_input_tokens":10170,"output_tokens":2078,"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":1269},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXpCVcgyH6tKGgXM7cjK","requestRef":"dfc8f94a-099d-4e5c-8ffb-d0660602d3f7","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"de28621d-8451-4c89-863e-d51a81d7b4ff","timestamp":"2026-10-30T04:46:55.179Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0160/out/state","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}40{"parentUuid":"de28621d-8451-4c89-863e-d51a81d7b4ff","isSidechain":false,"promptId":"4c063876-53fe-4181-ad58-eb5eab9fc815","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 2\n(eval):1: no such file or directory: out/analysis.py\n/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/simon-pierreboucher/wp29/runs/RC0160/out/state/out/analysis.py': [Errno 2] No such file or directory","is_error":true,"tool_use_id":"toolu_01CknpmQT6kh2q6YK1bzJprg"}]},"uuid":"4d45ada8-7591-4add-bf4b-971795089099","timestamp":"2026-10-30T04:46:55.222Z","toolUseResult":"Error: Exit code 2\n(eval):1: no such file or directory: out/analysis.py\n/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/simon-pierreboucher/wp29/runs/RC0160/out/state/out/analysis.py': [Errno 2] No such file or directory","sandboxed":false,"sourceToolAssistantUUID":"de28621d-8451-4c89-863e-d51a81d7b4ff","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0160/out/state","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}41{"type":"api-request-blob","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","hash":"de65ccfb864b410da0aa7e42e75e679975f62dd2fbfddb99285f4a0a696bf5b6","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01CknpmQT6kh2q6YK1bzJprg","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F1-conf-03: LPM of next-year default on end-of-year-t predictors. Run from workspace root.\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nimport statsmodels.api as sm\n\nos.makedirs(\"out\", exist_ok=True)\nraw = pd.read_csv(\"data/firm_panel.csv\", dtype={\"firm_id\": str})\nn_raw = len(raw)\nPRED = [\"leverage\", \"cash_ratio\", \"log_sales\"]\nledger = []\ndef step(name, rule, before, after, why):\n    ledger.append(dict(step=name, rule=rule, n_before=before, n_after=after, n_removed=before - after, rationale=why))\n\nstep(\"0_read\", \"rows read from data/firm_panel.csv\", n_raw, n_raw, \"raw input\")\nn_exact_dup = int(raw.duplicated().sum())\nn_key_dup = int(raw.duplicated([\"firm_id\", \"year\"]).sum())\nd = raw.drop_duplicates().reset_index(drop=True)\nassert not d.duplicated([\"firm_id\", \"year\"]).any(), \"non-identical key duplicates present\"\nstep(\"1_dedupe\", \"drop exact duplicate rows (keep first)\", n_raw, len(d), \"assembly error: identical firm-year rows repeated\")\nstep(\"2_drop_rating_2025\", \"remove column rating_2025 (known 2025, uses realised defaults)\", len(d), len(d), \"violates information-availability rule; column removal only\")\nn_b = len(d)\nn_miss_by_year = d.loc[d.default_next.isna(), \"year\"].value_counts().to_dict()\nd = d[d.default_next.notna()].copy()\nstep(\"3_outcome_observed\", \"drop rows with missing default_next\", n_b, len(d), \"outcome year not observed at data cut (end 2022); no imputation\")\nn_b = len(d)\nd = d.dropna(subset=PRED)\nstep(\"4_complete_predictors\", \"drop rows with missing predictors\", n_b, len(d), \"listwise deletion (none expected)\")\n\nd[\"default_next\"] = d[\"default_next\"].astype(int)\nd = d.sort_values([\"firm_id\", \"year\"]).reset_index(drop=True)\nanalytic = d[[\"firm_id\", \"year\"] + PRED + [\"default_next\"]]\nanalytic.to_csv(\"out/analytic.csv\", index=False)\npd.DataFrame(ledger).to_csv(\"out/exclusions.csv\", index=False)\n\nX = sm.add_constant(analytic[PRED])\ngroups = pd.factorize(analytic[\"firm_id\"])[0]\nfit = sm.OLS(analytic[\"default_next\"], X).fit(cov_type=\"cluster\", cov_kwds={\"groups\": groups})\n\nres = {\n    \"task_id\": \"F1-conf-03\",\n    \"n_analysed\": int(len(analytic)),\n    \"n_firms\": int(analytic.firm_id.nunique()),\n    \"predictors_used\": PRED,\n    \"variables_excluded\": [\n        {\"variable\": \"rating_2025\", \"reason\": \"Assigned in the 2025 review using realised default history through 2022 and sample-average leverage; not known at end of year t (look-ahead leakage). In the data every observed default is in rating D.\"},\n        {\"variable\": \"firm_id\", \"reason\": \"identifier; used only for clustering, not as a predictor\"},\n        {\"variable\": \"year\", \"reason\": \"origin-year index; not part of the specified model\"},\n        {\"variable\": \"default_next\", \"reason\": \"outcome variable\"},\n    ],\n    \"beta_leverage\": float(fit.params[\"leverage\"]),\n    \"se_leverage_cluster\": float(fit.bse[\"leverage\"]),\n    \"r2\": float(fit.rsquared),\n    \"default_rate\": float(analytic.default_next.mean()),\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": [\n        {\"issue\": f\"{n_exact_dup} exact duplicate rows ({n_key_dup} duplicated firm_id-year keys, all identical copies)\", \"handling\": \"dropped duplicates keeping one copy; panel becomes 300 firms x 7 years\"},\n        {\"issue\": \"rating_2025 is a final-vintage field built from future defaults (information leakage)\", \"handling\": \"excluded from predictors and from out/analytic.csv\"},\n        {\"issue\": f\"default_next missing for {sum(n_miss_by_year.values())} deduplicated rows, by year: {{{', '.join(f'{int(k)}: {v}' for k, v in sorted(n_miss_by_year.items()))}}}\", \"handling\": \"excluded from estimation (outcome not observed at data cut); not imputed\"},\n    ],\n    \"n_input_rows\": {\"firm_panel.csv\": int(n_raw)},\n    \"random_seed\": None,\n    # supplementary\n    \"beta_cash_ratio\": float(fit.params[\"cash_ratio\"]),\n    \"beta_log_sales\": float(fit.params[\"log_sales\"]),\n    \"intercept\": float(fit.params[\"const\"]),\n    \"ci95_leverage_low\": float(fit.conf_int().loc[\"leverage\", 0]),\n    \"ci95_leverage_high\": float(fit.conf_int().loc[\"leverage\", 1]),\n    \"p_leverage_cluster\": float(fit.pvalues[\"leverage\"]),\n    \"se_leverage_classical\": float(sm.OLS(analytic[\"default_next\"], X).fit().bse[\"leverage\"]),\n    \"n_rows_after_dedupe\": int(n_raw - n_exact_dup),\n}\nwith open(\"out/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"n_analysed\", \"n_firms\", \"beta_leverage\", \"se_leverage_cluster\", \"r2\", \"default_rate\"]}, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && cat out/results.json | head -50"}}]}}42{"type":"api-request","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","id":"099cca45-7a2c-4612-912b-4df597530405","timestamp":"2026-10-30T04:46:55.225Z","version":"2.1.314","querySource":"sdk","shapeHash":"2dd02160d273680ea9621f287d43802879b3bf673adec2f3bf71bba355d178ed","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":"dfc8f94a-099d-4e5c-8ffb-d0660602d3f7","keep":10,"tail":["de65ccfb864b410da0aa7e42e75e679975f62dd2fbfddb99285f4a0a696bf5b6","f32b84300d80810d6f555034ada218ca5c480f8f74b5b3a243330730512afdcd"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"0378271e3133b756dbcac6665d3ad047f076b00b5e1fea64befcd76b83b940e1"}43{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"4d45ada8-7591-4add-bf4b-971795089099","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2"}44{"type":"atis-latch","atis":"","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2"}45{"parentUuid":"4d45ada8-7591-4add-bf4b-971795089099","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpDhgFURPqMPVNqpspA","type":"message","role":"assistant","content":[{"type":"text","text":"The shell was still in out/state; re-running from the root."