spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T04:45:46.524Z","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T04:45:46.530Z","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47"}4{"parentUuid":null,"isSidechain":false,"promptId":"1016f0b0-3824-44f7-afa9-92244e172823","type":"user","message":{"role":"user","content":"Begin"},"uuid":"8f71e108-279e-44bf-94aa-1e92652d35bc","timestamp":"2026-10-30T04:45:46.536Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0159","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","shapeHash":"03e0b194203a2592e804e85a4b4ac59f527f377b666c83f8d1983d4175dc695a","timestamp":"2026-10-30T04:45:46.551Z","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/RC0159\nDate: 2026-10-30\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n\nSEPARATE REVIEW (additional)\n- After the gate passes, a separate reviewer session that has not seen your work in progress will examine `TASK.md` and everything under `out/` and may return a list of issues (file `out/review/review.json`). You will then be resumed once to address them: fix what is justified, explain in `out/report.md` (Limitations and open issues) what you disagree with, re-run the gate, and update `out/claims.json` and `out/state/phases.json`. The reviewer has no reference answers; its role is to challenge unsupported claims, violated information rules, unverified computations, and inconsistencies between your report and your results.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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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.\nbundle.json\ndata\ngate_spec.json\nTASK.md\n\n./data:\nfirm_panel.csv\nREADME.md","is_error":true,"tool_use_id":"toolu_01P1ruUYTDRZYVvaETkfFpP8"}]},"uuid":"0a1f7e2f-4b2d-46e6-a14d-ea112cca2e09","timestamp":"2026-10-30T04:45:49.805Z","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\nfirm_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\nF062,2020,0.2496,0.2919,6.3207,B,0\nF123,2019,0.6604,0.18,5.617,D,1\nF086,2021,0.8782,0.0278,5.8368,D,0\nF133,2018,0.4562,0.0483,5.7967,D,1\nF269,2020,0.5944,0.3414,5.458,D,0\n    2141 data/firm_panel.csv\na1f1af88b1ab05900155991f2468cce09695af978923e5bc3ef9c28d45683b6f  data/firm_panel.csv\n26573e982ab6ca9e7f90d2b89bd2878aa6a670f90da21496bff787b59216b9a2  data/README.md","is_error":false}]},"uuid":"d5cb484b-1a13-4f70-a002-1d760feb4daa","timestamp":"2026-10-30T04:45:52.061Z","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\nfirm_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\nF062,2020,0.2496,0.2919,6.3207,B,0\nF123,2019,0.6604,0.18,5.617,D,1\nF086,2021,0.8782,0.0278,5.8368,D,0\nF133,2018,0.4562,0.0483,5.7967,D,1\nF269,2020,0.5944,0.3414,5.458,D,0\n    2141 data/firm_panel.csv\na1f1af88b1ab05900155991f2468cce09695af978923e5bc3ef9c28d45683b6f  data/firm_panel.csv\n26573e982ab6ca9e7f90d2b89bd2878aa6a670f90da21496bff787b59216b9a2  data/README.md","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"3eb4eae9-88e9-41eb-bcd7-e8e02e632409","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0159","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","version":"2.1.314","gitBranch":"HEAD"}21{"type":"api-request-blob","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","hash":"d5dd8ff5bd73e7206cdca9d8ef4149ac427d5d8ae0265793a4a01398c65bfa51","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01KxsWZDvhmBdBBaNgc7LUcM","name":"Bash","input":{"command":"cat data/README.md; cat gate_spec.json | head -50; head data/firm_panel.csv; wc -l data/firm_panel.csv; shasum -a 256 data/*","description":"Read codebook, gate spec, and data head"}}]}}22{"type":"api-request","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","id":"31990c95-7d2d-4f2a-bb8f-64c8ee5bb109","timestamp":"2026-10-30T04:45:52.065Z","version":"2.1.314","querySource":"sdk","shapeHash":"03e0b194203a2592e804e85a4b4ac59f527f377b666c83f8d1983d4175dc695a","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":"aeceaec8-1d90-4392-84fe-bc1666d1ced1","keep":4,"tail":["d5dd8ff5bd73e7206cdca9d8ef4149ac427d5d8ae0265793a4a01398c65bfa51","33cc0ecbb763b0c14c17e7472571548cd865255932860950d8c608df886b2be0"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"cbddf5d1560390eced8e59598a8d8a1ebe3d947ea4e4a4c304520ada1e2bdd11"}23{"parentUuid":"d5cb484b-1a13-4f70-a002-1d760feb4daa","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp94DdZCtnL1FP7SR36","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":1361,"cache_read_input_tokens":6048,"output_tokens":533,"output_tokens_details":{"thinking_tokens":34},"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":1361},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":269,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXp93xzY6Q1zGSzL1aCG","requestRef":"31990c95-7d2d-4f2a-bb8f-64c8ee5bb109","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"abdfe97d-e92e-48a3-aebf-64ce14140cb0","timestamp":"2026-10-30T04:45:53.836Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0159","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"abdfe97d-e92e-48a3-aebf-64ce14140cb0","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp94DdZCtnL1FP7SR36","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014UNrgfwam7uFoJ8XMjkiJn","name":"Bash","input":{"command":"sed -n 50,200p gate_spec.json; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/firm_panel.csv',dtype={'firm_id':str})\nprint(d.dtypes);print(d.describe(include='all').T)\nprint(d.isna().sum())\nprint('exact dup',d.duplicated().sum(),'key dup',d.duplicated(['firm_id','year']).sum(), 'key dup all', 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.firm_id.nunique())\nprint(d.default_next.value_counts(dropna=False))\nprint(d.groupby('year').default_next.apply(lambda s:s.isna().sum()))\nprint(d.groupby('firm_id').rating_2025.nunique().value_counts())\nfor c in ['leverage','cash_ratio','log_sales']: print(c,(d[c]<0).sum(),(d[c]>1).sum())\nprint(d.firm_id.str.match(r'^F\\d{3}$').all())\n\"","description":"Audit the panel data"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1361,"cache_read_input_tokens":6048,"output_tokens":533,"output_tokens_details":{"thinking_tokens":34},"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":1361},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXp93xzY6Q1zGSzL1aCG","requestRef":"31990c95-7d2d-4f2a-bb8f-64c8ee5bb109","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a39550df-2175-4ba7-be06-51918b29ccc7","timestamp":"2026-10-30T04:45:57.122Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0159","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","version":"2.1.314","gitBranch":"HEAD"}25{"parentUuid":"a39550df-2175-4ba7-be06-51918b29ccc7","isSidechain":false,"promptId":"1016f0b0-3824-44f7-afa9-92244e172823","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014UNrgfwam7uFoJ8XMjkiJn","type":"tool_result","content":"      \"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}firm_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\nexact dup 40 key dup 40 key dup all 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\n300\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\nrating_2025\n1    300\nName: count, dtype: int64\nleverage 0 0\ncash_ratio 0 0\nlog_sales 0 2140\nTrue","is_error":false}]},"uuid":"46b420c7-f064-4ce9-b08f-82209260d4b0","timestamp":"2026-10-30T04:45:57.362Z","toolUseResult":{"stdout":"      \"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}firm_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\nexact dup 40 key dup 40 key dup all 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\n300\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\nrating_2025\n1    300\nName: count, dtype: int64\nleverage 0 0\ncash_ratio 0 0\nlog_sales 0 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-n 50,200p gate_spec.json; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/firm_panel.csv',dtype={'firm_id':str})\nprint(d.dtypes);print(d.describe(include='all').T)\nprint(d.isna().sum())\nprint('exact dup',d.duplicated().sum(),'key dup',d.duplicated(['firm_id','year']).sum(), 'key dup all', 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.firm_id.nunique())\nprint(d.default_next.value_counts(dropna=False))\nprint(d.groupby('year').default_next.apply(lambda s:s.isna().sum()))\nprint(d.groupby('firm_id').rating_2025.nunique().value_counts())\nfor c in ['leverage','cash_ratio','log_sales']: print(c,(d[c]<0).sum(),(d[c]>1).sum())\nprint(d.firm_id.str.match(r'^F\\d{3}$').all())\n\"","description":"Audit the panel data"}}]}}27{"type":"api-request","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","id":"3fd2f61b-a40b-4e60-a3df-907e62a3f16a","timestamp":"2026-10-30T04:45:57.366Z","version":"2.1.314","querySource":"sdk","shapeHash":"03e0b194203a2592e804e85a4b4ac59f527f377b666c83f8d1983d4175dc695a","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":"31990c95-7d2d-4f2a-bb8f-64c8ee5bb109","keep":6,"tail":["1e17a399ddf4f421b1e289f56b6b894e2358a8e6d74622c156cb185d5e9590e9","aa7e02418f40501d9e77c2dbceb0d4e62695857183fdc287c836ec42cdf918ad"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"2587959f77ba2bbc24829ff163e0bc7f007ae217d3a16ec05e6e1a5f43fe5281"}28{"parentUuid":"46b420c7-f064-4ce9-b08f-82209260d4b0","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp9SwcirXyk79tT7t67","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":2153,"cache_read_input_tokens":7409,"output_tokens":453,"output_tokens_details":{"thinking_tokens":176},"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":2153},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1618,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXp9SeWQzPP938kiqa4E","requestRef":"3fd2f61b-a40b-4e60-a3df-907e62a3f16a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"36818550-99a0-47c9-8a9e-0eb31cb58dac","timestamp":"2026-10-30T04:46:00.259Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0159","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"36818550-99a0-47c9-8a9e-0eb31cb58dac","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXp9SwcirXyk79tT7t67","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KaobNw2KZep5RYAwfvQehS","name":"Bash","input":{"command":"mkdir -p out/state; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/firm_panel.csv',dtype={'firm_id':str}).drop_duplicates()\nprint(len(d), d.groupby('year').size().tolist())\nprint(d.groupby('firm_id').default_next.sum().value_counts().sort_index())\nprint(d.groupby('firm_id').size().value_counts())\nprint(d[['leverage','cash_ratio','log_sales']].describe())\nprint(d.groupby('rating_2025').default_next.mean())\n\"","description":"Check deduplicated structure"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2153,"cache_read_input_tokens":7409,"output_tokens":453,"output_tokens_details":{"thinking_tokens":176},"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":2153},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXp9SeWQzPP938kiqa4E","requestRef":"3fd2f61b-a40b-4e60-a3df-907e62a3f16a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4e14dcdb-e957-416a-973a-8639613b1b44","timestamp":"2026-10-30T04:46:02.050Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0159","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","version":"2.1.314","gitBranch":"HEAD"}30{"parentUuid":"4e14dcdb-e957-416a-973a-8639613b1b44","isSidechain":false,"promptId":"1016f0b0-3824-44f7-afa9-92244e172823","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KaobNw2KZep5RYAwfvQehS","type":"tool_result","content":"2100 [300, 300, 300, 300, 300, 300, 300]\ndefault_next\n0.0    161\n1.0     83\n2.0     39\n3.0     11\n4.0      6\nName: count, dtype: int64\n7    300\nName: count, dtype: int64\n          leverage   cash_ratio    log_sales\ncount  2100.000000  2100.000000  2100.000000\nmean      0.395826     0.196952     4.988362\nstd       0.209957     0.119556     0.988936\nmin       0.020000     0.004600     1.288200\n25%       0.234100     0.103925     4.330550\n50%       0.382250     0.177200     4.996200\n75%       0.545200     0.265425     5.652200\nmax       0.950000     0.740600     8.878600\nrating_2025\nA    0.000000\nB    0.000000\nC    0.000000\nD    0.227083\nName: default_next, dtype: float64","is_error":false}]},"uuid":"d036e349-04ae-4fbd-8d99-9cdeeafe8928","timestamp":"2026-10-30T04:46:02.297Z","toolUseResult":{"stdout":"2100 [300, 300, 300, 300, 300, 300, 300]\ndefault_next\n0.0    161\n1.0     83\n2.0     39\n3.0     11\n4.0      6\nName: count, dtype: int64\n7    300\nName: count, dtype: int64\n          leverage   cash_ratio    log_sales\ncount  2100.000000  2100.000000  2100.000000\nmean      0.395826     0.196952     4.988362\nstd       0.209957     0.119556     0.988936\nmin       0.020000     0.004600     1.288200\n25%       0.234100     0.103925     4.330550\n50%       0.382250     0.177200     4.996200\n75%       0.545200     0.265425     5.652200\nmax       0.950000     0.740600     8.878600\nrating_2025\nA    0.000000\nB    0.000000\nC    0.000000\nD    0.227083\nName: default_next, dtype: 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-p out/state; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/firm_panel.csv',dtype={'firm_id':str}).drop_duplicates()\nprint(len(d), d.groupby('year').size().tolist())\nprint(d.groupby('firm_id').default_next.sum().value_counts().sort_index())\nprint(d.groupby('firm_id').size().value_counts())\nprint(d[['leverage','cash_ratio','log_sales']].describe())\nprint(d.groupby('rating_2025').default_next.mean())\n\"","description":"Check deduplicated 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> out/state/contract.md <<'EOF'\n# Contract — F1-conf-03\n**Question.** Association between firm leverage at end of year t and default in year t+1, using only information known at end of t.\n**Estimand.** b1 in OLS LPM default_next ~ 1 + leverage + cash_ratio + log_sales, on firm-years with observed outcome; SE clustered by firm_id. Meaningful as a conditional association (not causal) under correct construction of one row per firm-year and no look-ahead predictors.\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 (firm_id, year, predictors, default_next; n_analysed rows); out/exclusions.csv (step, rule, n_before, n_after, n_removed, rationale; chained); out/report.md (6 sections); out/analysis.py; out/claims.json.\n**Information rules.** Predictor allowed 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 (no imputation). Only data/firm_panel.csv.\n**Invalidity.** Using rating_2025; imputing outcomes; keeping duplicate firm-years (double weighting); missing columns making the model unestimable.\nEOF\ncat > out/state/log.md <<'EOF'\n# Log\n- 2026-10-30 P0 passed: contract written.\n- 2026-10-30 P1: audit found 40 exact duplicate rows (identical on all columns, 40 duplicated firm-year keys), 307 rows with missing default_next all in year 2022 (300 after dedup). rating_2025 is a look-ahead (2025, built from realised defaults; perfectly separates defaults: only D firms default) → excluded. Decision: drop exact duplicates keeping first, then drop missing outcomes.\nEOF","description":"Write contract and log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":752,"cache_read_input_tokens":9562,"output_tokens":836,"output_tokens_details":{"thinking_tokens":63},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":752},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXp9okGC9DsXc2Pmvepf","requestRef":"959b7167-2866-4aeb-8132-3ca6cbc86b43","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1b5c7b8a-d77a-4d06-873b-9147b7dfa059","timestamp":"2026-10-30T04:46:10.270Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0159","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","version":"2.1.314","gitBranch":"HEAD"}37{"parentUuid":"1b5c7b8a-d77a-4d06-873b-9147b7dfa059","isSidechain":false,"promptId":"1016f0b0-3824-44f7-afa9-92244e172823","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01P5bGJUhqqNuh3wjLraYHio","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"021142fd-c53e-45dc-9771-f18bf37dd21b","timestamp":"2026-10-30T04:46:10.294Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"1b5c7b8a-d77a-4d06-873b-9147b7dfa059","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0159","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","version":"2.1.314","gitBranch":"HEAD"}38{"type":"api-request-blob","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","hash":"c2dfc6d0e89c08294f3724e8393db87be5735a3e8668c40a7109118044b2e732","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01P5bGJUhqqNuh3wjLraYHio","name":"Bash","input":{"command":"cat > out/state/contract.md <<'EOF'\n# Contract — F1-conf-03\n**Question.** Association between firm leverage at end of year t and default in year t+1, using only information known at end of t.\n**Estimand.** b1 in OLS LPM default_next ~ 1 + leverage + cash_ratio + log_sales, on firm-years with observed outcome; SE clustered by firm_id. Meaningful as a conditional association (not causal) under correct construction of one row per firm-year and no look-ahead predictors.\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 (firm_id, year, predictors, default_next; n_analysed rows); out/exclusions.csv (step, rule, n_before, n_after, n_removed, rationale; chained); out/report.md (6 sections); out/analysis.py; out/claims.json.\n**Information rules.** Predictor allowed 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 (no imputation). Only data/firm_panel.csv.\n**Invalidity.** Using rating_2025; imputing outcomes; keeping duplicate firm-years (double weighting); missing columns making the model unestimable.\nEOF\ncat > out/state/log.md <<'EOF'\n# Log\n- 2026-10-30 P0 passed: contract written.\n- 2026-10-30 P1: audit found 40 exact duplicate rows (identical on all columns, 40 duplicated firm-year keys), 307 rows with missing default_next all in year 2022 (300 after dedup). rating_2025 is a look-ahead (2025, built from realised defaults; perfectly separates defaults: only D firms default) → excluded. Decision: drop exact duplicates keeping first, then drop missing outcomes.\nEOF","description":"Write contract and log"}}]}}39{"type":"api-request","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","id":"6110ce88-30f5-45bf-a450-357a60758da9","timestamp":"2026-10-30T04:46:10.297Z","version":"2.1.314","querySource":"sdk","shapeHash":"03e0b194203a2592e804e85a4b4ac59f527f377b666c83f8d1983d4175dc695a","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":"959b7167-2866-4aeb-8132-3ca6cbc86b43","keep":10,"tail":["c2dfc6d0e89c08294f3724e8393db87be5735a3e8668c40a7109118044b2e732","c4177ec121d410751cd813877bb3064680f8aeb46f90886e25791695ea1b4bd7"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"851f712b9cfe77658417f389dc86af6f46d499e3cf6a1c7e480de9b612cefae5"}40{"parentUuid":"021142fd-c53e-45dc-9771-f18bf37dd21b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpAQBTMcHDjkdGkEMZe","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_019mGvUXjPtyRaTwinodE3wn","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F1-conf-03: information-respecting LPM of next-year default on leverage.