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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`. Below is some general advice about doing careful empirical research; it adds nothing specific to this task and you are free to ignore it.\n\nCareful empirical work usually begins by reading the task statement slowly and more than once, because the most common errors in applied research come not from difficult mathematics but from a misunderstanding of what exactly is being asked, which quantity is of interest, and which inputs are legitimately available. It is worth pausing to think about what the quantity of interest actually means, under what conditions it is well defined, and what would make an estimate of it misleading. Many published results have been overturned not because the estimator was wrong but because the sample was not what the authors believed it to be, or because information that should not have been available at the time of a decision had quietly entered the analysis.\n\nData deserve suspicion. Files assembled from several sources often carry inconsistencies: duplicated records, mixed units, shifted dates, columns whose definitions changed mid-sample, or values that were revised after the fact. Good practice is to look at the data before modelling them: distributions, ranges, missing values, identifiers, and whether counts add up. When something looks odd, it is better to document it and decide explicitly how to handle it than to let a default silently decide. The same applies to transformations: scaling, differencing, winsorising and imputing all change what is being estimated, and each deserves a sentence of justification.\n\nSimple baselines are underrated. A naïve or unconditional estimate computed with exactly the same evaluation rule as the main method tells the reader how much the method actually adds and protects against the illusion of progress created by a complicated pipeline. If the elaborate method does not beat the simple one, that is a finding, not a failure.\n\nUncertainty is part of the result. Standard errors must respect the dependence structure of the data; a confidence interval that ignores clustering, serial correlation or overlapping targets is not conservative, it is wrong. Where the sampling distribution is unknown, resampling or simulation can help, but the assumptions behind them should be stated. A result that is not statistically distinguishable from zero should be reported as such, in plain words.\n\nReproducibility is a courtesy to the reader and a safeguard for the author. Keeping the analysis in a script that reads the raw inputs and regenerates every reported number makes it possible to check the work, to find mistakes, and to rerun the analysis when the inputs change. Fixing random seeds, recording software versions and keeping intermediate files out of the final results all reduce the chance that a number in the report cannot be traced back to a computation.\n\nFinally, honesty about limitations is what distinguishes research from advocacy. Every analysis has weaknesses: assumptions that may not hold, data that may not be representative, methods that trade one bias for another. Stating them clearly, together with what would be needed to resolve them, is more valuable than a confident conclusion that the evidence does not support. When a task cannot be completed as specified, saying so, and saying why, is the correct outcome.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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the user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0015","sessionId":"1a2c49fb-1651-4b69-9452-d6f39db0c089","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"1a2c49fb-1651-4b69-9452-d6f39db0c089"}12{"parentUuid":"9e0615bf-266c-43c5-a4d9-7b3ecf9d1bfa","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsuNoeQxKuYZUyDt6XS","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KBu2F491oVRZwkUv7JzQMH","name":"Bash","input":{"command":"cat TASK.md; 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The estimand is the coefficient on `leverage` in the linear-probability model default_next = a + b1·leverage + b2·cash_ratio + b3·log_sales + e estimated by OLS on the firm-years whose outcome is observed, with standard errors clustered by firm. The analytic dataset must contain only predictors whose values were known at the origin year.\n\n## Inputs\n- `data/firm_panel.csv` — simulated data (synthetic firm-year panel; not observed data), 300 firms, years 2016–2022, one intended row per firm-year. Columns `firm_id`, `year`, `leverage`, `cash_ratio`, `log_sales`, `rating_2025`, `default_next`; definitions, units and the date at which each field becomes known are in `data/README.md` (codebook). Empty cells mean the value is not observed. The extract may contain assembly errors.\n\n## Information-availability rules\n- A variable may enter the model for origin year t only if its value would have been known at the end of year t (see the codebook's last column). Any variable that does not satisfy this rule must be excluded from the analytic dataset and listed in `variables_excluded`.\n- Firm-years whose outcome `default_next` is not observed cannot be used for estimation and must be listed in the exclusion ledger; do not impute outcomes.\n- Use only `data/firm_panel.csv`.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F1-conf-03\" |\n| `n_analysed` | integer | firm-years | observations in the estimation sample |\n| `n_firms` | integer | firms | distinct firms in the estimation sample |\n| `predictors_used` | array of strings | — | column names entered as regressors (excluding the intercept) |\n| `variables_excluded` | array of {\"variable\",\"reason\"} | — | columns of the file deliberately not used as predictors and why |\n| `beta_leverage` | number | probability points | OLS coefficient on `leverage` |\n| `se_leverage_cluster` | number | — | standard error of `beta_leverage` clustered by `firm_id` |\n| `r2` | number | — | in-sample R² of the model |\n| `default_rate` | number | — | baseline: share of firm-years with default_next = 1 in the estimation sample |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | every data problem detected and how it was handled (empty list if none) |\n| `n_input_rows` | object | rows | {\"firm_panel.csv\": number of data rows read (excluding the header)} |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/analytic.csv` — the estimation sample: one row per firm-year used, columns `firm_id`, `year`, the predictors used and `default_next`; exactly `n_analysed` rows and no column that violates the information rule.