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2240,"cache_read_input_tokens":11439,"output_tokens":2128,"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":2240},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXpDhPssELPGrqK5b2FD","requestRef":"099cca45-7a2c-4612-912b-4df597530405","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1b7aaf9a-cda4-465b-a014-6c65bff40b2e","timestamp":"2026-10-30T04:46:56.276Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0160/out/state","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}46{"parentUuid":"1b7aaf9a-cda4-465b-a014-6c65bff40b2e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpDhgFURPqMPVNqpspA","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013kg9gjVUkPbJDrnVxQSxkZ","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0160 && ls out out/state && cat > out/analysis.py <<'EOF'\n\"\"\"F1-conf-03: LPM of next-year default on end-of-year-t predictors. Run from workspace root.\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nimport statsmodels.api as sm\n\nos.makedirs(\"out\", exist_ok=True)\nraw = pd.read_csv(\"data/firm_panel.csv\", dtype={\"firm_id\": str})\nn_raw = len(raw)\nPRED = [\"leverage\", \"cash_ratio\", \"log_sales\"]\nledger = []\ndef step(name, rule, before, after, why):\n    ledger.append(dict(step=name, rule=rule, n_before=before, n_after=after, n_removed=before - after, rationale=why))\n\nstep(\"0_read\", \"rows read from data/firm_panel.csv\", n_raw, n_raw, \"raw input\")\nn_exact_dup = int(raw.duplicated().sum())\nn_key_dup = int(raw.duplicated([\"firm_id\", \"year\"]).sum())\nd = raw.drop_duplicates().reset_index(drop=True)\nassert not d.duplicated([\"firm_id\", \"year\"]).any(), \"non-identical key duplicates present\"\nstep(\"1_dedupe\", \"drop exact duplicate rows (keep first)\", n_raw, len(d), \"assembly error: identical firm-year rows repeated\")\nstep(\"2_drop_rating_2025\", \"remove column rating_2025 (known 2025, uses realised defaults)\", len(d), len(d), \"violates information-availability rule; column removal only\")\nn_b = len(d)\nn_miss_by_year = d.loc[d.default_next.isna(), \"year\"].value_counts().to_dict()\nd = d[d.default_next.notna()].copy()\nstep(\"3_outcome_observed\", \"drop rows with missing default_next\", n_b, len(d), \"outcome year not observed at data cut (end 2022); no imputation\")\nn_b = len(d)\nd = d.dropna(subset=PRED)\nstep(\"4_complete_predictors\", \"drop rows with missing predictors\", n_b, len(d), \"listwise deletion (none expected)\")\n\nd[\"default_next\"] = d[\"default_next\"].astype(int)\nd = d.sort_values([\"firm_id\", \"year\"]).reset_index(drop=True)\nanalytic = d[[\"firm_id\", \"year\"] + PRED + [\"default_next\"]]\nanalytic.to_csv(\"out/analytic.csv\", index=False)\npd.DataFrame(ledger).to_csv(\"out/exclusions.csv\", index=False)\n\nX = sm.add_constant(analytic[PRED])\ngroups = pd.factorize(analytic[\"firm_id\"])[0]\nfit = sm.OLS(analytic[\"default_next\"], X).fit(cov_type=\"cluster\", cov_kwds={\"groups\": groups})\n\nres = {\n    \"task_id\": \"F1-conf-03\",\n    \"n_analysed\": int(len(analytic)),\n    \"n_firms\": int(analytic.firm_id.nunique()),\n    \"predictors_used\": PRED,\n    \"variables_excluded\": [\n        {\"variable\": \"rating_2025\", \"reason\": \"Assigned in the 2025 review using realised default history through 2022 and sample-average leverage; not known at end of year t (look-ahead leakage). In the data every observed default is in rating D.\"},\n        {\"variable\": \"firm_id\", \"reason\": \"identifier; used only for clustering, not as a predictor\"},\n        {\"variable\": \"year\", \"reason\": \"origin-year index; not part of the specified model\"},\n        {\"variable\": \"default_next\", \"reason\": \"outcome variable\"},\n    ],\n    \"beta_leverage\": float(fit.params[\"leverage\"]),\n    \"se_leverage_cluster\": float(fit.bse[\"leverage\"]),\n    \"r2\": float(fit.rsquared),\n    \"default_rate\": float(analytic.default_next.mean()),\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": [\n        {\"issue\": f\"{n_exact_dup} exact duplicate rows ({n_key_dup} duplicated firm_id-year keys, all identical copies)\", \"handling\": \"dropped duplicates keeping one copy; panel becomes 300 firms x 7 years\"},\n        {\"issue\": \"rating_2025 is a final-vintage field built from future defaults (information leakage)\", \"handling\": \"excluded from predictors and from out/analytic.csv\"},\n        {\"issue\": f\"default_next missing for {sum(n_miss_by_year.values())} deduplicated rows, by year: {{{', '.join(f'{int(k)}: {v}' for k, v in sorted(n_miss_by_year.items()))}}}\", \"handling\": \"excluded from estimation (outcome not observed at data cut); not imputed\"},\n    ],\n    \"n_input_rows\": {\"firm_panel.csv\": int(n_raw)},\n    \"random_seed\": None,\n    # supplementary\n    \"beta_cash_ratio\": float(fit.params[\"cash_ratio\"]),\n    \"beta_log_sales\": float(fit.params[\"log_sales\"]),\n    \"intercept\": float(fit.params[\"const\"]),\n    \"ci95_leverage_low\": float(fit.conf_int().loc[\"leverage\", 0]),\n    \"ci95_leverage_high\": float(fit.conf_int().loc[\"leverage\", 1]),\n    \"p_leverage_cluster\": float(fit.pvalues[\"leverage\"]),\n    \"se_leverage_classical\": float(sm.OLS(analytic[\"default_next\"], X).fit().bse[\"leverage\"]),\n    \"n_rows_after_dedupe\": int(n_raw - n_exact_dup),\n}\nwith open(\"out/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"n_analysed\", \"n_firms\", \"beta_leverage\", \"se_leverage_cluster\", \"r2\", \"default_rate\"]}, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && cat out/results.json"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2240,"cache_read_input_tokens":11439,"output_tokens":2128,"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":2240},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpDhPssELPGrqK5b2FD","requestRef":"099cca45-7a2c-4612-912b-4df597530405","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"25f31303-9e4b-4c63-b4ae-d981e69a8b55","timestamp":"2026-10-30T04:47:09.395Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0160/out/state","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}47{"parentUuid":"25f31303-9e4b-4c63-b4ae-d981e69a8b55","isSidechain":false,"promptId":"4c063876-53fe-4181-ad58-eb5eab9fc815","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013kg9gjVUkPbJDrnVxQSxkZ","type":"tool_result","content":"out:\nstate\n\nout/state:\ncontract.md\ndata_audit.md\nlog.md\n{\n  \"n_analysed\": 1800,\n  \"n_firms\": 300,\n  \"beta_leverage\": 0.2637836102938267,\n  \"se_leverage_cluster\": 0.0407569280434615,\n  \"r2\": 0.03490564212216729,\n  \"default_rate\": 0.12111111111111111\n}\nstep,rule,n_before,n_after,n_removed,rationale\n0_read,rows read from data/firm_panel.csv,2140,2140,0,raw input\n1_dedupe,drop exact duplicate rows (keep first),2140,2100,40,assembly error: identical firm-year rows repeated\n2_drop_rating_2025,\"remove column rating_2025 (known 2025, uses realised defaults)\",2100,2100,0,violates information-availability rule; column removal only\n3_outcome_observed,drop rows with missing default_next,2100,1800,300,outcome year not observed at data cut (end 2022); no imputation\n4_complete_predictors,drop rows with missing predictors,1800,1800,0,listwise deletion (none expected)\n{\n  \"task_id\": \"F1-conf-03\",\n  \"n_analysed\": 1800,\n  \"n_firms\": 300,\n  \"predictors_used\": [\n    \"leverage\",\n    \"cash_ratio\",\n    \"log_sales\"\n  ],\n  \"variables_excluded\": [\n    {\n      \"variable\": \"rating_2025\",\n      \"reason\": \"Assigned in the 2025 review using realised default history through 2022 and sample-average leverage; not known at end of year t (look-ahead leakage). In the data every observed default is in rating D.