{"type":"atis-latch","atis":"","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9"} {"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T04:35:50.736Z","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","content":"Begin"} {"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T04:35:50.742Z","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9"} {"parentUuid":null,"isSidechain":false,"promptId":"987711b9-2da1-48f1-95c0-984a823a90af","type":"user","message":{"role":"user","content":"Begin"},"uuid":"a979baea-4e8a-4360-a183-d4e2e45251fa","timestamp":"2026-10-30T04:35:50.746Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"type":"api-request-shape","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","shapeHash":"1cefe355e71f8bb33a44ea933fac791086e15c215724bd3d4e47a4860724ab4b","timestamp":"2026-10-30T04:35:50.759Z","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/RC0118\nDate: 2026-10-30\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. Never use words like \"complex\" or \"risk\" in the description - just describe what it does.\n\nSay what the command does in plain words: do not echo the command's text, its flags, or file paths - the user reads this description, often without seeing the command.\n\nFor simple commands (git, npm, standard CLI tools), keep it brief (5-10 words):\n- ls → \"List files in current directory\"\n- git status → \"Show working tree status\"\n- npm install → \"Install package dependencies\"\n\nFor commands that are harder to parse at a glance (piped commands, obscure flags, etc.), add enough context to clarify what it does:\n- find . -name \"*.tmp\" -exec rm {} \\; → \"Find and delete all .tmp files recursively\"\n- git reset --hard origin/main → \"Discard all local changes and match remote main\"\n- curl -s url | jq '.data[]' → \"Fetch JSON from URL and extract data array elements\"","type":"string"},"dangerouslyDisableSandbox":{"description":"Set this to true to dangerously override sandbox mode and run commands without sandboxing.","type":"boolean"}},"required":["command"],"additionalProperties":false}},{"name":"Edit","description":"modify file contents in place","input_schema":{"$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":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","hash":"d43ab0e0b8ec9db790a6459cb2f4bc4ffce02825ca05063162023be60912ced9","message":{"role":"system","content":[],"output_config":{"effort":"medium"}}} {"type":"api-request","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","id":"8950ff53-51dc-4d89-b23d-8972afac6def","timestamp":"2026-10-30T04:35:50.759Z","version":"2.1.314","querySource":"sdk","shapeHash":"1cefe355e71f8bb33a44ea933fac791086e15c215724bd3d4e47a4860724ab4b","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":"78648b89ec50780926648e0201e31443fb5a508f2520b0d942d43b5aa722ffd0"} {"parentUuid":"a979baea-4e8a-4360-a183-d4e2e45251fa","isSidechain":false,"attachment":{"type":"session_context","context":{}},"type":"attachment","uuid":"add086e7-e8b7-4e0b-9fe9-771fc032ef0e","timestamp":"2026-10-30T04:35:50.751Z","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"add086e7-e8b7-4e0b-9fe9-771fc032ef0e","isSidechain":false,"attachment":{"type":"date","date":"2026-10-30"},"type":"attachment","uuid":"d49ddb67-e112-45fc-bfb5-42cd3a792668","timestamp":"2026-10-30T04:35:50.751Z","rendered":[{"content":"\nToday's date is 2026-10-30.\n"}],"renderedRole":"system","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"d49ddb67-e112-45fc-bfb5-42cd3a792668","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":"4b378e89-3caa-411e-8d23-c02e7b82705b","timestamp":"2026-10-30T04:35:50.752Z","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/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"type":"tools-used","tools":["Bash"],"sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9"} {"parentUuid":"4b378e89-3caa-411e-8d23-c02e7b82705b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoNkMraY4wFRSA5buNB","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":2603,"cache_read_input_tokens":1504,"output_tokens":117,"output_tokens_details":{"thinking_tokens":15},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2603},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":387,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXoNk5FoGQ83mcUkxxXi","requestRef":"8950ff53-51dc-4d89-b23d-8972afac6def","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c77a109b-c5f8-4f07-8257-2136a499e51b","timestamp":"2026-10-30T04:35:52.004Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"c77a109b-c5f8-4f07-8257-2136a499e51b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoNkMraY4wFRSA5buNB","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01NoW9z5YHHajYDsmD2BYYoo","name":"Bash","input":{"command":"cat TASK.md; ls -la; ls -la data out 2>/dev/null","description":"Read task and list workspace"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":2603,"cache_read_input_tokens":1504,"output_tokens":117,"output_tokens_details":{"thinking_tokens":15},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2603},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXoNk5FoGQ83mcUkxxXi","requestRef":"8950ff53-51dc-4d89-b23d-8972afac6def","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9a09ac58-8463-495f-b7a5-30acc932e58e","timestamp":"2026-10-30T04:35:52.413Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"9a09ac58-8463-495f-b7a5-30acc932e58e","isSidechain":false,"promptId":"987711b9-2da1-48f1-95c0-984a823a90af","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\n# F2-conf-01 — Effect of a school-level programme on student scores, with valid uncertainty\n\n## Scientific question\nA programme was assigned at the school level: every student in a treated school is exposed, no student in a control school is. Estimate the average effect of the programme on the student test score and report a valid measure of its sampling uncertainty. The estimand τ is the difference between the mean score of students in treated schools and the mean score of students in control schools (student-weighted means, i.e. the OLS coefficient of `score` on `treated` with an intercept). Uncertainty must reflect the design: the programme varies only between schools, and students within the same school share unobserved school-level influences; schools are independent of each other. Decide, at the 5% level (two-sided), whether the data support a non-zero effect.\n\n## Inputs\n- `data/schools.csv` — simulated data (a synthetic dataset generated for this exercise; it is not observed data). Columns: `school_id` (integer school identifier), `student_id` (integer student identifier, unique), `treated` (1 if the student's school received the programme, 0 otherwise — constant within a school), `score` (test score, points). One row per student; rows are in random order. The design is balanced: every school has the same number of students.\n\n## Information-availability rules\n- Use only `data/schools.csv`. No other input exists.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | – | \"F2-conf-01\" |\n| `tau_hat` | number | points | estimated programme effect (OLS coefficient of score on treated, with intercept) |\n| `se_iid` | number | points | conventional OLS standard error of `tau_hat` assuming independent, homoskedastic errors — the naïve baseline |\n| `se_cluster` | number | points | standard error of `tau_hat` that is valid under the two-level design described above (your main uncertainty measure) |\n| `p_value` | number | – | two-sided p-value of H0: τ = 0 using `se_cluster` |\n| `tau_ci_low`, `tau_ci_high` | number | points | 95% confidence interval for τ built from `se_cluster` |\n| `effect_significant_5pct` | boolean | – | true if and only if H0: τ = 0 is rejected at the 5% level with the valid uncertainty measure |\n| `icc_hat` | number | – | estimated intraclass correlation of the score residual (share of residual variance that is between schools, after removing the treatment effect) |\n| `n_schools` | integer | – | number of distinct schools |\n| `n_treated_schools` | integer | – | number of treated schools |\n| `n_analysed` | integer | – | students used in the estimate |\n| `feasibility` | string | – | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | – | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | – | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | – | {\"schools.csv\": rows read} |\n| `random_seed` | integer or null | – | seed used, if any (e.g. for a bootstrap) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]. Include one claim about whether the programme has a non-zero effect at the 5% level.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 15 minutes of work.\ntotal 24\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:29 .\ndrwxr-xr-x 77 simon-pierreboucher staff 2464 oct. 30 00:35 ..\n-rw-r--r-- 1 simon-pierreboucher staff 421 oct. 29 23:29 bundle.json\ndrwxr-xr-x 3 simon-pierreboucher staff 96 oct. 29 23:26 data\n-rw-r--r-- 1 simon-pierreboucher staff 1609 oct. 29 23:28 gate_spec.json\n-rw-r--r-- 1 simon-pierreboucher staff 3925 oct. 29 23:28 TASK.md\ndata:\ntotal 40\ndrwxr-xr-x 3 simon-pierreboucher staff 96 oct. 29 23:26 .\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:29 ..\n-rw-r--r-- 1 simon-pierreboucher staff 16818 oct. 29 23:26 schools.csv","is_error":true,"tool_use_id":"toolu_01NoW9z5YHHajYDsmD2BYYoo"}]},"uuid":"03039f5a-4c84-404b-8e58-68753e469ea4","timestamp":"2026-10-30T04:35:52.494Z","toolUseResult":"Error: Exit code 1\n# F2-conf-01 — Effect of a school-level programme on student scores, with valid uncertainty\n\n## Scientific question\nA programme was assigned at the school level: every student in a treated school is exposed, no student in a control school is. Estimate the average effect of the programme on the student test score and report a valid measure of its sampling uncertainty. The estimand τ is the difference between the mean score of students in treated schools and the mean score of students in control schools (student-weighted means, i.e. the OLS coefficient of `score` on `treated` with an intercept). Uncertainty must reflect the design: the programme varies only between schools, and students within the same school share unobserved school-level influences; schools are independent of each other. Decide, at the 5% level (two-sided), whether the data support a non-zero effect.\n\n## Inputs\n- `data/schools.csv` — simulated data (a synthetic dataset generated for this exercise; it is not observed data). Columns: `school_id` (integer school identifier), `student_id` (integer student identifier, unique), `treated` (1 if the student's school received the programme, 0 otherwise — constant within a school), `score` (test score, points). One row per student; rows are in random order. The design is balanced: every school has the same number of students.\n\n## Information-availability rules\n- Use only `data/schools.csv`. No other input exists.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | – | \"F2-conf-01\" |\n| `tau_hat` | number | points | estimated programme effect (OLS coefficient of score on treated, with intercept) |\n| `se_iid` | number | points | conventional OLS standard error of `tau_hat` assuming independent, homoskedastic errors — the naïve baseline |\n| `se_cluster` | number | points | standard error of `tau_hat` that is valid under the two-level design described above (your main uncertainty measure) |\n| `p_value` | number | – | two-sided p-value of H0: τ = 0 using `se_cluster` |\n| `tau_ci_low`, `tau_ci_high` | number | points | 95% confidence interval for τ built from `se_cluster` |\n| `effect_significant_5pct` | boolean | – | true if and only if H0: τ = 0 is rejected at the 5% level with the valid uncertainty measure |\n| `icc_hat` | number | – | estimated intraclass correlation of the score residual (share of residual variance that is between schools, after removing the treatment effect) |\n| `n_schools` | integer | – | number of distinct schools |\n| `n_treated_schools` | integer | – | number of treated schools |\n| `n_analysed` | integer | – | students used in the estimate |\n| `feasibility` | string | – | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | – | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | – | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | – | {\"schools.csv\": rows read} |\n| `random_seed` | integer or null | – | seed used, if any (e.g. for a bootstrap) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]. Include one claim about whether the programme has a non-zero effect at the 5% level.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 15 minutes of work.