{"type":"atis-latch","atis":"","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9"}
{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T04:07:58.165Z","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","content":"Begin"}
{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T04:07:58.169Z","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9"}
{"parentUuid":null,"isSidechain":false,"promptId":"a3229195-e300-4111-9f74-fbabf01c67ff","type":"user","message":{"role":"user","content":"Begin"},"uuid":"8a724740-dcf4-461d-a407-d19f696e642f","timestamp":"2026-10-30T04:07:58.173Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0024","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","version":"2.1.314","gitBranch":"HEAD"}
{"type":"api-request-shape","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","shapeHash":"a54eeb670b8878f1ed772600f5a887c0cd432e0281ba2d394cba769bb793c219","timestamp":"2026-10-30T04:07:58.185Z","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/RC0024\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`.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. Never use words like \"complex\" or \"risk\" in the description - just describe what it does.\n\nSay what the command does in plain words: do not echo the command's text, its flags, or file paths - the user reads this description, often without seeing the command.\n\nFor simple commands (git, npm, standard CLI tools), keep it brief (5-10 words):\n- ls → \"List files in current directory\"\n- git status → \"Show working tree status\"\n- npm install → \"Install package dependencies\"\n\nFor commands that are harder to parse at a glance (piped commands, obscure flags, etc.), add enough context to clarify what it does:\n- find . -name \"*.tmp\" -exec rm {} \\; → \"Find and delete all .tmp files recursively\"\n- git reset --hard origin/main → \"Discard all local changes and match remote main\"\n- curl -s url | jq '.data[]' → \"Fetch JSON from URL and extract data array elements\"","type":"string"},"dangerouslyDisableSandbox":{"description":"Set this to true to dangerously override sandbox mode and run commands without sandboxing.","type":"boolean"}},"required":["command"],"additionalProperties":false}},{"name":"Edit","description":"modify file contents in place","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"file_path":{"description":"The absolute path to the file to modify","type":"string"},"old_string":{"description":"The text to replace","type":"string"},"new_string":{"description":"The text to replace it with (must be different from old_string)","type":"string"},"replace_all":{"description":"Replace all occurrences of old_string (default false)","default":false,"type":"boolean"}},"required":["file_path","old_string","new_string"],"additionalProperties":false}},{"name":"Read","description":"read files, images, PDFs, notebooks","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"file_path":{"description":"The absolute path to the file to read","type":"string"},"offset":{"description":"The line number to start reading from. Only provide if the file is too large to read at once","type":"integer","minimum":0,"maximum":9007199254740991},"limit":{"description":"The number of lines to read. Only provide if the file is too large to read at once.","type":"integer","exclusiveMinimum":0,"maximum":9007199254740991},"pages":{"description":"Page range for PDF files (e.g., \"1-5\", \"3\", \"10-20\"). Only applicable to PDF files. Maximum 20 pages per request.","type":"string"},"allow_large":{"description":"Set to true to read a text file, or a line range of one, that is over the usual size limits, up to what still fits in your context. Only use this when you genuinely need all of it or the user asked for the whole file; otherwise read it in parts with offset and limit, or search it.","type":"boolean"}},"required":["file_path"],"additionalProperties":false}}]}}
{"type":"api-request-blob","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","hash":"d43ab0e0b8ec9db790a6459cb2f4bc4ffce02825ca05063162023be60912ced9","message":{"role":"system","content":[],"output_config":{"effort":"medium"}}}
{"type":"api-request","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","id":"d72fcb5f-40f4-4363-ac05-47c7c9c99200","timestamp":"2026-10-30T04:07:58.185Z","version":"2.1.314","querySource":"sdk","shapeHash":"a54eeb670b8878f1ed772600f5a887c0cd432e0281ba2d394cba769bb793c219","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"keep":0,"tail":["252097215a6af3e04abe3f3c5a1cce73f5f388825c554cef2907d381aaf4102e","d43ab0e0b8ec9db790a6459cb2f4bc4ffce02825ca05063162023be60912ced9"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",0,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"af43cd2667921f2d836ce4eaf285e250ff9d9b032add709c2c1a707f4c767e4e"}
{"parentUuid":"8a724740-dcf4-461d-a407-d19f696e642f","isSidechain":false,"attachment":{"type":"session_context","context":{}},"type":"attachment","uuid":"5ccb7ac5-22a1-4c40-8a3b-a0559ff84ead","timestamp":"2026-10-30T04:07:58.178Z","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0024","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"5ccb7ac5-22a1-4c40-8a3b-a0559ff84ead","isSidechain":false,"attachment":{"type":"date","date":"2026-10-30"},"type":"attachment","uuid":"767d45d0-0d26-4141-a365-0cbde3df97f8","timestamp":"2026-10-30T04:07:58.178Z","rendered":[{"content":"\nToday's date is 2026-10-30.\n"}],"renderedRole":"system","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0024","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"767d45d0-0d26-4141-a365-0cbde3df97f8","isSidechain":false,"attachment":{"type":"remote_session_change","url":null,"commit":"Co-Authored-By: Claude Opus 5.5 ","pr":"🤖 Generated with [Claude Code](https://claude.com/claude-code)","sendUserFileHint":false,"managedCommit":false,"managedPr":false},"type":"attachment","uuid":"a7b22837-fcc1-453a-a870-f38e3c30a484","timestamp":"2026-10-30T04:07:58.178Z","rendered":[{"content":"\nAttribution for git commits and pull requests you create from here on (this replaces Claude Code's own earlier attribution guidance, such as a previous copy of this reminder; the user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 \n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0024","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","version":"2.1.314","gitBranch":"HEAD"}
{"type":"tools-used","tools":["Bash"],"sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9"}
{"parentUuid":"a7b22837-fcc1-453a-a870-f38e3c30a484","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmFXwKPRG5rWRwZsAkc","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SyUSsZBvTKcbvJfSwdB1XP","name":"Bash","input":{"command":"cat TASK.md; ls -la data out 2>/dev/null"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":730,"cache_read_input_tokens":1504,"output_tokens":68,"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":730},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXmFXfSj2UWi5pN3TbKd","requestRef":"d72fcb5f-40f4-4363-ac05-47c7c9c99200","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1100503c-76b3-477d-8f25-2422214adcd3","timestamp":"2026-10-30T04:08:01.263Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0024","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"1100503c-76b3-477d-8f25-2422214adcd3","isSidechain":false,"promptId":"a3229195-e300-4111-9f74-fbabf01c67ff","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\n# F6-conf-04 — Does workforce training raise productivity growth? A mini-project on simulated firm data\n\n## Scientific question\nA one-year training programme offered firms a variable number of training hours per employee. Using the simulated firm-level data provided, estimate the effect of one additional training hour per employee on productivity growth in the following year. The estimand is the causal effect of `training_hours` on `productivity_growth` (percentage points per hour) under a selection-on-observables assumption: conditional on the pre-programme information in the file, training hours are as good as randomly assigned, and the effect is linear and homogeneous. Report both the unadjusted association (the required baseline) and the estimate that targets the estimand, with uncertainty. The chain required is: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report.\n\n## Inputs\n- `data/firms.csv` — simulated data (generated for this exercise; it is not observed data). One row per firm. Columns: `firm_id` (integer), `skill_index` (standardised index of the workforce's baseline skill, measured in the year *before* the programme), `training_hours` (hours of training per employee received during the programme year), `productivity_growth` (percent change in labour productivity in the year *after* the programme year). Rows are in random order.\n\n## Information-availability rules\n- Use only `data/firms.csv`. No other variables exist; nothing needs to be imputed.\n- The \"unadjusted\" model is the OLS regression of `productivity_growth` on `training_hours` with an intercept only.