\"\n    },\n    {\n      \"variable\": \"firm_id\",\n      \"reason\": \"identifier; used only for clustering, not as a predictor\"\n    },\n    {\n      \"variable\": \"year\",\n      \"reason\": \"origin-year index; not part of the specified model\"\n    },\n    {\n      \"variable\": \"default_next\",\n      \"reason\": \"outcome variable\"\n    }\n  ],\n  \"beta_leverage\": 0.2637836102938267,\n  \"se_leverage_cluster\": 0.0407569280434615,\n  \"r2\": 0.03490564212216729,\n  \"default_rate\": 0.12111111111111111,\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [\n    {\n      \"issue\": \"40 exact duplicate rows (40 duplicated firm_id-year keys, all identical copies)\",\n      \"handling\": \"dropped duplicates keeping one copy; panel becomes 300 firms x 7 years\"\n    },\n    {\n      \"issue\": \"rating_2025 is a final-vintage field built from future defaults (information leakage)\",\n      \"handling\": \"excluded from predictors and from out/analytic.csv\"\n    },\n    {\n      \"issue\": \"default_next missing for 300 deduplicated rows, by year: {2022: 300}\",\n      \"handling\": \"excluded from estimation (outcome not observed at data cut); not imputed\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"firm_panel.csv\": 2140\n  },\n  \"random_seed\": null,\n  \"beta_cash_ratio\": -0.18594837465723182,\n  \"beta_log_sales\": -0.007342181807581363,\n  \"intercept\": 0.08997926409935522,\n  \"ci95_leverage_low\": 0.18390149920815163,\n  \"ci95_leverage_high\": 0.34366572137950174,\n  \"p_leverage_cluster\": 9.663918872288981e-11,\n  \"se_leverage_classical\": 0.035820571091069786,\n  \"n_rows_after_dedupe\": 2100\n}","is_error":false}]},"uuid":"e5ddc4a7-f7fc-4525-a8ab-9f65f190a294","timestamp":"2026-10-30T04:47:10.063Z","toolUseResult":{"stdout":"out:\nstate\n\nout/state:\ncontract.md\ndata_audit.md\nlog.md\n{\n  \"n_analysed\": 1800,\n  \"n_firms\": 300,\n  \"beta_leverage\": 0.2637836102938267,\n  \"se_leverage_cluster\": 0.0407569280434615,\n  \"r2\": 0.03490564212216729,\n  \"default_rate\": 0.12111111111111111\n}\nstep,rule,n_before,n_after,n_removed,rationale\n0_read,rows read from data/firm_panel.csv,2140,2140,0,raw input\n1_dedupe,drop exact duplicate rows (keep first),2140,2100,40,assembly error: identical firm-year rows repeated\n2_drop_rating_2025,\"remove column rating_2025 (known 2025, uses realised defaults)\",2100,2100,0,violates information-availability rule; column removal only\n3_outcome_observed,drop rows with missing default_next,2100,1800,300,outcome year not observed at data cut (end 2022); no imputation\n4_complete_predictors,drop rows with missing predictors,1800,1800,0,listwise deletion (none expected)\n{\n  \"task_id\": \"F1-conf-03\",\n  \"n_analysed\": 1800,\n  \"n_firms\": 300,\n  \"predictors_used\": [\n    \"leverage\",\n    \"cash_ratio\",\n    \"log_sales\"\n  ],\n  \"variables_excluded\": [\n    {\n      \"variable\": \"rating_2025\",\n      \"reason\": \"Assigned in the 2025 review using realised default history through 2022 and sample-average leverage; not known at end of year t (look-ahead leakage). In the data every observed default is in rating D.\"\n    },\n    {\n      \"variable\": \"firm_id\",\n      \"reason\": \"identifier; used only for clustering, not as a predictor\"\n    },\n    {\n      \"variable\": \"year\",\n      \"reason\": \"origin-year index; not part of the specified model\"\n    },\n    {\n      \"variable\": \"default_next\",\n      \"reason\": \"outcome variable\"\n    }\n  ],\n  \"beta_leverage\": 0.2637836102938267,\n  \"se_leverage_cluster\": 0.0407569280434615,\n  \"r2\": 0.03490564212216729,\n  \"default_rate\": 0.12111111111111111,\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [\n    {\n      \"issue\": \"40 exact duplicate rows (40 duplicated firm_id-year keys, all identical copies)\",\n      \"handling\": \"dropped duplicates keeping one copy; panel becomes 300 firms x 7 years\"\n    },\n    {\n      \"issue\": \"rating_2025 is a final-vintage field built from future defaults (information leakage)\",\n      \"handling\": \"excluded from predictors and from out/analytic.csv\"\n    },\n    {\n      \"issue\": \"default_next missing for 300 deduplicated rows, by year: {2022: 300}\",\n      \"handling\": \"excluded from estimation (outcome not observed at data cut); not imputed\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"firm_panel.csv\": 2140\n  },\n  \"random_seed\": null,\n  \"beta_cash_ratio\": -0.18594837465723182,\n  \"beta_log_sales\": -0.007342181807581363,\n  \"intercept\": 0.08997926409935522,\n  \"ci95_leverage_low\": 0.18390149920815163,\n  \"ci95_leverage_high\": 0.34366572137950174,\n  \"p_leverage_cluster\": 9.663918872288981e-11,\n  \"se_leverage_classical\": 0.035820571091069786,\n  \"n_rows_after_dedupe\": 2100\n}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"25f31303-9e4b-4c63-b4ae-d981e69a8b55","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0160","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}48{"type":"api-request-blob","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","hash":"4ae3ba6bf1b5024989306aba92640b548a1dfa508650b93ac0b3166a710b0763","message":{"role":"assistant","content":[{"type":"text","text":"The shell was still in out/state; re-running from the root."},{"type":"tool_use","id":"toolu_013kg9gjVUkPbJDrnVxQSxkZ","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0160 && ls out out/state && cat > out/analysis.py <<'EOF'\n\"\"\"F1-conf-03: LPM of next-year default on end-of-year-t predictors. Run from workspace root.\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nimport statsmodels.api as sm\n\nos.makedirs(\"out\", exist_ok=True)\nraw = pd.read_csv(\"data/firm_panel.csv\", dtype={\"firm_id\": str})\nn_raw = len(raw)\nPRED = [\"leverage\", \"cash_ratio\", \"log_sales\"]\nledger = []\ndef step(name, rule, before, after, why):\n    ledger.append(dict(step=name, rule=rule, n_before=before, n_after=after, n_removed=before - after, rationale=why))\n\nstep(\"0_read\", \"rows read from data/firm_panel.csv\", n_raw, n_raw, \"raw input\")\nn_exact_dup = int(raw.duplicated().sum())\nn_key_dup = int(raw.duplicated([\"firm_id\", \"year\"]).sum())\nd = raw.drop_duplicates().reset_index(drop=True)\nassert not d.duplicated([\"firm_id\", \"year\"]).any(), \"non-identical key duplicates present\"\nstep(\"1_dedupe\", \"drop exact duplicate rows (keep first)\", n_raw, len(d), \"assembly error: identical firm-year rows repeated\")\nstep(\"2_drop_rating_2025\", \"remove column rating_2025 (known 2025, uses realised defaults)\", len(d), len(d), \"violates information-availability rule; column removal only\")\nn_b = len(d)\nn_miss_by_year = d.loc[d.default_next.isna(), \"year\"].value_counts().to_dict()\nd = d[d.default_next.notna()].copy()\nstep(\"3_outcome_observed\", \"drop rows with missing default_next\", n_b, len(d), \"outcome year not observed at data cut (end 2022); no imputation\")\nn_b = len(d)\nd = d.dropna(subset=PRED)\nstep(\"4_complete_predictors\", \"drop rows with missing predictors\", n_b, len(d), \"listwise deletion (none expected)\")\n\nd[\"default_next\"] = d[\"default_next\"].astype(int)\nd = d.sort_values([\"firm_id\", \"year\"]).reset_index(drop=True)\nanalytic = d[[\"firm_id\", \"year\"] + PRED + [\"default_next\"]]\nanalytic.to_csv(\"out/analytic.csv\", index=False)\npd.DataFrame(ledger).to_csv(\"out/exclusions.csv\", index=False)\n\nX = sm.add_constant(analytic[PRED])\ngroups = pd.factorize(analytic[\"firm_id\"])[0]\nfit = sm.OLS(analytic[\"default_next\"], X).fit(cov_type=\"cluster\", cov_kwds={\"groups\": groups})\n\nres = {\n    \"task_id\": \"F1-conf-03\",\n    \"n_analysed\": int(len(analytic)),\n    \"n_firms\": int(analytic.firm_id.nunique()),\n    \"predictors_used\": PRED,\n    \"variables_excluded\": [\n        {\"variable\": \"rating_2025\", \"reason\": \"Assigned in the 2025 review using realised default history through 2022 and sample-average leverage; not known at end of year t (look-ahead leakage). In the data every observed default is in rating D.