\n- `out/exclusions.csv` — the exclusion ledger with columns `step`, `rule`, `n_before`, `n_after`, `n_removed`, `rationale`; one row per step from the number of rows read to `n_analysed` (`n_before − n_removed = n_after` on every row; each step's `n_before` equals the previous step's `n_after`). Column removals may be listed with `n_removed = 0`.\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/analytic.csv` and `out/exclusions.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work.\nfirm_panel.csv\nREADME.md","is_error":false}]},"uuid":"770325e7-8d75-41b4-82c1-0fea1c0a9646","timestamp":"2026-10-30T05:35:15.956Z","toolUseResult":{"stdout":"# 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). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work.\nfirm_panel.csv\nREADME.md","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"f799851d-64ec-472f-be42-490821e8f96a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0015","sessionId":"1a2c49fb-1651-4b69-9452-d6f39db0c089","version":"2.1.314","gitBranch":"HEAD"}14{"type":"api-request-blob","sessionId":"1a2c49fb-1651-4b69-9452-d6f39db0c089","hash":"925d86974fe2f05cfdcb35cea6a8fc9163fadf5b094ab2a89c7a4628d86cb83e","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01KBu2F491oVRZwkUv7JzQMH","name":"Bash","input":{"command":"cat TASK.md; ls -R data | head -50"}}]}}15{"type":"api-request","sessionId":"1a2c49fb-1651-4b69-9452-d6f39db0c089","id":"883cdfdf-15c6-4c2b-8bb2-4e063492bbe6","timestamp":"2026-10-30T05:35:15.960Z","version":"2.1.314","querySource":"sdk","shapeHash":"dbecb1913cd4f99ee9b180f50569e03a2eaa3fd36e283248a5d20ac285f42bec","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":"ac26db06-0b62-4419-a134-450721325d34","keep":2,"tail":["925d86974fe2f05cfdcb35cea6a8fc9163fadf5b094ab2a89c7a4628d86cb83e","6fe9b57344f892edd16b4853e7e949cf616cb6ad1db675c42f745729104532d0"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"a7fd0f504b69d0bc150554ead78afc04d1b4badee1118509ab01fea27fe119de"}16{"parentUuid":"770325e7-8d75-41b4-82c1-0fea1c0a9646","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsuXxpe2okmLmXVvjsA","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_016ufY2CLVz1UwH7X2Gdb91X","name":"Bash","input":{"command":"cat data/README.md; cd data; head -5 firm_panel.csv; ~/wp29/venv/bin/python -c \"\nimport pandas as pd;d=pd.read_csv('firm_panel.csv');print(d.describe(include='all').T);print(d.dtypes);print(d.isna().sum());print(d.duplicated().sum(),d.duplicated(['firm_id','year']).sum());print(d.year.value_counts().sort_index());print(d.default_next.value_counts(dropna=False))\nfor c in ['leverage','cash_ratio','log_sales']: print(c, d[c].sort_values().head(5).tolist(), d[c].sort_values().dropna().tail(5).tolist())\n\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1615,"cache_read_input_tokens":3186,"output_tokens":284,"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":1615},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsuXgCuBsca6G7VxfHe","requestRef":"883cdfdf-15c6-4c2b-8bb2-4e063492bbe6","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5b24f249-223c-45e1-9aec-2e9a67590bdd","timestamp":"2026-10-30T05:35:18.845Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0015","sessionId":"1a2c49fb-1651-4b69-9452-d6f39db0c089","version":"2.1.314","gitBranch":"HEAD"}17{"parentUuid":"5b24f249-223c-45e1-9aec-2e9a67590bdd","isSidechain":false,"promptId":"64d26444-90a3-4c8b-b196-b621861df13e","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016ufY2CLVz1UwH7X2Gdb91X","type":"tool_result","content":"# Codebook — F1-conf-03 (`firm_panel.csv`)\n\nSimulated data (a synthetic firm-year panel generated for this exercise; it is not observed data). One intended row per firm and year, years 2016–2022, 300 firms. The extract was assembled from the firm master file and the annual accounts and may contain assembly errors.\n\n| column | type | definition | when the value becomes known |\n|---|---|---|---|\n| `firm_id` | string | firm identifier | at origin |\n| `year` | integer | fiscal year t (the origin year) | at origin |\n| `leverage` | number | total debt / total assets at the end of year t | end of year t |\n| `cash_ratio` | number | cash and equivalents / total assets at the end of year t | end of year t |\n| `log_sales` | number | natural log of sales in year t (thousands USD) | end of year t |\n| `rating_2025` | string (A/B/C/D) | credit rating assigned to the firm in the 2025 portfolio review; the review used the firm's complete realised default history through 2022 and its average leverage over the sample; one value per firm, constant across years (final-vintage field) | 2025 |\n| `default_next` | integer 0/1 | 1 if the firm defaulted in year t+1, 0 otherwise; empty when the outcome year had not yet been observed at the data cut (end of 2022) | end of year t+1 |\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\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 object\nyear int64\nleverage float64\ncash_ratio float64\nlog_sales float64\nrating_2025 object\ndefault_next float64\ndtype: object\nfirm_id 0\nyear 0\nleverage 0\ncash_ratio 0\nlog_sales 0\nrating_2025 0\ndefault_next 307\ndtype: int64\n40 40\nyear\n2016 305\n2017 301\n2018 309\n2019 305\n2020 306\n2021 307\n2022 307\nName: count, dtype: int64\ndefault_next\n0.0 1609\nNaN 307\n1.0 224\nName: count, dtype: int64\nleverage [0.02, 0.02, 0.02, 0.02, 0.02] [0.9111, 0.9249, 0.9383, 0.95, 0.95]\ncash_ratio [0.0046, 0.0052, 0.0059, 0.0061, 0.0066] [0.6295, 0.6392, 0.688, 0.688, 0.7406]\nlog_sales [1.2882, 1.5626, 1.6894, 1.9413, 1.9791] [7.803, 7.8546, 8.1378, 8.1917, 8.8786]","is_error":false}]},"uuid":"a346c079-b8e5-4d9e-8a4d-fc752124ec4f","timestamp":"2026-10-30T05:35:19.066Z","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 |\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\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 object\nyear int64\nleverage float64\ncash_ratio float64\nlog_sales float64\nrating_2025 object\ndefault_next float64\ndtype: object\nfirm_id 0\nyear 0\nleverage 0\ncash_ratio 0\nlog_sales 0\nrating_2025 0\ndefault_next 