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `naive_estimate` | number | pp per hour | OLS slope of the unadjusted model (baseline) |\n| `naive_ci_low`, `naive_ci_high` | number | pp per hour | 95% confidence interval for the unadjusted slope |\n| `baseline_metric` | number | percentage points | RMSE of the unadjusted model's residuals, sqrt(mean(e²)) |\n| `model_metric` | number | percentage points | RMSE of the residuals of the model that targets the estimand |\n| `effect_estimate` | number | pp per hour | estimate of the estimand (coefficient on `training_hours` in the model you justify) |\n| `effect_se` | number | pp per hour | its standard error (conventional or heteroskedasticity-robust; state which) |\n| `effect_ci_low`, `effect_ci_high` | number | pp per hour | 95% confidence interval for the effect |\n| `skill_index_coefficient` | number or null | pp per index unit | coefficient on `skill_index` in the model that targets the estimand (null if that model does not include it) |\n| `conclusion` | string | — | one of the codes below, based on `effect_ci_low`/`effect_ci_high` |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — scatter of `productivity_growth` against `training_hours` with the unadjusted fitted line and the fitted line of the model that targets the estimand (evaluated at the mean of any other regressor), produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per model with columns `model`, `estimate`, `se`, `ci_low`, `ci_high` (header + at least two data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. This task is designed to be completed in well under 20 minutes of work; prefer a short, correct chain over an elaborate one.\ndata:\ntotal 64\ndrwxr-xr-x 3 simon-pierreboucher staff 96 oct. 29 23:31 .\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:43 ..\n-rw-r--r-- 1 simon-pierreboucher staff 31657 oct. 29 23:43 firms.csv","is_error":true,"tool_use_id":"toolu_01SyUSsZBvTKcbvJfSwdB1XP"}]},"uuid":"35c5082c-c9e6-4823-a1c2-4be90875b329","timestamp":"2026-10-30T04:08:01.326Z","toolUseResult":"Error: Exit code 1\n# F6-conf-04 — Does workforce training raise productivity growth? A mini-project on simulated firm data\n\n## Scientific question\nA one-year training programme offered firms a variable number of training hours per employee. Using the simulated firm-level data provided, estimate the effect of one additional training hour per employee on productivity growth in the following year. The estimand is the causal effect of `training_hours` on `productivity_growth` (percentage points per hour) under a selection-on-observables assumption: conditional on the pre-programme information in the file, training hours are as good as randomly assigned, and the effect is linear and homogeneous. Report both the unadjusted association (the required baseline) and the estimate that targets the estimand, with uncertainty. The chain required is: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report.\n\n## Inputs\n- `data/firms.csv` — simulated data (generated for this exercise; it is not observed data). One row per firm. Columns: `firm_id` (integer), `skill_index` (standardised index of the workforce's baseline skill, measured in the year *before* the programme), `training_hours` (hours of training per employee received during the programme year), `productivity_growth` (percent change in labour productivity in the year *after* the programme year). Rows are in random order.\n\n## Information-availability rules\n- Use only `data/firms.csv`. No other variables exist; nothing needs to be imputed.\n- The \"unadjusted\" model is the OLS regression of `productivity_growth` on `training_hours` with an intercept only.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `naive_estimate` | number | pp per hour | OLS slope of the unadjusted model (baseline) |\n| `naive_ci_low`, `naive_ci_high` | number | pp per hour | 95% confidence interval for the unadjusted slope |\n| `baseline_metric` | number | percentage points | RMSE of the unadjusted model's residuals, sqrt(mean(e²)) |\n| `model_metric` | number | percentage points | RMSE of the residuals of the model that targets the estimand |\n| `effect_estimate` | number | pp per hour | estimate of the estimand (coefficient on `training_hours` in the model you justify) |\n| `effect_se` | number | pp per hour | its standard error (conventional or heteroskedasticity-robust; state which) |\n| `effect_ci_low`, `effect_ci_high` | number | pp per hour | 95% confidence interval for the effect |\n| `skill_index_coefficient` | number or null | pp per index unit | coefficient on `skill_index` in the model that targets the estimand (null if that model does not include it) |\n| `conclusion` | string | — | one of the codes below, based on `effect_ci_low`/`effect_ci_high` |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — scatter of `productivity_growth` against `training_hours` with the unadjusted fitted line and the fitted line of the model that targets the estimand (evaluated at the mean of any other regressor), produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per model with columns `model`, `estimate`, `se`, `ci_low`, `ci_high` (header + at least two data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. This task is designed to be completed in well under 20 minutes of work; prefer a short, correct chain over an elaborate one.\ndata:\ntotal 64\ndrwxr-xr-x 3 simon-pierreboucher staff 96 oct. 29 23:31 .\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:43 ..\n-rw-r--r-- 1 simon-pierreboucher staff 31657 oct. 29 23:43 firms.csv","sandboxed":false,"sourceToolAssistantUUID":"1100503c-76b3-477d-8f25-2422214adcd3","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0024","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","version":"2.1.314","gitBranch":"HEAD"}
{"type":"api-request-blob","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","hash":"e297cca09ff81129d11f57ade55ff19ecd4ad9408640bfb466151c19e598adb8","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01SyUSsZBvTKcbvJfSwdB1XP","name":"Bash","input":{"command":"cat TASK.md; ls -la data out 2>/dev/null"}}]}}
{"type":"api-request","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","id":"14b3baf4-2786-40cf-b80a-2e91e8878d8f","timestamp":"2026-10-30T04:08:01.330Z","version":"2.1.314","querySource":"sdk","shapeHash":"a54eeb670b8878f1ed772600f5a887c0cd432e0281ba2d394cba769bb793c219","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":"d72fcb5f-40f4-4363-ac05-47c7c9c99200","keep":2,"tail":["e297cca09ff81129d11f57ade55ff19ecd4ad9408640bfb466151c19e598adb8","dc657460eaed7f22297133e637fa1244d1da551329941807e69bd9df931327a4"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"58f2cfcfe99da6de6c443714822b7c70885c208e15e597fdcdefb00df33d7778"}
{"parentUuid":"35c5082c-c9e6-4823-a1c2-4be90875b329","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmFf9ARqVNAV4xEtfqs","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013zq4JYZzP8RcHJ2yR6T7o6","name":"Bash","input":{"command":"cd data; head -5 firms.csv; ~/wp29/venv/bin/python -c \"\nimport pandas as pd; d=pd.read_csv('firms.csv'); print(d.describe(include='all')); print(d.isna().sum()); print(d.dtypes); print(d.firm_id.duplicated().sum(), d.duplicated().sum()); print(d.corr())\nfor c in d.columns: print(c, d[c].nsmallest(5).tolist(), d[c].nlargest(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":2161,"cache_read_input_tokens":2234,"output_tokens":218,"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":2161},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXmFerHnQdzv2sdkHppk","requestRef":"14b3baf4-2786-40cf-b80a-2e91e8878d8f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"cf1cca20-7297-461a-9cdf-bc0fc0397e81","timestamp":"2026-10-30T04:08:03.675Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0024","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"cf1cca20-7297-461a-9cdf-bc0fc0397e81","isSidechain":false,"promptId":"a3229195-e300-4111-9f74-fbabf01c67ff","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013zq4JYZzP8RcHJ2yR6T7o6","type":"tool_result","content":"firm_id,skill_index,training_hours,productivity_growth\n1748,-2.0128,12.679,12.167\n1202,0.0756,20.0,7.3943\n1860,1.0082,21.618,3.9187\n1341,-1.6259,8.305,13.8987\n firm_id skill_index training_hours productivity_growth\ncount 1200.000000 1200.000000 1200.000000 1200.000000\nmean 1600.500000 -0.001467 19.945735 7.083012\nstd 346.554469 0.994503 8.243620 4.020107\nmin 1001.000000 -3.827900 -7.585000 -4.038000\n25% 1300.750000 -0.642375 14.580750 4.444350\n50% 1600.500000 0.010250 19.676500 7.061400\n75% 1900.250000 0.680000 25.502000 9.987000\nmax 2200.000000 2.962300 51.770000 19.388900\nfirm_id 0\nskill_index 0\ntraining_hours 0\nproductivity_growth 0\ndtype: int64\nfirm_id int64\nskill_index float64\ntraining_hours float64\nproductivity_growth float64\ndtype: object\n0 0\n firm_id skill_index training_hours productivity_growth\nfirm_id 1.000000 0.047429 0.054160 -0.030509\nskill_index 0.047429 1.000000 0.693530 -0.786787\ntraining_hours 0.054160 0.693530 1.000000 -0.277219\nproductivity_growth -0.030509 -0.786787 -0.277219 1.000000\nfirm_id [1001, 1002, 1003, 1004, 1005] [2200, 2199, 2198, 2197, 2196]\nskill_index [-3.8279, -3.3725, -3.0307, -2.995, -2.9694] [2.9623, 2.707, 2.6714, 2.6393, 2.6033]\ntraining_hours [-7.585, -6.388, -5.672, -5.383, -4.006] [51.77, 47.697, 45.489, 44.37, 43.266]\nproductivity_growth [-4.038, -3.5604, -3.5072, -3.4112, -3.4061] [19.3889, 19.2839, 19.1087, 19.0637, 18.9096]","is_error":false}]},"uuid":"d8d72097-1b02-491e-8637-a06c881a170c","timestamp":"2026-10-30T04:08:03.914Z","toolUseResult":{"stdout":"firm_id,skill_index,training_hours,productivity_growth\n1748,-2.0128,12.679,12.167\n1202,0.0756,20.0,7.3943\n1860,1.0082,21.618,3.9187\n1341,-1.6259,8.305,13.8987\n firm_id skill_index training_hours productivity_growth\ncount 1200.000000 1200.000000 1200.000000 1200.000000\nmean 1600.500000 -0.001467 19.945735 7.083012\nstd 346.554469 0.994503 8.243620 4.020107\nmin 1001.000000 -3.827900 -7.585000 -4.038000\n25% 1300.750000 -0.642375 14.580750 4.444350\n50% 1600.500000 0.010250 19.676500 7.061400\n75% 1900.250000 0.680000 25.502000 9.987000\nmax 2200.000000 2.962300 51.770000 19.388900\nfirm_id 0\nskill_index 0\ntraining_hours 0\nproductivity_growth 0\ndtype: int64\nfirm_id int64\nskill_index float64\ntraining_hours float64\nproductivity_growth float64\ndtype: object\n0 0\n firm_id skill_index training_hours productivity_growth\nfirm_id 1.000000 0.047429 0.054160 -0.030509\nskill_index 0.047429 1.000000 0.693530 -0.786787\ntraining_hours 0.054160 0.693530 1.000000 -0.277219\nproductivity_growth -0.030509 -0.786787 -0.277219 1.000000\nfirm_id [1001, 1002, 1003, 1004, 1005] [2200, 2199, 2198, 2197, 2196]\nskill_index [-3.8279, -3.3725, -3.0307, -2.995, -2.9694] [2.9623, 2.707, 2.6714, 2.6393, 2.6033]\ntraining_hours [-7.585, -6.388, -5.672, -5.383, -4.006] [51.77, 47.697, 45.489, 44.37, 43.266]\nproductivity_growth [-4.038, -3.5604, -3.5072, -3.4112, -3.4061] [19.3889, 19.2839, 19.1087, 19.0637, 18.9096]","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"cf1cca20-7297-461a-9cdf-bc0fc0397e81","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0024/data","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","version":"2.1.314","gitBranch":"HEAD"}