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/firm_panel.csv; writes out/results.json, out/analytic.csv, out/exclusions.csv.\nNo randomness is used (random_seed = null).\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nimport statsmodels.api as sm\n\nDATA = \"data/firm_panel.csv\"\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\n\nraw = pd.read_csv(DATA, dtype={\"firm_id\": str})\nn_raw = len(raw)\nledger = []\nissues = []\ndef step(name, rule, before, after, why):\n    ledger.append(dict(step=name, rule=rule, n_before=before, n_after=after,\n                       n_removed=before - after, rationale=why))\n\nstep(\"0_read\", \"rows read from data/firm_panel.csv (excluding header)\", n_raw, n_raw,\n     \"starting point\")\n\n# 1. Remove look-ahead column (column removal, no rows removed)\nPREDICTORS = [\"leverage\", \"cash_ratio\", \"log_sales\"]\nexcluded = [{\"variable\": \"rating_2025\",\n             \"reason\": \"Known only in 2025 (final-vintage field) and constructed from the firm's realised \"\n                       \"default history through 2022 and sample-average leverage; not available at end of \"\n                       \"origin year t and mechanically contains the outcome (look-ahead leakage).\"}]\ndf = raw.drop(columns=[\"rating_2025\"])\nstep(\"1_drop_rating_2025\", \"drop column rating_2025 (known 2025, uses realised outcomes)\", len(df), len(df),\n     \"information-availability rule; column removal only\")\nissues.append({\"issue\": \"Column rating_2025 is a final-vintage (2025) field built from realised defaults through 2022 \"\n                        \"(look-ahead; in the data only rating D firms ever default).\",\n               \"handling\": \"Excluded from the analytic dataset and listed in variables_excluded.\"})\n\n# 2. Exact duplicate rows\nn_keydup = int(raw.duplicated([\"firm_id\", \"year\"]).sum())\nn_exdup = int(raw.duplicated().sum())\nbefore = len(df)\ndf = df.loc[~raw.duplicated()].copy()  # duplicates judged on all original columns\nstep(\"2_drop_exact_duplicates\", \"drop rows identical on all columns to an earlier row (keep first)\", before, len(df),\n     \"assembly error: one intended row per firm-year; duplicates would double-weight observations\")\nassert not df.duplicated([\"firm_id\", \"year\"]).any(), \"conflicting firm-year duplicates remain\"\nissues.append({\"issue\": f\"{n_exdup} exact duplicate rows ({n_keydup} duplicated firm_id-year keys; all duplicates identical \"\n                        f\"on every column, no conflicting values).\",\n               \"handling\": \"Kept the first occurrence of each, giving one row per firm-year (300 firms x 7 years).\"})\n\n# 3. Missing outcome\nn_miss = int(df[\"default_next\"].isna().sum())\nmiss_years = sorted(df.loc[df[\"default_next\"].isna(), \"year\"].unique().tolist())\nbefore = len(df)\ndf = df.loc[df[\"default_next\"].notna()].copy()\nstep(\"3_drop_missing_outcome\", \"drop firm-years with default_next empty (outcome year not observed by end-2022 cut)\",\n     before, len(df), \"outcome not observed; no imputation allowed\")\nissues.append({\"issue\": f\"{n_miss} firm-years (after de-duplication) have missing default_next, all in origin year(s) {miss_years} \"\n                        \"(outcome year 2023 beyond the data cut).\",\n               \"handling\": \"Excluded from estimation (no imputation); recorded in the exclusion ledger.\"})\n\n# 4. Missing predictors (none expected; ledger step kept for completeness)\nbefore = len(df)\ndf = df.dropna(subset=PREDICTORS)\nstep(\"4_drop_missing_predictors\", \"drop rows with missing leverage, cash_ratio or log_sales\", before, len(df),\n     \"complete-case for regressors\")\n\ndf[\"default_next\"] = df[\"default_next\"].astype(int)\ndf = df.sort_values([\"firm_id\", \"year\"]).reset_index(drop=True)\ndf[[\"firm_id\", \"year\"] + PREDICTORS + [\"default_next\"]].to_csv(f\"{OUT}/analytic.csv\", index=False)\npd.DataFrame(ledger).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\n# Baseline\ndefault_rate = float(df[\"default_next\"].mean())\n\n# Model: OLS LPM, SE clustered by firm\nX = sm.add_constant(df[PREDICTORS])\ny = df[\"default_next\"]\ngroups = pd.factorize(df[\"firm_id\"])[0]\nfit = sm.OLS(y, X).fit(cov_type=\"cluster\", cov_kwds={\"groups\": groups})\nb = float(fit.params[\"leverage\"]); se = float(fit.bse[\"leverage\"])\n\nres = {\n    \"task_id\": \"F1-conf-03\",\n    \"n_analysed\": int(len(df)),\n    \"n_firms\": int(df[\"firm_id\"].nunique()),\n    \"predictors_used\": PREDICTORS,\n    \"variables_excluded\": excluded,\n    \"beta_leverage\": b,\n    \"se_leverage_cluster\": se,\n    \"r2\": float(fit.rsquared),\n    \"default_rate\": default_rate,\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": issues,\n    \"n_input_rows\": {\"firm_panel.csv\": int(n_raw)},\n    \"random_seed\": None,\n    # supplementary (not required)\n    \"ci95_leverage_cluster\": [float(fit.conf_int().loc[\"leverage\", 0]), float(fit.conf_int().loc[\"leverage\", 1])],\n    \"pvalue_leverage_cluster\": float(fit.pvalues[\"leverage\"]),\n    \"beta_cash_ratio\": float(fit.params[\"cash_ratio\"]),\n    \"se_cash_ratio_cluster\": float(fit.bse[\"cash_ratio\"]),\n    \"beta_log_sales\": float(fit.params[\"log_sales\"]),\n    \"se_log_sales_cluster\": float(fit.bse[\"log_sales\"]),\n    \"intercept\": float(fit.params[\"const\"]),\n    \"n_clusters\": int(len(np.unique(groups))),\n    \"cov_type\": \"cluster-robust by firm_id (statsmodels default small-sample correction)\",\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\nprint(f\"n_analysed={res['n_analysed']} n_firms={res['n_firms']} beta_leverage={b:.6f} se={se:.6f} \"\n      f\"r2={res['r2']:.6f} default_rate={default_rate:.6f}\")\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && cat out/results.json | head -30","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":851,"cache_read_input_tokens":10314,"output_tokens":2525,"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":851},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXpAPvKUPokUvQqmpswh","requestRef":"6110ce88-30f5-45bf-a450-357a60758da9","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b97cd113-a833-4ed6-b3a3-d9bae99bd7d6","timestamp":"2026-10-30T04:46:29.752Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0159","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","version":"2.1.314","gitBranch":"HEAD"}41{"parentUuid":"b97cd113-a833-4ed6-b3a3-d9bae99bd7d6","isSidechain":false,"promptId":"1016f0b0-3824-44f7-afa9-92244e172823","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019mGvUXjPtyRaTwinodE3wn","type":"tool_result","content":"n_analysed=1800 n_firms=300 beta_leverage=0.263784 se=0.040757 r2=0.034906 default_rate=0.121111\nstep,rule,n_before,n_after,n_removed,rationale\n0_read,rows read from data/firm_panel.csv (excluding header),2140,2140,0,starting point\n1_drop_rating_2025,\"drop column rating_2025 (known 2025, uses realised outcomes)\",2140,2140,0,information-availability rule; column removal only\n2_drop_exact_duplicates,drop rows identical on all columns to an earlier row (keep first),2140,2100,40,assembly error: one intended row per firm-year; duplicates would double-weight observations\n3_drop_missing_outcome,drop firm-years with default_next empty (outcome year not observed by end-2022 cut),2100,1800,300,outcome not observed; no imputation allowed\n4_drop_missing_predictors,\"drop rows with missing leverage, cash_ratio or log_sales\",1800,1800,0,complete-case for regressors\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\": \"Known only in 2025 (final-vintage field) and constructed from the firm's realised default history through 2022 and sample-average leverage; not available at end of origin year t and mechanically contains the outcome (look-ahead leakage).\"\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\": \"Column rating_2025 is a final-vintage (2025) field built from realised defaults through 2022 (look-ahead; in the data only rating D firms ever default).\",\n      \"handling\": \"Excluded from the analytic dataset and listed in variables_excluded.\"\n    },\n    {\n      \"issue\": \"40 exact duplicate rows (40 duplicated firm_id-year keys; all duplicates identical on every column, no conflicting values).\",\n      \"handling\": \"Kept the first occurrence of each, giving one row per firm-year (300 firms x 7 years).\"\n    },","is_error":false}]},"uuid":"26be9442-4379-4d3b-99f7-475ffbe7e047","timestamp":"2026-10-30T04:46:30.692Z","toolUseResult":{"stdout":"n_analysed=1800 n_firms=300 beta_leverage=0.263784 se=0.040757 r2=0.034906 default_rate=0.121111\nstep,rule,n_before,n_after,n_removed,rationale\n0_read,rows read from data/firm_panel.csv (excluding header),2140,2140,0,starting point\n1_drop_rating_2025,\"drop column rating_2025 (known 2025, uses realised outcomes)\",2140,2140,0,information-availability rule; column removal only\n2_drop_exact_duplicates,drop rows identical on all columns to an earlier row (keep first),2140,2100,40,assembly error: one intended row per firm-year; duplicates would double-weight observations\n3_drop_missing_outcome,drop firm-years with default_next empty (outcome year not observed by end-2022 cut),2100,1800,300,outcome not observed; no imputation allowed\n4_drop_missing_predictors,\"drop rows with missing leverage, cash_ratio or log_sales\",1800,1800,0,complete-case for regressors\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\": \"Known only in 2025 (final-vintage field) and constructed from the firm's realised default history through 2022 and sample-average leverage; not available at end of origin year t and mechanically contains the outcome (look-ahead leakage).