307\ndtype: int64\n40 40\nyear\n2016 305\n2017 301\n2018 309\n2019 305\n2020 306\n2021 307\n2022 307\nName: count, dtype: int64\ndefault_next\n0.0 1609\nNaN 307\n1.0 224\nName: count, dtype: int64\nleverage [0.02, 0.02, 0.02, 0.02, 0.02] [0.9111, 0.9249, 0.9383, 0.95, 0.95]\ncash_ratio [0.0046, 0.0052, 0.0059, 0.0061, 0.0066] [0.6295, 0.6392, 0.688, 0.688, 0.7406]\nlog_sales [1.2882, 1.5626, 1.6894, 1.9413, 1.9791] [7.803, 7.8546, 8.1378, 8.1917, 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data; ~/wp29/venv/bin/python -c \"\nimport pandas as pd;d=pd.read_csv('firm_panel.csv').drop_duplicates();print(len(d));print(d.groupby('year').default_next.apply(lambda s:s.isna().sum()));print(d.groupby('firm_id').size().value_counts());print(d.groupby('firm_id').rating_2025.nunique().value_counts());print(sorted(d.firm_id.unique())[:3],sorted(d.firm_id.unique())[-3:]);print((d.leverage==0.02).sum(),(d.leverage==0.95).sum());print(d.groupby('rating_2025').default_next.mean())\nprint(d[d.default_next.isna()&(d.year<2022)])\n\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1585,"cache_read_input_tokens":4801,"output_tokens":421,"output_tokens_details":{"thinking_tokens":115},"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":1585},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsukyAB9xYqBgTTXNbk","requestRef":"3972a668-e155-4db5-abed-31389d957920","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"44c7b1e7-aa79-440b-a858-720c246ff669","timestamp":"2026-10-30T05:35:23.005Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0015/data","sessionId":"1a2c49fb-1651-4b69-9452-d6f39db0c089","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"44c7b1e7-aa79-440b-a858-720c246ff669","isSidechain":false,"promptId":"64d26444-90a3-4c8b-b196-b621861df13e","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01C6BCgeA8m1DeegodiLUBnH","type":"tool_result","content":"(eval):cd:1: no such file or directory: data\n2100\nyear\n2016 0\n2017 0\n2018 0\n2019 0\n2020 0\n2021 0\n2022 300\nName: default_next, dtype: int64\n7 300\nName: count, dtype: int64\nrating_2025\n1 300\nName: count, dtype: int64\n['F001', 'F002', 'F003'] ['F298', 'F299', 'F300']\n57 1\nrating_2025\nA 0.000000\nB 0.000000\nC 0.000000\nD 0.227083\nName: default_next, dtype: float64\nEmpty DataFrame\nColumns: [firm_id, year, leverage, cash_ratio, log_sales, rating_2025, default_next]\nIndex: []","is_error":false}]},"uuid":"786f5061-faf4-455c-a946-e947cff6e9fb","timestamp":"2026-10-30T05:35:23.203Z","toolUseResult":{"stdout":"(eval):cd:1: no such file or directory: data\n2100\nyear\n2016 0\n2017 0\n2018 0\n2019 0\n2020 0\n2021 0\n2022 300\nName: default_next, dtype: int64\n7 300\nName: count, dtype: int64\nrating_2025\n1 300\nName: count, dtype: int64\n['F001', 'F002', 'F003'] ['F298', 'F299', 'F300']\n57 1\nrating_2025\nA 0.000000\nB 0.000000\nC 0.000000\nD 0.227083\nName: default_next, dtype: float64\nEmpty DataFrame\nColumns: [firm_id, year, leverage, cash_ratio, log_sales, rating_2025, default_next]\nIndex: []","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"44c7b1e7-aa79-440b-a858-720c246ff669","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0015/data","sessionId":"1a2c49fb-1651-4b69-9452-d6f39db0c089","version":"2.1.314","gitBranch":"HEAD"}25{"type":"api-request-blob","sessionId":"1a2c49fb-1651-4b69-9452-d6f39db0c089","hash":"e04988de10fa0b7292cf1219dc40b91734acf527e56a2137080abdcb82f0eb92","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01C6BCgeA8m1DeegodiLUBnH","name":"Bash","input":{"command":"cd data; ~/wp29/venv/bin/python -c \"\nimport pandas as pd;d=pd.read_csv('firm_panel.csv').drop_duplicates();print(len(d));print(d.groupby('year').default_next.apply(lambda s:s.isna().sum()));print(d.groupby('firm_id').size().value_counts());print(d.groupby('firm_id').rating_2025.nunique().value_counts());print(sorted(d.firm_id.unique())[:3],sorted(d.firm_id.unique())[-3:]);print((d.leverage==0.02).sum(),(d.leverage==0.95).sum());print(d.groupby('rating_2025').default_next.mean())\nprint(d[d.default_next.isna()&(d.year<2022)])\n\""}}]}}26{"type":"api-request","sessionId":"1a2c49fb-1651-4b69-9452-d6f39db0c089","id":"1ee5a06f-9368-472e-acd1-71639aad8fba","timestamp":"2026-10-30T05:35:23.205Z","version":"2.1.314","querySource":"sdk","shapeHash":"dbecb1913cd4f99ee9b180f50569e03a2eaa3fd36e283248a5d20ac285f42bec","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":"3972a668-e155-4db5-abed-31389d957920","keep":6,"tail":["e04988de10fa0b7292cf1219dc40b91734acf527e56a2137080abdcb82f0eb92","ccb9c4d7d9007ccc92ad7772927860803e239c4e82719dfc9d7f966d57db6677"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"5479c56f80302f894fa981b881a388b56cdaadc55df6ab645365d0fee6c940d0"}27{"parentUuid":"786f5061-faf4-455c-a946-e947cff6e9fb","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsv4wdmMxE3DAtoNMf6","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":671,"cache_read_input_tokens":6386,"output_tokens":2338,"output_tokens_details":{"thinking_tokens":103},"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":671},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":616,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsv4fXS9aRmA2iWBnGk","requestRef":"1ee5a06f-9368-472e-acd1-71639aad8fba","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e08bc458-96f0-4345-8262-d322750aef89","timestamp":"2026-10-30T05:35:25.009Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0015/data","sessionId":"1a2c49fb-1651-4b69-9452-d6f39db0c089","version":"2.1.314","gitBranch":"HEAD"}28{"parentUuid":"e08bc458-96f0-4345-8262-d322750aef89","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsv4wdmMxE3DAtoNMf6","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012Mcyz7srjANwbWd2FKvSRw","name":"Bash","input":{"command":"cd ..; pwd; mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"F1-conf-03: regenerate results.json, analytic.csv, exclusions.csv from data/ only.