\"},\n        {\"variable\": \"firm_id\", \"reason\": \"identifier; used only for clustering, not as a predictor\"},\n        {\"variable\": \"year\", \"reason\": \"origin-year index; not part of the specified model\"},\n        {\"variable\": \"default_next\", \"reason\": \"outcome variable\"},\n    ],\n    \"beta_leverage\": float(fit.params[\"leverage\"]),\n    \"se_leverage_cluster\": float(fit.bse[\"leverage\"]),\n    \"r2\": float(fit.rsquared),\n    \"default_rate\": float(analytic.default_next.mean()),\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": [\n        {\"issue\": f\"{n_exact_dup} exact duplicate rows ({n_key_dup} duplicated firm_id-year keys, all identical copies)\", \"handling\": \"dropped duplicates keeping one copy; panel becomes 300 firms x 7 years\"},\n        {\"issue\": \"rating_2025 is a final-vintage field built from future defaults (information leakage)\", \"handling\": \"excluded from predictors and from out/analytic.csv\"},\n        {\"issue\": f\"default_next missing for {sum(n_miss_by_year.values())} deduplicated rows, by year: {{{', '.join(f'{int(k)}: {v}' for k, v in sorted(n_miss_by_year.items()))}}}\", \"handling\": \"excluded from estimation (outcome not observed at data cut); not imputed\"},\n    ],\n    \"n_input_rows\": {\"firm_panel.csv\": int(n_raw)},\n    \"random_seed\": None,\n    # supplementary\n    \"beta_cash_ratio\": float(fit.params[\"cash_ratio\"]),\n    \"beta_log_sales\": float(fit.params[\"log_sales\"]),\n    \"intercept\": float(fit.params[\"const\"]),\n    \"ci95_leverage_low\": float(fit.conf_int().loc[\"leverage\", 0]),\n    \"ci95_leverage_high\": float(fit.conf_int().loc[\"leverage\", 1]),\n    \"p_leverage_cluster\": float(fit.pvalues[\"leverage\"]),\n    \"se_leverage_classical\": float(sm.OLS(analytic[\"default_next\"], X).fit().bse[\"leverage\"]),\n    \"n_rows_after_dedupe\": int(n_raw - n_exact_dup),\n}\nwith open(\"out/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"n_analysed\", \"n_firms\", \"beta_leverage\", \"se_leverage_cluster\", \"r2\", \"default_rate\"]}, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && cat out/results.json"}}]}}49{"type":"api-request","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","id":"9cee81b3-0adc-4513-8c74-4dd5d32db7d0","timestamp":"2026-10-30T04:47:10.066Z","version":"2.1.314","querySource":"sdk","shapeHash":"2dd02160d273680ea9621f287d43802879b3bf673adec2f3bf71bba355d178ed","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":"099cca45-7a2c-4612-912b-4df597530405","keep":12,"tail":["4ae3ba6bf1b5024989306aba92640b548a1dfa508650b93ac0b3166a710b0763","d1b70852cbe80395e61386789a47af98bb984bb56104689cfb57605e87fc9354"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"752a837e4dba6e40c8e39d1ff607de572dc62d3e9be07acff6e5fc99ec73be57"}50{"parentUuid":"e5ddc4a7-f7fc-4525-a8ab-9f65f190a294","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpEo8Sm7JoRRcAJYMuo","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":3349,"cache_read_input_tokens":13679,"output_tokens":615,"output_tokens_details":{"thinking_tokens":30},"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":3349},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":353,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXpEnqLhv9TGxvZ8WW5S","requestRef":"9cee81b3-0adc-4513-8c74-4dd5d32db7d0","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"cca69d62-330c-4743-a799-9f2268cf8887","timestamp":"2026-10-30T04:47:11.397Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0160","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}51{"parentUuid":"cca69d62-330c-4743-a799-9f2268cf8887","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpEo8Sm7JoRRcAJYMuo","type":"message","role":"assistant","content":[{"type":"text","text":"Now verification: re-run from clean state and check consistency."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3349,"cache_read_input_tokens":13679,"output_tokens":615,"output_tokens_details":{"thinking_tokens":30},"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":3349},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpEnqLhv9TGxvZ8WW5S","requestRef":"9cee81b3-0adc-4513-8c74-4dd5d32db7d0","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5d781c38-3e6e-4742-b87d-0534a00b4b38","timestamp":"2026-10-30T04:47:11.399Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0160","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}52{"parentUuid":"5d781c38-3e6e-4742-b87d-0534a00b4b38","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpEo8Sm7JoRRcAJYMuo","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DkNiF5JWX1ZY33KqsJh8AD","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 -c \"\nimport pandas as pd,json\nr=json.load(open('out/results.json'));a=pd.read_csv('out/analytic.csv');e=pd.read_csv('out/exclusions.csv')\nprint('rows',len(a)==r['n_analysed'], 'cols',list(a.columns))\nprint('ledger', (e.n_before-e.n_removed==e.n_after).all(), (e.n_before.iloc[1:].values==e.n_after.iloc[:-1].values).all(), e.n_before.iloc[0]==2140, e.n_after.iloc[-1]==r['n_analysed'])\nprint('no rating', 'rating_2025' not in a.columns, 'max year', a.year.max(), 'dup keys', a.duplicated(['firm_id','year']).sum())\nprint('se>0', r['se_leverage_cluster']>0, 'ci contains', r['ci95_leverage_low']<r['beta_leverage']<r['ci95_leverage_high'], 'r2 in [0,1]', 0<=r['r2']<=1, 'rate', a.default_next.mean()==r['default_rate'])\n\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3349,"cache_read_input_tokens":13679,"output_tokens":615,"output_tokens_details":{"thinking_tokens":30},"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":3349},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXpEnqLhv9TGxvZ8WW5S","requestRef":"9cee81b3-0adc-4513-8c74-4dd5d32db7d0","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6737c691-3f8d-4465-9865-c71d15421869","timestamp":"2026-10-30T04:47:15.308Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0160","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}53{"parentUuid":"6737c691-3f8d-4465-9865-c71d15421869","isSidechain":false,"promptId":"4c063876-53fe-4181-ad58-eb5eab9fc815","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DkNiF5JWX1ZY33KqsJh8AD","type":"tool_result","content":"IDENTICAL\nrows True cols ['firm_id', 'year', 'leverage', 'cash_ratio', 'log_sales', 'default_next']\nledger True True True True\nno rating True max year 2021 dup keys 0\nse>0 True ci contains True r2 in [0,1] True rate True","is_error":false}]},"uuid":"915a2467-acca-457e-871b-e310115c52c2","timestamp":"2026-10-30T04:47:16.152Z","toolUseResult":{"stdout":"IDENTICAL\nrows True cols ['firm_id', 'year', 'leverage', 'cash_ratio', 'log_sales', 'default_next']\nledger True True True True\nno rating True max year 2021 dup keys 0\nse>0 True ci contains True r2 in [0,1] True rate True","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"6737c691-3f8d-4465-9865-c71d15421869","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0160","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}54{"type":"api-request-blob","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","hash":"4089653206e19b5f9e93c5957fce4b0827f36403bfed3e4ab7e13ed3dc13c257","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"Now verification: re-run from clean state and check consistency."