\"\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\": \"Column rating_2025 is a final-vintage (2025) field built from realised defaults through 2022 (look-ahead; in the data only rating D firms ever default).\",\n      \"handling\": \"Excluded from the analytic dataset and listed in variables_excluded.\"\n    },\n    {\n      \"issue\": \"40 exact duplicate rows (40 duplicated firm_id-year keys; all duplicates identical on every column, no conflicting values).\",\n      \"handling\": \"Kept the first occurrence of each, giving one row per firm-year (300 firms x 7 years).\"\n    },","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"b97cd113-a833-4ed6-b3a3-d9bae99bd7d6","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0159","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","version":"2.1.314","gitBranch":"HEAD"}42{"type":"api-request-blob","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","hash":"a3c0618f797c847019c16f42d073d27d9b3962aa19ec972dccb739401c4859bf","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_019mGvUXjPtyRaTwinodE3wn","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F1-conf-03: information-respecting LPM of next-year default on leverage.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/firm_panel.csv; writes out/results.json, out/analytic.csv, out/exclusions.csv.\nNo randomness is used (random_seed = null).\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nimport statsmodels.api as sm\n\nDATA = \"data/firm_panel.csv\"\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\n\nraw = pd.read_csv(DATA, dtype={\"firm_id\": str})\nn_raw = len(raw)\nledger = []\nissues = []\ndef step(name, rule, before, after, why):\n    ledger.append(dict(step=name, rule=rule, n_before=before, n_after=after,\n                       n_removed=before - after, rationale=why))\n\nstep(\"0_read\", \"rows read from data/firm_panel.csv (excluding header)\", n_raw, n_raw,\n     \"starting point\")\n\n# 1. Remove look-ahead column (column removal, no rows removed)\nPREDICTORS = [\"leverage\", \"cash_ratio\", \"log_sales\"]\nexcluded = [{\"variable\": \"rating_2025\",\n             \"reason\": \"Known only in 2025 (final-vintage field) and constructed from the firm's realised \"\n                       \"default history through 2022 and sample-average leverage; not available at end of \"\n                       \"origin year t and mechanically contains the outcome (look-ahead leakage).\"}]\ndf = raw.drop(columns=[\"rating_2025\"])\nstep(\"1_drop_rating_2025\", \"drop column rating_2025 (known 2025, uses realised outcomes)\", len(df), len(df),\n     \"information-availability rule; column removal only\")\nissues.append({\"issue\": \"Column rating_2025 is a final-vintage (2025) field built from realised defaults through 2022 \"\n                        \"(look-ahead; in the data only rating D firms ever default).\",\n               \"handling\": \"Excluded from the analytic dataset and listed in variables_excluded.\"})\n\n# 2. Exact duplicate rows\nn_keydup = int(raw.duplicated([\"firm_id\", \"year\"]).sum())\nn_exdup = int(raw.duplicated().sum())\nbefore = len(df)\ndf = df.loc[~raw.duplicated()].copy()  # duplicates judged on all original columns\nstep(\"2_drop_exact_duplicates\", \"drop rows identical on all columns to an earlier row (keep first)\", before, len(df),\n     \"assembly error: one intended row per firm-year; duplicates would double-weight observations\")\nassert not df.duplicated([\"firm_id\", \"year\"]).any(), \"conflicting firm-year duplicates remain\"\nissues.append({\"issue\": f\"{n_exdup} exact duplicate rows ({n_keydup} duplicated firm_id-year keys; all duplicates identical \"\n                        f\"on every column, no conflicting values).\",\n               \"handling\": \"Kept the first occurrence of each, giving one row per firm-year (300 firms x 7 years).\"})\n\n# 3. Missing outcome\nn_miss = int(df[\"default_next\"].isna().sum())\nmiss_years = sorted(df.loc[df[\"default_next\"].isna(), \"year\"].unique().tolist())\nbefore = len(df)\ndf = df.loc[df[\"default_next\"].notna()].copy()\nstep(\"3_drop_missing_outcome\", \"drop firm-years with default_next empty (outcome year not observed by end-2022 cut)\",\n     before, len(df), \"outcome not observed; no imputation allowed\")\nissues.append({\"issue\": f\"{n_miss} firm-years (after de-duplication) have missing default_next, all in origin year(s) {miss_years} \"\n                        \"(outcome year 2023 beyond the data cut).\",\n               \"handling\": \"Excluded from estimation (no imputation); recorded in the exclusion ledger.\"})\n\n# 4. Missing predictors (none expected; ledger step kept for completeness)\nbefore = len(df)\ndf = df.dropna(subset=PREDICTORS)\nstep(\"4_drop_missing_predictors\", \"drop rows with missing leverage, cash_ratio or log_sales\", before, len(df),\n     \"complete-case for regressors\")\n\ndf[\"default_next\"] = df[\"default_next\"].astype(int)\ndf = df.sort_values([\"firm_id\", \"year\"]).reset_index(drop=True)\ndf[[\"firm_id\", \"year\"] + PREDICTORS + [\"default_next\"]].to_csv(f\"{OUT}/analytic.csv\", index=False)\npd.DataFrame(ledger).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\n# Baseline\ndefault_rate = float(df[\"default_next\"].mean())\n\n# Model: OLS LPM, SE clustered by firm\nX = sm.add_constant(df[PREDICTORS])\ny = df[\"default_next\"]\ngroups = pd.factorize(df[\"firm_id\"])[0]\nfit = sm.OLS(y, X).fit(cov_type=\"cluster\", cov_kwds={\"groups\": groups})\nb = float(fit.params[\"leverage\"]); se = float(fit.bse[\"leverage\"])\n\nres = {\n    \"task_id\": \"F1-conf-03\",\n    \"n_analysed\": int(len(df)),\n    \"n_firms\": int(df[\"firm_id\"].nunique()),\n    \"predictors_used\": PREDICTORS,\n    \"variables_excluded\": excluded,\n    \"beta_leverage\": b,\n    \"se_leverage_cluster\": se,\n    \"r2\": float(fit.rsquared),\n    \"default_rate\": default_rate,\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": issues,\n    \"n_input_rows\": {\"firm_panel.csv\": int(n_raw)},\n    \"random_seed\": None,\n    # supplementary (not required)\n    \"ci95_leverage_cluster\": [float(fit.conf_int().loc[\"leverage\", 0]), float(fit.conf_int().loc[\"leverage\", 1])],\n    \"pvalue_leverage_cluster\": float(fit.pvalues[\"leverage\"]),\n    \"beta_cash_ratio\": float(fit.params[\"cash_ratio\"]),\n    \"se_cash_ratio_cluster\": float(fit.bse[\"cash_ratio\"]),\n    \"beta_log_sales\": float(fit.params[\"log_sales\"]),\n    \"se_log_sales_cluster\": float(fit.bse[\"log_sales\"]),\n    \"intercept\": float(fit.params[\"const\"]),\n    \"n_clusters\": int(len(np.unique(groups))),\n    \"cov_type\": \"cluster-robust by firm_id (statsmodels default small-sample correction)\",\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\nprint(f\"n_analysed={res['n_analysed']} n_firms={res['n_firms']} beta_leverage={b:.6f} se={se:.6f} \"\n      f\"r2={res['r2']:.6f} default_rate={default_rate:.6f}\")\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && cat out/results.json | head -30","description":"Write and run analysis script"}}]}}43{"type":"api-request","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","id":"226009fc-d52c-4a51-a224-50ade12d8f4c","timestamp":"2026-10-30T04:46:30.696Z","version":"2.1.314","querySource":"sdk","shapeHash":"03e0b194203a2592e804e85a4b4ac59f527f377b666c83f8d1983d4175dc695a","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":"6110ce88-30f5-45bf-a450-357a60758da9","keep":12,"tail":["a3c0618f797c847019c16f42d073d27d9b3962aa19ec972dccb739401c4859bf","3027c9fdf39f66ec43eac91f4552855a4dde7ad6e2377a6fc7ea144782130ce1"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"ee3894540003a424ff7062288b5d2c28ecb57b5e1c2e7b09e35d7ee333aad351"}44{"parentUuid":"26be9442-4379-4d3b-99f7-475ffbe7e047","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpBuPtdDgUZqFya7EZL","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSqwgKEAgTGAI4AUIIdGhpbmtpbmcSDKOHtY71Hu8j/rfjUBoMmmwwyHMjJ3HaGTFoIjDxz0+csVWffzqgBXJWcEMJ+TFVJster13km9SesVmWbwkku7YcRl8e1J9A07i2KBkqyAeCepUiNpvTykW36aBOZzaIsUc8TG3AI4MXxnSB1S1qvoY6FNRjda0/0skjEER02y/n1km6kv+0+MBN0+wgP/+GPj0BO9mjBwiFJ51hg9PcF01iuMI2/EXH4Osmc0tzUn7dgfPCYI249QdQ0rh2P7abh1wJiLPC7GoWxUu5IxminB+PspVa/8vEavtbN9G1U0xLX2gfvG7MDgHdvu7F53sAz2DQ7H9kZQuLhESwA4mQkXAdlgzFyVAYYKosLGlC4gABvKOHu8WJNY7vPrAL8xf/gO6eXYUrpOridmFx5eoZcOMrovuy3Gq8Tb+xxD+cihRBNQlElm2khFz0/ppF9/ZwSAVekFZALoCwVByeG1RMkEKl3YgR4DMhCU0QyMze7NXFmTbRq2dYsIqaZaoDKnaD3/MYGuvywvwRoYPpoTck2uJrVgluovadMbVGF8QgqXnM5g49Z1iYj8Bds97sn2o4ZOW4jgYmlroBUPPNc/S24ay/XFn5he/M4PmwBX7dcNOR/35ip1CqiTxWjSWnWEABWsYJq8yLHkTG0NdfY4iIlarxsPTTlFD3NvbBYulIlyTLD3f/FrCFCf/TAtLo6o95oiTCUkR+YVTgtm75Rj1jsDLMF0Js/f23lYanz5dFFhD6O3FmK0CLsfHNGlrkDM6zd3BPPOEqKq9v+Lvw6wpGTT/uT/tk4RmZCht/YxiBy0pi1kN8fWihU/S2IDcxXnlfykSNCJnqURr00JUjrHAUz9WIIbREoXvLaNTwCR1kCT+9v7WEP24BA37VE6TINVg6IPNimijzSZSDDwgqccPHMkmsRmWXc2VSsbfxyfp1h8eGHpTU7QlKIrpJL0zK4BzfhnFq2hju8vEGu2Fln0d3X0oYU+fARoUGXct9CbvozyQt8yDW0aNld1mb0GlJW3IVtsLPU5TtbwgPSgvf367o9/Y7lIlBY5LakYLqB+Ld/tXmzPU60qX2hSD8nwhVqPuG+vHNt3gtfcYwNrbSC1uiVkzNF8pBRGIIx2zAz790q2prOXyM8pYSbUejsr7wR0DGkmcI/OGfKe5VBmrpP7pzlEq1QYQD+jsPeBpO7Eroygin2Qoa4vg9S5PznFKyx2b1V4y8J7Yy7/t13O3aVDZLGaplPWaBYChA39osLuddPksrnE+j0JQaP/6BRBePcgQVyW4rAV+gKC3OtF26MhQhXqvCLUgnEHP0SXLAV834kulIWRWOHxrlujMqYeIQBq+V2UJljG0wMGSTp6rvC0g6dPOo1jZvsPgrB+39FYz91YspZvrd0mfqlxgB"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3384,"cache_read_input_tokens":11165,"output_tokens":1461,"output_tokens_details":{"thinking_tokens":106},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":3384},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1030,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXpBu8X9SxBEbc29G1mD","requestRef":"226009fc-d52c-4a51-a224-50ade12d8f4c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"93e49fc6-e23c-477e-90b4-e5c0e91c6b60","timestamp":"2026-10-30T04:46:33.145Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0159","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","version":"2.1.314","gitBranch":"HEAD"}45{"parentUuid":"93e49fc6-e23c-477e-90b4-e5c0e91c6b60","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpBuPtdDgUZqFya7EZL","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01HjeUFhP1tRbkPiN3xNkNyt","name":"Bash","input":{"command":"cd out && ~/wp29/venv/bin/python - <<'EOF'\ns=open('analysis.py').read()\ns=s.replace('''issues.append({\"issue\": \"Column rating_2025 is a final-vintage (2025) field built from realised defaults through 2022 \"\n                        \"(look-ahead; in the data only rating D firms ever default).