\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nimport statsmodels.formula.api as smf\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nOUT = os.path.join(ROOT, \"out\")\nraw = pd.read_csv(os.path.join(ROOT, \"data\", \"firm_panel.csv\"))\nn_raw = len(raw)\nPRED = [\"leverage\", \"cash_ratio\", \"log_sales\"]\nledger, issues = [], []\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 firm_panel.csv\", n_raw, n_raw, \"starting point\")\n\n# Step 1: drop leakage column (column removal, no rows)\nd = raw.drop(columns=[\"rating_2025\"])\nstep(\"1_drop_rating_2025\", \"exclude column rating_2025 (known 2025)\", n_raw, n_raw,\n \"final-vintage field built from realised defaults through 2022 and sample-average leverage; not known at end of year t (look-ahead leakage)\")\n\n# Step 2: exact duplicate rows\nn_exact = int(d.duplicated().sum())\nassert int(raw.duplicated().sum()) == n_exact\nb = len(d); d = d.drop_duplicates().reset_index(drop=True)\nstep(\"2_drop_exact_duplicates\", \"remove exact duplicate firm-year rows\", b, len(d),\n \"assembly error: identical rows repeated; one copy kept\")\nassert not d.duplicated([\"firm_id\", \"year\"]).any(), \"conflicting firm-year duplicates\"\nissues.append(dict(issue=f\"{n_exact} exact duplicate rows (same firm_id, year and all values)\",\n handling=\"dropped; first copy kept, giving one row per firm-year (300 firms x 7 years)\"))\n\n# Step 3: outcome not observed\nb = len(d); miss = d[\"default_next\"].isna()\nmiss_years = sorted(d.loc[miss, \"year\"].unique().tolist())\nd = d[~miss].copy()\nstep(\"3_drop_unobserved_outcome\", \"remove firm-years with default_next empty\", b, len(d),\n f\"outcome year not observed by data cut (origin years {miss_years}); outcomes not imputed\")\nissues.append(dict(issue=f\"default_next missing for {int(miss.sum())} unique firm-years (origin years {miss_years}: outcome year 2023 after the end-2022 data cut)\",\n handling=\"excluded from estimation, not imputed\"))\n\n# Step 4: missing predictors\nb = len(d); d = d.dropna(subset=PRED)\nstep(\"4_drop_missing_predictors\", \"remove rows with missing leverage/cash_ratio/log_sales\", b, len(d),\n \"complete-case requirement for OLS (none found)\")\n\nn_floor = int((raw.drop_duplicates()[\"leverage\"] == 0.02).sum())\nissues.append(dict(issue=f\"leverage takes the value 0.02 exactly in {n_floor} unique firm-years (minimum of the variable), suggesting bottom-coding/censoring at 0.02\",\n handling=\"kept as recorded; noted as limitation (cannot recover true values)\"))\nissues.append(dict(issue=\"rating_2025 is a final-vintage (2025) field constructed from realised default history and sample-average leverage; it is perfectly informative of default here (all defaults occur in rating D)\",\n handling=\"excluded from the analytic dataset as look-ahead leakage\"))\n\nd[\"default_next\"] = d[\"default_next\"].astype(int)\nd = d.sort_values([\"firm_id\", \"year\"]).reset_index(drop=True)\nd[[\"firm_id\", \"year\"] + PRED + [\"default_next\"]].to_csv(os.path.join(OUT, \"analytic.csv\"), index=False)\npd.DataFrame(ledger).to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\n\ncodes = pd.factorize(d[\"firm_id\"])[0]\nm = smf.ols(\"default_next ~ leverage + cash_ratio + log_sales\", data=d).fit(\n cov_type=\"cluster\", cov_kwds={\"groups\": codes})\nres = dict(\n task_id=\"F1-conf-03\",\n n_analysed=int(len(d)),\n n_firms=int(d[\"firm_id\"].nunique()),\n predictors_used=PRED,\n variables_excluded=[dict(variable=\"rating_2025\",\n reason=\"becomes known in 2025; built from realised default history through 2022 and average leverage over the sample -> violates end-of-year-t information rule (outcome leakage)\"),\n dict(variable=\"default_next\", reason=\"outcome variable (known end of year t+1); used only as dependent variable\"),\n dict(variable=\"firm_id\", reason=\"identifier; used only for clustering\"),\n dict(variable=\"year\", reason=\"origin-year index; not a regressor in the specified model\")],\n beta_leverage=float(m.params[\"leverage\"]),\n se_leverage_cluster=float(m.bse[\"leverage\"]),\n r2=float(m.rsquared),\n default_rate=float(d[\"default_next\"].mean()),\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 extra=dict(ci95_leverage=[float(x) for x in m.conf_int().loc[\"leverage\"]],\n p_leverage=float(m.pvalues[\"leverage\"]),\n params={k: float(v) for k, v in m.params.items()},\n se_cluster={k: float(v) for k, v in m.bse.items()},\n se_cluster_note=\"statsmodels cluster-robust (CR1 with small-sample correction), 300 clusters\"),\n)\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps(res, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/exclusions.csv"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":671,"cache_read_input_tokens":6386,"output_tokens":2338,"output_tokens_details":{"thinking_tokens":103},"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":671},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsv4fXS9aRmA2iWBnGk","requestRef":"1ee5a06f-9368-472e-acd1-71639aad8fba","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6a3fe973-29d8-4591-b380-63f9aba30411","timestamp":"2026-10-30T05:35:42.043Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0015/data","sessionId":"1a2c49fb-1651-4b69-9452-d6f39db0c089","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"6a3fe973-29d8-4591-b380-63f9aba30411","isSidechain":false,"promptId":"64d26444-90a3-4c8b-b196-b621861df13e","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012Mcyz7srjANwbWd2FKvSRw","type":"tool_result","content":"/Users/simon-pierreboucher/wp29/runs/RA0015\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\": \"becomes known in 2025; built from realised default history through 2022 and average leverage over the sample -> violates end-of-year-t information rule (outcome leakage)\"\n },\n {\n \"variable\": \"default_next\",\n \"reason\": \"outcome variable (known end of year t+1); used only as dependent variable\"\n },\n {\n \"variable\": \"firm_id\",\n \"reason\": \"identifier; used only for clustering\"\n },\n {\n \"variable\": \"year\",\n \"reason\": \"origin-year index; not a regressor in the specified model\"\n }\n ],\n \"beta_leverage\": 0.2637836102938267,\n \"se_leverage_cluster\": 0.04075692804346149,\n \"r2\": 0.03490564212216729,\n \"default_rate\": 0.12111111111111111,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"40 exact duplicate rows (same firm_id, year and all values)\",\n \"handling\": \"dropped; first copy kept, giving one row per firm-year (300 firms x 7 years)\"\n },\n {\n \"issue\": \"default_next missing for 300 unique firm-years (origin years [2022]: outcome year 2023 after the end-2022 data cut)\",\n \"handling\": \"excluded from estimation, not imputed\"\n },\n {\n \"issue\": \"leverage takes the value 0.02 exactly in 57 unique firm-years (minimum of the variable), suggesting