{"type":"api-request-blob","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","hash":"92675da10b40984bb74ba9e2d725d207f69abd716aae7653eca41dd60c005553","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_013zq4JYZzP8RcHJ2yR6T7o6","name":"Bash","input":{"command":"cd data; head -5 firms.csv; ~/wp29/venv/bin/python -c \"\nimport pandas as pd; d=pd.read_csv('firms.csv'); print(d.describe(include='all')); print(d.isna().sum()); print(d.dtypes); print(d.firm_id.duplicated().sum(), d.duplicated().sum()); print(d.corr())\nfor c in d.columns: print(c, d[c].nsmallest(5).tolist(), d[c].nlargest(5).tolist())\n\""}}]}}
{"type":"api-request","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","id":"7dc45ead-8f5c-40b9-8713-ebff4d1c752f","timestamp":"2026-10-30T04:08:03.917Z","version":"2.1.314","querySource":"sdk","shapeHash":"a54eeb670b8878f1ed772600f5a887c0cd432e0281ba2d394cba769bb793c219","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":"14b3baf4-2786-40cf-b80a-2e91e8878d8f","keep":4,"tail":["92675da10b40984bb74ba9e2d725d207f69abd716aae7653eca41dd60c005553","5eb290b7cc1cd20f1f8a249849aa093190eb5dd73dd9a10c40ea11eec833558b"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"d5a16deaebb1e0d8ee87e451e4219418a4d23e71069f61ae5d7368a2da2a5770"}
{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"d8d72097-1b02-491e-8637-a06c881a170c","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9"}
{"type":"atis-latch","atis":"","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9"}
{"parentUuid":"d8d72097-1b02-491e-8637-a06c881a170c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmFrCR4gWGQ5pyPmntE","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":976,"cache_read_input_tokens":4395,"output_tokens":322,"output_tokens_details":{"thinking_tokens":36},"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":976},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":388,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXmFqvJ8pn2TvQmSHeQA","requestRef":"7dc45ead-8f5c-40b9-8713-ebff4d1c752f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e9ac1929-f8f5-4533-910f-986f2a70b9ea","timestamp":"2026-10-30T04:08:05.750Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0024/data","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"e9ac1929-f8f5-4533-910f-986f2a70b9ea","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmFrCR4gWGQ5pyPmntE","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01AawfKLRxSsqDptjHLeaqWY","name":"Bash","input":{"command":"cd data; ~/wp29/venv/bin/python -c \"\nimport pandas as pd; d=pd.read_csv('firms.csv'); print((d.training_hours<0).sum(), (d.training_hours==0).sum()); print(d.training_hours.value_counts().head(8)); print(d.skill_index.value_counts().head(3)); print(d.productivity_growth.value_counts().head(3))\nprint(d[d.training_hours<0])\nimport statsmodels.formula.api as smf\nfor s in [d, d[d.training_hours>=0]]:\n m=smf.ols('productivity_growth~training_hours+skill_index',s).fit(cov_type='HC1'); print(m.params, m.bse)\n\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":976,"cache_read_input_tokens":4395,"output_tokens":322,"output_tokens_details":{"thinking_tokens":36},"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":976},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmFqvJ8pn2TvQmSHeQA","requestRef":"7dc45ead-8f5c-40b9-8713-ebff4d1c752f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2e535fb8-d073-467d-818b-a5d70b02ef96","timestamp":"2026-10-30T04:08:07.449Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0024/data","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"2e535fb8-d073-467d-818b-a5d70b02ef96","isSidechain":false,"promptId":"a3229195-e300-4111-9f74-fbabf01c67ff","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01AawfKLRxSsqDptjHLeaqWY","type":"tool_result","content":"(eval):cd:1: no such file or directory: data\n12 0\ntraining_hours\n26.584 2\n19.412 2\n26.494 2\n30.526 2\n10.148 2\n29.737 2\n30.812 2\n14.113 2\nName: count, dtype: int64\nskill_index\n-1.2509 2\n-0.5832 2\n 0.1411 2\nName: count, dtype: int64\nproductivity_growth\n8.9942 2\n11.4315 2\n10.5167 2\nName: count, dtype: int64\n firm_id skill_index training_hours productivity_growth\n127 1276 -2.4212 -0.133 19.1087\n372 1425 -1.6253 -4.006 8.9215\n518 1463 -2.1087 -5.383 10.3419\n521 1755 -1.2527 -1.335 7.7379\n688 1248 -1.2167 -3.000 5.3598\n772 1280 -0.9997 -2.351 6.7451\n880 1927 -2.0138 -6.388 10.4497\n893 2037 -2.9950 -2.887 13.2197\n1009 1483 -2.8599 -5.672 13.5980\n1082 1353 -1.8188 -1.514 10.0230\n1140 1247 -2.9694 -7.585 13.3106\n1150 1886 -2.4104 -2.726 15.8426\nIntercept 2.045418\ntraining_hours 0.252224\nskill_index -4.630436\ndtype: float64 Intercept 0.200567\ntraining_hours 0.009479\nskill_index 0.078089\ndtype: float64\nIntercept 2.013217\ntraining_hours 0.253538\nskill_index -4.624621\ndtype: float64 Intercept 0.208482\ntraining_hours 0.009812\nskill_index 0.078164\ndtype: float64","is_error":false}]},"uuid":"5563bb89-31bc-422e-907b-43646433cab4","timestamp":"2026-10-30T04:08:08.068Z","toolUseResult":{"stdout":"(eval):cd:1: no such file or directory: data\n12 0\ntraining_hours\n26.584 2\n19.412 2\n26.494 2\n30.526 2\n10.148 2\n29.737 2\n30.812 2\n14.113 2\nName: count, dtype: int64\nskill_index\n-1.2509 2\n-0.5832 2\n 0.1411 2\nName: count, dtype: int64\nproductivity_growth\n8.9942 2\n11.4315 2\n10.5167 2\nName: count, dtype: int64\n firm_id skill_index training_hours productivity_growth\n127 1276 -2.4212 -0.133 19.1087\n372 1425 -1.6253 -4.006 8.9215\n518 1463 -2.1087 -5.383 10.3419\n521 1755 -1.2527 -1.335 7.7379\n688 1248 -1.2167 -3.000 5.3598\n772 1280 -0.9997 -2.351 6.7451\n880 1927 -2.0138 -6.388 10.4497\n893 2037 -2.9950 -2.887 13.2197\n1009 1483 -2.8599 -5.672 13.5980\n1082 1353 -1.8188 -1.514 10.0230\n1140 1247 -2.9694 -7.585 13.3106\n1150 1886 -2.4104 -2.726 15.8426\nIntercept 2.045418\ntraining_hours 0.252224\nskill_index -4.630436\ndtype: float64 Intercept 0.200567\ntraining_hours 0.009479\nskill_index 0.078089\ndtype: float64\nIntercept 2.013217\ntraining_hours 0.253538\nskill_index -4.624621\ndtype: float64 Intercept 0.208482\ntraining_hours 0.009812\nskill_index 0.078164\ndtype: float64","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"2e535fb8-d073-467d-818b-a5d70b02ef96","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0024/data","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","version":"2.1.314","gitBranch":"HEAD"}
{"type":"api-request-blob","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","hash":"3fb1d38e518045c31e8b02d262175c95f07c6f6562dc68714a1b43be489f62d6","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01AawfKLRxSsqDptjHLeaqWY","name":"Bash","input":{"command":"cd data; ~/wp29/venv/bin/python -c \"\nimport pandas as pd; d=pd.read_csv('firms.csv'); print((d.training_hours<0).sum(), (d.training_hours==0).sum()); print(d.training_hours.value_counts().head(8)); print(d.skill_index.value_counts().head(3)); print(d.productivity_growth.value_counts().head(3))\nprint(d[d.training_hours<0])\nimport statsmodels.formula.api as smf\nfor s in [d, d[d.training_hours>=0]]:\n m=smf.ols('productivity_growth~training_hours+skill_index',s).fit(cov_type='HC1'); print(m.params, m.bse)\n\""}}]}}
{"type":"api-request","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","id":"4edc75c2-ca03-4c2e-b0e9-178a356326c4","timestamp":"2026-10-30T04:08:08.074Z","version":"2.1.314","querySource":"sdk","shapeHash":"a54eeb670b8878f1ed772600f5a887c0cd432e0281ba2d394cba769bb793c219","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":"7dc45ead-8f5c-40b9-8713-ebff4d1c752f","keep":6,"tail":["3fb1d38e518045c31e8b02d262175c95f07c6f6562dc68714a1b43be489f62d6","885f4af20760a944df7052df256e37fb71b3fa52b1c5ced25eb95c72034255c1"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"78bec8c21b129b4ace90cf8e928a010dace869269fdcb2509e0086eef7ab9c18"}