\ntotal 24\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:29 .\ndrwxr-xr-x 77 simon-pierreboucher staff 2464 oct. 30 00:35 ..\n-rw-r--r-- 1 simon-pierreboucher staff 421 oct. 29 23:29 bundle.json\ndrwxr-xr-x 3 simon-pierreboucher staff 96 oct. 29 23:26 data\n-rw-r--r-- 1 simon-pierreboucher staff 1609 oct. 29 23:28 gate_spec.json\n-rw-r--r-- 1 simon-pierreboucher staff 3925 oct. 29 23:28 TASK.md\ndata:\ntotal 40\ndrwxr-xr-x 3 simon-pierreboucher staff 96 oct. 29 23:26 .\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:29 ..\n-rw-r--r-- 1 simon-pierreboucher staff 16818 oct. 29 23:26 schools.csv","sandboxed":false,"sourceToolAssistantUUID":"9a09ac58-8463-495f-b7a5-30acc932e58e","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"type":"api-request-blob","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","hash":"2535a62ca9500ad23852f94974672f7eaea109c14b0fdeddb5a59befb9c67c06","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01NoW9z5YHHajYDsmD2BYYoo","name":"Bash","input":{"command":"cat TASK.md; ls -la; ls -la data out 2>/dev/null","description":"Read task and list workspace"}}]}} {"type":"api-request","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","id":"b547a730-49b9-4257-a89a-8f1362b80cc7","timestamp":"2026-10-30T04:35:52.499Z","version":"2.1.314","querySource":"sdk","shapeHash":"1cefe355e71f8bb33a44ea933fac791086e15c215724bd3d4e47a4860724ab4b","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":"8950ff53-51dc-4d89-b23d-8972afac6def","keep":2,"tail":["2535a62ca9500ad23852f94974672f7eaea109c14b0fdeddb5a59befb9c67c06","919ff6a043a4e1791f78005e0f05c86fb838ee5a9f691112953ea5392c120480"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"6fbf909897267cbae4ff313bd732f3b9aeab6afd404da95d000b80401519e58c"} {"parentUuid":"03039f5a-4c84-404b-8e58-68753e469ea4","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoNr6Q1Auf1LhHbAuaa","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01VRSnLv6Af3HmYBWo2dzJoB","name":"Bash","input":{"command":"cat gate_spec.json bundle.json; mkdir -p out/state; cd data; shasum -a 256 schools.csv; head -5 schools.csv; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('schools.csv');print(d.shape);print(d.dtypes);print(d.describe());print(d.isna().sum())\nprint('dup student',d.student_id.duplicated().sum(),'dup rows',d.duplicated().sum())\ng=d.groupby('school_id').agg(n=('score','size'),t=('treated','nunique'),tr=('treated','first'),m=('score','mean'))\nprint(g.n.value_counts());print(g.t.value_counts());print(g.tr.value_counts());print(g)\nprint(d.treated.value_counts())\n\"","description":"Audit data file"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1884,"cache_read_input_tokens":4107,"output_tokens":345,"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":1884},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXoNqonR2GsdkcDF2Bqe","requestRef":"b547a730-49b9-4257-a89a-8f1362b80cc7","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"31ac6ae2-4128-46b1-8031-1e97c545e734","timestamp":"2026-10-30T04:35:55.384Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"type":"last-prompt","lastPrompt":"Begin","leafUuid":"31ac6ae2-4128-46b1-8031-1e97c545e734","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9"} {"type":"atis-latch","atis":"","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9"} {"parentUuid":"31ac6ae2-4128-46b1-8031-1e97c545e734","isSidechain":false,"promptId":"987711b9-2da1-48f1-95c0-984a823a90af","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01VRSnLv6Af3HmYBWo2dzJoB","type":"tool_result","content":"{\n \"task_id\": \"F2-conf-01\",\n \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\"],\n \"results_keys\": {\n \"task_id\": {\"type\": \"string\", \"nullable\": false},\n \"tau_hat\": {\"type\": \"number\", \"nullable\": false},\n \"se_iid\": {\"type\": \"number\", \"nullable\": false},\n \"se_cluster\": {\"type\": \"number\", \"nullable\": false},\n \"p_value\": {\"type\": \"number\", \"nullable\": false},\n \"tau_ci_low\": {\"type\": \"number\", \"nullable\": false},\n \"tau_ci_high\": {\"type\": \"number\", \"nullable\": false},\n \"effect_significant_5pct\": {\"type\": \"boolean\", \"nullable\": false},\n \"icc_hat\": {\"type\": \"number\", \"nullable\": false},\n \"n_schools\": {\"type\": \"integer\", \"nullable\": false},\n \"n_treated_schools\": {\"type\": \"integer\", \"nullable\": false},\n \"n_analysed\": {\"type\": \"integer\", \"nullable\": false},\n \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n \"data_issues\": {\"type\": \"array\", \"nullable\": false},\n \"n_input_rows\": {\"type\": \"object\", \"nullable\": false},\n \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n },\n \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n \"family_checks\": [\"ci_contains_estimate\", \"positive_se\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n \"ci_pairs\": [[\"tau_hat\", \"tau_ci_low\", \"tau_ci_high\"]],\n \"positive_keys\": [\"se_iid\", \"se_cluster\"],\n \"chronology_pairs\": [],\n \"analysis_timeout_s\": 300,\n \"reproducibility_tolerance_rel\": 1e-6\n}\n{\n \"task_id\": \"F2-conf-01\",\n \"family\": \"F2\",\n \"split\": \"conf\",\n \"version\": \"1.0\",\n \"created_utc\": \"2026-10-30T03:29:07.013143+00:00\",\n \"files\": {\n \"TASK.md\": \"114f43f61885b229d7f982f413fd2d347e7fa7be0509555a18067c7cbf1b7cad\",\n \"data/schools.csv\": \"0f1d8378fff2f0898762feaa08dd94d9ad04156dad6330572d98997c9005c88a\",\n \"gate_spec.json\": \"9785bd6f17ce8946989d00b4f847cf13136c7bc3b33f2c7a4c1ff010c214ada4\"\n }\n}0f1d8378fff2f0898762feaa08dd94d9ad04156dad6330572d98997c9005c88a schools.csv\nschool_id,student_id,treated,score\n124,603,0,63.956\n116,414,1,59.642\n120,518,0,53.895\n115,385,1,51.222\n(1000, 4)\nschool_id int64\nstudent_id int64\ntreated int64\nscore float64\ndtype: object\n school_id student_id treated score\ncount 1000.000000 1000.000000 1000.00000 1000.000000\nmean 119.500000 500.500000 0.50000 50.801531\nstd 11.549172 288.819436 0.50025 4.483969\nmin 100.000000 1.000000 0.00000 35.721000\n25% 109.750000 250.750000 0.00000 47.923000\n50% 119.500000 500.500000 0.50000 50.714000\n75% 129.250000 750.250000 1.00000 53.933500\nmax 139.000000 1000.000000 1.00000 64.173000\nschool_id 0\nstudent_id 0\ntreated 0\nscore 0\ndtype: int64\ndup student 0 dup rows 0\nn\n25 40\nName: count, dtype: int64\nt\n1 40\nName: count, dtype: int64\ntr\n1 20\n0 20\nName: count, dtype: int64\n n t tr m\nschool_id \n100 25 1 1 53.12896\n101 25 1 1 51.81364\n102 25 1 0 49.47196\n103 25 1 0 53.80840\n104 25 1 1 52.45492\n105 25 1 1 52.11980\n106 25 1 0 47.29140\n107 25 1 0 47.96636\n108 25 1 0 46.21216\n109 25 1 0 52.58432\n110 25 1 1 47.97892\n111 25 1 0 52.93580\n112 25 1 1 51.11876\n113 25 1 0 50.01260\n114 25 1 1 51.58000\n115 25 1 1 53.60212\n116 25 1 1 55.60588\n117 25 1 1 50.12952\n118 25 1 0 48.74272\n119 25 1 0 48.11464\n120 25 1 0 47.84768\n121 25 1 1 53.10032\n122 25 1 0 52.65696\n123 25 1 1 51.01732\n124 25 1 0 53.63332\n125 25 1 1 54.08012\n126 25 1 1 52.99944\n127 25 1 1 46.70260\n128 25 1 0 54.05464\n129 25 1 0 52.42144\n130 25 1 0 50.58732\n131 25 1 0 48.54192\n132 25 1 0 48.89652\n133 25 1 1 49.46216\n134 25 1 1 50.01984\n135 25 1 1 49.80852\n136 25 1 1 48.84240\n137 25 1 0 51.92500\n138 25 1 1 50.07656\n139 25 1 0 48.71428\ntreated\n0 500\n1 500\nName: count, dtype: int64","is_error":false}]},"uuid":"b1a18ad5-695b-4592-8844-9cc89faf262c","timestamp":"2026-10-30T04:35:55.699Z","toolUseResult":{"stdout":"{\n \"task_id\": \"F2-conf-01\",\n \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\"],\n \"results_keys\": {\n \"task_id\": {\"type\": \"string\", \"nullable\": false},\n \"tau_hat\": {\"type\": \"number\", \"nullable\": false},\n \"se_iid\": {\"type\": \"number\", \"nullable\": false},\n \"se_cluster\": {\"type\": \"number\", \"nullable\": false},\n \"p_value\": {\"type\": \"number\", \"nullable\": false},\n \"tau_ci_low\": {\"type\": \"number\", \"nullable\": false},\n \"tau_ci_high\": {\"type\": \"number\", \"nullable\": false},\n \"effect_significant_5pct\": {\"type\": \"boolean\", \"nullable\": false},\n \"icc_hat\": {\"type\": \"number\", \"nullable\": false},\n \"n_schools\": {\"type\": \"integer\", \"nullable\": false},\n \"n_treated_schools\": {\"type\": \"integer\", \"nullable\": false},\n \"n_analysed\": {\"type\": \"integer\", \"nullable\": false},\n \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n \"data_issues\": {\"type\": \"array\", \"nullable\": false},\n \"n_input_rows\": {\"type\": \"object\", \"nullable\": false},\n \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n },\n \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n \"family_checks\": [\"ci_contains_estimate\", \"positive_se\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n \"ci_pairs\": [[\"tau_hat\", \"tau_ci_low\", \"tau_ci_high\"]],\n \"positive_keys\": [\"se_iid\", \"se_cluster\"],\n \"chronology_pairs\": [],\n \"analysis_timeout_s\": 300,\n \"reproducibility_tolerance_rel\": 1e-6\n}\n{\n \"task_id\": \"F2-conf-01\",\n \"family\": \"F2\",\n \"split\": \"conf\",\n \"version\": \"1.0\",\n \"created_utc\": \"2026-10-30T03:29:07.013143+00:00\",\n \"files\": {\n \"TASK.md\": \"114f43f61885b229d7f982f413fd2d347e7fa7be0509555a18067c7cbf1b7cad\",\n \"data/schools.csv\": \"0f1d8378fff2f0898762feaa08dd94d9ad04156dad6330572d98997c9005c88a\",\n \"gate_spec.json\": \"9785bd6f17ce8946989d00b4f847cf13136c7bc3b33f2c7a4c1ff010c214ada4\"\n }\n}0f1d8378fff2f0898762feaa08dd94d9ad04156dad6330572d98997c9005c88a schools.csv\nschool_id,student_id,treated,score\n124,603,0,63.956\n116,414,1,59.642\n120,518,0,53.895\n115,385,1,51.222\n(1000, 4)\nschool_id int64\nstudent_id int64\ntreated int64\nscore float64\ndtype: object\n school_id student_id treated score\ncount 1000.000000 1000.000000 1000.00000 1000.000000\nmean 119.500000 500.500000 0.50000 50.801531\nstd 11.549172 288.819436 0.50025 4.483969\nmin 100.000000 1.000000 0.00000 35.721000\n25% 109.750000 250.750000 0.00000 47.923000\n50% 119.500000 500.500000 0.50000 50.714000\n75% 129.250000 750.250000 1.00000 53.933500\nmax 139.000000 1000.000000 1.00000 64.173000\nschool_id 0\nstudent_id 0\ntreated 0\nscore 0\ndtype: int64\ndup student 0 dup rows 0\nn\n25 40\nName: count, dtype: int64\nt\n1 40\nName: count, dtype: int64\ntr\n1 20\n0 20\nName: count, dtype: int64\n n t tr m\nschool_id \n100 25 1 1 53.12896\n101 25 1 1 51.81364\n102 25 1 0 49.47196\n103 25 1 0 53.80840\n104 25 1 1 52.45492\n105 25 1 1 52.11980\n106 25 1 0 47.29140\n107 25 1 0 47.96636\n108 25 1 0 46.21216\n109 25 1 0 52.58432\n110 25 1 1 47.97892\n111 25 1 0 52.93580\n112 25 1 1 51.11876\n113 25 1 0 50.01260\n114 25 1 1 51.58000\n115 25 1 1 53.60212\n116 25 1 1 55.60588\n117 25 1 1 50.12952\n118 25 1 0 48.74272\n119 25 1 0 48.11464\n120 25 1 0 47.84768\n121 25 1 1 53.10032\n122 25 1 0 52.65696\n123 25 1 1 51.01732\n124 25 1 0 53.63332\n125 25 1 1 54.08012\n126 25 1 1 52.99944\n127 25 1 1 46.70260\n128 25 1 0 54.05464\n129 25 1 0 52.42144\n130 25 1 0 50.58732\n131 25 1 0 48.54192\n132 25 1 0 48.89652\n133 25 1 1 49.46216\n134 25 1 1 50.01984\n135 25 1 1 49.80852\n136 25 1 1 48.84240\n137 25 1 0 51.92500\n138 25 1 1 50.07656\n139 25 1 0 48.71428\ntreated\n0 500\n1 500\nName: count, dtype: int64","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"31ac6ae2-4128-46b1-8031-1e97c545e734","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118/data","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"type":"api-request-blob","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","hash":"f9be2e0fa8ade4bcb228559b494827ccaf07b5fef1b77f61fba3d75ab89bd17e","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01VRSnLv6Af3HmYBWo2dzJoB","name":"Bash","input":{"command":"cat gate_spec.json bundle.json; mkdir -p out/state; cd data; shasum -a 256 schools.csv; head -5 schools.csv; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('schools.csv');print(d.shape);print(d.dtypes);print(d.describe());print(d.isna().sum())\nprint('dup student',d.student_id.duplicated().sum(),'dup rows',d.duplicated().sum())\ng=d.groupby('school_id').agg(n=('score','size'),t=('treated','nunique'),tr=('treated','first'),m=('score','mean'))\nprint(g.n.value_counts());print(g.t.value_counts());print(g.tr.value_counts());print(g)\nprint(d.treated.value_counts())\n\"","description":"Audit data file"}}]}} {"type":"api-request","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","id":"c3516d4d-3abe-458b-9948-c9a55d35f534","timestamp":"2026-10-30T04:35:55.702Z","version":"2.1.314","querySource":"sdk","shapeHash":"1cefe355e71f8bb33a44ea933fac791086e15c215724bd3d4e47a4860724ab4b","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":"b547a730-49b9-4257-a89a-8f1362b80cc7","keep":4,"tail":["f9be2e0fa8ade4bcb228559b494827ccaf07b5fef1b77f61fba3d75ab89bd17e","4eec499e6837442c0072458a665c43c2e2b5f077d1a206b2ca0937450ffd62e1"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"1e28341bb9ff49c4b5bd4956336e29b30f5d65cde5752524ea47eadf208ae027"} {"parentUuid":"b1a18ad5-695b-4592-8844-9cc89faf262c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoP5z4srww631oN3zXr","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":2294,"cache_read_input_tokens":5991,"output_tokens":1287,"output_tokens_details":{"thinking_tokens":308},"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":2294},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":2693,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXoP5W3K6h1EATY8cHHy","requestRef":"c3516d4d-3abe-458b-9948-c9a55d35f534","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a748b0dc-f7d5-46ba-966c-7f11e0c30c2b","timestamp":"2026-10-30T04:35:59.318Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118/data","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"a748b0dc-f7d5-46ba-966c-7f11e0c30c2b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoP5z4srww631oN3zXr","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0147TZXDGU3B23PUo5FjyLAD","name":"Bash","input":{"command":"cat > out/state/contract.md <<'EOF'\n# Contract (P0)\n- Question: average effect of a school-level programme on student test score, with design-valid uncertainty; decide at 5% two-sided whether effect != 0.