bottom-coding/censoring at 0.02\",\n \"handling\": \"kept as recorded; noted as limitation (cannot recover true values)\"\n },\n {\n \"issue\": \"rating_2025 is a final-vintage (2025) field constructed from realised default history and sample-average leverage; it is perfectly informative of default here (all defaults occur in rating D)\",\n \"handling\": \"excluded from the analytic dataset as look-ahead leakage\"\n }\n ],\n \"n_input_rows\": {\n \"firm_panel.csv\": 2140\n },\n \"random_seed\": null,\n \"extra\": {\n \"ci95_leverage\": [\n 0.18390149920815163,\n 0.34366572137950174\n ],\n \"p_leverage\": 9.663918872288908e-11,\n \"params\": {\n \"Intercept\": 0.08997926409935522,\n \"leverage\": 0.2637836102938267,\n \"cash_ratio\": -0.18594837465723182,\n \"log_sales\": -0.007342181807581363\n },\n \"se_cluster\": {\n \"Intercept\": 0.04351602070943619,\n \"leverage\": 0.04075692804346149,\n \"cash_ratio\": 0.058886083766457535,\n \"log_sales\": 0.007626106514531851\n },\n \"se_cluster_note\": \"statsmodels cluster-robust (CR1 with small-sample correction), 300 clusters\"\n }\n}\nstep,rule,n_before,n_after,n_removed,rationale\n0_read,rows read from firm_panel.csv,2140,2140,0,starting point\n1_drop_rating_2025,exclude column rating_2025 (known 2025),2140,2140,0,final-vintage field built from realised defaults through 2022 and sample-average leverage; not known at end of year t (look-ahead leakage)\n2_drop_exact_duplicates,remove exact duplicate firm-year rows,2140,2100,40,assembly error: identical rows repeated; one copy kept\n3_drop_unobserved_outcome,remove firm-years with default_next empty,2100,1800,300,outcome year not observed by data cut (origin years [2022]); outcomes not imputed\n4_drop_missing_predictors,remove rows with missing leverage/cash_ratio/log_sales,1800,1800,0,complete-case requirement for OLS (none found)","is_error":false}]},"uuid":"4ce3d3cb-7c09-4dcf-a0a5-b0f50d1c2abd","timestamp":"2026-10-30T05:35:42.566Z","toolUseResult":{"stdout":"/Users/simon-pierreboucher/wp29/runs/RA0015\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\": \"becomes known in 2025; built from realised default history through 2022 and average leverage over the sample -> violates end-of-year-t information rule (outcome leakage)\"\n },\n {\n \"variable\": \"default_next\",\n \"reason\": \"outcome variable (known end of year t+1); used only as dependent variable\"\n },\n {\n \"variable\": \"firm_id\",\n \"reason\": \"identifier; used only for clustering\"\n },\n {\n \"variable\": \"year\",\n \"reason\": \"origin-year index; not a regressor in the specified model\"\n }\n ],\n \"beta_leverage\": 0.2637836102938267,\n \"se_leverage_cluster\": 0.04075692804346149,\n \"r2\": 0.03490564212216729,\n \"default_rate\": 0.12111111111111111,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"40 exact duplicate rows (same firm_id, year and all values)\",\n \"handling\": \"dropped; first copy kept, giving one row per firm-year (300 firms x 7 years)\"\n },\n {\n \"issue\": \"default_next missing for 300 unique firm-years (origin years [2022]: outcome year 2023 after the end-2022 data cut)\",\n \"handling\": \"excluded from estimation, not imputed\"\n },\n {\n \"issue\": \"leverage takes the value 0.02 exactly in 57 unique firm-years (minimum of the variable), suggesting bottom-coding/censoring at 0.02\",\n \"handling\": \"kept as recorded; noted as limitation (cannot recover true values)\"\n },\n {\n \"issue\": \"rating_2025 is a final-vintage (2025) field constructed from realised default history and sample-average leverage; it is perfectly informative of default here (all defaults occur in rating D)\",\n \"handling\": \"excluded from the analytic dataset as look-ahead leakage\"\n }\n ],\n \"n_input_rows\": {\n \"firm_panel.csv\": 2140\n },\n \"random_seed\": null,\n \"extra\": {\n \"ci95_leverage\": [\n 0.18390149920815163,\n 0.34366572137950174\n ],\n \"p_leverage\": 9.663918872288908e-11,\n \"params\": {\n \"Intercept\": 0.08997926409935522,\n \"leverage\": 0.2637836102938267,\n \"cash_ratio\": -0.18594837465723182,\n \"log_sales\": -0.007342181807581363\n },\n \"se_cluster\": {\n \"Intercept\": 0.04351602070943619,\n \"leverage\": 0.04075692804346149,\n \"cash_ratio\": 0.058886083766457535,\n \"log_sales\": 0.007626106514531851\n },\n \"se_cluster_note\": \"statsmodels cluster-robust (CR1 with small-sample correction), 300 clusters\"\n }\n}\nstep,rule,n_before,n_after,n_removed,rationale\n0_read,rows read from firm_panel.csv,2140,2140,0,starting point\n1_drop_rating_2025,exclude column rating_2025 (known 2025),2140,2140,0,final-vintage field built from realised defaults through 2022 and sample-average leverage; not known at end of year t (look-ahead leakage)\n2_drop_exact_duplicates,remove exact duplicate firm-year rows,2140,2100,40,assembly error: identical rows repeated; one copy kept\n3_drop_unobserved_outcome,remove firm-years with default_next empty,2100,1800,300,outcome year not observed by data cut (origin years [2022]); outcomes not imputed\n4_drop_missing_predictors,remove rows with missing leverage/cash_ratio/log_sales,1800,1800,0,complete-case requirement for OLS (none found)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"6a3fe973-29d8-4591-b380-63f9aba30411","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0015","sessionId":"1a2c49fb-1651-4b69-9452-d6f39db0c089","version":"2.1.314","gitBranch":"HEAD"}30{"type":"api-request-blob","sessionId":"1a2c49fb-1651-4b69-9452-d6f39db0c089","hash":"35d40c6b29cd0f6f37312a757a442245e25ee1458a6caf2cbc3c8e40cf1b1fae","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_012Mcyz7srjANwbWd2FKvSRw","name":"Bash","input":{"command":"cd ..; pwd; mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"F1-conf-03: regenerate results.json, analytic.csv, exclusions.csv from data/ only.