},{"type":"tool_use","id":"toolu_01DkNiF5JWX1ZY33KqsJh8AD","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 -c \"\nimport pandas as pd,json\nr=json.load(open('out/results.json'));a=pd.read_csv('out/analytic.csv');e=pd.read_csv('out/exclusions.csv')\nprint('rows',len(a)==r['n_analysed'], 'cols',list(a.columns))\nprint('ledger', (e.n_before-e.n_removed==e.n_after).all(), (e.n_before.iloc[1:].values==e.n_after.iloc[:-1].values).all(), e.n_before.iloc[0]==2140, e.n_after.iloc[-1]==r['n_analysed'])\nprint('no rating', 'rating_2025' not in a.columns, 'max year', a.year.max(), 'dup keys', a.duplicated(['firm_id','year']).sum())\nprint('se>0', r['se_leverage_cluster']>0, 'ci contains', r['ci95_leverage_low']<r['beta_leverage']<r['ci95_leverage_high'], 'r2 in [0,1]', 0<=r['r2']<=1, 'rate', a.default_next.mean()==r['default_rate'])\n\""}}]}}55{"type":"api-request","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","id":"a4a1b7fd-06a8-4a21-88f8-81599423c25a","timestamp":"2026-10-30T04:47:16.154Z","version":"2.1.314","querySource":"sdk","shapeHash":"2dd02160d273680ea9621f287d43802879b3bf673adec2f3bf71bba355d178ed","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":"9cee81b3-0adc-4513-8c74-4dd5d32db7d0","keep":14,"tail":["4089653206e19b5f9e93c5957fce4b0827f36403bfed3e4ab7e13ed3dc13c257","4f5e47786ffacbe37974ff487b56078e2219b09b0caa3c0ef1159dd1b017dc52"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"50762ca1003b13ffce68686ddc7238d734981fb9c382eaaad5ffda78fcbb8789"}56{"parentUuid":"915a2467-acca-457e-871b-e310115c52c2","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpFF97tCrTn5ZTQqUW7","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01HyZUVkDqY2oewauuxCFGTF","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n- Clean re-run of out/analysis.py (deleted results/analytic/exclusions first): results.json byte-identical — PASS.\n- analytic.csv rows == n_analysed (1800) — PASS; columns firm_id, year, leverage, cash_ratio, log_sales, default_next; no rating_2025 — PASS.\n- Exclusion ledger: n_before − n_removed = n_after on every row; chained; starts at 2140 (n_input_rows), ends at 1800 — PASS. 2140 − 40 − 300 = 1800.\n- No duplicate firm-year keys in analytic sample — PASS. Max origin year 2021 (outcome 2022 ≤ data cut) — PASS.\n- se_leverage_cluster > 0 (0.0408) — PASS; 95% CI [0.184, 0.344] contains beta 0.264 — PASS; R² in [0,1] — PASS.\n- default_rate (baseline, 0.121) equals mean of default_next in analytic.csv — PASS. Magnitude: beta 0.26 per unit leverage; leverage IQR ~0.31 → ~8 pp change, plausible vs 12% base rate.\n- Cluster SE (0.0408) > classical SE (0.0358): expected with within-firm correlation.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"contract.md written covering question, estimand, outputs, info rules, invalidity\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"sha256 recorded\",\"schema/missing/duplicates/ranges audited\",\"rating_2025 leakage identified and excluded\",\"exclusion ledger sums verified\"],\"notes\":\"40 exact dups; 300 missing outcomes (2022)\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"baseline default_rate=0.1211 on estimation sample written to results.json\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, writes results/analytic/exclusions; no randomness (seed null)\"],\"notes\":\"\"},\n\"P4\":{\"status\":\"running\",\"checks\":[\"clean re-run byte-identical\",\"consistency checks in checks.md all PASS\"],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P2/P3: baseline + LPM computed (beta 0.264, cluster SE 0.041, n=1800). P4: checks pass; running gate.\" >> out/state/log.md\n~/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":692,"cache_read_input_tokens":17028,"output_tokens":983,"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":692},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXpFEsWSUvCcw6zT19jy","requestRef":"a4a1b7fd-06a8-4a21-88f8-81599423c25a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2af74e02-c434-4689-8c3d-f84bc11d2810","timestamp":"2026-10-30T04:47:24.757Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0160","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}57{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"2af74e02-c434-4689-8c3d-f84bc11d2810","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2"}58{"type":"atis-latch","atis":"","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2"}59{"parentUuid":"2af74e02-c434-4689-8c3d-f84bc11d2810","isSidechain":false,"promptId":"4c063876-53fe-4181-ad58-eb5eab9fc815","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HyZUVkDqY2oewauuxCFGTF","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — FAILED (10 of 32 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] file:out/analytic.csv: present\n  [ok] file:out/exclusions.csv: present\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:n_analysed: ok\n  [ok] key:n_firms: ok\n  [ok] key:predictors_used: ok\n  [ok] key:variables_excluded: ok\n  [ok] key:beta_leverage: ok\n  [ok] key:se_leverage_cluster: ok\n  [ok] key:r2: ok\n  [ok] key:default_rate: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] exclusion_ledger: 5 steps consistent\n  [ok] positive:se_leverage_cluster: 0.0407569280434615\n  [ok] reproducibility: regenerated in 0.6s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0160/out/state/gate_receipts/20261030T044725.418435Z.json\n20261030T044725.418435Z.json","is_error":false}]},"uuid":"f9842ee8-b1a6-47b4-9e5b-70188daa34ef","timestamp":"2026-10-30T04:47:25.425Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — FAILED (10 of 32 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] file:out/analytic.csv: present\n  [ok] file:out/exclusions.csv: present\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:n_analysed: ok\n  [ok] key:n_firms: ok\n  [ok] key:predictors_used: ok\n  [ok] key:variables_excluded: ok\n  [ok] key:beta_leverage: ok\n  [ok] key:se_leverage_cluster: ok\n  [ok] key:r2: ok\n  [ok] key:default_rate: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] exclusion_ledger: 5 steps consistent\n  [ok] positive:se_leverage_cluster: 0.0407569280434615\n  [ok] reproducibility: regenerated in 0.6s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0160/out/state/gate_receipts/20261030T044725.418435Z.json\n20261030T044725.418435Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"2af74e02-c434-4689-8c3d-f84bc11d2810","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0160","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}60{"type":"api-request-blob","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","hash":"2fa21e7a7efbcb316a7c3e7e3247a44e2186e99d3652a77a2a9435373ed9d653","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01HyZUVkDqY2oewauuxCFGTF","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n- Clean re-run of out/analysis.py (deleted results/analytic/exclusions first): results.json byte-identical — PASS.\n- analytic.csv rows == n_analysed (1800) — PASS; columns firm_id, year, leverage, cash_ratio, log_sales, default_next; no rating_2025 — PASS.\n- Exclusion ledger: n_before − n_removed = n_after on every row; chained; starts at 2140 (n_input_rows), ends at 1800 — PASS. 2140 − 40 − 300 = 1800.\n- No duplicate firm-year keys in analytic sample — PASS. Max origin year 2021 (outcome 2022 ≤ data cut) — PASS.\n- se_leverage_cluster > 0 (0.0408) — PASS; 95% CI [0.184, 0.344] contains beta 0.264 — PASS; R² in [0,1] — PASS.\n- default_rate (baseline, 0.121) equals mean of default_next in analytic.csv — PASS. Magnitude: beta 0.26 per unit leverage; leverage IQR ~0.31 → ~8 pp change, plausible vs 12% base rate.\n- Cluster SE (0.0408) > classical SE (0.0358): expected with within-firm correlation.