\",''','''_dr = raw.drop_duplicates().groupby(\"rating_2025\")[\"default_next\"].mean()\nleak_rate_by_rating = {k: float(v) for k, v in _dr.items()}\nissues.append({\"issue\": \"Column rating_2025 is a final-vintage (2025) field built from realised defaults through 2022 \"\n                        f\"(look-ahead; observed default rate by rating: {leak_rate_by_rating}).\",''')\ns=s.replace('''    \"n_clusters\"''','''    \"default_rate_by_rating_2025_diagnostic\": leak_rate_by_rating,\n    \"n_clusters\"''')\nopen('analysis.py','w').write(s)\nEOF\ncd .. && rm -f out/results.json out/analytic.csv out/exclusions.csv && ~/wp29/venv/bin/python out/analysis.py && shasum -a256 out/results.json out/analytic.csv out/exclusions.csv > /tmp/h1 && ~/wp29/venv/bin/python out/analysis.py >/dev/null && shasum -a256 out/results.json out/analytic.csv out/exclusions.csv | diff - /tmp/h1 && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd,numpy as np,json\nr=json.load(open('out/results.json')); a=pd.read_csv('out/analytic.csv'); e=pd.read_csv('out/exclusions.csv')\nX=np.column_stack([np.ones(len(a)),a[['leverage','cash_ratio','log_sales']]]);y=a.default_next.values\nXtXi=np.linalg.inv(X.T@X);b=XtXi@X.T@y;u=y-X@b\nM=sum(np.outer(X[g].T@u[g],X[g].T@u[g]) for g in a.groupby('firm_id').indices.values())\nn,k=X.shape;G=a.firm_id.nunique();c=G/(G-1)*(n-1)/(n-k)\nse=np.sqrt(np.diag(c*XtXi@M@XtXi))\nprint('beta diff',b[1]-r['beta_leverage'],'se diff',se[1]-r['se_leverage_cluster'])\nprint('r2 diff',1-u@u/((y-y.mean())@(y-y.mean()))-r['r2'])\nprint('rows',len(a)==r['n_analysed'],'firms',a.firm_id.nunique()==r['n_firms'],'rate',a.default_next.mean()==r['default_rate'])\nprint('chain',(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('cols',list(a.columns),'dupkeys',a.duplicated(['firm_id','year']).sum(),'years',sorted(a.year.unique()),'na',a.isna().sum().sum())\nprint('ci contains',r['ci95_leverage_cluster'][0]<r['beta_leverage']<r['ci95_leverage_cluster'][1])\nprint(r['ci95_leverage_cluster'],r['pvalue_leverage_cluster'],r['default_rate_by_rating_2025_diagnostic'])\nEOF","description":"Compute leak diagnostic in script, rerun, verify independently"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3384,"cache_read_input_tokens":11165,"output_tokens":1461,"output_tokens_details":{"thinking_tokens":106},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":3384},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpBu8X9SxBEbc29G1mD","requestRef":"226009fc-d52c-4a51-a224-50ade12d8f4c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"01f4fafe-3989-4368-ac54-2c90caf87dd9","timestamp":"2026-10-30T04:46:42.944Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0159","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","version":"2.1.314","gitBranch":"HEAD"}46{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"01f4fafe-3989-4368-ac54-2c90caf87dd9","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47"}47{"type":"atis-latch","atis":"","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47"}48{"parentUuid":"01f4fafe-3989-4368-ac54-2c90caf87dd9","isSidechain":false,"promptId":"1016f0b0-3824-44f7-afa9-92244e172823","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HjeUFhP1tRbkPiN3xNkNyt","type":"tool_result","content":"n_analysed=1800 n_firms=300 beta_leverage=0.263784 se=0.040757 r2=0.034906 default_rate=0.121111\nIDENTICAL\nbeta diff -2.220446049250313e-16 se diff 4.163336342344337e-17\nr2 diff 0.0\nrows True firms True rate True\nchain True True True True\ncols ['firm_id', 'year', 'leverage', 'cash_ratio', 'log_sales', 'default_next'] dupkeys 0 years [np.int64(2016), np.int64(2017), np.int64(2018), np.int64(2019), np.int64(2020), np.int64(2021)] na 0\nci contains True\n[0.18390149920815163, 0.34366572137950174] 9.663918872288981e-11 {'A': 0.0, 'B': 0.0, 'C': 0.0, 'D': 0.22708333333333333}","is_error":false}]},"uuid":"1de1316f-8cc6-4db6-8c22-8277a2a814a4","timestamp":"2026-10-30T04:46:44.869Z","toolUseResult":{"stdout":"n_analysed=1800 n_firms=300 beta_leverage=0.263784 se=0.040757 r2=0.034906 default_rate=0.121111\nIDENTICAL\nbeta diff -2.220446049250313e-16 se diff 4.163336342344337e-17\nr2 diff 0.0\nrows True firms True rate True\nchain True True True True\ncols ['firm_id', 'year', 'leverage', 'cash_ratio', 'log_sales', 'default_next'] dupkeys 0 years [np.int64(2016), np.int64(2017), np.int64(2018), np.int64(2019), np.int64(2020), np.int64(2021)] na 0\nci contains True\n[0.18390149920815163, 0.34366572137950174] 9.663918872288981e-11 {'A': 0.0, 'B': 0.0, 'C': 0.0, 'D': 0.22708333333333333}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"01f4fafe-3989-4368-ac54-2c90caf87dd9","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0159","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","version":"2.1.314","gitBranch":"HEAD"}49{"type":"api-request-blob","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","hash":"9a9a256bfc9ecb492cac940732edf498fd722f7eabb555f80a8de047c4f05cc8","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01HjeUFhP1tRbkPiN3xNkNyt","name":"Bash","input":{"command":"cd out && ~/wp29/venv/bin/python - <<'EOF'\ns=open('analysis.py').read()\ns=s.replace('''issues.append({\"issue\": \"Column rating_2025 is a final-vintage (2025) field built from realised defaults through 2022 \"\n                        \"(look-ahead; in the data only rating D firms ever default).\",''','''_dr = raw.drop_duplicates().groupby(\"rating_2025\")[\"default_next\"].mean()\nleak_rate_by_rating = {k: float(v) for k, v in _dr.items()}\nissues.append({\"issue\": \"Column rating_2025 is a final-vintage (2025) field built from realised defaults through 2022 \"\n                        f\"(look-ahead; observed default rate by rating: {leak_rate_by_rating}).\",''')\ns=s.replace('''    \"n_clusters\"''','''    \"default_rate_by_rating_2025_diagnostic\": leak_rate_by_rating,\n    \"n_clusters\"''')\nopen('analysis.py','w').write(s)\nEOF\ncd .. && rm -f out/results.json out/analytic.csv out/exclusions.csv && ~/wp29/venv/bin/python out/analysis.py && shasum -a256 out/results.json out/analytic.csv out/exclusions.csv > /tmp/h1 && ~/wp29/venv/bin/python out/analysis.py >/dev/null && shasum -a256 out/results.json out/analytic.csv out/exclusions.csv | diff - /tmp/h1 && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd,numpy as np,json\nr=json.load(open('out/results.json')); a=pd.read_csv('out/analytic.csv'); e=pd.read_csv('out/exclusions.csv')\nX=np.column_stack([np.ones(len(a)),a[['leverage','cash_ratio','log_sales']]]);y=a.default_next.values\nXtXi=np.linalg.inv(X.T@X);b=XtXi@X.T@y;u=y-X@b\nM=sum(np.outer(X[g].T@u[g],X[g].T@u[g]) for g in a.groupby('firm_id').indices.values())\nn,k=X.shape;G=a.firm_id.nunique();c=G/(G-1)*(n-1)/(n-k)\nse=np.sqrt(np.diag(c*XtXi@M@XtXi))\nprint('beta diff',b[1]-r['beta_leverage'],'se diff',se[1]-r['se_leverage_cluster'])\nprint('r2 diff',1-u@u/((y-y.mean())@(y-y.mean()))-r['r2'])\nprint('rows',len(a)==r['n_analysed'],'firms',a.firm_id.nunique()==r['n_firms'],'rate',a.default_next.mean()==r['default_rate'])\nprint('chain',(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('cols',list(a.columns),'dupkeys',a.duplicated(['firm_id','year']).sum(),'years',sorted(a.year.unique()),'na',a.isna().sum().sum())\nprint('ci contains',r['ci95_leverage_cluster'][0]<r['beta_leverage']<r['ci95_leverage_cluster'][1])\nprint(r['ci95_leverage_cluster'],r['pvalue_leverage_cluster'],r['default_rate_by_rating_2025_diagnostic'])\nEOF","description":"Compute leak diagnostic in script, rerun, verify independently"}}]}}50{"type":"api-request","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","id":"091f6aae-d11c-4c4f-9b62-e53b04813dd5","timestamp":"2026-10-30T04:46:44.873Z","version":"2.1.314","querySource":"sdk","shapeHash":"03e0b194203a2592e804e85a4b4ac59f527f377b666c83f8d1983d4175dc695a","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":"226009fc-d52c-4a51-a224-50ade12d8f4c","keep":14,"tail":["9a9a256bfc9ecb492cac940732edf498fd722f7eabb555f80a8de047c4f05cc8","8e32b0bf35254a7ba210eb6a762498f8bb3fd3942dfcd3daf43fb8d6307a7b1e"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"016a398dccbb1afd5d45d30d16063db1c418418f94e606556fa38221b1a42997"}51{"parentUuid":"1de1316f-8cc6-4db6-8c22-8277a2a814a4","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpCx4AWTAfTWPTP3QF6","type":"message","role":"assistant","content":[{"type":"text","text":"Now the audit, checks, phase ledger, and gate run."