{"parentUuid":"5563bb89-31bc-422e-907b-43646433cab4","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmG9xWXySzJoG8fuYaS","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":923,"cache_read_input_tokens":5371,"output_tokens":2568,"output_tokens_details":{"thinking_tokens":127},"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":923},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1223,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXmG9gtEzURYSeDrs2rG","requestRef":"4edc75c2-ca03-4c2e-b0e9-178a356326c4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9cc308cc-ebe7-4abc-a74e-96751bfb7a3f","timestamp":"2026-10-30T04:08:10.761Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0024/data","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"9cc308cc-ebe7-4abc-a74e-96751bfb7a3f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmG9xWXySzJoG8fuYaS","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013hUNJSA4HfTrf7qhRSVig9","name":"Bash","input":{"command":"mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"Replication script for F6-conf-04. Run from workspace root:\n ~/wp29/venv/bin/python out/analysis.py\n\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\nimport statsmodels.formula.api as smf\n\nos.makedirs(\"out\", exist_ok=True)\nraw = pd.read_csv(\"data/firms.csv\")\nn_raw = len(raw)\nissues = []\n\n# ---- audit ----\nassert raw.isna().sum().sum() == 0\nassert not raw.firm_id.duplicated().any()\nneg = raw.training_hours < 0\nissues.append({\n \"issue\": f\"{int(neg.sum())} rows have negative training_hours (min {raw.training_hours.min():.3f}), which is physically impossible (hours per employee cannot be < 0); likely an untruncated simulation draw or recording error.\",\n \"handling\": \"Excluded from the main analysis; sensitivity analysis re-estimates with them included (results reported in table1.csv and report).\"})\nd = raw[~neg].copy()\n\n# ---- models (HC1 robust SEs) ----\nnaive = smf.ols(\"productivity_growth ~ training_hours\", d).fit(cov_type=\"HC1\")\nadj = smf.ols(\"productivity_growth ~ training_hours + skill_index\", d).fit(cov_type=\"HC1\")\nadj_all = smf.ols(\"productivity_growth ~ training_hours + skill_index\", raw).fit(cov_type=\"HC1\")\nnaive_all = smf.ols(\"productivity_growth ~ training_hours\", raw).fit(cov_type=\"HC1\")\n# diagnostic: nonlinearity / heterogeneity checks\nquad = smf.ols(\"productivity_growth ~ training_hours + I(training_hours**2) + skill_index + I(skill_index**2)\", d).fit(cov_type=\"HC1\")\ninter = smf.ols(\"productivity_growth ~ training_hours * skill_index\", d).fit(cov_type=\"HC1\")\n\nrmse = lambda m: float(np.sqrt(np.mean(m.resid ** 2)))\ndef row(name, m):\n ci = m.conf_int().loc[\"training_hours\"]\n return {\"model\": name, \"estimate\": m.params[\"training_hours\"], \"se\": m.bse[\"training_hours\"],\n \"ci_low\": ci[0], \"ci_high\": ci[1]}\ntable = pd.DataFrame([\n row(\"unadjusted (main sample)\", naive),\n row(\"adjusted for skill_index (main sample) [targets estimand]\", adj),\n row(\"unadjusted (all rows incl. negative hours)\", naive_all),\n row(\"adjusted for skill_index (all rows incl. negative hours)\", adj_all),\n row(\"adjusted + quadratics (diagnostic)\", quad),\n])\ntable.to_csv(\"out/table1.csv\", index=False)\n\neci = adj.conf_int().loc[\"training_hours\"]; nci = naive.conf_int().loc[\"training_hours\"]\nif eci[0] > 0: concl = \"positive_effect\"\nelif eci[1] < 0: concl = \"negative_effect\"\nelse: concl = \"no_detectable_effect\"\n\nres = {\n \"task_id\": \"F6-conf-04\",\n \"naive_estimate\": float(naive.params[\"training_hours\"]),\n \"naive_ci_low\": float(nci[0]), \"naive_ci_high\": float(nci[1]),\n \"baseline_metric\": rmse(naive),\n \"model_metric\": rmse(adj),\n \"effect_estimate\": float(adj.params[\"training_hours\"]),\n \"effect_se\": float(adj.bse[\"training_hours\"]),\n \"effect_ci_low\": float(eci[0]), \"effect_ci_high\": float(eci[1]),\n \"skill_index_coefficient\": float(adj.params[\"skill_index\"]),\n \"conclusion\": concl,\n \"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"firms.csv\": int(n_raw)},\n \"n_analysed\": int(adj.nobs),\n \"random_seed\": None,\n \"diagnostics\": {\n \"se_type\": \"HC1 heteroskedasticity-robust\",\n \"corr_training_skill\": float(d[[\"training_hours\", \"skill_index\"]].corr().iloc[0, 1]),\n \"sensitivity_all_rows_effect\": float(adj_all.params[\"training_hours\"]),\n \"sensitivity_all_rows_se\": float(adj_all.bse[\"training_hours\"]),\n \"quad_training_sq_coef\": float(quad.params[\"I(training_hours ** 2)\"]),\n \"quad_training_sq_p\": float(quad.pvalues[\"I(training_hours ** 2)\"]),\n \"quad_skill_sq_p\": float(quad.pvalues[\"I(skill_index ** 2)\"]),\n \"interaction_coef\": float(inter.params[\"training_hours:skill_index\"]),\n \"interaction_p\": float(inter.pvalues[\"training_hours:skill_index\"]),\n \"conventional_se_effect\": float(smf.ols(\"productivity_growth ~ training_hours + skill_index\", d).fit().bse[\"training_hours\"]),\n \"r2_naive\": float(naive.rsquared), \"r2_adjusted\": float(adj.rsquared),\n },\n}\nwith open(\"out/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\n\n# ---- figure ----\nfig, ax = plt.subplots(figsize=(7, 5))\nax.scatter(raw.training_hours[~neg], raw.productivity_growth[~neg], s=8, alpha=0.4, label=\"firms (analysed)\")\nax.scatter(raw.training_hours[neg], raw.productivity_growth[neg], s=14, marker=\"x\", c=\"k\", label=\"excluded (negative hours)\")\nx = np.linspace(d.training_hours.min(), d.training_hours.max(), 100)\nax.plot(x, naive.params[\"Intercept\"] + naive.params[\"training_hours\"] * x, \"r-\",\n label=f\"unadjusted: slope {naive.params['training_hours']:.3f}\")\nsm = d.skill_index.mean()\nax.plot(x, adj.params[\"Intercept\"] + adj.params[\"training_hours\"] * x + adj.params[\"skill_index\"] * sm, \"g-\",\n label=f\"adjusted for skill (at mean skill): slope {adj.params['training_hours']:.3f}\")\nax.set_xlabel(\"training hours per employee\"); ax.set_ylabel(\"productivity growth (%)\")\nax.set_title(\"Productivity growth vs training hours (simulated data)\")\nax.legend(fontsize=8)\nfig.tight_layout(); fig.savefig(\"out/figure1.png\", dpi=120)\nprint(json.dumps(res, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/table1.csv"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":923,"cache_read_input_tokens":5371,"output_tokens":2568,"output_tokens_details":{"thinking_tokens":127},"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":923},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmG9gtEzURYSeDrs2rG","requestRef":"4edc75c2-ca03-4c2e-b0e9-178a356326c4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a6a97db3-ccfe-4490-b041-79bceae456ce","timestamp":"2026-10-30T04:08:27.938Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0024/data","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"a6a97db3-ccfe-4490-b041-79bceae456ce","isSidechain":false,"promptId":"a3229195-e300-4111-9f74-fbabf01c67ff","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nTraceback (most recent call last):\n File \"/Users/simon-pierreboucher/wp29/runs/RC0024/data/out/analysis.py\", line 12, in \n raw = pd.read_csv(\"data/firms.csv\")\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n return _read(filepath_or_buffer, kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n parser = TextFileReader(filepath_or_buffer, **kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n self._engine = self._make_engine(f, self.engine)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n self.handles = get_handle(\n ~~~~~~~~~~^\n f,\n ^^\n ...<6 lines>...\n storage_options=self.options.get(\"storage_options\", None),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n handle = open(\n handle,\n ...<3 lines>...\n newline=\"\",\n )\nFileNotFoundError: [Errno 2] No such file or directory: 'data/firms.csv'\ncat: out/table1.csv: No such file or directory","is_error":true,"tool_use_id":"toolu_013hUNJSA4HfTrf7qhRSVig9"}]},"uuid":"1d6cad5a-ffc5-48ab-afdf-48ca4550105d","timestamp":"2026-10-30T04:08:28.758Z","toolUseResult":"Error: Exit code 1\nTraceback (most recent call last):\n File \"/Users/simon-pierreboucher/wp29/runs/RC0024/data/out/analysis.py\", line 12, in \n raw = pd.read_csv(\"data/firms.csv\")\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n return _read(filepath_or_buffer, kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n parser = TextFileReader(filepath_or_buffer, **kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n self._engine = self._make_engine(f, self.engine)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n self.handles = get_handle(\n ~~~~~~~~~~^\n f,\n ^^\n ...<6 lines>...\n storage_options=self.options.get(\"storage_options\", None),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n handle = open(\n handle,\n ...<3 lines>...\n newline=\"\",\n )\nFileNotFoundError: [Errno 2] No such file or directory: 'data/firms.csv'\ncat: out/table1.csv: No such file or directory","sandboxed":false,"sourceToolAssistantUUID":"a6a97db3-ccfe-4490-b041-79bceae456ce","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0024/data","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","version":"2.1.314","gitBranch":"HEAD"}