\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nimport statsmodels.formula.api as smf\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nOUT = os.path.join(ROOT, \"out\")\nraw = pd.read_csv(os.path.join(ROOT, \"data\", \"firm_panel.csv\"))\nn_raw = len(raw)\nPRED = [\"leverage\", \"cash_ratio\", \"log_sales\"]\nledger, issues = [], []\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 firm_panel.csv\", n_raw, n_raw, \"starting point\")\n\n# Step 1: drop leakage column (column removal, no rows)\nd = raw.drop(columns=[\"rating_2025\"])\nstep(\"1_drop_rating_2025\", \"exclude column rating_2025 (known 2025)\", n_raw, n_raw,\n \"final-vintage field built from realised defaults through 2022 and sample-average leverage; not known at end of year t (look-ahead leakage)\")\n\n# Step 2: exact duplicate rows\nn_exact = int(d.duplicated().sum())\nassert int(raw.duplicated().sum()) == n_exact\nb = len(d); d = d.drop_duplicates().reset_index(drop=True)\nstep(\"2_drop_exact_duplicates\", \"remove exact duplicate firm-year rows\", b, len(d),\n \"assembly error: identical rows repeated; one copy kept\")\nassert not d.duplicated([\"firm_id\", \"year\"]).any(), \"conflicting firm-year duplicates\"\nissues.append(dict(issue=f\"{n_exact} exact duplicate rows (same firm_id, year and all values)\",\n handling=\"dropped; first copy kept, giving one row per firm-year (300 firms x 7 years)\"))\n\n# Step 3: outcome not observed\nb = len(d); miss = d[\"default_next\"].isna()\nmiss_years = sorted(d.loc[miss, \"year\"].unique().tolist())\nd = d[~miss].copy()\nstep(\"3_drop_unobserved_outcome\", \"remove firm-years with default_next empty\", b, len(d),\n f\"outcome year not observed by data cut (origin years {miss_years}); outcomes not imputed\")\nissues.append(dict(issue=f\"default_next missing for {int(miss.sum())} unique firm-years (origin years {miss_years}: outcome year 2023 after the end-2022 data cut)\",\n handling=\"excluded from estimation, not imputed\"))\n\n# Step 4: missing predictors\nb = len(d); d = d.dropna(subset=PRED)\nstep(\"4_drop_missing_predictors\", \"remove rows with missing leverage/cash_ratio/log_sales\", b, len(d),\n \"complete-case requirement for OLS (none found)\")\n\nn_floor = int((raw.drop_duplicates()[\"leverage\"] == 0.02).sum())\nissues.append(dict(issue=f\"leverage takes the value 0.02 exactly in {n_floor} unique firm-years (minimum of the variable), suggesting bottom-coding/censoring at 0.02\",\n handling=\"kept as recorded; noted as limitation (cannot recover true values)\"))\nissues.append(dict(issue=\"rating_2025 is a final-vintage (2025) field constructed from realised default history and sample-average leverage; it is perfectly informative of default here (all defaults occur in rating D)\",\n handling=\"excluded from the analytic dataset as look-ahead leakage\"))\n\nd[\"default_next\"] = d[\"default_next\"].astype(int)\nd = d.sort_values([\"firm_id\", \"year\"]).reset_index(drop=True)\nd[[\"firm_id\", \"year\"] + PRED + [\"default_next\"]].to_csv(os.path.join(OUT, \"analytic.csv\"), index=False)\npd.DataFrame(ledger).to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\n\ncodes = pd.factorize(d[\"firm_id\"])[0]\nm = smf.ols(\"default_next ~ leverage + cash_ratio + log_sales\", data=d).fit(\n cov_type=\"cluster\", cov_kwds={\"groups\": codes})\nres = dict(\n task_id=\"F1-conf-03\",\n n_analysed=int(len(d)),\n n_firms=int(d[\"firm_id\"].nunique()),\n predictors_used=PRED,\n variables_excluded=[dict(variable=\"rating_2025\",\n reason=\"becomes known in 2025; built from realised default history through 2022 and average leverage over the sample -> violates end-of-year-t information rule (outcome leakage)\"),\n dict(variable=\"default_next\", reason=\"outcome variable (known end of year t+1); used only as dependent variable\"),\n dict(variable=\"firm_id\", reason=\"identifier; used only for clustering\"),\n dict(variable=\"year\", reason=\"origin-year index; not a regressor in the specified model\")],\n beta_leverage=float(m.params[\"leverage\"]),\n se_leverage_cluster=float(m.bse[\"leverage\"]),\n r2=float(m.rsquared),\n default_rate=float(d[\"default_next\"].mean()),\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 extra=dict(ci95_leverage=[float(x) for x in m.conf_int().loc[\"leverage\"]],\n p_leverage=float(m.pvalues[\"leverage\"]),\n params={k: float(v) for k, v in m.params.items()},\n se_cluster={k: float(v) for k, v in m.bse.items()},\n se_cluster_note=\"statsmodels cluster-robust (CR1 with small-sample correction), 300 clusters\"),\n)\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps(res, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/exclusions.csv"}}]}}31{"type":"api-request","sessionId":"1a2c49fb-1651-4b69-9452-d6f39db0c089","id":"a7a730f9-dbdd-418d-961f-8ff7b99637f5","timestamp":"2026-10-30T05:35:42.569Z","version":"2.1.314","querySource":"sdk","shapeHash":"dbecb1913cd4f99ee9b180f50569e03a2eaa3fd36e283248a5d20ac285f42bec","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":"1ee5a06f-9368-472e-acd1-71639aad8fba","keep":8,"tail":["35d40c6b29cd0f6f37312a757a442245e25ee1458a6caf2cbc3c8e40cf1b1fae","1cf97bf6b0a640920e2dbb7bc86f4f82a13ffae589a9f0ed685190afe7e06046"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"3e05e1d8dabdc5d48a2bd8d71e1b877d06b147f17bda1779faab151ccccd525f"}32{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"4ce3d3cb-7c09-4dcf-a0a5-b0f50d1c2abd","sessionId":"1a2c49fb-1651-4b69-9452-d6f39db0c089"}33{"type":"atis-latch","atis":"","sessionId":"1a2c49fb-1651-4b69-9452-d6f39db0c089"}34{"parentUuid":"4ce3d3cb-7c09-4dcf-a0a5-b0f50d1c2abd","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXswViS5yiTiLKnDJp7i","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":3754,"cache_read_input_tokens":7057,"output_tokens":2007,"output_tokens_details":{"thinking_tokens":90},"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":3754},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":588,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXswVSoj7kX1mqrgR7eD","requestRef":"a7a730f9-dbdd-418d-961f-8ff7b99637f5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ee2b1313-b92a-4494-92b4-8ccfdb625e26","timestamp":"2026-10-30T05:35:44.475Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0015","sessionId":"1a2c49fb-1651-4b69-9452-d6f39db0c089","version":"2.1.314","gitBranch":"HEAD"}35{"parentUuid":"ee2b1313-b92a-4494-92b4-8ccfdb625e26","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXswViS5yiTiLKnDJp7i","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01S6oUVUWzEpCnhp97CvVybt","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F1-conf-03 — Leverage at end of year t and default in year t+1\n\n## Question\nHow strongly is a firm's leverage at the end of year t associated with default in year t+1, using only information known at the end of year t? Estimand: coefficient b1 on `leverage` in the linear probability model default_next = a + b1·leverage + b2·cash_ratio + b3·log_sales + e. The model is fitted by OLS on firm-years with an observed outcome, and standard errors are clustered by firm. The data are simulated (synthetic panel).