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1769,"cache_read_input_tokens":14549,"output_tokens":1614,"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":1769},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXpCwmYLkHZpC8TGQWCE","requestRef":"091f6aae-d11c-4c4f-9b62-e53b04813dd5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8138a6b5-b2be-41e7-9f13-c8eb80f10373","timestamp":"2026-10-30T04:46:46.079Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0159","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","version":"2.1.314","gitBranch":"HEAD"}52{"parentUuid":"8138a6b5-b2be-41e7-9f13-c8eb80f10373","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpCx4AWTAfTWPTP3QF6","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_019o3EKPKSVTJie6Um28Dpah","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit\n| file | sha256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/firm_panel.csv | a1f1af88b1ab05900155991f2468cce09695af978923e5bc3ef9c28d45683b6f | 2140 | 7 |\n| data/README.md (codebook) | 26573e982ab6ca9e7f90d2b89bd2878aa6a670f90da21496bff787b59216b9a2 | – | – |\n\nSchema: firm_id (str, F### pattern, 300 distinct), year (int 2016–2022), leverage (ratio, 0.02–0.95), cash_ratio (ratio, 0.0046–0.7406), log_sales (log thousand USD, 1.29–8.88), rating_2025 (A/B/C/D, constant within firm), default_next (0/1, 307 empty).\n- Missing: only default_next, 307 rows, all year 2022 (outcome 2023 is after the end-2022 cut). After de-duplication: 300.\n- Duplicates: 40 rows exactly identical to another row on all columns; 40 duplicated (firm_id, year) keys; no conflicting duplicates. After removal: 2100 rows = 300 firms × 7 years, balanced.\n- Range anomalies: none (ratios in [0,1], no negatives).\n- Information availability: rating_2025 known in 2025 and built from realised defaults through 2022 → violates rule; excluded. Diagnostic: default rate is 0 for A/B/C and ~0.227 for D (leakage).\n- Observation: firms can default in several years (up to 4 defaults per firm); the codebook defines default per year and gives no exit rule, so rows are kept as given (noted as limitation).\nEOF\ncat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n1. Reproducibility: removed outputs, ran analysis.py twice; SHA-256 of results.json, analytic.csv, exclusions.csv identical. PASS\n2. Independent numpy re-computation of OLS beta, CR1 cluster SE (G/(G-1)·(n-1)/(n-k)) and R²: differences ≤ 2.3e-16. PASS\n3. analytic.csv rows = n_analysed (1800); distinct firms = n_firms (300); mean default = default_rate. PASS\n4. Exclusion ledger: n_before − n_removed = n_after on every row; chained; starts at 2140 rows read, ends at 1800. 2140 − 40 − 300 = 1800. PASS\n5. analytic.csv columns = firm_id, year, leverage, cash_ratio, log_sales, default_next (no rating_2025); no duplicate keys; no NA; years 2016–2021 only. PASS\n6. SE positive (0.0408); 95% CI [0.184, 0.344] contains beta 0.264. PASS\n7. Magnitude: beta 0.264 per unit leverage vs baseline default rate 0.121; a 0.21 (1 SD) increase in leverage ↔ ~5.5 pp, plausible. PASS\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 flagged as look-ahead\"],\"notes\":\"data_audit.md\"},\n \"P2\":{\"status\":\"passed\",\"checks\":[\"baseline default_rate=0.1211 on estimation sample\"],\"notes\":\"\"},\n \"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py runs and writes results.json, analytic.csv, exclusions.csv\"],\"notes\":\"no randomness; random_seed null\"},\n \"P4\":{\"status\":\"running\",\"checks\":[\"clean rerun identical hashes\",\"independent numpy recomputation matches\",\"ledger chain sums\",\"checks.md items 1-7 PASS\"],\"notes\":\"\"},\n \"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P2/P3 passed; P4 checks all PASS, running gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .; ls out/state/gate_receipts","description":"Write audit, checks, phases; run gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1769,"cache_read_input_tokens":14549,"output_tokens":1614,"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":1769},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpCwmYLkHZpC8TGQWCE","requestRef":"091f6aae-d11c-4c4f-9b62-e53b04813dd5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1bb21b19-e618-4ef0-a1ac-a7f68c47ddad","timestamp":"2026-10-30T04:47:00.157Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0159","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","version":"2.1.314","gitBranch":"HEAD"}53{"parentUuid":"1bb21b19-e618-4ef0-a1ac-a7f68c47ddad","isSidechain":false,"promptId":"1016f0b0-3824-44f7-afa9-92244e172823","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019o3EKPKSVTJie6Um28Dpah","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 0.8s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0159/out/state/gate_receipts/20261030T044701.027797Z.json\n20261030T044701.027797Z.json","is_error":false}]},"uuid":"027a77bf-0f89-4bf5-a263-db7971356b1c","timestamp":"2026-10-30T04:47:01.038Z","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 0.8s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0159/out/state/gate_receipts/20261030T044701.027797Z.json\n20261030T044701.027797Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"1bb21b19-e618-4ef0-a1ac-a7f68c47ddad","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0159","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","version":"2.1.314","gitBranch":"HEAD"}54{"type":"api-request-blob","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","hash":"0cee53827122dd452f7a8c86960d569f45218678db54b0702a7f232ba3062799","message":{"role":"assistant","content":[{"type":"text","text":"Now the audit, checks, phase ledger, and gate run."},{"type":"tool_use","id":"toolu_019o3EKPKSVTJie6Um28Dpah","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit\n| file | sha256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/firm_panel.csv | a1f1af88b1ab05900155991f2468cce09695af978923e5bc3ef9c28d45683b6f | 2140 | 7 |\n| data/README.md (codebook) | 26573e982ab6ca9e7f90d2b89bd2878aa6a670f90da21496bff787b59216b9a2 | – | – |\n\nSchema: firm_id (str, F### pattern, 300 distinct), year (int 2016–2022), leverage (ratio, 0.02–0.95), cash_ratio (ratio, 0.0046–0.7406), log_sales (log thousand USD, 1.29–8.88), rating_2025 (A/B/C/D, constant within firm), default_next (0/1, 307 empty).\n- Missing: only default_next, 307 rows, all year 2022 (outcome 2023 is after the end-2022 cut). After de-duplication: 300.\n- Duplicates: 40 rows exactly identical to another row on all columns; 40 duplicated (firm_id, year) keys; no conflicting duplicates. After removal: 2100 rows = 300 firms × 7 years, balanced.\n- Range anomalies: none (ratios in [0,1], no negatives).\n- Information availability: rating_2025 known in 2025 and built from realised defaults through 2022 → violates rule; excluded. Diagnostic: default rate is 0 for A/B/C and ~0.227 for D (leakage).\n- Observation: firms can default in several years (up to 4 defaults per firm); the codebook defines default per year and gives no exit rule, so rows are kept as given (noted as limitation).\nEOF\ncat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n1. Reproducibility: removed outputs, ran analysis.py twice; SHA-256 of results.json, analytic.csv, exclusions.csv identical. PASS\n2. Independent numpy re-computation of OLS beta, CR1 cluster SE (G/(G-1)·(n-1)/(n-k)) and R²: differences ≤ 2.3e-16. PASS\n3. analytic.csv rows = n_analysed (1800); distinct firms = n_firms (300); mean default = default_rate. PASS\n4. Exclusion ledger: n_before − n_removed = n_after on every row; chained; starts at 2140 rows read, ends at 1800. 2140 − 40 − 300 = 1800. PASS\n5. analytic.csv columns = firm_id, year, leverage, cash_ratio, log_sales, default_next (no rating_2025); no duplicate keys; no NA; years 2016–2021 only. PASS\n6. SE positive (0.0408); 95% CI [0.184, 0.344] contains beta 0.264. PASS\n7. Magnitude: beta 0.264 per unit leverage vs baseline default rate 0.121; a 0.21 (1 SD) increase in leverage ↔ ~5.5 pp, plausible. PASS\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 flagged as look-ahead\"],\"notes\":\"data_audit.md\"},\n \"P2\":{\"status\":\"passed\",\"checks\":[\"baseline default_rate=0.1211 on estimation sample\"],\"notes\":\"\"},\n \"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py runs and writes results.json, analytic.csv, exclusions.csv\"],\"notes\":\"no randomness; random_seed null\"},\n \"P4\":{\"status\":\"running\",\"checks\":[\"clean rerun identical hashes\",\"independent numpy recomputation matches\",\"ledger chain sums\",\"checks.md items 1-7 PASS\"],\"notes\":\"\"},\n \"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P2/P3 passed; P4 checks all PASS, running gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .; 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The output-contract and reproducibility checks pass. Next I'll write the report and claims."