{"type":"api-request-blob","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","hash":"70b5f9a2b273dc9d49d4108fdfeb771ba035052a1d45612852750f06cded3f7c","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_013hUNJSA4HfTrf7qhRSVig9","name":"Bash","input":{"command":"mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"Replication script for F6-conf-04. Run from workspace root:\n ~/wp29/venv/bin/python out/analysis.py\n\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\nimport statsmodels.formula.api as smf\n\nos.makedirs(\"out\", exist_ok=True)\nraw = pd.read_csv(\"data/firms.csv\")\nn_raw = len(raw)\nissues = []\n\n# ---- audit ----\nassert raw.isna().sum().sum() == 0\nassert not raw.firm_id.duplicated().any()\nneg = raw.training_hours < 0\nissues.append({\n \"issue\": f\"{int(neg.sum())} rows have negative training_hours (min {raw.training_hours.min():.3f}), which is physically impossible (hours per employee cannot be < 0); likely an untruncated simulation draw or recording error.\",\n \"handling\": \"Excluded from the main analysis; sensitivity analysis re-estimates with them included (results reported in table1.csv and report).\"})\nd = raw[~neg].copy()\n\n# ---- models (HC1 robust SEs) ----\nnaive = smf.ols(\"productivity_growth ~ training_hours\", d).fit(cov_type=\"HC1\")\nadj = smf.ols(\"productivity_growth ~ training_hours + skill_index\", d).fit(cov_type=\"HC1\")\nadj_all = smf.ols(\"productivity_growth ~ training_hours + skill_index\", raw).fit(cov_type=\"HC1\")\nnaive_all = smf.ols(\"productivity_growth ~ training_hours\", raw).fit(cov_type=\"HC1\")\n# diagnostic: nonlinearity / heterogeneity checks\nquad = smf.ols(\"productivity_growth ~ training_hours + I(training_hours**2) + skill_index + I(skill_index**2)\", d).fit(cov_type=\"HC1\")\ninter = smf.ols(\"productivity_growth ~ training_hours * skill_index\", d).fit(cov_type=\"HC1\")\n\nrmse = lambda m: float(np.sqrt(np.mean(m.resid ** 2)))\ndef row(name, m):\n ci = m.conf_int().loc[\"training_hours\"]\n return {\"model\": name, \"estimate\": m.params[\"training_hours\"], \"se\": m.bse[\"training_hours\"],\n \"ci_low\": ci[0], \"ci_high\": ci[1]}\ntable = pd.DataFrame([\n row(\"unadjusted (main sample)\", naive),\n row(\"adjusted for skill_index (main sample) [targets estimand]\", adj),\n row(\"unadjusted (all rows incl. negative hours)\", naive_all),\n row(\"adjusted for skill_index (all rows incl. negative hours)\", adj_all),\n row(\"adjusted + quadratics (diagnostic)\", quad),\n])\ntable.to_csv(\"out/table1.csv\", index=False)\n\neci = adj.conf_int().loc[\"training_hours\"]; nci = naive.conf_int().loc[\"training_hours\"]\nif eci[0] > 0: concl = \"positive_effect\"\nelif eci[1] < 0: concl = \"negative_effect\"\nelse: concl = \"no_detectable_effect\"\n\nres = {\n \"task_id\": \"F6-conf-04\",\n \"naive_estimate\": float(naive.params[\"training_hours\"]),\n \"naive_ci_low\": float(nci[0]), \"naive_ci_high\": float(nci[1]),\n \"baseline_metric\": rmse(naive),\n \"model_metric\": rmse(adj),\n \"effect_estimate\": float(adj.params[\"training_hours\"]),\n \"effect_se\": float(adj.bse[\"training_hours\"]),\n \"effect_ci_low\": float(eci[0]), \"effect_ci_high\": float(eci[1]),\n \"skill_index_coefficient\": float(adj.params[\"skill_index\"]),\n \"conclusion\": concl,\n \"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"firms.csv\": int(n_raw)},\n \"n_analysed\": int(adj.nobs),\n \"random_seed\": None,\n \"diagnostics\": {\n \"se_type\": \"HC1 heteroskedasticity-robust\",\n \"corr_training_skill\": float(d[[\"training_hours\", \"skill_index\"]].corr().iloc[0, 1]),\n \"sensitivity_all_rows_effect\": float(adj_all.params[\"training_hours\"]),\n \"sensitivity_all_rows_se\": float(adj_all.bse[\"training_hours\"]),\n \"quad_training_sq_coef\": float(quad.params[\"I(training_hours ** 2)\"]),\n \"quad_training_sq_p\": float(quad.pvalues[\"I(training_hours ** 2)\"]),\n \"quad_skill_sq_p\": float(quad.pvalues[\"I(skill_index ** 2)\"]),\n \"interaction_coef\": float(inter.params[\"training_hours:skill_index\"]),\n \"interaction_p\": float(inter.pvalues[\"training_hours:skill_index\"]),\n \"conventional_se_effect\": float(smf.ols(\"productivity_growth ~ training_hours + skill_index\", d).fit().bse[\"training_hours\"]),\n \"r2_naive\": float(naive.rsquared), \"r2_adjusted\": float(adj.rsquared),\n },\n}\nwith open(\"out/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\n\n# ---- figure ----\nfig, ax = plt.subplots(figsize=(7, 5))\nax.scatter(raw.training_hours[~neg], raw.productivity_growth[~neg], s=8, alpha=0.4, label=\"firms (analysed)\")\nax.scatter(raw.training_hours[neg], raw.productivity_growth[neg], s=14, marker=\"x\", c=\"k\", label=\"excluded (negative hours)\")\nx = np.linspace(d.training_hours.min(), d.training_hours.max(), 100)\nax.plot(x, naive.params[\"Intercept\"] + naive.params[\"training_hours\"] * x, \"r-\",\n label=f\"unadjusted: slope {naive.params['training_hours']:.3f}\")\nsm = d.skill_index.mean()\nax.plot(x, adj.params[\"Intercept\"] + adj.params[\"training_hours\"] * x + adj.params[\"skill_index\"] * sm, \"g-\",\n label=f\"adjusted for skill (at mean skill): slope {adj.params['training_hours']:.3f}\")\nax.set_xlabel(\"training hours per employee\"); ax.set_ylabel(\"productivity growth (%)\")\nax.set_title(\"Productivity growth vs training hours (simulated data)\")\nax.legend(fontsize=8)\nfig.tight_layout(); fig.savefig(\"out/figure1.png\", dpi=120)\nprint(json.dumps(res, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/table1.csv"}}]}}
{"type":"api-request","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","id":"7593e5a6-f0a7-4193-ab71-e38c6367c50b","timestamp":"2026-10-30T04:08:28.761Z","version":"2.1.314","querySource":"sdk","shapeHash":"a54eeb670b8878f1ed772600f5a887c0cd432e0281ba2d394cba769bb793c219","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":"4edc75c2-ca03-4c2e-b0e9-178a356326c4","keep":8,"tail":["70b5f9a2b273dc9d49d4108fdfeb771ba035052a1d45612852750f06cded3f7c","2d49d2b9683e9570a0dce0fbbf7698ed2a9f1afa0cabead7771f6630b4a250e2"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"6a11fdd95ea694b8bc30ea56ca698d86001fd5e85737320ac5892721e3bfb9d4"}
{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"1d6cad5a-ffc5-48ab-afdf-48ca4550105d","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9"}
{"type":"atis-latch","atis":"","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9"}
{"parentUuid":"1d6cad5a-ffc5-48ab-afdf-48ca4550105d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmHgQMx1LhtPnHxmLgw","type":"message","role":"assistant","content":[{"type":"text","text":"Shell was still in `data/`; I created `data/out` by mistake — moving it out."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3296,"cache_read_input_tokens":6294,"output_tokens":170,"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":3296},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXmHg8EtaH4FZCnFW58p","requestRef":"7593e5a6-f0a7-4193-ab71-e38c6367c50b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d3d30196-451e-47b4-aacc-392de2dad19e","timestamp":"2026-10-30T04:08:30.618Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0024/data","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"d3d30196-451e-47b4-aacc-392de2dad19e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmHgQMx1LhtPnHxmLgw","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01RT3gxFTMNX6Zm7jcN6njRw","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0024 && mkdir -p out && mv data/out/analysis.py out/ && rmdir data/out && ls data && ~/wp29/venv/bin/python out/analysis.py && cat out/table1.csv"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3296,"cache_read_input_tokens":6294,"output_tokens":170,"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":3296},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmHg8EtaH4FZCnFW58p","requestRef":"7593e5a6-f0a7-4193-ab71-e38c6367c50b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"700e948d-975a-4fc0-a6b9-92ceb2031ab7","timestamp":"2026-10-30T04:08:31.453Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0024/data","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"700e948d-975a-4fc0-a6b9-92ceb2031ab7","isSidechain":false,"promptId":"a3229195-e300-4111-9f74-fbabf01c67ff","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RT3gxFTMNX6Zm7jcN6njRw","type":"tool_result","content":"firms.csv\n{\n \"task_id\": \"F6-conf-04\",\n \"naive_estimate\": -0.13169790632300882,\n \"naive_ci_low\": -0.1603518326928757,\n \"naive_ci_high\": -0.10304397995314195,\n \"baseline_metric\": 3.8601022336733584,\n \"model_metric\": 1.9758571828823268,\n \"effect_estimate\": 0.25353820594501253,\n \"effect_se\": 0.009812212326345536,\n \"effect_ci_low\": 0.2343066231767153,\n \"effect_ci_high\": 0.27276978871330976,\n \"skill_index_coefficient\": -4.624620879345756,\n \"conclusion\": \"positive_effect\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"12 rows have negative training_hours (min -7.585), which is physically impossible (hours per employee cannot be < 0); likely an untruncated simulation draw or recording error.\",\n \"handling\": \"Excluded from the main analysis; sensitivity analysis re-estimates with them included (results reported in table1.csv and report).\"\n }\n ],\n \"n_input_rows\": {\n \"firms.csv\": 1200\n },\n \"n_analysed\": 1188,\n \"random_seed\": null,\n \"diagnostics\": {\n \"se_type\": \"HC1 heteroskedasticity-robust\",\n \"corr_training_skill\": 0.6775720280598019,\n \"sensitivity_all_rows_effect\": 0.25222449623309495,\n \"sensitivity_all_rows_se\": 0.00947885217664965,\n \"quad_training_sq_coef\": 0.0002835295601785778,\n \"quad_training_sq_p\": 0.6596601958920358,\n \"quad_skill_sq_p\": 0.475062983936557,\n \"interaction_coef\": -0.00032835690944043455,\n \"interaction_p\": 0.9564572077696775,\n \"conventional_se_effect\": 0.0098410870694658,\n \"r2_naive\": 0.06821334219243413,\n \"r2_adjusted\": 0.7558650404794334\n }\n}\nmodel,estimate,se,ci_low,ci_high\nunadjusted (main sample),-0.13169790632300882,0.014619618827634278,-0.1603518326928757,-0.10304397995314195\nadjusted for skill_index (main sample) [targets estimand],0.25353820594501253,0.009812212326345536,0.2343066231767153,0.27276978871330976\nunadjusted (all rows incl. negative hours),-0.13518960504279864,0.013896968221774055,-0.16242716225177342,-0.10795204783382387\nadjusted for skill_index (all rows incl. negative hours),0.25222449623309495,0.00947885217664965,0.23364628735208254,0.27080270511410737\nadjusted + quadratics (diagnostic),0.2420067817564457,0.028944809103468614,0.18527599837426012,0.29873756513863126","is_error":false}]},"uuid":"d8544c79-d1c3-45d5-b7d0-ffde53cb6d70","timestamp":"2026-10-30T04:08:32.373Z","toolUseResult":{"stdout":"firms.csv\n{\n \"task_id\": \"F6-conf-04\",\n \"naive_estimate\": -0.13169790632300882,\n \"naive_ci_low\": -0.1603518326928757,\n \"naive_ci_high\": -0.10304397995314195,\n \"baseline_metric\": 3.8601022336733584,\n \"model_metric\": 1.9758571828823268,\n \"effect_estimate\": 0.25353820594501253,\n \"effect_se\": 0.009812212326345536,\n \"effect_ci_low\": 0.2343066231767153,\n \"effect_ci_high\": 0.27276978871330976,\n \"skill_index_coefficient\": -4.624620879345756,\n \"conclusion\": \"positive_effect\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"12 rows have negative training_hours (min -7.585), which is physically impossible (hours per employee cannot be < 0); likely an untruncated simulation draw or recording error.