\n- Estimand tau: difference in student-weighted mean score, treated minus control schools = OLS coef of score on treated (with intercept). Meaningful as causal effect under random (or as-good-as-random) assignment of schools; inference assumes independence across schools, arbitrary dependence within a school.\n- Main SE: cluster-robust (by school_id) SE; naive iid OLS SE as baseline. p-value and 95% CI from cluster SE (t with G-1 df). icc_hat: share of residual variance (after removing arm means) between schools.\n- Outputs: out/results.json (task_id, tau_hat, se_iid, se_cluster, p_value, tau_ci_low, tau_ci_high, effect_significant_5pct, icc_hat, n_schools, n_treated_schools, n_analysed, feasibility, feasibility_reason, data_issues, n_input_rows, random_seed), out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues), out/analysis.py, out/claims.json, plus out/exclusions.csv, out/state/*.\n- Information rule: only data/schools.csv.\n- Invalidating: treatment varying within school, too few clusters, missing/duplicate data, using iid SE for inference.\nEOF\ncat > out/state/log.md <<'EOF'\n# Log\n- 2026-10-30 P0 contract written.\nEOF\ncat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\nFile: data/schools.csv, SHA-256 0f1d8378fff2f0898762feaa08dd94d9ad04156dad6330572d98997c9005c88a (matches bundle.json).\n- 1000 rows x 4 columns: school_id (int, 100–139), student_id (int, 1–1000, unique), treated (int 0/1), score (float, points, 35.72–64.17, mean 50.80, sd 4.48).\n- Missing values: none in any column. Duplicate rows: 0. Duplicate student_id: 0.\n- 40 schools, each with exactly 25 students (balanced as stated). treated is constant within every school; 20 treated schools (500 students), 20 control schools (500 students).\n- No dates (no date ranges applicable). No anomalies detected; no out-of-range scores evident.\n- Information availability: only input is schools.csv, as permitted. No violations.\n- Exclusions: none (see out/exclusions.csv).\nEOF","description":"Write contract, log, audit"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2294,"cache_read_input_tokens":5991,"output_tokens":1287,"output_tokens_details":{"thinking_tokens":308},"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":2294},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXoP5W3K6h1EATY8cHHy","requestRef":"c3516d4d-3abe-458b-9948-c9a55d35f534","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a1730f06-9461-4f74-acec-d4d45141c036","timestamp":"2026-10-30T04:36:07.711Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118/data","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"a1730f06-9461-4f74-acec-d4d45141c036","isSidechain":false,"promptId":"987711b9-2da1-48f1-95c0-984a823a90af","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\n(eval):1: no such file or directory: out/state/contract.md\n(eval):10: no such file or directory: out/state/log.md\n(eval):14: no such file or directory: out/state/data_audit.md","is_error":true,"tool_use_id":"toolu_0147TZXDGU3B23PUo5FjyLAD"}]},"uuid":"9ea7be76-e386-4de2-a666-b7b9829d39b0","timestamp":"2026-10-30T04:36:07.747Z","toolUseResult":"Error: Exit code 1\n(eval):1: no such file or directory: out/state/contract.md\n(eval):10: no such file or directory: out/state/log.md\n(eval):14: no such file or directory: out/state/data_audit.md","sandboxed":false,"sourceToolAssistantUUID":"a1730f06-9461-4f74-acec-d4d45141c036","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118/data","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"type":"api-request-blob","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","hash":"0bc1025a8695d87240b56906a7ff117827817a8eb6d977a8b06da9b5c0f7426d","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_0147TZXDGU3B23PUo5FjyLAD","name":"Bash","input":{"command":"cat > out/state/contract.md <<'EOF'\n# Contract (P0)\n- Question: average effect of a school-level programme on student test score, with design-valid uncertainty; decide at 5% two-sided whether effect != 0.\n- Estimand tau: difference in student-weighted mean score, treated minus control schools = OLS coef of score on treated (with intercept). Meaningful as causal effect under random (or as-good-as-random) assignment of schools; inference assumes independence across schools, arbitrary dependence within a school.\n- Main SE: cluster-robust (by school_id) SE; naive iid OLS SE as baseline. p-value and 95% CI from cluster SE (t with G-1 df). icc_hat: share of residual variance (after removing arm means) between schools.\n- Outputs: out/results.json (task_id, tau_hat, se_iid, se_cluster, p_value, tau_ci_low, tau_ci_high, effect_significant_5pct, icc_hat, n_schools, n_treated_schools, n_analysed, feasibility, feasibility_reason, data_issues, n_input_rows, random_seed), out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues), out/analysis.py, out/claims.json, plus out/exclusions.csv, out/state/*.\n- Information rule: only data/schools.csv.\n- Invalidating: treatment varying within school, too few clusters, missing/duplicate data, using iid SE for inference.\nEOF\ncat > out/state/log.md <<'EOF'\n# Log\n- 2026-10-30 P0 contract written.\nEOF\ncat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\nFile: data/schools.csv, SHA-256 0f1d8378fff2f0898762feaa08dd94d9ad04156dad6330572d98997c9005c88a (matches bundle.json).\n- 1000 rows x 4 columns: school_id (int, 100–139), student_id (int, 1–1000, unique), treated (int 0/1), score (float, points, 35.72–64.17, mean 50.80, sd 4.48).\n- Missing values: none in any column. Duplicate rows: 0. Duplicate student_id: 0.\n- 40 schools, each with exactly 25 students (balanced as stated). treated is constant within every school; 20 treated schools (500 students), 20 control schools (500 students).\n- No dates (no date ranges applicable). No anomalies detected; no out-of-range scores evident.\n- Information availability: only input is schools.csv, as permitted. No violations.\n- Exclusions: none (see out/exclusions.csv).\nEOF","description":"Write contract, log, audit"}}]}} {"type":"api-request","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","id":"2c24c4ee-06fa-4227-b03f-3020b7a044c5","timestamp":"2026-10-30T04:36:07.751Z","version":"2.1.314","querySource":"sdk","shapeHash":"1cefe355e71f8bb33a44ea933fac791086e15c215724bd3d4e47a4860724ab4b","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":"c3516d4d-3abe-458b-9948-c9a55d35f534","keep":6,"tail":["0bc1025a8695d87240b56906a7ff117827817a8eb6d977a8b06da9b5c0f7426d","9704a41a0a35e1f6791aee7c933a5a43e28152b6b226e5cbd41f9dac67e9f7bd"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"06764836f0eb13f8e9d1f682592fe7963f8d59d73d687f8f9a211e8f3ae0811e"} {"parentUuid":"9ea7be76-e386-4de2-a666-b7b9829d39b0","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoPykJVJaNnDKWCD7Hf","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":1371,"cache_read_input_tokens":8285,"output_tokens":199,"output_tokens_details":{"thinking_tokens":90},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1371},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":483,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXoPyUBgZT5jqjXSjJXq","requestRef":"2c24c4ee-06fa-4227-b03f-3020b7a044c5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8934ffe0-b165-469d-8e63-24ab056b9343","timestamp":"2026-10-30T04:36:09.112Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118/data","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"8934ffe0-b165-469d-8e63-24ab056b9343","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoPykJVJaNnDKWCD7Hf","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01FE5Q2Zz76VeEKjpyULJYGx","name":"Bash","input":{"command":"pwd; cd /Users/simon-pierreboucher/wp29/runs/RC0118 && ls out data","description":"Check working directory"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1371,"cache_read_input_tokens":8285,"output_tokens":199,"output_tokens_details":{"thinking_tokens":90},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1371},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXoPyUBgZT5jqjXSjJXq","requestRef":"2c24c4ee-06fa-4227-b03f-3020b7a044c5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6ee1eb38-fc2f-405c-bbcf-230b858be164","timestamp":"2026-10-30T04:36:09.803Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118/data","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"6ee1eb38-fc2f-405c-bbcf-230b858be164","isSidechain":false,"promptId":"987711b9-2da1-48f1-95c0-984a823a90af","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01FE5Q2Zz76VeEKjpyULJYGx","type":"tool_result","content":"/Users/simon-pierreboucher/wp29/runs/RC0118/data\ndata:\nschools.csv\n\nout:\nstate","is_error":false}]},"uuid":"276974eb-0f81-4ecb-8efc-5e813ef37fb2","timestamp":"2026-10-30T04:36:09.826Z","toolUseResult":{"stdout":"/Users/simon-pierreboucher/wp29/runs/RC0118/data\ndata:\nschools.csv\n\nout:\nstate","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"6ee1eb38-fc2f-405c-bbcf-230b858be164","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"type":"api-request-blob","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","hash":"dcfa4d329bd693ba323625540a3c260be16e7e2da7ebd6dac03089aaf77a748a","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01FE5Q2Zz76VeEKjpyULJYGx","name":"Bash","input":{"command":"pwd; cd /Users/simon-pierreboucher/wp29/runs/RC0118 && ls out data","description":"Check working directory"}}]}} {"type":"api-request","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","id":"69d463fa-f297-40dd-bd3c-9e8de7199583","timestamp":"2026-10-30T04:36:09.829Z","version":"2.1.314","querySource":"sdk","shapeHash":"1cefe355e71f8bb33a44ea933fac791086e15c215724bd3d4e47a4860724ab4b","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":"2c24c4ee-06fa-4227-b03f-3020b7a044c5","keep":8,"tail":["dcfa4d329bd693ba323625540a3c260be16e7e2da7ebd6dac03089aaf77a748a","acd68be85f9a439263471b0b191acafe95755773ef235fef44b96e35e00deb71"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"db8dc3b8404906e023633b5604e11f42638cd198ac53ed7286b938e83a768d1b"} {"type":"last-prompt","lastPrompt":"Begin","leafUuid":"276974eb-0f81-4ecb-8efc-5e813ef37fb2","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9"} {"type":"atis-latch","atis":"","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9"} {"parentUuid":"276974eb-0f81-4ecb-8efc-5e813ef37fb2","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoQ8brDfx85tADBPnmk","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":245,"cache_read_input_tokens":9656,"output_tokens":1171,"output_tokens_details":{"thinking_tokens":110},"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":245},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":772,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXoQ8JzwHeq59g1jTX3i","requestRef":"69d463fa-f297-40dd-bd3c-9e8de7199583","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1602e800-34eb-4edd-bf10-99f8ac95e3ff","timestamp":"2026-10-30T04:36:11.457Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"1602e800-34eb-4edd-bf10-99f8ac95e3ff","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoQ8brDfx85tADBPnmk","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01CoA12pUpJAm8oKMUcnTgxp","name":"Bash","input":{"command":"ls -a data && cat > out/state/contract.md <<'EOF'\n# Contract (P0)\n- Question: average effect of a school-level programme on student test score, with design-valid uncertainty; decide at 5% two-sided whether effect != 0.