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2322,"cache_read_input_tokens":16318,"output_tokens":2492,"output_tokens_details":{"thinking_tokens":46},"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":2322},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpE8unxJjDV6SEsMxzQ","requestRef":"ca9e5d58-7ad1-4be7-bb5a-2c3e45cbbb92","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8d61c0b3-809b-48de-8a0b-590931a1b132","timestamp":"2026-10-30T04:47:03.171Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0159","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","version":"2.1.314","gitBranch":"HEAD"}58{"parentUuid":"8d61c0b3-809b-48de-8a0b-590931a1b132","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpE9Bfhbpz8f2Sj7NjH","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01G8Kv64m8His1vgPpxntgqN","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F1-conf-03 — Leverage and next-year default (information-respecting)\n\n## Question\nHow is a firm's leverage at the end of year t associated with default in year t+1, using only information available at the end of year t? Estimand: coefficient b1 on `leverage` in the OLS linear-probability model default_next = a + b1·leverage + b2·cash_ratio + b3·log_sales + e. The model is fit on firm-years with an observed outcome, with standard errors clustered by firm. Data are simulated (synthetic panel).\n\n## Data and exclusions\nInput: `data/firm_panel.csv`, 2140 data rows (`n_input_rows`). Exclusion ledger (`out/exclusions.csv`):\n- Column `rating_2025` dropped (no rows removed). It is a 2025 final-vintage field built from realised defaults through 2022 and sample-average leverage, so it was not known at the end of t (`variables_excluded`). Diagnostic: the observed default rate is 0 for ratings A/B/C and about 0.227 for D, so the rating effectively encodes the outcome (`default_rate_by_rating_2025_diagnostic`).\n- 40 exact duplicate rows removed (identical on every column, no conflicting values). That leaves 2100 rows = 300 firms × 7 years (`data_issues`).\n- 300 firm-years with empty `default_next` removed, all with origin year 2022 (the outcome year 2023 lies after the data cut). No outcomes were imputed (`data_issues`).\n- No rows had missing predictors.\nEstimation sample: `n_analysed` = 1800 firm-years, `n_firms` = 300, origin years 2016–2021. It is written to `out/analytic.csv` with columns firm_id, year, leverage, cash_ratio, log_sales, default_next.\n\n## Method\nBaseline: the unconditional default rate in the estimation sample (`default_rate`). Main model: OLS with an intercept and `predictors_used` = leverage, cash_ratio, log_sales (statsmodels). Standard errors are cluster-robust by `firm_id` (300 clusters, `n_clusters`), using the standard CR1 small-sample correction. An independent numpy recomputation matched the coefficient, SE and R² to within 1e-15 (`out/state/checks.md`). The analysis uses no randomness (`random_seed` = null).\n\n## Results\n- Baseline: the default rate is 0.121 (`default_rate`).\n- `beta_leverage` = 0.264: holding cash ratio and log sales fixed, one unit more leverage (debt/assets going from 0 to 1) goes with a 26.4 percentage-point higher probability of default the next year. A one-SD change in leverage (≈0.21, from the audit) therefore corresponds to about 5.5 pp, which is large next to the 12.1% base rate.\n- Other coefficients: `beta_cash_ratio` = −0.002 with SE 0.065, so not distinguishable from zero. `beta_log_sales` = 0.012 with SE 0.009. `intercept` = −0.04 (numbers rounded from results.json).\n- `r2` = 0.035: the three predictors explain little of the variation in individual defaults.\n\n## Uncertainty\n`se_leverage_cluster` = 0.041 (clustered by firm). The 95% CI is [0.184, 0.344] (`ci95_leverage_cluster`), with p ≈ 1e-10 (`pvalue_leverage_cluster`). These rely on large-cluster asymptotics; 300 clusters is ample.\n\n## Limitations and open issues\n- This is an association in a linear-probability model, not a causal effect. LPM fitted values may fall outside [0,1].\n- The data are synthetic. Results describe this simulated panel only.\n- Firms keep appearing after a default year, and some default in several years (up to 4). The codebook defines default per year and gives no exit rule, so all firm-years were kept as provided.\n- I kept the first copy of each duplicate. Because the copies are identical, the choice does not affect the results.\n- I did not test alternatives such as logit/probit or year fixed effects, because the task specifies the model.\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 duplicates and 300 firm-years with unobserved outcomes (origin year 2022).\",\"results_keys\":[\"n_analysed\",\"n_firms\",\"n_input_rows\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Duplicates were identical, so keeping the first copy has no effect.\"},\n {\"id\":\"C2\",\"text\":\"rating_2025 was excluded as a look-ahead variable; in the data only rating-D firms ever default.\",\"results_keys\":[\"variables_excluded\",\"default_rate_by_rating_2025_diagnostic\"],\"status\":\"supported\",\"caveat\":\"The diagnostic only shows leakage; it is not used in estimation.\"},\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, a one-unit increase in leverage is associated with a 0.264 higher probability of next-year default (firm-clustered SE 0.041, 95% CI 0.184-0.344).\",\"results_keys\":[\"beta_leverage\",\"se_leverage_cluster\",\"ci95_leverage_cluster\",\"pvalue_leverage_cluster\"],\"status\":\"supported\",\"caveat\":\"Association in an LPM on synthetic data; not causal.\"},\n {\"id\":\"C5\",\"text\":\"The model explains little individual-level variation (R2 = 0.035).\",\"results_keys\":[\"r2\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P4']['status']='passed'; p['P4']['checks'].append('gate run receipt 20261030T044701.027797Z.json: contract keys+reproducibility ok; failures only P5 files (report/claims) and non-terminal phases, expected before P5')\np['P5']['status']='passed'; p['P5']['checks']=['report.md with 6 sections citing keys','claims.json C1-C5 referencing existing keys','final gate run (see notes)']\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate receipt 20261030T044701); P5 report+claims written; running final gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v '\\[ok\\]'","description":"Write report, claims, update phases, rerun gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2322,"cache_read_input_tokens":16318,"output_tokens":2492,"output_tokens_details":{"thinking_tokens":46},"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":2322},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXpE8unxJjDV6SEsMxzQ","requestRef":"ca9e5d58-7ad1-4be7-bb5a-2c3e45cbbb92","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f258f521-2469-4b3a-94e0-d4169c0312d6","timestamp":"2026-10-30T04:47:22.620Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0159","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","version":"2.1.314","gitBranch":"HEAD"}59{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"f258f521-2469-4b3a-94e0-d4169c0312d6","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47"}60{"type":"atis-latch","atis":"","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47"}61{"parentUuid":"f258f521-2469-4b3a-94e0-d4169c0312d6","isSidechain":false,"promptId":"1016f0b0-3824-44f7-afa9-92244e172823","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01G8Kv64m8His1vgPpxntgqN","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 32 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0159/out/state/gate_receipts/20261030T044723.544715Z.json","is_error":false}]},"uuid":"2887f638-8142-4c7f-9045-5875cd4337f4","timestamp":"2026-10-30T04:47:23.552Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 32 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0159/out/state/gate_receipts/20261030T044723.544715Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"f258f521-2469-4b3a-94e0-d4169c0312d6","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0159","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","version":"2.1.314","gitBranch":"HEAD"}62{"type":"api-request-blob","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","hash":"3344840936e8881867bf590ee9dc77a9ed5b12232c0b34f6b1059f2a64b916c8","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQS+wYKEAgTGAI4AUIIdGhpbmtpbmcSDJFnlplQYyCoZliMKBoM/PgoiokbOEJvyLOJIjCsSvz8POVqTdmtkO3MXV5tQyBUI1ByN6im/vWGvRtYmpwWQioVBA6hynQpjJRC/6kqmAZ4scr1WxerqcxsetrRUX4cVT+ev2PhBrHrI/F4ZkJsECePdYApTcC/taC3iEdPRycwBpo1BfH6wqB5mIf2Sta+QkvktSm0IEdhPy4OMDWXKeX9dSouwqDiirR12AAYB8i4eYMlCmr416sfV3h+JSzb+btg8pznWoeEItsMmSAof6uTOlscpzPSwSw27dlo9SJmnKARsuo8jd0hENEEAEP+8o4xOLoYTk+U1quoZiED9Pen9HIzVEKm/q6qc7ZwYLBdBYAyJOVJH2+Lfgsr9DP8LS9exeIK16uaRds5UTf12TzC+2hhJds60oskvOivxjnQqxcECrq4VpeItkQli/AlQ3OTiLE85Xnc7LEGDuukCWzCLa7rIUrFn2MgBJBR6C/KsVAFYAxpurgl04mbjOxKa5SMna7iWWwrhgTZkNNhxZrGFhQUI+fiZCrbRcWLVGdopQWSIPk52gGX/JFdweYvitDKA/5WcwSieVfbnpT4u+2g1GnL3NS99dNT62eTc3FwnIujQTFMkVVxpTcbM2hYFmgfA3JNYy/leAIfxzTZ/gsTpNXMVcIDb0JQ6NrKA8/KbM8dAwxIib+qgaMGlY4xM4IvK9OvN+iy5u4cgCTfXZNObuxpwOq3KzGhBsoIkuPofGYKIv0bLn7Zr1dN+bljyY1scix1jHOP1F9GG19AvoQDpTygsKFggdCxw9/8oVt4hzwqgz2I15Typ7Pw2M5HCKEWyoOb0WiC5tClXBYoEhr/keVupQbJZsasq3o7f2VyP2G6kZLIN3s0Xwh75mi4V581zfwHvGSnLT2GzpIYK/dYh//zxtY49D79XZuYHkYeuZjivz1tFn3QrXtShTKPLk4W0gs8/rqKBBthdB/Yxar5UAJGD+oHihrODjsXfo5XHbckEkTd91/ZtQXgv6ywIn1u0340BvOSl9xgHfY8BIi/SflIdD15iBwKCFzrA72h6LUJ/YSQ4QrTBwDCkocSC7yBKdXI12LcsREZxd/84zNpiPHLc71/R2OmgCYKfnA6Klbl2Ro5DyUKwq4Mpw4iRIqMGClIcccYAQ=="},{"type":"text","text":"Remaining failures are the P5 deliverables, which is expected at this stage. The output-contract and reproducibility checks pass. Next I'll write the report and claims."},{"type":"tool_use","id":"toolu_01G8Kv64m8His1vgPpxntgqN","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F1-conf-03 — Leverage and next-year default (information-respecting)\n\n## Question\nHow is a firm's leverage at the end of year t associated with default in year t+1, using only information available at the end of year t? Estimand: coefficient b1 on `leverage` in the OLS linear-probability model default_next = a + b1·leverage + b2·cash_ratio + b3·log_sales + e. The model is fit on firm-years with an observed outcome, with standard errors clustered by firm. Data are simulated (synthetic panel).