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"contract.md written covering question, estimand, outputs, info rules, invalidity\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"sha256 recorded\",\"schema/missing/duplicates/ranges audited\",\"rating_2025 leakage identified and excluded\",\"exclusion ledger sums verified\"],\"notes\":\"40 exact dups; 300 missing outcomes (2022)\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"baseline default_rate=0.1211 on estimation sample written to results.json\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, writes results/analytic/exclusions; no randomness (seed null)\"],\"notes\":\"\"},\n\"P4\":{\"status\":\"running\",\"checks\":[\"clean re-run byte-identical\",\"consistency checks in checks.md all PASS\"],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P2/P3: baseline + LPM computed (beta 0.264, cluster SE 0.041, n=1800). P4: checks pass; running gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts"}}]}}61{"type":"api-request","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","id":"47d17c8f-6c9c-4f81-9a69-1be7520859e6","timestamp":"2026-10-30T04:47:25.428Z","version":"2.1.314","querySource":"sdk","shapeHash":"2dd02160d273680ea9621f287d43802879b3bf673adec2f3bf71bba355d178ed","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":"a4a1b7fd-06a8-4a21-88f8-81599423c25a","keep":16,"tail":["2fa21e7a7efbcb316a7c3e7e3247a44e2186e99d3652a77a2a9435373ed9d653","9861fa96ba8d98674fe35ee2c99d0b7aed32d33fcee46dd175d02d815307d8d8"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",17,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"dbd2d0c4fc6f424fa26f764f8dde086f1e48e509770e2e868632fee614a06a86"}62{"parentUuid":"f9842ee8-b1a6-47b4-9e5b-70188daa34ef","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpFvicy5zyn1UVWwfmJ","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":1691,"cache_read_input_tokens":17720,"output_tokens":2484,"output_tokens_details":{"thinking_tokens":57},"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":1691},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":741,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXpFvXU8ri3ezCyV5YAs","requestRef":"47d17c8f-6c9c-4f81-9a69-1be7520859e6","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"461183e9-ec6a-4a16-b6f8-59f3abac6948","timestamp":"2026-10-30T04:47:27.096Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0160","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}63{"parentUuid":"461183e9-ec6a-4a16-b6f8-59f3abac6948","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpFvicy5zyn1UVWwfmJ","type":"message","role":"assistant","content":[{"type":"text","text":"The P4 gate run passed everything that belongs to P4 (keys, reproducibility, exclusion ledger, positive SE). The remaining failures are the P5 deliverables, which I write next."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1691,"cache_read_input_tokens":17720,"output_tokens":2484,"output_tokens_details":{"thinking_tokens":57},"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":1691},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpFvXU8ri3ezCyV5YAs","requestRef":"47d17c8f-6c9c-4f81-9a69-1be7520859e6","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a64db686-9f35-43a4-b440-30396f41862d","timestamp":"2026-10-30T04:47:27.098Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0160","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}64{"parentUuid":"a64db686-9f35-43a4-b440-30396f41862d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpFvicy5zyn1UVWwfmJ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01BFiqwmW45tMkq12o8homd3","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F1-conf-03 — Leverage and next-year default (information-respecting)\n\n## Question\nWhat is the association between a firm's leverage at the end of year t and its default in year t+1? We use only information available at the end of year t. The estimand is b1 in the linear-probability model default_next = a + b1·leverage + b2·cash_ratio + b3·log_sales + e. It is estimated by OLS on firm-years with an observed outcome, with standard errors clustered by firm. The data are simulated (a synthetic panel).\n\n## Data and exclusions\n- Input: `data/firm_panel.csv`, 2140 data rows (`n_input_rows`), 300 firms, 2016–2022.\n- Data problems found (`data_issues`):\n  1. 40 rows are exact duplicates of another row (assembly error). One copy of each was kept. This leaves 2100 rows = 300 firms × 7 years (`n_rows_after_dedupe`).\n  2. `rating_2025` was assigned in 2025 using each firm's realised defaults through 2022, so it was not known at the end of year t. It is excluded (`variables_excluded`). In the deduplicated data, every observed default falls in rating D, which confirms that the field leaks the outcome.\n  3. `default_next` is missing for 300 rows, all with origin year 2022 (outcome year 2023 lies after the data cut). These rows are excluded and not imputed.\n- Exclusion ledger (`out/exclusions.csv`): 2140 → 2100 (deduplication) → 2100 (column removal of rating_2025) → 1800 (outcome observed) → 1800 (complete predictors). Estimation sample: `n_analysed` = 1800 firm-years, `n_firms` = 300, origin years 2016–2021.\n\n## Method\n- Baseline: the unconditional default rate in the estimation sample (`default_rate`).\n- Main model: OLS of `default_next` on an intercept plus `predictors_used` = leverage, cash_ratio and log_sales, all measured at the end of year t.\n- Standard errors are cluster-robust by `firm_id` (statsmodels, default small-sample correction).\n- No randomness is involved (`random_seed` = null).\n- `out/analysis.py` regenerates all outputs from `data/`.\n\n## Results\n- Baseline default rate: 0.121 (`default_rate`).\n- `beta_leverage` = 0.264. Holding cash ratio and log sales fixed, a 0.10 higher leverage ratio is associated with about 2.6 percentage points higher probability of default in the next year.\n- Other coefficients: cash_ratio −0.186 (`beta_cash_ratio`); log_sales −0.0073 (`beta_log_sales`); intercept 0.090 (`intercept`).\n- In-sample R² = 0.035 (`r2`). The model explains little of the individual-level variation in default, as is typical for a binary outcome.\n- Feasibility: complete (`feasibility`).\n\n## Uncertainty\n- The firm-clustered SE of the leverage coefficient is 0.0408 (`se_leverage_cluster`). The 95% CI is [0.184, 0.344] (`ci95_leverage_low`, `ci95_leverage_high`), with p ≈ 1e-10 (`p_leverage_cluster`).\n- The classical (non-clustered) SE is smaller, 0.0358 (`se_leverage_classical`). This is consistent with outcomes being correlated within firms.\n\n## Limitations and open issues\n- This is an association, not a causal effect. Leverage may proxy for unobserved firm risk.\n- A linear-probability model can produce fitted values outside [0,1]. The coefficient is an average linear approximation.\n- Duplicates were resolved by keeping one copy. This is unambiguous here because all copies were identical.\n- Firms that default stay in the panel. The codebook does not say how post-default years are defined, so all observed firm-years are used as given.\n- The data are synthetic, so the results say nothing about real firms.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The estimation sample has 1800 firm-years from 300 firms after removing 40 exact duplicate rows and 300 rows with unobserved outcome (origin year 2022).\",\"results_keys\":[\"n_analysed\",\"n_firms\",\"n_input_rows\",\"n_rows_after_dedupe\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Duplicates were identical copies; one copy retained.\"},\n {\"id\":\"C2\",\"text\":\"rating_2025 violates the information-availability rule (built in 2025 from realised defaults) and is excluded from the predictors.