\n- Estimand tau: difference in student-weighted mean score, treated minus control schools = OLS coef of score on treated (with intercept). Meaningful as causal effect under random (or as-good-as-random) assignment of schools; inference assumes independence across schools, arbitrary dependence within a school.\n- Main SE: cluster-robust (by school_id) SE; naive iid OLS SE as baseline. p-value and 95% CI from cluster SE (t with G-1 df). icc_hat: share of residual variance (after removing arm means) between schools.\n- Outputs: out/results.json (task_id, tau_hat, se_iid, se_cluster, p_value, tau_ci_low, tau_ci_high, effect_significant_5pct, icc_hat, n_schools, n_treated_schools, n_analysed, feasibility, feasibility_reason, data_issues, n_input_rows, random_seed), out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues), out/analysis.py, out/claims.json, plus out/exclusions.csv, out/state/*.\n- Information rule: only data/schools.csv.\n- Invalidating: treatment varying within school, too few clusters, missing/duplicate data, using iid SE for inference.\nEOF\ncat > out/state/log.md <<'EOF'\n# Log\n- 2026-10-30 P0 contract written; P0 passed.\n- 2026-10-30 P1 data audit done: clean, balanced 40x25, no exclusions; P1 passed.\nEOF\ncat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\nFile: data/schools.csv, SHA-256 0f1d8378fff2f0898762feaa08dd94d9ad04156dad6330572d98997c9005c88a (matches bundle.json).\n- 1000 rows x 4 columns: school_id (int, 100–139), student_id (int, 1–1000, unique), treated (int 0/1), score (float, points, 35.72–64.17, mean 50.80, sd 4.48).\n- Missing values: none in any column. Duplicate rows: 0. Duplicate student_id: 0.\n- 40 schools, each with exactly 25 students (balanced as stated). treated is constant within every school; 20 treated schools (500 students), 20 control schools (500 students).\n- No dates (no date ranges applicable). No anomalies detected; no out-of-range scores evident.\n- Information availability: only input is schools.csv, as permitted. No violations.\n- Exclusions: none (see out/exclusions.csv).\nEOF","description":"Write contract, log, audit"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":245,"cache_read_input_tokens":9656,"output_tokens":1171,"output_tokens_details":{"thinking_tokens":110},"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":245},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXoQ8JzwHeq59g1jTX3i","requestRef":"69d463fa-f297-40dd-bd3c-9e8de7199583","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"526af0f5-e2eb-421f-bea5-62c94dd82135","timestamp":"2026-10-30T04:36:18.656Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"526af0f5-e2eb-421f-bea5-62c94dd82135","isSidechain":false,"promptId":"987711b9-2da1-48f1-95c0-984a823a90af","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01CoA12pUpJAm8oKMUcnTgxp","type":"tool_result","content":".\n..\nschools.csv","is_error":false}]},"uuid":"7cb39576-d498-40dc-b9f0-7c82d2103688","timestamp":"2026-10-30T04:36:18.687Z","toolUseResult":{"stdout":".\n..\nschools.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"526af0f5-e2eb-421f-bea5-62c94dd82135","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"type":"api-request-blob","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","hash":"5612f8ff573a75ff4781b92867f56970c2881e47bb00a96c696b8da419714501","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01CoA12pUpJAm8oKMUcnTgxp","name":"Bash","input":{"command":"ls -a data && cat > out/state/contract.md <<'EOF'\n# Contract (P0)\n- Question: average effect of a school-level programme on student test score, with design-valid uncertainty; decide at 5% two-sided whether effect != 0.\n- Estimand tau: difference in student-weighted mean score, treated minus control schools = OLS coef of score on treated (with intercept). Meaningful as causal effect under random (or as-good-as-random) assignment of schools; inference assumes independence across schools, arbitrary dependence within a school.\n- Main SE: cluster-robust (by school_id) SE; naive iid OLS SE as baseline. p-value and 95% CI from cluster SE (t with G-1 df). icc_hat: share of residual variance (after removing arm means) between schools.\n- Outputs: out/results.json (task_id, tau_hat, se_iid, se_cluster, p_value, tau_ci_low, tau_ci_high, effect_significant_5pct, icc_hat, n_schools, n_treated_schools, n_analysed, feasibility, feasibility_reason, data_issues, n_input_rows, random_seed), out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues), out/analysis.py, out/claims.json, plus out/exclusions.csv, out/state/*.\n- Information rule: only data/schools.csv.\n- Invalidating: treatment varying within school, too few clusters, missing/duplicate data, using iid SE for inference.\nEOF\ncat > out/state/log.md <<'EOF'\n# Log\n- 2026-10-30 P0 contract written; P0 passed.\n- 2026-10-30 P1 data audit done: clean, balanced 40x25, no exclusions; P1 passed.\nEOF\ncat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\nFile: data/schools.csv, SHA-256 0f1d8378fff2f0898762feaa08dd94d9ad04156dad6330572d98997c9005c88a (matches bundle.json).\n- 1000 rows x 4 columns: school_id (int, 100–139), student_id (int, 1–1000, unique), treated (int 0/1), score (float, points, 35.72–64.17, mean 50.80, sd 4.48).\n- Missing values: none in any column. Duplicate rows: 0. Duplicate student_id: 0.\n- 40 schools, each with exactly 25 students (balanced as stated). treated is constant within every school; 20 treated schools (500 students), 20 control schools (500 students).\n- No dates (no date ranges applicable). No anomalies detected; no out-of-range scores evident.\n- Information availability: only input is schools.csv, as permitted. No violations.\n- Exclusions: none (see out/exclusions.csv).\nEOF","description":"Write contract, log, audit"}}]}} {"type":"api-request","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","id":"8fd0b359-ac68-496c-b635-77f110a0d5ae","timestamp":"2026-10-30T04:36:18.690Z","version":"2.1.314","querySource":"sdk","shapeHash":"1cefe355e71f8bb33a44ea933fac791086e15c215724bd3d4e47a4860724ab4b","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":"69d463fa-f297-40dd-bd3c-9e8de7199583","keep":10,"tail":["5612f8ff573a75ff4781b92867f56970c2881e47bb00a96c696b8da419714501","03fc2789ac37caa4c57b96e04796e621293e9b3cfc25fcf94565635758595bcd"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"50b744e1a947143a472ab1f6ec2b186f5b6cf68f7d67b6768d8c04801192610b"} {"parentUuid":"7cb39576-d498-40dc-b9f0-7c82d2103688","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoQnUfz5ERDV9D6UPes","type":"message","role":"assistant","content":[{"type":"text","text":"Now the analysis script."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1156,"cache_read_input_tokens":9901,"output_tokens":2284,"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":1156},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXoQnBZ2Yc4uup7Uc9Lb","requestRef":"8fd0b359-ac68-496c-b635-77f110a0d5ae","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8e2d38df-197a-4af0-abb9-045397fb005b","timestamp":"2026-10-30T04:36:19.313Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"8e2d38df-197a-4af0-abb9-045397fb005b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoQnUfz5ERDV9D6UPes","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01RS3YvPHpqLuU8RVEc5y9NG","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F2-conf-01: school-level programme effect with cluster-robust inference.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/schools.csv; writes out/results.json and out/exclusions.csv. No randomness.\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nimport statsmodels.formula.api as smf\nfrom scipy import stats\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\ndf = pd.read_csv(os.path.join(ROOT, \"data\", \"schools.csv\"))\nn_raw = len(df)\n\nissues = []\nexcl = []\n# Step 1: rows with missing values in required columns\nn0 = len(df)\ndf = df.dropna(subset=[\"school_id\", \"student_id\", \"treated\", \"score\"])\nexcl.append((\"1\", \"drop rows with missing school_id/student_id/treated/score\", n0, len(df), n0 - len(df),\n \"required for estimation\"))\n# Step 2: duplicate student ids\nn0 = len(df)\ndf = df.drop_duplicates(subset=[\"student_id\"])\nexcl.append((\"2\", \"drop duplicate student_id\", n0, len(df), n0 - len(df), \"student_id must be unique\"))\nfor s, r, a, b, k, why in excl:\n if k > 0:\n issues.append({\"issue\": f\"{k} rows removed: {r}\", \"handling\": \"excluded\"})\n# Design checks\nwithin = df.groupby(\"school_id\")[\"treated\"].nunique()\nif (within > 1).any():\n issues.append({\"issue\": f\"treated varies within {int((within>1).sum())} schools\", \"handling\": \"reported; not altered\"})\nsizes = df.groupby(\"school_id\").size()\nif sizes.nunique() != 1:\n issues.append({\"issue\": \"unbalanced school sizes\", \"handling\": \"kept; CR1 SE handles unequal clusters\"})\n\npd.DataFrame(excl, columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]).to_csv(\n os.path.join(ROOT, \"out\", \"exclusions.csv\"), index=False)\n\nG = df[\"school_id\"].nunique()\nG1 = int(df.groupby(\"school_id\")[\"treated\"].first().sum())\nN = len(df)\n\n# Baseline: naive OLS with iid homoskedastic SE\nols = smf.ols(\"score ~ treated\", data=df).fit()\ntau = float(ols.params[\"treated\"])\nse_iid = float(ols.bse[\"treated\"])\n\n# Main: cluster-robust (CR1, Stata-style small-sample correction) by school, t with G-1 df\ncl = smf.ols(\"score ~ treated\", data=df).fit(cov_type=\"cluster\", cov_kwds={\"groups\": df[\"school_id\"]},\n use_t=True)\nse_cl = float(cl.bse[\"treated\"])\ndof = G - 1\ntcrit = float(stats.t.ppf(0.975, dof))\ntstat = tau / se_cl\np = float(2 * stats.t.sf(abs(tstat), dof))\nlo, hi = tau - tcrit * se_cl, tau + tcrit * se_cl\n\n# Cross-check: two-sample Welch t-test on school means (aggregated analysis)\nsm = df.groupby(\"school_id\").agg(m=(\"score\", \"mean\"), t=(\"treated\", \"first\"))\na, b = sm.m[sm.t == 1], sm.m[sm.t == 0]\nagg = stats.ttest_ind(a, b, equal_var=False)\nse_agg = float(np.sqrt(a.var(ddof=1) / len(a) + b.var(ddof=1) / len(b)))\n\n# ICC of residual (after removing arm means), one-way ANOVA estimator\ndf = df.assign(res=df[\"score\"] - df.groupby(\"treated\")[\"score\"].transform(\"mean\"))\ngrp = df.groupby(\"school_id\")[\"res\"]\nng = grp.size(); mg = grp.mean()\nssw = float(((df[\"res\"] - grp.transform(\"mean\")) ** 2).sum())\nssb = float((ng * (mg - df[\"res\"].mean()) ** 2).sum())\nmsw = ssw / (N - G)\nmsb = ssb / (G - 2) # two df used by arm means\nn0_eff = (N - (ng ** 2).sum() / N) / (G - 1)\nsb2 = max((msb - msw) / n0_eff, 0.0)\nicc = sb2 / (sb2 + msw)\ndeff = 1 + (float(ng.mean()) - 1) * icc\n\nres = {\n \"task_id\": \"F2-conf-01\",\n \"tau_hat\": tau,\n \"se_iid\": se_iid,\n \"se_cluster\": se_cl,\n \"p_value\": p,\n \"tau_ci_low\": float(lo),\n \"tau_ci_high\": float(hi),\n \"effect_significant_5pct\": bool(p < 0.05),\n \"icc_hat\": float(icc),\n \"n_schools\": int(G),\n \"n_treated_schools\": G1,\n \"n_analysed\": int(N),\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"schools.csv\": int(n_raw)},\n \"random_seed\": None,\n # supplementary\n \"baseline_p_value_iid\": float(ols.pvalues[\"treated\"]),\n \"baseline_ci_iid_low\": float(ols.conf_int().loc[\"treated\", 0]),\n \"baseline_ci_iid_high\": float(ols.conf_int().loc[\"treated\", 1]),\n \"t_stat_cluster\": float(tstat),\n \"df_cluster\": int(dof),\n \"se_ratio_cluster_to_iid\": se_cl / se_iid,\n \"design_effect_from_icc\": float(deff),\n \"school_means_welch_se\": se_agg,\n \"school_means_welch_p\": float(agg.pvalue),\n \"mean_score_treated\": float(df.loc[df.treated == 1, \"score\"].mean()),\n \"mean_score_control\": float(df.loc[df.treated == 0, \"score\"].mean()),\n}\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps(res, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/exclusions.csv","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1156,"cache_read_input_tokens":9901,"output_tokens":2284,"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":1156},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXoQnBZ2Yc4uup7Uc9Lb","requestRef":"8fd0b359-ac68-496c-b635-77f110a0d5ae","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a2559837-599c-4d8e-9b7e-34aec941653a","timestamp":"2026-10-30T04:36:36.431Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"a2559837-599c-4d8e-9b7e-34aec941653a","isSidechain":false,"promptId":"987711b9-2da1-48f1-95c0-984a823a90af","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RS3YvPHpqLuU8RVEc5y9NG","type":"tool_result","content":"{\n \"task_id\": \"F2-conf-01\",\n \"tau_hat\": 0.9611179999999999,\n \"se_iid\": 0.28209729504903186,\n \"se_cluster\": 0.7289578392511658,\n \"p_value\": 0.19503319180357392,\n \"tau_ci_low\": -0.5133384025429495,\n \"tau_ci_high\": 2.435574402542949,\n \"effect_significant_5pct\": false,\n \"icc_hat\": 0.24029707512276074,\n \"n_schools\": 40,\n \"n_treated_schools\": 20,\n \"n_analysed\": 1000,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [],\n \"n_input_rows\": {\n \"schools.csv\": 1000\n },\n \"random_seed\": null,\n \"baseline_p_value_iid\": 0.0006829117319841643,\n \"baseline_ci_iid_low\": 0.4075461090262589,\n \"baseline_ci_iid_high\": 1.514689890973741,\n \"t_stat_cluster\": 1.3184822883410166,\n \"df_cluster\": 39,\n \"se_ratio_cluster_to_iid\": 2.5840653279729757,\n \"design_effect_from_icc\": 6.767129802946258,\n \"school_means_welch_se\": 0.7381173986381983,\n \"school_means_welch_p\": 0.20081230523957236,\n \"mean_score_treated\": 51.28209,\n \"mean_score_control\": 50.320972000000005\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with missing school_id/student_id/treated/score,1000,1000,0,required for estimation\n2,drop duplicate student_id,1000,1000,0,student_id must be unique","is_error":false}]},"uuid":"e6660cf2-abe1-47d0-94d7-288daf53d283","timestamp":"2026-10-30T04:36:37.139Z","toolUseResult":{"stdout":"{\n \"task_id\": \"F2-conf-01\",\n \"tau_hat\": 0.9611179999999999,\n \"se_iid\": 0.28209729504903186,\n \"se_cluster\": 0.7289578392511658,\n \"p_value\": 0.19503319180357392,\n \"tau_ci_low\": -0.5133384025429495,\n \"tau_ci_high\": 2.435574402542949,\n \"effect_significant_5pct\": false,\n \"icc_hat\": 0.24029707512276074,\n \"n_schools\": 40,\n \"n_treated_schools\": 20,\n \"n_analysed\": 1000,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [],\n \"n_input_rows\": {\n \"schools.csv\": 1000\n },\n \"random_seed\": null,\n \"baseline_p_value_iid\": 0.0006829117319841643,\n \"baseline_ci_iid_low\": 0.4075461090262589,\n \"baseline_ci_iid_high\": 1.514689890973741,\n \"t_stat_cluster\": 1.3184822883410166,\n \"df_cluster\": 39,\n \"se_ratio_cluster_to_iid\": 2.5840653279729757,\n \"design_effect_from_icc\": 6.767129802946258,\n \"school_means_welch_se\": 0.7381173986381983,\n \"school_means_welch_p\": 0.20081230523957236,\n \"mean_score_treated\": 51.28209,\n \"mean_score_control\": 50.320972000000005\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with missing school_id/student_id/treated/score,1000,1000,0,required for estimation\n2,drop duplicate student_id,1000,1000,0,student_id must be unique","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"a2559837-599c-4d8e-9b7e-34aec941653a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"type":"api-request-blob","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","hash":"deae3d68d74bd5edc756535ccc666519c5ddf3301be417d07cf5bda8247418df","message":{"role":"assistant","content":[{"type":"text","text":"Now the analysis script."},{"type":"tool_use","id":"toolu_01RS3YvPHpqLuU8RVEc5y9NG","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F2-conf-01: school-level programme effect with cluster-robust inference.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/schools.csv; writes out/results.json and out/exclusions.csv. No randomness.\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nimport statsmodels.formula.api as smf\nfrom scipy import stats\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\ndf = pd.read_csv(os.path.join(ROOT, \"data\", \"schools.csv\"))\nn_raw = len(df)\n\nissues = []\nexcl = []\n# Step 1: rows with missing values in required columns\nn0 = len(df)\ndf = df.dropna(subset=[\"school_id\", \"student_id\", \"treated\", \"score\"])\nexcl.append((\"1\", \"drop rows with missing school_id/student_id/treated/score\", n0, len(df), n0 - len(df),\n \"required for estimation\"))\n# Step 2: duplicate student ids\nn0 = len(df)\ndf = df.drop_duplicates(subset=[\"student_id\"])\nexcl.append((\"2\", \"drop duplicate student_id\", n0, len(df), n0 - len(df), \"student_id must be unique\"))\nfor s, r, a, b, k, why in excl:\n if k > 0:\n issues.append({\"issue\": f\"{k} rows removed: {r}\", \"handling\": \"excluded\"})\n# Design checks\nwithin = df.groupby(\"school_id\")[\"treated\"].nunique()\nif (within > 1).any():\n issues.append({\"issue\": f\"treated varies within {int((within>1).sum())} schools\", \"handling\": \"reported; not altered\"})\nsizes = df.groupby(\"school_id\").size()\nif sizes.nunique() != 1:\n issues.append({\"issue\": \"unbalanced school sizes\", \"handling\": \"kept; CR1 SE handles unequal clusters\"})\n\npd.DataFrame(excl, columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]).to_csv(\n os.path.join(ROOT, \"out\", \"exclusions.csv\"), index=False)\n\nG = df[\"school_id\"].nunique()\nG1 = int(df.groupby(\"school_id\")[\"treated\"].first().sum())\nN = len(df)\n\n# Baseline: naive OLS with iid homoskedastic SE\nols = smf.ols(\"score ~ treated\", data=df).fit()\ntau = float(ols.params[\"treated\"])\nse_iid = float(ols.bse[\"treated\"])\n\n# Main: cluster-robust (CR1, Stata-style small-sample correction) by school, t with G-1 df\ncl = smf.ols(\"score ~ treated\", data=df).fit(cov_type=\"cluster\", cov_kwds={\"groups\": df[\"school_id\"]},\n use_t=True)\nse_cl = float(cl.bse[\"treated\"])\ndof = G - 1\ntcrit = float(stats.t.ppf(0.975, dof))\ntstat = tau / se_cl\np = float(2 * stats.t.sf(abs(tstat), dof))\nlo, hi = tau - tcrit * se_cl, tau + tcrit * se_cl\n\n# Cross-check: two-sample Welch t-test on school means (aggregated analysis)\nsm = df.groupby(\"school_id\").agg(m=(\"score\", \"mean\"), t=(\"treated\", \"first\"))\na, b = sm.m[sm.t == 1], sm.m[sm.t == 0]\nagg = stats.ttest_ind(a, b, equal_var=False)\nse_agg = float(np.sqrt(a.var(ddof=1) / len(a) + b.var(ddof=1) / len(b)))\n\n# ICC of residual (after removing arm means), one-way ANOVA estimator\ndf = df.assign(res=df[\"score\"] - df.groupby(\"treated\")[\"score\"].transform(\"mean\"))\ngrp = df.groupby(\"school_id\")[\"res\"]\nng = grp.size(); mg = grp.mean()\nssw = float(((df[\"res\"] - grp.transform(\"mean\")) ** 2).sum())\nssb = float((ng * (mg - df[\"res\"].mean()) ** 2).sum())\nmsw = ssw / (N - G)\nmsb = ssb / (G - 2) # two df used by arm means\nn0_eff = (N - (ng ** 2).sum() / N) / (G - 1)\nsb2 = max((msb - msw) / n0_eff, 0.0)\nicc = sb2 / (sb2 + msw)\ndeff = 1 + (float(ng.mean()) - 1) * icc\n\nres = {\n \"task_id\": \"F2-conf-01\",\n \"tau_hat\": tau,\n \"se_iid\": se_iid,\n \"se_cluster\": se_cl,\n \"p_value\": p,\n \"tau_ci_low\": float(lo),\n \"tau_ci_high\": float(hi),\n \"effect_significant_5pct\": bool(p < 0.05),\n \"icc_hat\": float(icc),\n \"n_schools\": int(G),\n \"n_treated_schools\": G1,\n \"n_analysed\": int(N),\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"schools.csv\": int(n_raw)},\n \"random_seed\": None,\n # supplementary\n \"baseline_p_value_iid\": float(ols.pvalues[\"treated\"]),\n \"baseline_ci_iid_low\": float(ols.conf_int().loc[\"treated\", 0]),\n \"baseline_ci_iid_high\": float(ols.conf_int().loc[\"treated\", 1]),\n \"t_stat_cluster\": float(tstat),\n \"df_cluster\": int(dof),\n \"se_ratio_cluster_to_iid\": se_cl / se_iid,\n \"design_effect_from_icc\": float(deff),\n \"school_means_welch_se\": se_agg,\n \"school_means_welch_p\": float(agg.pvalue),\n \"mean_score_treated\": float(df.loc[df.treated == 1, \"score\"].mean()),\n \"mean_score_control\": float(df.loc[df.treated == 0, \"score\"].mean()),\n}\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps(res, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/exclusions.csv","description":"Write and run analysis script"}}]}} {"type":"api-request","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","id":"787c5b18-557d-44fa-acab-710d6653b209","timestamp":"2026-10-30T04:36:37.143Z","version":"2.1.314","querySource":"sdk","shapeHash":"1cefe355e71f8bb33a44ea933fac791086e15c215724bd3d4e47a4860724ab4b","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":"8fd0b359-ac68-496c-b635-77f110a0d5ae","keep":12,"tail":["deae3d68d74bd5edc756535ccc666519c5ddf3301be417d07cf5bda8247418df","e3a372e45cfc3461ee48f66fbd436fd8f374de16d7550d72e4728962d872fcb6"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"8fb6577fc9ee300665615b4e5840bcfc7091ecbebb17fd7770dd547fa5a6cd1a"} {"parentUuid":"e6660cf2-abe1-47d0-94d7-288daf53d283","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoS9MJgqHTuLYrowveZ","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2888,"cache_read_input_tokens":11057,"output_tokens":721,"output_tokens_details":{"thinking_tokens":62},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2888},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":398,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXoS95wBnoD4u3U4yH6H","requestRef":"787c5b18-557d-44fa-acab-710d6653b209","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f8983aa1-dc78-41dd-a09a-685f02503d2e","timestamp":"2026-10-30T04:36:38.457Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"f8983aa1-dc78-41dd-a09a-685f02503d2e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoS9MJgqHTuLYrowveZ","type":"message","role":"assistant","content":[{"type":"text","text":"P4: reproducibility and consistency checks."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2888,"cache_read_input_tokens":11057,"output_tokens":721,"output_tokens_details":{"thinking_tokens":62},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2888},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXoS95wBnoD4u3U4yH6H","requestRef":"787c5b18-557d-44fa-acab-710d6653b209","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"bc833b87-bf54-4e2f-9994-80fd03e10459","timestamp":"2026-10-30T04:36:38.457Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"bc833b87-bf54-4e2f-9994-80fd03e10459","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoS9MJgqHTuLYrowveZ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LC71F8M18grmUR4wxVqz8V","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json && rm out/results.json out/exclusions.csv && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp /tmp/r1.json out/results.json && echo IDENTICAL\n~/wp29/venv/bin/python -c \"\nimport json,numpy as np\nr=json.load(open('out/results.json'))\nc=[]\nc.append(('ci contains tau',r['tau_ci_low']0 and r['se_cluster']>0))\nc.append(('tau = diff in means',abs(r['tau_hat']-(r['mean_score_treated']-r['mean_score_control']))<1e-9))\nc.append(('signif consistent with p and CI',(r['p_value']<0.05)==r['effect_significant_5pct']==(not(r['tau_ci_low']<=0<=r['tau_ci_high']))))\nc.append(('n_analysed = 1000 - 0 removed',r['n_analysed']==r['n_input_rows']['schools.csv']-0))\nc.append(('icc in [0,1]',0<=r['icc_hat']<=1))\nc.append(('se ratio^2 ~ design effect (within 10%)',abs(r['se_ratio_cluster_to_iid']**2/r['design_effect_from_icc']-1)<0.1))\nc.append(('cluster se ~ school-means Welch se (within 5%)',abs(r['se_cluster']/r['school_means_welch_se']-1)<0.05))\nfor k,v in c: print(k,v)\n\"","description":"Rerun for reproducibility and check identities"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2888,"cache_read_input_tokens":11057,"output_tokens":721,"output_tokens_details":{"thinking_tokens":62},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2888},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXoS95wBnoD4u3U4yH6H","requestRef":"787c5b18-557d-44fa-acab-710d6653b209","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"42420ed2-d685-4f5b-a272-ddc63ef43595","timestamp":"2026-10-30T04:36:43.234Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"type":"last-prompt","lastPrompt":"Begin","leafUuid":"42420ed2-d685-4f5b-a272-ddc63ef43595","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9"} {"type":"atis-latch","atis":"","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9"} {"parentUuid":"42420ed2-d685-4f5b-a272-ddc63ef43595","isSidechain":false,"promptId":"987711b9-2da1-48f1-95c0-984a823a90af","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LC71F8M18grmUR4wxVqz8V","type":"tool_result","content":"IDENTICAL\nci contains tau True\nse positive True\ntau = diff in means True\nsignif consistent with p and CI True\nn_analysed = 1000 - 0 removed True\nicc in [0,1] True\nse ratio^2 ~ design effect (within 10%) True\ncluster se ~ school-means Welch se (within 5%) True","is_error":false}]},"uuid":"0cb4bfba-3368-4b4a-b810-ec9f80bbecf5","timestamp":"2026-10-30T04:36:44.004Z","toolUseResult":{"stdout":"IDENTICAL\nci contains tau True\nse positive True\ntau = diff in means True\nsignif consistent with p and CI True\nn_analysed = 1000 - 0 removed True\nicc in [0,1] True\nse ratio^2 ~ design effect (within 10%) True\ncluster se ~ school-means Welch se (within 5%) True","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"42420ed2-d685-4f5b-a272-ddc63ef43595","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"type":"api-request-blob","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","hash":"041785fa1c6b14af664b00941e9338bd8c6512f8cbd8e61b1c0c4c7d47d23f55","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"P4: reproducibility and consistency checks."