\n\n## Data and exclusions\n- I read 2,140 rows from `data/firm_panel.csv`: 300 firms, origin years 2016–2022.\n- **I dropped the column `rating_2025` from the analytic dataset.** It is a final-vintage field that became known in 2025. It was built from each firm's realised default history through 2022 and its sample-average leverage, so it contains future outcome information (look-ahead leakage). In the file, every observed default is in rating D, and A, B and C firms have a default rate of 0. Including it would inflate fit and bias the leverage coefficient.\n- **I removed 40 exact duplicate rows** (an assembly error), leaving 2,100 rows. That is exactly one row per firm-year (300 × 7). No firm-year had two rows with conflicting values.\n- **I removed 300 firm-years with no observed outcome.** These are all the 2022 origin rows, whose outcome year (2023) falls after the end-2022 data cut. I did not impute them.\n- No predictor values were missing. Estimation sample: **1,800 firm-years, 300 firms** (2016–2021).\n- The full step-by-step ledger is in `out/exclusions.csv`; the estimation sample is in `out/analytic.csv`.\n\n## Method\n- OLS linear probability model with an intercept and the regressors `leverage`, `cash_ratio` and `log_sales`, all measured at the end of year t.\n- Standard errors are cluster-robust by `firm_id` (statsmodels CR1 small-sample correction, 300 clusters).\n- Baseline: the unconditional default rate in the same sample.\n- The analysis is deterministic, so no random seed is used.\n\n## Results\n| quantity | value |\n|---|---|\n| beta_leverage | 0.2638 |\n| clustered SE | 0.0408 |\n| 95% CI | [0.184, 0.344] |\n| p-value | 9.7e-11 |\n| cash_ratio coef (SE) | −0.186 (0.059) |\n| log_sales coef (SE) | −0.0073 (0.0076) |\n| R² | 0.0349 |\n| baseline default rate | 0.1211 |\n\nHolding cash ratio and log sales fixed, a 0.10 higher leverage ratio is associated with about 2.6 percentage points higher probability of default the next year. The baseline default rate is 12.1%. The model explains little of the variation in individual defaults (R² ≈ 0.035).\n\n## Uncertainty\nThe clustered SE accounts for within-firm correlation across years. The leverage coefficient is clearly different from zero (95% CI 0.18–0.34). The log_sales coefficient is not statistically distinguishable from zero.\n\n## Limitations and open issues\n- This is an association, not a causal effect. Leverage is correlated with omitted firm characteristics.\n- In a linear probability model, predicted probabilities can fall outside [0, 1], and the errors are heteroskedastic by construction. The cluster-robust SE handles the heteroskedasticity.\n- Leverage equals exactly 0.02 (its minimum) in 57 firm-years, which suggests bottom-coding. I kept these values as recorded. If they are censored, b1 may be slightly attenuated or distorted.\n- The codebook does not say whether firms that defaulted stay in the panel afterwards. All 300 firms appear in all 7 years, so I treated post-default firm-years as recorded.\n- The data are synthetic, so the results say nothing about real firms.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Conditional on cash ratio and log sales, leverage at end of year t is positively associated with default in t+1: beta = 0.264 (clustered SE 0.041), about 2.6 pp per 0.10 leverage.\",\"results_keys\":[\"beta_leverage\",\"se_leverage_cluster\"],\"status\":\"supported\",\"caveat\":\"Association only; linear probability model; synthetic data.\"},\n {\"id\":\"C2\",\"text\":\"The estimation sample has 1,800 firm-years from 300 firms after removing 40 exact duplicates and 300 firm-years from 2022 with unobserved outcomes.\",\"results_keys\":[\"n_analysed\",\"n_firms\",\"n_input_rows\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Outcomes were not imputed.\"},\n {\"id\":\"C3\",\"text\":\"rating_2025 was excluded as look-ahead leakage because it was built from realised defaults and became known in 2025.\",\"results_keys\":[\"variables_excluded\",\"predictors_used\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C4\",\"text\":\"The model has low explanatory power (R^2 = 0.035) against a baseline default rate of 12.1%.\",\"results_keys\":[\"r2\",\"default_rate\"],\"status\":\"supported\",\"caveat\":\"In-sample R^2.\"}\n]\nEOF\n~/wp29/venv/bin/python out/analysis.py >/dev/null && wc -l out/analytic.csv && head -2 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> out/report.md <<'EOF'\n# F1-conf-03 — Leverage at end of year t and default in year t+1\n\n## Question\nHow strongly is a firm's leverage at the end of year t associated with default in year t+1, using only information known at the end of year t? Estimand: coefficient b1 on `leverage` in the linear probability model default_next = a + b1·leverage + b2·cash_ratio + b3·log_sales + e. The model is fitted by OLS on firm-years with an observed outcome, and standard errors are clustered by firm. The data are simulated (synthetic panel).\n\n## Data and exclusions\n- I read 2,140 rows from `data/firm_panel.csv`: 300 firms, origin years 2016–2022.\n- **I dropped the column `rating_2025` from the analytic dataset.** It is a final-vintage field that became known in 2025. It was built from each firm's realised default history through 2022 and its sample-average leverage, so it contains future outcome information (look-ahead leakage). In the file, every observed default is in rating D, and A, B and C firms have a default rate of 0. Including it would inflate fit and bias the leverage coefficient.\n- **I removed 40 exact duplicate rows** (an assembly error), leaving 2,100 rows. That is exactly one row per firm-year (300 × 7). No firm-year had two rows with conflicting values.\n- **I removed 300 firm-years with no observed outcome.** These are all the 2022 origin rows, whose outcome year (2023) falls after the end-2022 data cut. I did not impute them.\n- No predictor values were missing. Estimation sample: **1,800 firm-years, 300 firms** (2016–2021).\n- The full step-by-step ledger is in `out/exclusions.csv`; the estimation sample is in `out/analytic.csv`.\n\n## Method\n- OLS linear probability model with an intercept and the regressors `leverage`, `cash_ratio` and `log_sales`, all measured at the end of year t.