\",\"results_keys\":[\"variables_excluded\",\"predictors_used\"],\"status\":\"supported\",\"caveat\":\"Based on the codebook; corroborated by all observed defaults falling in rating D.\"},\n {\"id\":\"C3\",\"text\":\"Baseline next-year default rate in the estimation sample is 0.121.\",\"results_keys\":[\"default_rate\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C4\",\"text\":\"Conditional on cash ratio and log sales, leverage at end of year t is positively associated with default in t+1: coefficient 0.264 (firm-clustered SE 0.041, 95% CI 0.184 to 0.344).\",\"results_keys\":[\"beta_leverage\",\"se_leverage_cluster\",\"ci95_leverage_low\",\"ci95_leverage_high\",\"p_leverage_cluster\"],\"status\":\"supported\",\"caveat\":\"Associational LPM estimate on synthetic data; not causal.\"},\n {\"id\":\"C5\",\"text\":\"The model has low explanatory power (R^2 = 0.035).\",\"results_keys\":[\"r2\"],\"status\":\"supported\",\"caveat\":\"R^2 is naturally low for binary outcomes.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json,glob\np=json.load(open('out/state/phases.json'))\nrec=sorted(glob.glob('out/state/gate_receipts/*.json'))\np['P4']['status']='passed'; p['P4']['checks'].append(f\"gate run (P4): {rec[-1]} — all P4-relevant checks ok (keys, reproducibility, exclusion ledger, positive SE); failures only for P5 deliverables not yet written\")\np['P5']['status']='passed'; p['P5']['checks']=[\"report.md with 6 sections, numbers cite results keys\",\"claims.json written, keys exist\",\"final gate run: see next receipt\"]\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate receipt 1). P5: report and claims written; running final gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v \"\\[ok\\]\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1691,"cache_read_input_tokens":17720,"output_tokens":2484,"output_tokens_details":{"thinking_tokens":57},"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":1691},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXpFvXU8ri3ezCyV5YAs","requestRef":"47d17c8f-6c9c-4f81-9a69-1be7520859e6","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"75a3dac0-8eea-477d-a1a5-60436cf66da3","timestamp":"2026-10-30T04:47:45.869Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0160","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}65{"parentUuid":"75a3dac0-8eea-477d-a1a5-60436cf66da3","isSidechain":false,"promptId":"4c063876-53fe-4181-ad58-eb5eab9fc815","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BFiqwmW45tMkq12o8homd3","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 32 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0160/out/state/gate_receipts/20261030T044746.604469Z.json","is_error":false}]},"uuid":"24031408-e480-4be9-a18f-e5fe45eb5932","timestamp":"2026-10-30T04:47:46.611Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 32 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0160/out/state/gate_receipts/20261030T044746.604469Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"75a3dac0-8eea-477d-a1a5-60436cf66da3","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0160","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}66{"type":"api-request-blob","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","hash":"0f7b633f1d86ae3865d706ee7bad5d287103a437de4009f173a6c445b44cc692","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"The P4 gate run passed everything that belongs to P4 (keys, reproducibility, exclusion ledger, positive SE). The remaining failures are the P5 deliverables, which I write next."},{"type":"tool_use","id":"toolu_01BFiqwmW45tMkq12o8homd3","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F1-conf-03 — Leverage and next-year default (information-respecting)\n\n## Question\nWhat is the association between a firm's leverage at the end of year t and its default in year t+1? We use only information available at the end of year t. The estimand is b1 in the linear-probability model default_next = a + b1·leverage + b2·cash_ratio + b3·log_sales + e. It is estimated by OLS on firm-years with an observed outcome, with standard errors clustered by firm. The data are simulated (a synthetic panel).\n\n## Data and exclusions\n- Input: `data/firm_panel.csv`, 2140 data rows (`n_input_rows`), 300 firms, 2016–2022.\n- Data problems found (`data_issues`):\n  1. 40 rows are exact duplicates of another row (assembly error). One copy of each was kept. This leaves 2100 rows = 300 firms × 7 years (`n_rows_after_dedupe`).\n  2. `rating_2025` was assigned in 2025 using each firm's realised defaults through 2022, so it was not known at the end of year t. It is excluded (`variables_excluded`). In the deduplicated data, every observed default falls in rating D, which confirms that the field leaks the outcome.\n  3. `default_next` is missing for 300 rows, all with origin year 2022 (outcome year 2023 lies after the data cut). These rows are excluded and not imputed.\n- Exclusion ledger (`out/exclusions.csv`): 2140 → 2100 (deduplication) → 2100 (column removal of rating_2025) → 1800 (outcome observed) → 1800 (complete predictors). Estimation sample: `n_analysed` = 1800 firm-years, `n_firms` = 300, origin years 2016–2021.\n\n## Method\n- Baseline: the unconditional default rate in the estimation sample (`default_rate`).\n- Main model: OLS of `default_next` on an intercept plus `predictors_used` = leverage, cash_ratio and log_sales, all measured at the end of year t.\n- Standard errors are cluster-robust by `firm_id` (statsmodels, default small-sample correction).\n- No randomness is involved (`random_seed` = null).\n- `out/analysis.py` regenerates all outputs from `data/`.\n\n## Results\n- Baseline default rate: 0.121 (`default_rate`).\n- `beta_leverage` = 0.264. Holding cash ratio and log sales fixed, a 0.10 higher leverage ratio is associated with about 2.6 percentage points higher probability of default in the next year.\n- Other coefficients: cash_ratio −0.186 (`beta_cash_ratio`); log_sales −0.0073 (`beta_log_sales`); intercept 0.090 (`intercept`).\n- In-sample R² = 0.035 (`r2`). The model explains little of the individual-level variation in default, as is typical for a binary outcome.\n- Feasibility: complete (`feasibility`).\n\n## Uncertainty\n- The firm-clustered SE of the leverage coefficient is 0.0408 (`se_leverage_cluster`). The 95% CI is [0.184, 0.344] (`ci95_leverage_low`, `ci95_leverage_high`), with p ≈ 1e-10 (`p_leverage_cluster`).\n- The classical (non-clustered) SE is smaller, 0.0358 (`se_leverage_classical`). This is consistent with outcomes being correlated within firms.\n\n## Limitations and open issues\n- This is an association, not a causal effect. Leverage may proxy for unobserved firm risk.\n- A linear-probability model can produce fitted values outside [0,1]. The coefficient is an average linear approximation.\n- Duplicates were resolved by keeping one copy. This is unambiguous here because all copies were identical.\n- Firms that default stay in the panel. The codebook does not say how post-default years are defined, so all observed firm-years are used as given.\n- The data are synthetic, so the results say nothing about real firms.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The estimation sample has 1800 firm-years from 300 firms after removing 40 exact duplicate rows and 300 rows with unobserved outcome (origin year 2022).\",\"results_keys\":[\"n_analysed\",\"n_firms\",\"n_input_rows\",\"n_rows_after_dedupe\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Duplicates were identical copies; one copy retained.\"},\n {\"id\":\"C2\",\"text\":\"rating_2025 violates the information-availability rule (built in 2025 from realised defaults) and is excluded from the predictors.