\",\n \"handling\": \"Excluded from the main analysis; sensitivity analysis re-estimates with them included (results reported in table1.csv and report).\"\n }\n ],\n \"n_input_rows\": {\n \"firms.csv\": 1200\n },\n \"n_analysed\": 1188,\n \"random_seed\": null,\n \"diagnostics\": {\n \"se_type\": \"HC1 heteroskedasticity-robust\",\n \"corr_training_skill\": 0.6775720280598019,\n \"sensitivity_all_rows_effect\": 0.25222449623309495,\n \"sensitivity_all_rows_se\": 0.00947885217664965,\n \"quad_training_sq_coef\": 0.0002835295601785778,\n \"quad_training_sq_p\": 0.6596601958920358,\n \"quad_skill_sq_p\": 0.475062983936557,\n \"interaction_coef\": -0.00032835690944043455,\n \"interaction_p\": 0.9564572077696775,\n \"conventional_se_effect\": 0.0098410870694658,\n \"r2_naive\": 0.06821334219243413,\n \"r2_adjusted\": 0.7558650404794334\n }\n}\nmodel,estimate,se,ci_low,ci_high\nunadjusted (main sample),-0.13169790632300882,0.014619618827634278,-0.1603518326928757,-0.10304397995314195\nadjusted for skill_index (main sample) [targets estimand],0.25353820594501253,0.009812212326345536,0.2343066231767153,0.27276978871330976\nunadjusted (all rows incl. negative hours),-0.13518960504279864,0.013896968221774055,-0.16242716225177342,-0.10795204783382387\nadjusted for skill_index (all rows incl. negative hours),0.25222449623309495,0.00947885217664965,0.23364628735208254,0.27080270511410737\nadjusted + quadratics (diagnostic),0.2420067817564457,0.028944809103468614,0.18527599837426012,0.29873756513863126","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"700e948d-975a-4fc0-a6b9-92ceb2031ab7","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0024","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","version":"2.1.314","gitBranch":"HEAD"}
{"type":"api-request-blob","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","hash":"5e036231527a81fda74a8a5183179b720b85f13d63d0fccb29d2b4973c3dee73","message":{"role":"assistant","content":[{"type":"text","text":"Shell was still in `data/`; I created `data/out` by mistake — moving it out."},{"type":"tool_use","id":"toolu_01RT3gxFTMNX6Zm7jcN6njRw","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0024 && mkdir -p out && mv data/out/analysis.py out/ && rmdir data/out && ls data && ~/wp29/venv/bin/python out/analysis.py && cat out/table1.csv"}}]}}
{"type":"api-request","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","id":"33a8b313-8c4e-4ee1-a48e-4be1789dd371","timestamp":"2026-10-30T04:08:32.376Z","version":"2.1.314","querySource":"sdk","shapeHash":"a54eeb670b8878f1ed772600f5a887c0cd432e0281ba2d394cba769bb793c219","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":"7593e5a6-f0a7-4193-ab71-e38c6367c50b","keep":10,"tail":["5e036231527a81fda74a8a5183179b720b85f13d63d0fccb29d2b4973c3dee73","369213cf1b29c33631a7c606a1fbb1ed109c8d0bf2db8dccd6ec4b522ed3908c"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"594bc3c3e25d5cf8d6d813128276f79c5f97ef8cd6981fb6abb6a25645e56faf"}
{"type":"tools-used","tools":["Bash","Read"],"sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9"}
{"parentUuid":"d8544c79-d1c3-45d5-b7d0-ffde53cb6d70","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmHwrJNiJQJWHWafEJj","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":1194,"cache_read_input_tokens":9590,"output_tokens":2962,"output_tokens_details":{"thinking_tokens":29},"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":1194},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":343,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXmHwZvfeqPEEFFfJyG8","requestRef":"33a8b313-8c4e-4ee1-a48e-4be1789dd371","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"15da925a-41bb-4f81-8485-d68585e334ad","timestamp":"2026-10-30T04:08:33.748Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0024","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"15da925a-41bb-4f81-8485-d68585e334ad","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmHwrJNiJQJWHWafEJj","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014ymoYciZ6tqaa5XcHEc5sb","name":"Read","input":{"file_path":"/Users/simon-pierreboucher/wp29/runs/RC0024/out/figure1.png"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1194,"cache_read_input_tokens":9590,"output_tokens":2962,"output_tokens_details":{"thinking_tokens":29},"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":1194},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmHwZvfeqPEEFFfJyG8","requestRef":"33a8b313-8c4e-4ee1-a48e-4be1789dd371","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"58dc232c-c507-4801-a0cc-0c8ab0e280b3","timestamp":"2026-10-30T04:08:34.180Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0024","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"58dc232c-c507-4801-a0cc-0c8ab0e280b3","isSidechain":false,"promptId":"a3229195-e300-4111-9f74-fbabf01c67ff","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014ymoYciZ6tqaa5XcHEc5sb","type":"tool_result","content":[{"type":"image","source":{"type":"base64","data":"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","media_type":"image/png"}}]}]},"uuid":"d8f70e44-aa2f-40ba-a6aa-2a405da7e247","timestamp":"2026-10-30T04:08:34.196Z","toolUseResult":{"type":"image","file":{"base64":"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","type":"image/png","originalSize":97603,"dimensions":{"originalWidth":840,"originalHeight":600,"displayWidth":840,"displayHeight":600}}},"sourceToolAssistantUUID":"58dc232c-c507-4801-a0cc-0c8ab0e280b3","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0024","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"d8f70e44-aa2f-40ba-a6aa-2a405da7e247","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmHwrJNiJQJWHWafEJj","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01D2wtNH1BsWBRDaPiLyiveP","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F6-conf-04 — Effect of training hours on productivity growth (simulated data)\n\n## Question\nWhat is the effect of one additional training hour per employee on next-year productivity growth (pp per hour)? The estimand is causal under selection on observables: conditional on the pre-programme `skill_index`, training hours are as good as randomly assigned, and the effect is linear and homogeneous. We report the unadjusted OLS slope (baseline) and an estimate that targets the estimand.\n\n## Data and exclusions\n- `data/firms.csv`: 1200 rows, 4 columns; no missing values, no duplicate `firm_id`s, no duplicate rows.\n- **Issue:** 12 firms have negative `training_hours` (min −7.585). Hours per employee cannot be negative, so these rows are invalid. They are **excluded** from the main analysis, leaving **n = 1188**. A sensitivity check that keeps them barely changes the result (adjusted slope 0.2522 vs 0.2535).\n- `skill_index` is measured the year before the programme, so it is a valid pre-treatment covariate and not a mediator. Training and skill are strongly correlated (r = 0.68 in the analysed sample).\n\n## Method\n1. Baseline: OLS `productivity_growth ~ training_hours` (intercept only, as specified).\n2. Main model: OLS `productivity_growth ~ training_hours + skill_index`. Why: `skill_index` is the only pre-programme variable. Under the stated assumption it is the conditioning set that makes treatment ignorable, and it is strongly linked to both treatment (+) and outcome (−), so leaving it out causes confounding bias. Under linearity and homogeneity, the coefficient on `training_hours` is the estimand.\n3. Standard errors: HC1 heteroskedasticity-robust (conventional SEs are almost identical: 0.00984 vs 0.00981). 95% CIs are normal-based.\n4. Diagnostics: added quadratic terms in both regressors, and a training × skill interaction, to check the linearity and homogeneity assumptions.\n\n## Results\n| model | estimate (pp/hour) | robust SE | 95% CI |\n|---|---|---|---|\n| Unadjusted (n=1188) | −0.132 | 0.0146 | [−0.160, −0.103] |\n| Adjusted for skill (n=1188), **main** | **0.254** | 0.0098 | [0.234, 0.273] |\n| Unadjusted, all 1200 rows | −0.135 | 0.0139 | [−0.162, −0.108] |\n| Adjusted, all 1200 rows | 0.252 | 0.0095 | [0.234, 0.271] |\n| Adjusted + quadratics (diagnostic; linear term only) | 0.242 | 0.0289 | [0.185, 0.299] |\n\n(Full precision is in `out/table1.csv`; the figure is `out/figure1.png`.)\n\n- The unadjusted slope is **negative** (−0.132 pp/hour). After adjusting for baseline skill, the effect is **positive**: +0.254 pp of productivity growth per extra training hour. That is about 2.5 pp for 10 extra hours.\n- Coefficient on `skill_index`: −4.62 pp per index unit. Higher-skill firms got more training but grew more slowly, which explains why the raw sign is reversed (Simpson's-paradox-type confounding).\n- Residual RMSE falls from 3.86 pp (baseline) to 1.98 pp (adjusted). R² rises from 0.07 to 0.76.\n- Conclusion code: **positive_effect**, because the 95% CI lies entirely above zero.\n\n## Uncertainty\n- Robust SE 0.0098; 95% CI [0.234, 0.273]. Sampling uncertainty is small compared with the estimate.\n- Dropping or keeping the 12 invalid rows changes the estimate by about 0.001.