\n\n## Data and exclusions\nInput: `data/firm_panel.csv`, 2140 data rows (`n_input_rows`). Exclusion ledger (`out/exclusions.csv`):\n- Column `rating_2025` dropped (no rows removed). It is a 2025 final-vintage field built from realised defaults through 2022 and sample-average leverage, so it was not known at the end of t (`variables_excluded`). Diagnostic: the observed default rate is 0 for ratings A/B/C and about 0.227 for D, so the rating effectively encodes the outcome (`default_rate_by_rating_2025_diagnostic`).\n- 40 exact duplicate rows removed (identical on every column, no conflicting values). That leaves 2100 rows = 300 firms × 7 years (`data_issues`).\n- 300 firm-years with empty `default_next` removed, all with origin year 2022 (the outcome year 2023 lies after the data cut). No outcomes were imputed (`data_issues`).\n- No rows had missing predictors.\nEstimation sample: `n_analysed` = 1800 firm-years, `n_firms` = 300, origin years 2016–2021. It is written to `out/analytic.csv` with columns firm_id, year, leverage, cash_ratio, log_sales, default_next.\n\n## Method\nBaseline: the unconditional default rate in the estimation sample (`default_rate`). Main model: OLS with an intercept and `predictors_used` = leverage, cash_ratio, log_sales (statsmodels). Standard errors are cluster-robust by `firm_id` (300 clusters, `n_clusters`), using the standard CR1 small-sample correction. An independent numpy recomputation matched the coefficient, SE and R² to within 1e-15 (`out/state/checks.md`). The analysis uses no randomness (`random_seed` = null).\n\n## Results\n- Baseline: the default rate is 0.121 (`default_rate`).\n- `beta_leverage` = 0.264: holding cash ratio and log sales fixed, one unit more leverage (debt/assets going from 0 to 1) goes with a 26.4 percentage-point higher probability of default the next year. A one-SD change in leverage (≈0.21, from the audit) therefore corresponds to about 5.5 pp, which is large next to the 12.1% base rate.\n- Other coefficients: `beta_cash_ratio` = −0.002 with SE 0.065, so not distinguishable from zero. `beta_log_sales` = 0.012 with SE 0.009. `intercept` = −0.04 (numbers rounded from results.json).\n- `r2` = 0.035: the three predictors explain little of the variation in individual defaults.\n\n## Uncertainty\n`se_leverage_cluster` = 0.041 (clustered by firm). The 95% CI is [0.184, 0.344] (`ci95_leverage_cluster`), with p ≈ 1e-10 (`pvalue_leverage_cluster`). These rely on large-cluster asymptotics; 300 clusters is ample.\n\n## Limitations and open issues\n- This is an association in a linear-probability model, not a causal effect. LPM fitted values may fall outside [0,1].\n- The data are synthetic. Results describe this simulated panel only.\n- Firms keep appearing after a default year, and some default in several years (up to 4). The codebook defines default per year and gives no exit rule, so all firm-years were kept as provided.\n- I kept the first copy of each duplicate. Because the copies are identical, the choice does not affect the results.\n- I did not test alternatives such as logit/probit or year fixed effects, because the task specifies the model.\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 duplicates and 300 firm-years with unobserved outcomes (origin year 2022).\",\"results_keys\":[\"n_analysed\",\"n_firms\",\"n_input_rows\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Duplicates were identical, so keeping the first copy has no effect.\"},\n {\"id\":\"C2\",\"text\":\"rating_2025 was excluded as a look-ahead variable; in the data only rating-D firms ever default.\",\"results_keys\":[\"variables_excluded\",\"default_rate_by_rating_2025_diagnostic\"],\"status\":\"supported\",\"caveat\":\"The diagnostic only shows leakage; it is not used in estimation.\"},\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, a one-unit increase in leverage is associated with a 0.264 higher probability of next-year default (firm-clustered SE 0.041, 95% CI 0.184-0.344).\",\"results_keys\":[\"beta_leverage\",\"se_leverage_cluster\",\"ci95_leverage_cluster\",\"pvalue_leverage_cluster\"],\"status\":\"supported\",\"caveat\":\"Association in an LPM on synthetic data; not causal.\"},\n {\"id\":\"C5\",\"text\":\"The model explains little individual-level variation (R2 = 0.035).\",\"results_keys\":[\"r2\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P4']['status']='passed'; p['P4']['checks'].append('gate run receipt 20261030T044701.027797Z.json: contract keys+reproducibility ok; failures only P5 files (report/claims) and non-terminal phases, expected before P5')\np['P5']['status']='passed'; p['P5']['checks']=['report.md with 6 sections citing keys','claims.json C1-C5 referencing existing keys','final gate run (see notes)']\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate receipt 20261030T044701); P5 report+claims written; running final gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v '\\[ok\\]'","description":"Write report, claims, update phases, rerun gate"}}]}}63{"type":"api-request","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","id":"04c600d8-0f0a-4c43-af60-aeda8516f8ef","timestamp":"2026-10-30T04:47:23.556Z","version":"2.1.314","querySource":"sdk","shapeHash":"03e0b194203a2592e804e85a4b4ac59f527f377b666c83f8d1983d4175dc695a","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":"ca9e5d58-7ad1-4be7-bb5a-2c3e45cbbb92","keep":18,"tail":["3344840936e8881867bf590ee9dc77a9ed5b12232c0b34f6b1059f2a64b916c8","b07ef00cbc218d5fee508e83d2854c870a4708f457d7269dd281f926db2acdb2"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"6a20e4fb7bdbcb7be7a01e0c2cd033f981048b5d3c0b45491e1209f69c38662c"}64{"parentUuid":"2887f638-8142-4c7f-9045-5875cd4337f4","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpFoX32ErrZxS48mARj","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01RXDRB77XjedSFtsQdGUrVM","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P5']['notes']='final gate receipt 20261030T044723.544715Z.json: PASSED 0/32 failed'\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 Final gate PASSED (20261030T044723.544715Z). 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Holding cash ratio and log sales fixed, a firm with one unit more leverage at the end of year t (debt/assets going from 0 to 1) has a **0.264** higher probability of defaulting in year t+1. The standard error, clustered by firm, is 0.041 (95% CI 0.184 to 0.344). The final gate check passed with no failures, and re-running `out/analysis.py` reproduces the outputs exactly.\n\n**How the sample was built:**\n- **Rows:** I started from 2,140 rows, removed 40 exact duplicates (no conflicting copies) and then removed 300 firm-years from 2022 whose outcome year falls after the data cut. That leaves 1,800 firm-years from 300 firms (2016–2021); no outcomes were filled in.\n- **Excluded variable:** I left out `rating_2025`. It was assigned in 2025 using the firms' actual default history, so it wasn't known at the end of year t. In the data, only firms rated D ever default, which confirms it effectively contains the outcome.\n- **Predictors used:** leverage, cash_ratio and log_sales.\n\n**Other results:**\n- The baseline default rate in the sample is 0.121. A one-standard-deviation rise in leverage (about 0.21) goes with roughly 5.5 percentage points more default risk, which is large against that base rate.\n- R² is 0.035, so the three predictors explain little of which individual firms default.\n- A separate numpy calculation matched the coefficient, standard error and R² to within about 1e-16.\n\n**Limitations:**\n- This is an association from a linear probability model on simulated data, not a causal effect.\n- Firms stay in the panel after defaulting, and some default in up to four different years. The codebook gives no rule for dropping firms after default, so I kept all firm-years as provided.\n\nFiles are in `out/`:\n- `results.json`\n- `analytic.csv`\n- `exclusions.csv`\n- `analysis.py`\n- `report.md`\n- `claims.json`\n- `state/` (working notes, data audit, checks and gate receipts)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":321,"cache_read_input_tokens":21227,"output_tokens":707,"output_tokens_details":{"thinking_tokens":14},"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":321},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpG5UUXcGwmgnME18YP","requestRef":"7f0e8881-4220-4b5b-84f0-ed2deb2bca85","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3e3e62b6-12f2-4817-8442-ee62c0464a59","timestamp":"2026-10-30T04:47:34.590Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0159","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","version":"2.1.314","gitBranch":"HEAD"}70{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"3e3e62b6-12f2-4817-8442-ee62c0464a59","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47"}71{"type":"cost-state","sessionId":"b7cfa568-69d6-43d7-8ac1-bbeeea8e0f47","totalCostUSD":0.34820619999999997,"totalAPIDuration":101912,"totalAPIDurationWithoutRetries":101907,"totalToolDuration":6113,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":108211,"startTime":1793335546388,"modelUsage":{"claude-opus-5-5":{"inputTokens":24,"outputTokens":11184,"thinkingTokens":466,"cacheReadInputTokens":121051,"cacheCreationInputTokens":20044,"cacheCreationEphemeral5mInputTokens":20044,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.34820619999999997}},"hasUnknownModelCost":false}72