\",\"results_keys\":[\"variables_excluded\",\"predictors_used\"],\"status\":\"supported\",\"caveat\":\"Based on the codebook; corroborated by all observed defaults falling in rating D.\"},\n {\"id\":\"C3\",\"text\":\"Baseline next-year default rate in the estimation sample is 0.121.\",\"results_keys\":[\"default_rate\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C4\",\"text\":\"Conditional on cash ratio and log sales, leverage at end of year t is positively associated with default in t+1: coefficient 0.264 (firm-clustered SE 0.041, 95% CI 0.184 to 0.344).\",\"results_keys\":[\"beta_leverage\",\"se_leverage_cluster\",\"ci95_leverage_low\",\"ci95_leverage_high\",\"p_leverage_cluster\"],\"status\":\"supported\",\"caveat\":\"Associational LPM estimate on synthetic data; not causal.\"},\n {\"id\":\"C5\",\"text\":\"The model has low explanatory power (R^2 = 0.035).\",\"results_keys\":[\"r2\"],\"status\":\"supported\",\"caveat\":\"R^2 is naturally low for binary outcomes.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json,glob\np=json.load(open('out/state/phases.json'))\nrec=sorted(glob.glob('out/state/gate_receipts/*.json'))\np['P4']['status']='passed'; p['P4']['checks'].append(f\"gate run (P4): {rec[-1]} — all P4-relevant checks ok (keys, reproducibility, exclusion ledger, positive SE); failures only for P5 deliverables not yet written\")\np['P5']['status']='passed'; p['P5']['checks']=[\"report.md with 6 sections, numbers cite results keys\",\"claims.json written, keys exist\",\"final gate run: see next receipt\"]\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate receipt 1). P5: report and claims written; running final gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v \"\\[ok\\]\""}}]}}67{"type":"api-request","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","id":"a46943e5-99cc-4e72-9b1c-f3f0a8503311","timestamp":"2026-10-30T04:47:46.614Z","version":"2.1.314","querySource":"sdk","shapeHash":"2dd02160d273680ea9621f287d43802879b3bf673adec2f3bf71bba355d178ed","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":"47d17c8f-6c9c-4f81-9a69-1be7520859e6","keep":18,"tail":["0f7b633f1d86ae3865d706ee7bad5d287103a437de4009f173a6c445b44cc692","cc5abcf4890c3586cc0ed761ccb9639ab3fa55d6e9f3ec41f7b97db9bbeeee44"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"7a3ea226e58a447a6a667415b3a502c5d914cafbe3f4556f13f9bc3eac1d908b"}68{"parentUuid":"24031408-e480-4be9-a18f-e5fe45eb5932","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpHVRAzNctL9zM7Uytk","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01JDHnYryXbByNTqFZwDeaEU","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json,glob\np=json.load(open('out/state/phases.json'))\nrec=sorted(glob.glob('out/state/gate_receipts/*.json'))[-1]\np['P5']['checks'][-1]=f\"final gate run: {rec} — PASSED 0/32 failed\"\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 Final gate PASSED. Task complete.\" >> out/state/log.md\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":2579,"cache_read_input_tokens":19411,"output_tokens":269,"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":2579},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXpHV7omR3ZFaM4cxfXR","requestRef":"a46943e5-99cc-4e72-9b1c-f3f0a8503311","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9b40c384-70b2-47f5-9fdf-97738b5442eb","timestamp":"2026-10-30T04:47:48.987Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0160","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}69{"parentUuid":"9b40c384-70b2-47f5-9fdf-97738b5442eb","isSidechain":false,"promptId":"4c063876-53fe-4181-ad58-eb5eab9fc815","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JDHnYryXbByNTqFZwDeaEU","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 32 checks failed)","is_error":false}]},"uuid":"e5f51ad6-a3d2-4103-ab85-d077fdb945a9","timestamp":"2026-10-30T04:47:49.794Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 32 checks failed)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"9b40c384-70b2-47f5-9fdf-97738b5442eb","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0160","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","version":"2.1.314","gitBranch":"HEAD"}70{"type":"api-request-blob","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","hash":"9362bd93709aad8bbcb9d1993505aec841a3f00799c924349f555f258d49dec4","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01JDHnYryXbByNTqFZwDeaEU","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json,glob\np=json.load(open('out/state/phases.json'))\nrec=sorted(glob.glob('out/state/gate_receipts/*.json'))[-1]\np['P5']['checks'][-1]=f\"final gate run: {rec} — PASSED 0/32 failed\"\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 Final gate PASSED. Task complete.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | head -1"}}]}}71{"type":"api-request","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2","id":"87fbd2dc-0bb6-4699-a1eb-b03f36890e0d","timestamp":"2026-10-30T04:47:49.797Z","version":"2.1.314","querySource":"sdk","shapeHash":"2dd02160d273680ea9621f287d43802879b3bf673adec2f3bf71bba355d178ed","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":"a46943e5-99cc-4e72-9b1c-f3f0a8503311","keep":20,"tail":["9362bd93709aad8bbcb9d1993505aec841a3f00799c924349f555f258d49dec4","6657cff612cd87a728657e943e1a94dad69f908e5232173ac1f3e19fb12d832c"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",21,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"ad1b9f9ffb20a256d935bd9fb9eee56327267069320628236675c95586a3482c"}72{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"e5f51ad6-a3d2-4103-ab85-d077fdb945a9","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2"}73{"type":"atis-latch","atis":"","sessionId":"57a3ce5f-2296-4855-8897-e48bebb0d9f2"}74{"parentUuid":"e5f51ad6-a3d2-4103-ab85-d077fdb945a9","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpHiyzpt5wpPQWnyub5","type":"message","role":"assistant","content":[{"type":"text","text":"I've finished the analysis. Higher leverage at the end of year t is clearly associated with a higher chance of defaulting in year t+1: the coefficient on leverage is **0.264** (standard error clustered by firm 0.041, 95% CI 0.184–0.344). Holding cash ratio and log sales fixed, 0.10 more leverage goes with about 2.6 percentage points higher default probability. This is an association, not a causal effect.\n\nThe final gate run passed (0 of 32 checks failed), and re-running `out/analysis.py` from scratch reproduces identical results.\n\n**Data and exclusions:**\n- **Duplicates:** the file has 2,140 rows. Forty are exact copies of another row, so I kept one copy of each. That leaves 2,100 rows: 300 firms × 7 years.\n- **Unknown outcomes:** 300 rows, all from 2022, have no `default_next` because the 2023 outcome falls after the data cut. I dropped them rather than filling them in.\n- **Estimation sample:** 1,800 firm-years from 300 firms, years 2016–2021. Every step is recorded in `out/exclusions.csv`.\n- **`rating_2025` left out:** it was assigned in 2025 using each firm's actual defaults, so it wasn't known at the end of year t. In the data, every observed default has rating D, which confirms it gives away the outcome.\n\n**Other results:**\n- The baseline default rate in the sample is 0.121.\n- The model explains little of who defaults (R² = 0.035), which is normal for a yes/no outcome.\n- The other coefficients are cash ratio −0.186 and log sales −0.0073.\n\n**Limitations:**\n- The data are simulated, so the results say nothing about real firms.\n- A linear probability model can predict probabilities below 0 or above 1.\n- The codebook doesn't say how years after a firm defaults should be treated, so I used every observed firm-year as given.\n\nEverything is in `out/`. 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