\n- The quadratic terms are not significant (training² p = 0.66; skill² p = 0.48), and neither is the interaction (p = 0.96). So there is no evidence against linearity or effect homogeneity. In the quadratic model the linear coefficient is less precise (SE 0.029) because the terms are collinear.\n- The CI covers sampling error only. It does not account for model or identification uncertainty, which is the dominant risk (see below).\n\n## Limitations and open issues\n- **Identifying assumption not testable.** The causal reading depends entirely on there being no unobserved confounder once `skill_index` is controlled for. Any omitted pre-programme factor linked to both training and growth would bias the estimate, for example management quality, sector, firm size, prior productivity trends, or a measurement error in the skill index itself. The data cannot check this. The huge gap between the raw and adjusted estimates (−0.13 vs +0.25) shows how sensitive the answer is to what is conditioned on. A second confounder of similar strength could move the estimate just as much.\n- **Measurement error in `skill_index`.** If the index measures true skill with noise, adjustment is incomplete. Because skill correlates positively with training and negatively with growth, residual confounding would push the training coefficient *down* (toward the naive value). Under that scenario the true effect could be larger, but this was not quantified.\n- **Negative training hours.** The 12 negative values suggest that `training_hours` itself may be measured with error or come from an untruncated process. Classical error in the treatment would attenuate the slope. We excluded these rows but cannot tell whether the remaining values are error-free.\n- **Functional form and overlap.** Linearity and homogeneity were checked only with quadratic and interaction terms. Overlap and common support across skill levels, and outliers or influential points, were not formally examined. With r = 0.68 between training and skill, the effect at extreme skill levels relies partly on extrapolation.\n- **Not checked:** spillovers between firms (SUTVA), selection into the programme or into the sample (survivorship), clustering (no group identifiers exist), and whether a one-year growth horizon captures the full effect.\n- The data are simulated, so nothing here is evidence about real training programmes.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The unadjusted OLS slope of productivity growth on training hours is negative (about -0.13 pp per hour, 95% CI excludes zero).\",\"results_keys\":[\"naive_estimate\",\"naive_ci_low\",\"naive_ci_high\"],\"status\":\"supported\",\"caveat\":\"Descriptive association only; confounded by baseline skill.\"},\n {\"id\":\"C2\",\"text\":\"Conditional on baseline skill_index, one extra training hour per employee raises next-year productivity growth by about 0.25 pp (95% CI 0.23 to 0.27).\",\"results_keys\":[\"effect_estimate\",\"effect_se\",\"effect_ci_low\",\"effect_ci_high\",\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Causal interpretation rests on the untestable selection-on-observables assumption with skill_index as the only control.\"},\n {\"id\":\"C3\",\"text\":\"Baseline skill is a strong confounder: it is negatively related to growth (about -4.6 pp per index unit) and positively related to training, reversing the sign of the raw association.\",\"results_keys\":[\"skill_index_coefficient\",\"naive_estimate\",\"effect_estimate\"],\"status\":\"supported\",\"caveat\":\"The positive skill-training correlation (0.68) is in diagnostics.corr_training_skill.\"},\n {\"id\":\"C4\",\"text\":\"Adjusting for skill roughly halves residual RMSE (3.86 to 1.98 pp).\",\"results_keys\":[\"baseline_metric\",\"model_metric\"],\"status\":\"supported\",\"caveat\":\"In-sample fit; not a validation of causal identification.\"},\n {\"id\":\"C5\",\"text\":\"Excluding the 12 rows with negative training hours does not materially change the estimate.\",\"results_keys\":[\"data_issues\",\"n_analysed\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"Sensitivity numbers are in results.json diagnostics (sensitivity_all_rows_effect).\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'))\"; ls out"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1194,"cache_read_input_tokens":9590,"output_tokens":2962,"output_tokens_details":{"thinking_tokens":29},"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":1194},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXmHwZvfeqPEEFFfJyG8","requestRef":"33a8b313-8c4e-4ee1-a48e-4be1789dd371","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f3477b23-7a16-4335-91d7-0506fcb65920","timestamp":"2026-10-30T04:08:58.701Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0024","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"f3477b23-7a16-4335-91d7-0506fcb65920","isSidechain":false,"promptId":"a3229195-e300-4111-9f74-fbabf01c67ff","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01D2wtNH1BsWBRDaPiLyiveP","type":"tool_result","content":"analysis.py\nclaims.json\nfigure1.png\nreport.md\nresults.json\ntable1.csv","is_error":false}]},"uuid":"ee726904-94bd-4e8e-8246-29bcdfea2676","timestamp":"2026-10-30T04:08:58.750Z","toolUseResult":{"stdout":"analysis.py\nclaims.json\nfigure1.png\nreport.md\nresults.json\ntable1.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"f3477b23-7a16-4335-91d7-0506fcb65920","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0024","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","version":"2.1.314","gitBranch":"HEAD"}
{"type":"api-request-blob","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","hash":"072c39eacbf975373fee1329e3c165d242efbbac34c86e08bdeca718f35258af","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_014ymoYciZ6tqaa5XcHEc5sb","name":"Read","input":{"file_path":"/Users/simon-pierreboucher/wp29/runs/RC0024/out/figure1.png"}},{"type":"tool_use","id":"toolu_01D2wtNH1BsWBRDaPiLyiveP","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F6-conf-04 — Effect of training hours on productivity growth (simulated data)\n\n## Question\nWhat is the effect of one additional training hour per employee on next-year productivity growth (pp per hour)? The estimand is causal under selection on observables: conditional on the pre-programme `skill_index`, training hours are as good as randomly assigned, and the effect is linear and homogeneous. We report the unadjusted OLS slope (baseline) and an estimate that targets the estimand.\n\n## Data and exclusions\n- `data/firms.csv`: 1200 rows, 4 columns; no missing values, no duplicate `firm_id`s, no duplicate rows.\n- **Issue:** 12 firms have negative `training_hours` (min −7.585). Hours per employee cannot be negative, so these rows are invalid. They are **excluded** from the main analysis, leaving **n = 1188**. A sensitivity check that keeps them barely changes the result (adjusted slope 0.2522 vs 0.2535).\n- `skill_index` is measured the year before the programme, so it is a valid pre-treatment covariate and not a mediator. Training and skill are strongly correlated (r = 0.68 in the analysed sample).\n\n## Method\n1. Baseline: OLS `productivity_growth ~ training_hours` (intercept only, as specified).\n2. Main model: OLS `productivity_growth ~ training_hours + skill_index`. Why: `skill_index` is the only pre-programme variable. Under the stated assumption it is the conditioning set that makes treatment ignorable, and it is strongly linked to both treatment (+) and outcome (−), so leaving it out causes confounding bias. Under linearity and homogeneity, the coefficient on `training_hours` is the estimand.\n3. Standard errors: HC1 heteroskedasticity-robust (conventional SEs are almost identical: 0.00984 vs 0.00981). 95% CIs are normal-based.\n4. Diagnostics: added quadratic terms in both regressors, and a training × skill interaction, to check the linearity and homogeneity assumptions.\n\n## Results\n| model | estimate (pp/hour) | robust SE | 95% CI |\n|---|---|---|---|\n| Unadjusted (n=1188) | −0.132 | 0.0146 | [−0.160, −0.103] |\n| Adjusted for skill (n=1188), **main** | **0.254** | 0.0098 | [0.234, 0.273] |\n| Unadjusted, all 1200 rows | −0.135 | 0.0139 | [−0.162, −0.108] |\n| Adjusted, all 1200 rows | 0.252 | 0.0095 | [0.234, 0.271] |\n| Adjusted + quadratics (diagnostic; linear term only) | 0.242 | 0.0289 | [0.185, 0.299] |\n\n(Full precision is in `out/table1.csv`; the figure is `out/figure1.png`.)\n\n- The unadjusted slope is **negative** (−0.132 pp/hour). After adjusting for baseline skill, the effect is **positive**: +0.254 pp of productivity growth per extra training hour. That is about 2.5 pp for 10 extra hours.\n- Coefficient on `skill_index`: −4.62 pp per index unit. Higher-skill firms got more training but grew more slowly, which explains why the raw sign is reversed (Simpson's-paradox-type confounding).\n- Residual RMSE falls from 3.86 pp (baseline) to 1.98 pp (adjusted). R² rises from 0.07 to 0.76.\n- Conclusion code: **positive_effect**, because the 95% CI lies entirely above zero.\n\n## Uncertainty\n- Robust SE 0.0098; 95% CI [0.234, 0.273]. Sampling uncertainty is small compared with the estimate.\n- Dropping or keeping the 12 invalid rows changes the estimate by about 0.001.\n- The quadratic terms are not significant (training² p = 0.66; skill² p = 0.48), and neither is the interaction (p = 0.96). So there is no evidence against linearity or effect homogeneity. In the quadratic model the linear coefficient is less precise (SE 0.029) because the terms are collinear.\n- The CI covers sampling error only. It does not account for model or identification uncertainty, which is the dominant risk (see below).