},{"type":"tool_use","id":"toolu_01LC71F8M18grmUR4wxVqz8V","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json && rm out/results.json out/exclusions.csv && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp /tmp/r1.json out/results.json && echo IDENTICAL\n~/wp29/venv/bin/python -c \"\nimport json,numpy as np\nr=json.load(open('out/results.json'))\nc=[]\nc.append(('ci contains tau',r['tau_ci_low']0 and r['se_cluster']>0))\nc.append(('tau = diff in means',abs(r['tau_hat']-(r['mean_score_treated']-r['mean_score_control']))<1e-9))\nc.append(('signif consistent with p and CI',(r['p_value']<0.05)==r['effect_significant_5pct']==(not(r['tau_ci_low']<=0<=r['tau_ci_high']))))\nc.append(('n_analysed = 1000 - 0 removed',r['n_analysed']==r['n_input_rows']['schools.csv']-0))\nc.append(('icc in [0,1]',0<=r['icc_hat']<=1))\nc.append(('se ratio^2 ~ design effect (within 10%)',abs(r['se_ratio_cluster_to_iid']**2/r['design_effect_from_icc']-1)<0.1))\nc.append(('cluster se ~ school-means Welch se (within 5%)',abs(r['se_cluster']/r['school_means_welch_se']-1)<0.05))\nfor k,v in c: print(k,v)\n\"","description":"Rerun for reproducibility and check identities"}}]}} {"type":"api-request","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","id":"8783b4da-f82a-4230-ac21-93222cb8c69a","timestamp":"2026-10-30T04:36:44.008Z","version":"2.1.314","querySource":"sdk","shapeHash":"1cefe355e71f8bb33a44ea933fac791086e15c215724bd3d4e47a4860724ab4b","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":"787c5b18-557d-44fa-acab-710d6653b209","keep":14,"tail":["041785fa1c6b14af664b00941e9338bd8c6512f8cbd8e61b1c0c4c7d47d23f55","1e17e59029f54c7a473a4b5cf59407ea9dc7d8a0d604ec0ad8dcb1fbcdd914c2"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"c19520e2c9aecbb70f8546824ff4bb86ae109652d0c7c296d184fcd161732366"} {"parentUuid":"0cb4bfba-3368-4b4a-b810-ec9f80bbecf5","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoSehuXNVew586kc9cZ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01MrTVq5uztWjmZDwLDSPH1w","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n| check | outcome |\n|---|---|\n| Clean re-run (results.json, exclusions.csv deleted) regenerates byte-identical results.json | PASS |\n| CI [tau_ci_low, tau_ci_high] contains tau_hat | PASS |\n| se_iid > 0, se_cluster > 0 | PASS |\n| tau_hat equals mean_score_treated − mean_score_control | PASS |\n| effect_significant_5pct consistent with p_value<0.05 and CI excluding 0 | PASS (all false) |\n| n_input_rows 1000 − 0 removed = n_analysed 1000 (exclusions.csv sums) | PASS |\n| icc_hat in [0,1] | PASS (0.240) |\n| (se_cluster/se_iid)^2 = 6.68 vs design effect 1+(25−1)·icc = 6.77 (within 10%) | PASS |\n| se_cluster (CR1) vs Welch SE on 40 school means 0.738 (within 5%); Welch p=0.201 vs cluster p=0.195 | PASS |\n| treated constant within school; 20/20 schools; balanced 25 per school | PASS (audit) |\nEOF\ncat > out/state/phases.json <<'EOF'\n{\n \"P0\": {\"status\": \"passed\", \"checks\": [\"TASK.md read fully\", \"contract.md written\"], \"notes\": \"\"},\n \"P1\": {\"status\": \"passed\", \"checks\": [\"sha256 matches bundle.json\", \"schema/missing/duplicates inspected\", \"treated constant within school\", \"exclusions.csv sums 1000-0=1000\"], \"notes\": \"no data issues\"},\n \"P2\": {\"status\": \"passed\", \"checks\": [\"naive OLS iid baseline computed: se_iid, baseline_p_value_iid\"], \"notes\": \"\"},\n \"P3\": {\"status\": \"passed\", \"checks\": [\"analysis.py runs, writes results.json deterministically, no RNG\"], \"notes\": \"CR1 cluster SE, t(G-1)\"},\n \"P4\": {\"status\": \"running\", \"checks\": [\"clean rerun byte-identical\", \"internal consistency checks in checks.md all PASS\"], \"notes\": \"\"},\n \"P5\": {\"status\": \"pending\", \"checks\": [], \"notes\": \"\"}\n}\nEOF\necho \"- 2026-10-30 P2/P3 passed: baseline iid and CR1 cluster SE computed. P4 checks all pass; running gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .; ls out/state/gate_receipts","description":"Record checks, phases, run gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":838,"cache_read_input_tokens":13945,"output_tokens":981,"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":838},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXoSeRn7d276weStjZG4","requestRef":"8783b4da-f82a-4230-ac21-93222cb8c69a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c71fe1cf-464c-4e4e-9e73-7c91501e6f19","timestamp":"2026-10-30T04:36:51.993Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"c71fe1cf-464c-4e4e-9e73-7c91501e6f19","isSidechain":false,"promptId":"987711b9-2da1-48f1-95c0-984a823a90af","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MrTVq5uztWjmZDwLDSPH1w","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — FAILED (10 of 35 checks failed)\n [ok] file:out/results.json: present\n [FAIL] file:out/report.md: missing or empty\n [ok] file:out/analysis.py: present\n [FAIL] file:out/claims.json: missing or empty\n [ok] results_json: parsed\n [ok] key:task_id: ok\n [ok] key:tau_hat: ok\n [ok] key:se_iid: ok\n [ok] key:se_cluster: ok\n [ok] key:p_value: ok\n [ok] key:tau_ci_low: ok\n [ok] key:tau_ci_high: ok\n [ok] key:effect_significant_5pct: ok\n [ok] key:icc_hat: ok\n [ok] key:n_schools: ok\n [ok] key:n_treated_schools: ok\n [ok] key:n_analysed: ok\n [ok] key:feasibility: ok\n [ok] key:feasibility_reason: ok\n [ok] key:data_issues: ok\n [ok] key:n_input_rows: ok\n [ok] key:random_seed: ok\n [FAIL] section:Question: heading not found\n [FAIL] section:Data and exclusions: heading not found\n [FAIL] section:Method: heading not found\n [FAIL] section:Results: heading not found\n [FAIL] section:Uncertainty: heading not found\n [FAIL] section:Limitations and open issues: heading not found\n [FAIL] claims_json: out/claims.json missing\n [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n [ok] exclusion_ledger: 2 steps consistent\n [ok] ci:tau_hat: -0.5133384025429495 <= 0.9611179999999999 <= 2.435574402542949\n [ok] positive:se_iid: 0.28209729504903186\n [ok] positive:se_cluster: 0.7289578392511658\n [ok] reproducibility: regenerated in 0.7s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0118/out/state/gate_receipts/20261030T043652.732683Z.json\n20261030T043652.732683Z.json","is_error":false}]},"uuid":"85596a57-394b-44a6-90ed-5ea9577a9fb3","timestamp":"2026-10-30T04:36:52.742Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — FAILED (10 of 35 checks failed)\n [ok] file:out/results.json: present\n [FAIL] file:out/report.md: missing or empty\n [ok] file:out/analysis.py: present\n [FAIL] file:out/claims.json: missing or empty\n [ok] results_json: parsed\n [ok] key:task_id: ok\n [ok] key:tau_hat: ok\n [ok] key:se_iid: ok\n [ok] key:se_cluster: ok\n [ok] key:p_value: ok\n [ok] key:tau_ci_low: ok\n [ok] key:tau_ci_high: ok\n [ok] key:effect_significant_5pct: ok\n [ok] key:icc_hat: ok\n [ok] key:n_schools: ok\n [ok] key:n_treated_schools: ok\n [ok] key:n_analysed: ok\n [ok] key:feasibility: ok\n [ok] key:feasibility_reason: ok\n [ok] key:data_issues: ok\n [ok] key:n_input_rows: ok\n [ok] key:random_seed: ok\n [FAIL] section:Question: heading not found\n [FAIL] section:Data and exclusions: heading not found\n [FAIL] section:Method: heading not found\n [FAIL] section:Results: heading not found\n [FAIL] section:Uncertainty: heading not found\n [FAIL] section:Limitations and open issues: heading not found\n [FAIL] claims_json: out/claims.json missing\n [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n [ok] exclusion_ledger: 2 steps consistent\n [ok] ci:tau_hat: -0.5133384025429495 <= 0.9611179999999999 <= 2.435574402542949\n [ok] positive:se_iid: 0.28209729504903186\n [ok] positive:se_cluster: 0.7289578392511658\n [ok] reproducibility: regenerated in 0.7s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0118/out/state/gate_receipts/20261030T043652.732683Z.json\n20261030T043652.732683Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"c71fe1cf-464c-4e4e-9e73-7c91501e6f19","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"type":"api-request-blob","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","hash":"1ed2b16b64d20cd436e3799cc6670c42e606aa9457705591ccb6f6d42f1f1128","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01MrTVq5uztWjmZDwLDSPH1w","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n| check | outcome |\n|---|---|\n| Clean re-run (results.json, exclusions.csv deleted) regenerates byte-identical results.json | PASS |\n| CI [tau_ci_low, tau_ci_high] contains tau_hat | PASS |\n| se_iid > 0, se_cluster > 0 | PASS |\n| tau_hat equals mean_score_treated − mean_score_control | PASS |\n| effect_significant_5pct consistent with p_value<0.05 and CI excluding 0 | PASS (all false) |\n| n_input_rows 1000 − 0 removed = n_analysed 1000 (exclusions.csv sums) | PASS |\n| icc_hat in [0,1] | PASS (0.240) |\n| (se_cluster/se_iid)^2 = 6.68 vs design effect 1+(25−1)·icc = 6.77 (within 10%) | PASS |\n| se_cluster (CR1) vs Welch SE on 40 school means 0.738 (within 5%); Welch p=0.201 vs cluster p=0.195 | PASS |\n| treated constant within school; 20/20 schools; balanced 25 per school | PASS (audit) |\nEOF\ncat > out/state/phases.json <<'EOF'\n{\n \"P0\": {\"status\": \"passed\", \"checks\": [\"TASK.md read fully\", \"contract.md written\"], \"notes\": \"\"},\n \"P1\": {\"status\": \"passed\", \"checks\": [\"sha256 matches bundle.json\", \"schema/missing/duplicates inspected\", \"treated constant within school\", \"exclusions.csv sums 1000-0=1000\"], \"notes\": \"no data issues\"},\n \"P2\": {\"status\": \"passed\", \"checks\": [\"naive OLS iid baseline computed: se_iid, baseline_p_value_iid\"], \"notes\": \"\"},\n \"P3\": {\"status\": \"passed\", \"checks\": [\"analysis.py runs, writes results.json deterministically, no RNG\"], \"notes\": \"CR1 cluster SE, t(G-1)\"},\n \"P4\": {\"status\": \"running\", \"checks\": [\"clean rerun byte-identical\", \"internal consistency checks in checks.md all PASS\"], \"notes\": \"\"},\n \"P5\": {\"status\": \"pending\", \"checks\": [], \"notes\": \"\"}\n}\nEOF\necho \"- 2026-10-30 P2/P3 passed: baseline iid and CR1 cluster SE computed. P4 checks all pass; running gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .; ls out/state/gate_receipts","description":"Record checks, phases, run gate"}}]}} {"type":"api-request","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","id":"fb95adec-7487-4a55-a61e-0929221a28b4","timestamp":"2026-10-30T04:36:52.746Z","version":"2.1.314","querySource":"sdk","shapeHash":"1cefe355e71f8bb33a44ea933fac791086e15c215724bd3d4e47a4860724ab4b","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":"8783b4da-f82a-4230-ac21-93222cb8c69a","keep":16,"tail":["1ed2b16b64d20cd436e3799cc6670c42e606aa9457705591ccb6f6d42f1f1128","51acd80790d4d726aa810801a8edf5c9eb98f41cf7703e0f1de73d34f1ae8fde"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",17,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"42e236a77efc1c38271fa1653bfc7dd6936399028c46a4a2be039cccd809f4d5"} {"parentUuid":"85596a57-394b-44a6-90ed-5ea9577a9fb3","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoTJ7C3YpCyTG8W9Gz9","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSlQcKEAgTGAI4AUIIdGhpbmtpbmcSDGa6Acj5skwx0J2JkxoM9osafjnKJOJtWaU+IjCjOEvuFfngqfQLH+oZ/nPxEFgyEc1/5jmI/9BzNu3pIp2fqnM6VAjltMQdELVUmu0qsgZY5eY8iLI40XuVFbkPnbHst9N/BA0ZMJGit0G1+BiFPFcxIRw1/hAg0TQh8A6MYzcRtOI50HFzyHIm1DHzRJ6xxcJE5s5sDAFz2E1aSQBDqm4/FN7h/ey5sR7IVD8IP5Kj7hz/kySsPVlf6CkCdeWkoFhh8M5DpOq1+g2TAbd67Oru6XDzUUrzMf95l10GfkMjybK6NVoEhHLbEnBvKxZmtYNj7fVOe4omayPwD3dGMKfW3Hgj5Q2F0Dzlh7bFo6/gu2wIBULKHrpCBXhv3DMznmUuIxHuzZ9tpvXdbP50Tsc0wP5hvnvd0GogXFmkcsE7FvJqelFNwehsRNUM4fffvSO0KCB0JOgth+rIq+4BngwV4ZtJCbkL0gh8ldeVNOgE/uBl1mKsp8Rf3VR4+toIpkEG+f9ZTRsbebpYR6bH07n2qjZgg+xyKX3vgGQ/4cw15LdzeH3XQ9uPm2Gq+OL/Td3nvvTcUj1oGnrhuDS8oAo6dUJCX17GDY3FPpF7HO0r3SPZ/Yh/R0fw4To0o1DOcwuY2eQuS2F5DZhbuHRSDM3zGi/XbBpQr5IFJwQ5GdOvo2oehCWhtO77iIQutiNuj4zvC/10AopQZTTd5HAKXhLmASBL+Ou70vXHGHh2/gYtHlObI1WhFLYF72/Qxed4Y3sjdiraqUxLLbgbE9iYhx1EsrwhN6xjgS6PyRD7dEKI45drtzl9Qofuxl2Q+0nX39JreXboKMlkekfZFzPQRfAZ2u40DIt8dvybaLWhJv3GyL5xW6GwqstxESyimJEQsn0jVkHKlXv3upq1FzRPnE0EjWJBIzLn6D60fYeeiMR6RRJpcbgfBl4Y65rqwEl1Hn7x0zkj9XUYb/Z4xADyoqQxaLM5TkrVTArGI0jsBcyFcjPU0tUaxkRI9/5YvOOBMJLUEPfBpEIkc5XR7RCd+9gqUgWcidtgf//mlrnfm6SKxqVzdlQCkRI1+toHQZrK8U8s9E+g46U34tQKeLaiM3DaHGnL1TA8/mWuhFOJ51Sv0SzSsCvyiBQkIL+J84bXQBVDI+EvEBMNjh2Ob6DVOROBI2LDV6DdkWMN6zVEEGCzbRgB"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1771,"cache_read_input_tokens":14783,"output_tokens":2965,"output_tokens_details":{"thinking_tokens":52},"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":1771},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":573,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXoTHn6jTK8uBHnTMHic","requestRef":"fb95adec-7487-4a55-a61e-0929221a28b4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3c350a71-8e89-40f3-9817-2d33c374ead8","timestamp":"2026-10-30T04:36:54.090Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"3c350a71-8e89-40f3-9817-2d33c374ead8","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoTJ7C3YpCyTG8W9Gz9","type":"message","role":"assistant","content":[{"type":"text","text":"All P4-scope gate checks pass (the remaining failures are P5 deliverables). Now the report and claims."