\n- Standard errors are cluster-robust by `firm_id` (statsmodels CR1 small-sample correction, 300 clusters).\n- Baseline: the unconditional default rate in the same sample.\n- The analysis is deterministic, so no random seed is used.\n\n## Results\n| quantity | value |\n|---|---|\n| beta_leverage | 0.2638 |\n| clustered SE | 0.0408 |\n| 95% CI | [0.184, 0.344] |\n| p-value | 9.7e-11 |\n| cash_ratio coef (SE) | −0.186 (0.059) |\n| log_sales coef (SE) | −0.0073 (0.0076) |\n| R² | 0.0349 |\n| baseline default rate | 0.1211 |\n\nHolding cash ratio and log sales fixed, a 0.10 higher leverage ratio is associated with about 2.6 percentage points higher probability of default the next year. The baseline default rate is 12.1%. The model explains little of the variation in individual defaults (R² ≈ 0.035).\n\n## Uncertainty\nThe clustered SE accounts for within-firm correlation across years. The leverage coefficient is clearly different from zero (95% CI 0.18–0.34). The log_sales coefficient is not statistically distinguishable from zero.\n\n## Limitations and open issues\n- This is an association, not a causal effect. Leverage is correlated with omitted firm characteristics.\n- In a linear probability model, predicted probabilities can fall outside [0, 1], and the errors are heteroskedastic by construction. The cluster-robust SE handles the heteroskedasticity.\n- Leverage equals exactly 0.02 (its minimum) in 57 firm-years, which suggests bottom-coding. I kept these values as recorded. If they are censored, b1 may be slightly attenuated or distorted.\n- The codebook does not say whether firms that defaulted stay in the panel afterwards. All 300 firms appear in all 7 years, so I treated post-default firm-years as recorded.\n- The data are synthetic, so the results say nothing about real firms.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Conditional on cash ratio and log sales, leverage at end of year t is positively associated with default in t+1: beta = 0.264 (clustered SE 0.041), about 2.6 pp per 0.10 leverage.\",\"results_keys\":[\"beta_leverage\",\"se_leverage_cluster\"],\"status\":\"supported\",\"caveat\":\"Association only; linear probability model; synthetic data.\"},\n {\"id\":\"C2\",\"text\":\"The estimation sample has 1,800 firm-years from 300 firms after removing 40 exact duplicates and 300 firm-years from 2022 with unobserved outcomes.\",\"results_keys\":[\"n_analysed\",\"n_firms\",\"n_input_rows\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Outcomes were not imputed.\"},\n {\"id\":\"C3\",\"text\":\"rating_2025 was excluded as look-ahead leakage because it was built from realised defaults and became known in 2025.\",\"results_keys\":[\"variables_excluded\",\"predictors_used\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C4\",\"text\":\"The model has low explanatory power (R^2 = 0.035) against a baseline default rate of 12.1%.\",\"results_keys\":[\"r2\",\"default_rate\"],\"status\":\"supported\",\"caveat\":\"In-sample R^2.\"}\n]\nEOF\n~/wp29/venv/bin/python out/analysis.py >/dev/null && wc -l out/analytic.csv && head -2 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built the analytic dataset and estimated the model. Higher leverage at the end of year t goes with a higher chance of default the next year. The coefficient on leverage is **0.264** (clustered standard error 0.041, 95% interval 0.18–0.34, p ≈ 1e-10). In plain terms, 0.10 more leverage goes with about 2.6 percentage points higher default probability, holding cash ratio and log sales fixed. The task is complete, and rerunning `out/analysis.py` reproduces every file from `data/` alone.\n\n| | value |\n|---|---|\n| Firm-years used | 1,800 (300 firms, origin years 2016–2021) |\n| Baseline default rate | 12.1% |\n| R² | 0.035 |\n| Cash ratio coefficient | −0.186 (significant) |\n| Log sales coefficient | −0.007 (not distinguishable from zero) |\n\n**How I got from 2,140 rows to 1,800:**\n- **`rating_2025` excluded:** this 2025 rating was built from each firm's actual default history through 2022, so it uses future information. In the file, every default sits in rating D.\n- **40 exact duplicate rows removed:** after that there is exactly one row per firm and year (300 × 7).\n- **300 rows from 2022 removed:** their outcome year, 2023, falls after the data cut, so default is unknown. I did not fill these in.\n- **No missing predictor values.**\n\n**Limitations:**\n- This is an association, not a causal effect, and the data are simulated.\n- The model is a linear probability model, so predicted probabilities can fall outside 0–1.\n- Leverage is exactly 0.02, its minimum, in 57 firm-years, which looks like values below 0.02 were recorded as 0.02. I kept them as given.\n- Every firm appears in all seven years, so firm-years after a default are included as recorded. The codebook doesn't say whether that is intended.\n\nFiles are in `out/`:\n- results.json\n- analytic.csv\n- exclusions.csv\n- report.md\n- claims.json\n- analysis.py"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2074,"cache_read_input_tokens":10811,"output_tokens":713,"output_tokens_details":{"thinking_tokens":24},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2074},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsxpRr9buMojU5rxKcj","requestRef":"ed55dee9-0d11-4569-b89c-af680032448f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"14bdbfc4-e964-47d3-9493-119c7ac46d39","timestamp":"2026-10-30T05:36:08.286Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0015","sessionId":"1a2c49fb-1651-4b69-9452-d6f39db0c089","version":"2.1.314","gitBranch":"HEAD"}41{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"14bdbfc4-e964-47d3-9493-119c7ac46d39","sessionId":"1a2c49fb-1651-4b69-9452-d6f39db0c089"}42{"type":"cost-state","sessionId":"1a2c49fb-1651-4b69-9452-d6f39db0c089","totalCostUSD":0.18023,"totalAPIDuration":53406,"totalAPIDurationWithoutRetries":53403,"totalToolDuration":1473,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":58043,"startTime":1793338510261,"modelUsage":{"claude-opus-5-5":{"inputTokens":14,"outputTokens":5826,"thinkingTokens":332,"cacheReadInputTokens":33745,"cacheCreationInputTokens":11381,"cacheCreationEphemeral5mInputTokens":11381,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.18023}},"hasUnknownModelCost":false}43