\n\n## Limitations and open issues\n- **Identifying assumption not testable.** The causal reading depends entirely on there being no unobserved confounder once `skill_index` is controlled for. Any omitted pre-programme factor linked to both training and growth would bias the estimate, for example management quality, sector, firm size, prior productivity trends, or a measurement error in the skill index itself. The data cannot check this. The huge gap between the raw and adjusted estimates (−0.13 vs +0.25) shows how sensitive the answer is to what is conditioned on. A second confounder of similar strength could move the estimate just as much.\n- **Measurement error in `skill_index`.** If the index measures true skill with noise, adjustment is incomplete. Because skill correlates positively with training and negatively with growth, residual confounding would push the training coefficient *down* (toward the naive value). Under that scenario the true effect could be larger, but this was not quantified.\n- **Negative training hours.** The 12 negative values suggest that `training_hours` itself may be measured with error or come from an untruncated process. Classical error in the treatment would attenuate the slope. We excluded these rows but cannot tell whether the remaining values are error-free.\n- **Functional form and overlap.** Linearity and homogeneity were checked only with quadratic and interaction terms. Overlap and common support across skill levels, and outliers or influential points, were not formally examined. With r = 0.68 between training and skill, the effect at extreme skill levels relies partly on extrapolation.\n- **Not checked:** spillovers between firms (SUTVA), selection into the programme or into the sample (survivorship), clustering (no group identifiers exist), and whether a one-year growth horizon captures the full effect.\n- The data are simulated, so nothing here is evidence about real training programmes.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The unadjusted OLS slope of productivity growth on training hours is negative (about -0.13 pp per hour, 95% CI excludes zero).\",\"results_keys\":[\"naive_estimate\",\"naive_ci_low\",\"naive_ci_high\"],\"status\":\"supported\",\"caveat\":\"Descriptive association only; confounded by baseline skill.\"},\n {\"id\":\"C2\",\"text\":\"Conditional on baseline skill_index, one extra training hour per employee raises next-year productivity growth by about 0.25 pp (95% CI 0.23 to 0.27).\",\"results_keys\":[\"effect_estimate\",\"effect_se\",\"effect_ci_low\",\"effect_ci_high\",\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Causal interpretation rests on the untestable selection-on-observables assumption with skill_index as the only control.\"},\n {\"id\":\"C3\",\"text\":\"Baseline skill is a strong confounder: it is negatively related to growth (about -4.6 pp per index unit) and positively related to training, reversing the sign of the raw association.\",\"results_keys\":[\"skill_index_coefficient\",\"naive_estimate\",\"effect_estimate\"],\"status\":\"supported\",\"caveat\":\"The positive skill-training correlation (0.68) is in diagnostics.corr_training_skill.\"},\n {\"id\":\"C4\",\"text\":\"Adjusting for skill roughly halves residual RMSE (3.86 to 1.98 pp).\",\"results_keys\":[\"baseline_metric\",\"model_metric\"],\"status\":\"supported\",\"caveat\":\"In-sample fit; not a validation of causal identification.\"},\n {\"id\":\"C5\",\"text\":\"Excluding the 12 rows with negative training hours does not materially change the estimate.\",\"results_keys\":[\"data_issues\",\"n_analysed\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"Sensitivity numbers are in results.json diagnostics (sensitivity_all_rows_effect).\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'))\"; ls out"}}]}}
{"type":"api-request-blob","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","hash":"a2f7087d3d335499e0f87f9362d2dc8ba8e5e83bd0e4515be03e3f81dda65171","message":{"role":"user","content":[{"tool_use_id":"toolu_014ymoYciZ6tqaa5XcHEc5sb","type":"tool_result","content":[{"type":"image","source":{"type":"base64","data":"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","media_type":"image/png"}}]},{"tool_use_id":"toolu_01D2wtNH1BsWBRDaPiLyiveP","type":"tool_result","content":"analysis.py\nclaims.json\nfigure1.png\nreport.md\nresults.json\ntable1.csv","is_error":false}]}}
{"type":"api-request","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","id":"366ec8cf-a5ef-4c17-a5f1-7e3477efbd25","timestamp":"2026-10-30T04:08:58.753Z","version":"2.1.314","querySource":"sdk","shapeHash":"a54eeb670b8878f1ed772600f5a887c0cd432e0281ba2d394cba769bb793c219","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":"33a8b313-8c4e-4ee1-a48e-4be1789dd371","keep":12,"tail":["072c39eacbf975373fee1329e3c165d242efbbac34c86e08bdeca718f35258af","a2f7087d3d335499e0f87f9362d2dc8ba8e5e83bd0e4515be03e3f81dda65171"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",1],"value":{"type":"ephemeral"}}],"requestDigest":"7b6cca74f93ea555bac7a0dd88a713ca1bfc183f1f4b884f35da58577ea9b831"}
{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"ee726904-94bd-4e8e-8246-29bcdfea2676","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9"}
{"type":"atis-latch","atis":"","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9"}
{"parentUuid":"ee726904-94bd-4e8e-8246-29bcdfea2676","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmKtg7LtpbMcLgxR6c2","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3684,"cache_read_input_tokens":10784,"output_tokens":809,"output_tokens_details":{"thinking_tokens":36},"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":3684},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":6387,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXmKtPkGi5fiiYgWKCM7","requestRef":"366ec8cf-a5ef-4c17-a5f1-7e3477efbd25","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9aeb48cc-65be-4b0d-9963-88fb7c93358d","timestamp":"2026-10-30T04:09:06.827Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0024","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"9aeb48cc-65be-4b0d-9963-88fb7c93358d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmKtg7LtpbMcLgxR6c2","type":"message","role":"assistant","content":[{"type":"text","text":"All the required files are in `out/`, and running `out/analysis.py` regenerates the results, table and figure. The main finding: once baseline skill is controlled for, one extra training hour per employee raises next-year productivity growth by about **0.254 pp** (95% CI 0.234 to 0.273). The conclusion code is `positive_effect`.\n\n| Model | Estimate (pp per hour) | Standard error | 95% CI | Residual RMSE |\n|---|---|---|---|---|\n| Unadjusted (the required baseline) | −0.132 | 0.0146 | −0.160 to −0.103 | 3.86 pp |\n| Adjusted for `skill_index` (main) | 0.254 | 0.0098 | 0.234 to 0.273 | 1.98 pp |\n\n- **Sign reversal:** the raw association is negative because higher-skill firms got more training (correlation 0.68) but grew more slowly. Each unit of the skill index lowers growth by 4.62 pp.\n- **Standard errors:** these are heteroskedasticity-robust; conventional ones are almost identical.\n- **Data problem:** 12 firms have negative training hours, which is impossible. I excluded them, so 1,188 firms are analysed. Keeping them changes the estimate by about 0.001.\n- **Checks:** adding squared terms and a training × skill interaction found nothing (p = 0.66, 0.48 and 0.96). So the data don't contradict the assumption of a linear, uniform effect.\n\n**Limitations:**\n- The causal reading depends entirely on there being no other confounder besides `skill_index`, and the data can't test that. The jump from −0.13 to +0.25 shows how much the answer depends on what is controlled for.\n- Noise in the skill index or in training hours could bias the estimate.\n- I did not formally check overlap across skill levels, influential points, spillovers between firms, or how firms were selected.\n- The data are simulated, so this says nothing about real training programmes.\n\nEarly in the run I briefly created an `out/` folder inside `data/` by mistake. I moved the script out and deleted the folder; `data/firms.csv` was never changed.\n\nFiles are in `out/`:\n- `results.json`\n- `report.md`\n- `analysis.py`\n- `table1.csv`\n- `figure1.png`\n- `claims.json`"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3684,"cache_read_input_tokens":10784,"output_tokens":809,"output_tokens_details":{"thinking_tokens":36},"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":3684},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmKtPkGi5fiiYgWKCM7","requestRef":"366ec8cf-a5ef-4c17-a5f1-7e3477efbd25","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b63536b0-8231-407c-b6db-0ad5a13cffdf","timestamp":"2026-10-30T04:09:06.984Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0024","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","version":"2.1.314","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"b63536b0-8231-407c-b6db-0ad5a13cffdf","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9"}
{"type":"cost-state","sessionId":"d6946970-8d2b-43e9-bddb-12a6dc9b27b9","totalCostUSD":0.21525840000000002,"totalAPIDuration":66195,"totalAPIDurationWithoutRetries":66188,"totalToolDuration":2621,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":68944,"startTime":1793333278056,"modelUsage":{"claude-opus-5-5":{"inputTokens":16,"outputTokens":7117,"thinkingTokens":228,"cacheReadInputTokens":40172,"cacheCreationInputTokens":12964,"cacheCreationEphemeral5mInputTokens":12964,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.21525840000000002}},"hasUnknownModelCost":false}