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1771,"cache_read_input_tokens":14783,"output_tokens":2965,"output_tokens_details":{"thinking_tokens":52},"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":1771},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXoTHn6jTK8uBHnTMHic","requestRef":"fb95adec-7487-4a55-a61e-0929221a28b4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"7feaad0c-68f5-4206-bf11-d9fe5a01ca67","timestamp":"2026-10-30T04:36:54.091Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"7feaad0c-68f5-4206-bf11-d9fe5a01ca67","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoTJ7C3YpCyTG8W9Gz9","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01NKbHexEnvJ8s5rng674an3","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F2-conf-01 — Programme effect on student scores with cluster-valid uncertainty\n\n## Question\nA programme was assigned to whole schools. We estimate τ, the difference between student-weighted mean scores in treated and control schools (OLS coefficient of `score` on `treated` with intercept), give a sampling uncertainty that respects the school-level design, and test H0: τ = 0 at the 5% level (two-sided).\n\n## Data and exclusions\n`data/schools.csv` (synthetic; SHA-256 0f1d8378…c88a) has 1000 rows (`n_input_rows`). No missing values, duplicate rows or duplicate student IDs were found. `out/exclusions.csv` therefore records two filtering steps that each removed 0 rows. All `n_analysed` = 1000 students were used. They come from `n_schools` = 40 schools of 25 students each, and `n_treated_schools` = 20 of those schools are treated. Treatment is constant within every school. `data_issues` is empty. Full audit: `out/state/data_audit.md`.\n\n## Method\n- Baseline: OLS of score on treated with the conventional iid, homoskedastic standard error (`se_iid`).\n- Main analysis: the same OLS point estimate (`tau_hat`), with a cluster-robust variance clustered on `school_id`. This uses the CR1 small-sample correction from statsmodels: G/(G−1)·(N−1)/(N−K). The SE allows any correlation among students in the same school and assumes schools are independent. Because there are only 40 clusters, the p-value and 95% CI use a t distribution with G−1 = 39 degrees of freedom (`df_cluster`).\n- Cross-check: a Welch two-sample t-test on the 40 school means (`school_means_welch_se`, `school_means_welch_p`).\n- ICC: I subtracted each arm's mean score to get residuals, then applied a one-way ANOVA variance-components estimator by school (2 between-school df used up by the arm means). `icc_hat` = σ²_between / (σ²_between + σ²_within). It implies a design effect of 1 + (25−1)·ICC (`design_effect_from_icc`).\n- The method is deterministic, so no random seed is used (`random_seed` = null).\n\n## Results\n- Mean score was 51.28 in treated schools (`mean_score_treated`) and 50.32 in control schools (`mean_score_control`).\n- `tau_hat` = 0.961 points.\n- Valid (cluster) inference: `se_cluster` = 0.729, `t_stat_cluster` = 1.32, `p_value` = 0.195. The 95% CI is [`tau_ci_low`, `tau_ci_high`] = [−0.513, 2.436].\n- **H0: τ = 0 is not rejected at the 5% level (`effect_significant_5pct` = false).** The data do not support a non-zero programme effect.\n- The naive baseline would have concluded otherwise: `se_iid` = 0.282, `baseline_p_value_iid` = 0.00068, CI [`baseline_ci_iid_low`, `baseline_ci_iid_high`] = [0.408, 1.515]. That conclusion is invalid because it treats 1000 correlated students as independent observations.\n\n## Uncertainty\n- `icc_hat` = 0.240: about a quarter of residual score variance lies between schools.\n- With 25 students per school, this gives a design effect of about 6.77 (`design_effect_from_icc`). That matches the observed ratio `se_ratio_cluster_to_iid` = 2.58, whose square is about 6.68.\n- The school-means Welch test gives almost the same SE and p-value (`school_means_welch_se` = 0.738, `school_means_welch_p` = 0.201), which supports the cluster-robust result.\n- The effective sample size is about 1000/6.8 ≈ 150 students, and the information really comes from 40 schools.\n\n## Limitations and open issues\n- The data are synthetic. Reading τ as causal assumes that schools were assigned to treatment as if at random; the data cannot verify this.\n- Cluster-robust inference is asymptotic in the number of clusters. With 40 balanced clusters (20/20), CR1 with t(39) is generally adequate, and the school-means analysis agrees closely. A wild-cluster bootstrap was not run because the task does not require it.\n- Not rejecting H0 does not show the effect is zero. The 95% CI includes effects from about −0.5 to +2.4 points.\n- No covariates were available, so precision could not be improved through adjustment.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\": \"C1\", \"text\": \"The estimated programme effect is 0.961 points (treated minus control student-weighted mean score).\", \"results_keys\": [\"tau_hat\", \"mean_score_treated\", \"mean_score_control\"], \"status\": \"supported\", \"caveat\": \"Causal reading requires as-if-random assignment of schools.\"},\n {\"id\": \"C2\", \"text\": \"With school-clustered (CR1, t with 39 df) uncertainty, H0: tau = 0 is not rejected at the 5% level (p = 0.195; 95% CI -0.513 to 2.436); the data do not support a non-zero effect.\", \"results_keys\": [\"se_cluster\", \"p_value\", \"tau_ci_low\", \"tau_ci_high\", \"effect_significant_5pct\", \"df_cluster\"], \"status\": \"supported\", \"caveat\": \"Non-rejection is not evidence of zero effect; CI is wide.\"},\n {\"id\": \"C3\", \"text\": \"The naive iid standard error (0.282) understates uncertainty by a factor of about 2.58 and would wrongly indicate significance (p = 0.00068).\", \"results_keys\": [\"se_iid\", \"se_ratio_cluster_to_iid\", \"baseline_p_value_iid\", \"baseline_ci_iid_low\", \"baseline_ci_iid_high\"], \"status\": \"supported\", \"caveat\": \"Baseline shown only for contrast; it is invalid under the design.\"},\n {\"id\": \"C4\", \"text\": \"The residual intraclass correlation is about 0.24, implying a design effect of about 6.8 consistent with the observed SE inflation.\", \"results_keys\": [\"icc_hat\", \"design_effect_from_icc\", \"se_ratio_cluster_to_iid\"], \"status\": \"supported\", \"caveat\": \"ANOVA ICC estimator; sampling uncertainty of ICC not quantified.\"},\n {\"id\": \"C5\", \"text\": \"A Welch t-test on the 40 school means gives nearly the same SE and p-value as the cluster-robust analysis.\", \"results_keys\": [\"school_means_welch_se\", \"school_means_welch_p\", \"se_cluster\", \"p_value\"], \"status\": \"supported\", \"caveat\": \"\"},\n {\"id\": \"C6\", \"text\": \"All 1000 students in 40 schools (20 treated) were analysed with no exclusions or data issues.\", \"results_keys\": [\"n_analysed\", \"n_schools\", \"n_treated_schools\", \"n_input_rows\", \"data_issues\"], \"status\": \"supported\", \"caveat\": \"\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P4']={\"status\":\"passed\",\"checks\":p['P4']['checks']+[\"gate run at end of P4: receipt out/state/gate_receipts/20261030T043652.732683Z.json; all P4-scope checks (keys, CI, positive SE, exclusion ledger, reproducibility) ok; failures only for P5 deliverables not yet written\"],\"notes\":\"\"}\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md written with 6 sections, numbers cite results keys\",\"claims.json written (6 claims, incl. 5% significance claim C2)\",\"final gate run (see receipt list)\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate receipt 20261030T043652). P5 report and claims written; running final gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | head -3; ls out/state/gate_receipts","description":"Write report, claims, update phases, run final gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1771,"cache_read_input_tokens":14783,"output_tokens":2965,"output_tokens_details":{"thinking_tokens":52},"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":1771},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXoTHn6jTK8uBHnTMHic","requestRef":"fb95adec-7487-4a55-a61e-0929221a28b4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3d024152-2ed6-4f58-9071-a41cb1c102b1","timestamp":"2026-10-30T04:37:17.528Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"3d024152-2ed6-4f58-9071-a41cb1c102b1","isSidechain":false,"promptId":"987711b9-2da1-48f1-95c0-984a823a90af","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NKbHexEnvJ8s5rng674an3","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 35 checks failed)\n [ok] file:out/results.json: present\n [ok] file:out/report.md: present\n20261030T043652.732683Z.json\n20261030T043718.316390Z.json","is_error":false}]},"uuid":"bfb1682d-d924-49e8-8657-4ee6b153c74f","timestamp":"2026-10-30T04:37:18.328Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 35 checks failed)\n [ok] file:out/results.json: present\n [ok] file:out/report.md: present\n20261030T043652.732683Z.json\n20261030T043718.316390Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"3d024152-2ed6-4f58-9071-a41cb1c102b1","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"type":"api-request-blob","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","hash":"3b24d7f9269b0206b792d3e86cfab35ef0c29bbb24533005b9effda685622dab","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"All P4-scope gate checks pass (the remaining failures are P5 deliverables). Now the report and claims."},{"type":"tool_use","id":"toolu_01NKbHexEnvJ8s5rng674an3","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F2-conf-01 — Programme effect on student scores with cluster-valid uncertainty\n\n## Question\nA programme was assigned to whole schools. We estimate τ, the difference between student-weighted mean scores in treated and control schools (OLS coefficient of `score` on `treated` with intercept), give a sampling uncertainty that respects the school-level design, and test H0: τ = 0 at the 5% level (two-sided).\n\n## Data and exclusions\n`data/schools.csv` (synthetic; SHA-256 0f1d8378…c88a) has 1000 rows (`n_input_rows`). No missing values, duplicate rows or duplicate student IDs were found. `out/exclusions.csv` therefore records two filtering steps that each removed 0 rows. All `n_analysed` = 1000 students were used. They come from `n_schools` = 40 schools of 25 students each, and `n_treated_schools` = 20 of those schools are treated. Treatment is constant within every school. `data_issues` is empty. Full audit: `out/state/data_audit.md`.\n\n## Method\n- Baseline: OLS of score on treated with the conventional iid, homoskedastic standard error (`se_iid`).\n- Main analysis: the same OLS point estimate (`tau_hat`), with a cluster-robust variance clustered on `school_id`. This uses the CR1 small-sample correction from statsmodels: G/(G−1)·(N−1)/(N−K). The SE allows any correlation among students in the same school and assumes schools are independent. Because there are only 40 clusters, the p-value and 95% CI use a t distribution with G−1 = 39 degrees of freedom (`df_cluster`).\n- Cross-check: a Welch two-sample t-test on the 40 school means (`school_means_welch_se`, `school_means_welch_p`).\n- ICC: I subtracted each arm's mean score to get residuals, then applied a one-way ANOVA variance-components estimator by school (2 between-school df used up by the arm means). `icc_hat` = σ²_between / (σ²_between + σ²_within). It implies a design effect of 1 + (25−1)·ICC (`design_effect_from_icc`).\n- The method is deterministic, so no random seed is used (`random_seed` = null).\n\n## Results\n- Mean score was 51.28 in treated schools (`mean_score_treated`) and 50.32 in control schools (`mean_score_control`).\n- `tau_hat` = 0.961 points.\n- Valid (cluster) inference: `se_cluster` = 0.729, `t_stat_cluster` = 1.32, `p_value` = 0.195. The 95% CI is [`tau_ci_low`, `tau_ci_high`] = [−0.513, 2.436].\n- **H0: τ = 0 is not rejected at the 5% level (`effect_significant_5pct` = false).** The data do not support a non-zero programme effect.\n- The naive baseline would have concluded otherwise: `se_iid` = 0.282, `baseline_p_value_iid` = 0.00068, CI [`baseline_ci_iid_low`, `baseline_ci_iid_high`] = [0.408, 1.515]. That conclusion is invalid because it treats 1000 correlated students as independent observations.\n\n## Uncertainty\n- `icc_hat` = 0.240: about a quarter of residual score variance lies between schools.\n- With 25 students per school, this gives a design effect of about 6.77 (`design_effect_from_icc`). That matches the observed ratio `se_ratio_cluster_to_iid` = 2.58, whose square is about 6.68.\n- The school-means Welch test gives almost the same SE and p-value (`school_means_welch_se` = 0.738, `school_means_welch_p` = 0.201), which supports the cluster-robust result.\n- The effective sample size is about 1000/6.8 ≈ 150 students, and the information really comes from 40 schools.\n\n## Limitations and open issues\n- The data are synthetic. Reading τ as causal assumes that schools were assigned to treatment as if at random; the data cannot verify this.\n- Cluster-robust inference is asymptotic in the number of clusters. With 40 balanced clusters (20/20), CR1 with t(39) is generally adequate, and the school-means analysis agrees closely. A wild-cluster bootstrap was not run because the task does not require it.\n- Not rejecting H0 does not show the effect is zero. The 95% CI includes effects from about −0.5 to +2.4 points.\n- No covariates were available, so precision could not be improved through adjustment.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\": \"C1\", \"text\": \"The estimated programme effect is 0.961 points (treated minus control student-weighted mean score).\", \"results_keys\": [\"tau_hat\", \"mean_score_treated\", \"mean_score_control\"], \"status\": \"supported\", \"caveat\": \"Causal reading requires as-if-random assignment of schools.\"},\n {\"id\": \"C2\", \"text\": \"With school-clustered (CR1, t with 39 df) uncertainty, H0: tau = 0 is not rejected at the 5% level (p = 0.195; 95% CI -0.513 to 2.436); the data do not support a non-zero effect.\", \"results_keys\": [\"se_cluster\", \"p_value\", \"tau_ci_low\", \"tau_ci_high\", \"effect_significant_5pct\", \"df_cluster\"], \"status\": \"supported\", \"caveat\": \"Non-rejection is not evidence of zero effect; CI is wide.\"},\n {\"id\": \"C3\", \"text\": \"The naive iid standard error (0.282) understates uncertainty by a factor of about 2.58 and would wrongly indicate significance (p = 0.00068).\", \"results_keys\": [\"se_iid\", \"se_ratio_cluster_to_iid\", \"baseline_p_value_iid\", \"baseline_ci_iid_low\", \"baseline_ci_iid_high\"], \"status\": \"supported\", \"caveat\": \"Baseline shown only for contrast; it is invalid under the design.\"},\n {\"id\": \"C4\", \"text\": \"The residual intraclass correlation is about 0.24, implying a design effect of about 6.8 consistent with the observed SE inflation.\", \"results_keys\": [\"icc_hat\", \"design_effect_from_icc\", \"se_ratio_cluster_to_iid\"], \"status\": \"supported\", \"caveat\": \"ANOVA ICC estimator; sampling uncertainty of ICC not quantified.\"},\n {\"id\": \"C5\", \"text\": \"A Welch t-test on the 40 school means gives nearly the same SE and p-value as the cluster-robust analysis.\", \"results_keys\": [\"school_means_welch_se\", \"school_means_welch_p\", \"se_cluster\", \"p_value\"], \"status\": \"supported\", \"caveat\": \"\"},\n {\"id\": \"C6\", \"text\": \"All 1000 students in 40 schools (20 treated) were analysed with no exclusions or data issues.\", \"results_keys\": [\"n_analysed\", \"n_schools\", \"n_treated_schools\", \"n_input_rows\", \"data_issues\"], \"status\": \"supported\", \"caveat\": \"\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P4']={\"status\":\"passed\",\"checks\":p['P4']['checks']+[\"gate run at end of P4: receipt out/state/gate_receipts/20261030T043652.732683Z.json; all P4-scope checks (keys, CI, positive SE, exclusion ledger, reproducibility) ok; failures only for P5 deliverables not yet written\"],\"notes\":\"\"}\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md written with 6 sections, numbers cite results keys\",\"claims.json written (6 claims, incl. 5% significance claim C2)\",\"final gate run (see receipt list)\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate receipt 20261030T043652). P5 report and claims written; running final gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | head -3; ls out/state/gate_receipts","description":"Write report, claims, update phases, run final gate"}}]}} {"type":"api-request","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","id":"3d12d8f9-beab-4994-aa3c-231fd24a44ee","timestamp":"2026-10-30T04:37:18.332Z","version":"2.1.314","querySource":"sdk","shapeHash":"1cefe355e71f8bb33a44ea933fac791086e15c215724bd3d4e47a4860724ab4b","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":"fb95adec-7487-4a55-a61e-0929221a28b4","keep":18,"tail":["3b24d7f9269b0206b792d3e86cfab35ef0c29bbb24533005b9effda685622dab","cef790b037e65de675075dc62e1250c3296fce03b52aadddbe08722c36bc9814"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"4df9f71a49a3e90f15640eeb86fa2c0f7f0ec594b2ac44e35220bd80d4e140df"} {"type":"last-prompt","lastPrompt":"Begin","leafUuid":"bfb1682d-d924-49e8-8657-4ee6b153c74f","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9"} {"type":"atis-latch","atis":"","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9"} {"parentUuid":"bfb1682d-d924-49e8-8657-4ee6b153c74f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoVBTthXLDw7NgzpjXN","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LtEbKTM72M9zBbwSyWFytr","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P5']['checks'][-1]=\"final gate run at end of P5: receipt out/state/gate_receipts/20261030T043718.316390Z.json, PASSED 0/35 failed\"\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 Final gate PASSED (receipt 20261030T043718). All phases passed; task complete.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | head -1","description":"Record final receipt and confirm gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3063,"cache_read_input_tokens":16554,"output_tokens":306,"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":3063},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXoVBBn5Yqs2xXayTZLS","requestRef":"3d12d8f9-beab-4994-aa3c-231fd24a44ee","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3072af78-141b-4536-aa07-21475bcdac1b","timestamp":"2026-10-30T04:37:20.863Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"3072af78-141b-4536-aa07-21475bcdac1b","isSidechain":false,"promptId":"987711b9-2da1-48f1-95c0-984a823a90af","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LtEbKTM72M9zBbwSyWFytr","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 35 checks failed)","is_error":false}]},"uuid":"e5135055-20ce-498e-9a88-559861d9772c","timestamp":"2026-10-30T04:37:21.620Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 35 checks failed)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"3072af78-141b-4536-aa07-21475bcdac1b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"type":"api-request-blob","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","hash":"767fee2b4fba00979973d7f5629d06731be8c559b628d49da8f7234f8b2b7de0","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01LtEbKTM72M9zBbwSyWFytr","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P5']['checks'][-1]=\"final gate run at end of P5: receipt out/state/gate_receipts/20261030T043718.316390Z.json, PASSED 0/35 failed\"\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 Final gate PASSED (receipt 20261030T043718). All phases passed; task complete.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | head -1","description":"Record final receipt and confirm gate"}}]}} {"type":"api-request","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","id":"73d0d5e5-cb16-49dd-91d7-25c05377a99d","timestamp":"2026-10-30T04:37:21.623Z","version":"2.1.314","querySource":"sdk","shapeHash":"1cefe355e71f8bb33a44ea933fac791086e15c215724bd3d4e47a4860724ab4b","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":"3d12d8f9-beab-4994-aa3c-231fd24a44ee","keep":20,"tail":["767fee2b4fba00979973d7f5629d06731be8c559b628d49da8f7234f8b2b7de0","5be25ae19dd0eb74563705e9b97b2f9135f8ff8aace72d891e0e4a5c8195661c"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",21,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"6b370f059537697c35af973b5cc48acb49fe987468ea74c42476abc278538fcb"} {"parentUuid":"e5135055-20ce-498e-9a88-559861d9772c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoVRZiKcE4E74sRKHRM","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":346,"cache_read_input_tokens":19617,"output_tokens":786,"output_tokens_details":{"thinking_tokens":21},"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":346},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":6471,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXoVRErsL6raFLaoNq2W","requestRef":"73d0d5e5-cb16-49dd-91d7-25c05377a99d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e157fa32-46e0-4809-8021-33308a9ac9cb","timestamp":"2026-10-30T04:37:28.885Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"parentUuid":"e157fa32-46e0-4809-8021-33308a9ac9cb","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoVRZiKcE4E74sRKHRM","type":"message","role":"assistant","content":[{"type":"text","text":"The estimated programme effect is **0.961 points**, but the data do not support a non-zero effect at the 5% level once uncertainty is measured at the school level (p = 0.195). The final gate run passed all 35 of its checks.\n\n| | Estimate | 95% interval | p-value |\n|---|---|---|---|\n| School-level (valid) standard error: 0.729 | 0.961 | −0.513 to 2.436 | 0.195 |\n| Naive standard error (treats students as independent): 0.282 | 0.961 | 0.408 to 1.515 | 0.00068 |\n\n- **Data:** all 1,000 students were used, in 40 schools of 25 students each, 20 of them treated. There were no missing values, duplicates or other problems, so nothing was excluded.\n- **Why the two answers differ:** about a quarter of the score variation (intraclass correlation 0.240) is between schools. That inflates the variance by about 6.8 times, which matches the naive standard error being 2.58 times too small. The naive figure therefore makes the effect look significant when it isn't.\n- **Cross-check:** comparing the 40 school averages directly gives nearly the same result (standard error 0.738, p = 0.201).\n- **Method:** a regression of score on treatment, with standard errors that allow students in the same school to be correlated. Because there are only 40 schools, the p-value and interval use a t distribution with 39 degrees of freedom. Nothing is random, so no seed was needed, and a clean re-run reproduced `results.json` exactly.\n\n**Limitations:**\n- The data are synthetic.\n- Reading the estimate as the programme's causal effect assumes schools were assigned as if at random, which the data can't confirm.\n- Not finding an effect doesn't mean there is none: the interval still allows effects up to about 2.4 points.\n- No other student or school variables were available to make the estimate more precise.\n- I didn't run a bootstrap check because the task doesn't call for one; with 40 evenly split schools the standard method should be adequate.\n\nEverything is in `out/`. The full explanation is in `report.md` and `analysis.py` regenerates the results; the working notes and checks are in `out/state/`.\n\nFiles are in `out/`:\n- results.json\n- report.md\n- analysis.py\n- claims.json\n- exclusions.csv"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":346,"cache_read_input_tokens":19617,"output_tokens":786,"output_tokens_details":{"thinking_tokens":21},"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":346},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXoVRErsL6raFLaoNq2W","requestRef":"73d0d5e5-cb16-49dd-91d7-25c05377a99d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6ca4a7e8-3729-498e-a896-514c877f626b","timestamp":"2026-10-30T04:37:29.418Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0118","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","version":"2.1.314","gitBranch":"HEAD"} {"type":"last-prompt","lastPrompt":"Begin","leafUuid":"6ca4a7e8-3729-498e-a896-514c877f626b","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9"} {"type":"cost-state","sessionId":"89d11f14-ac1c-41b7-a68f-9e67546a15a9","totalCostUSD":0.338711,"totalAPIDuration":94565,"totalAPIDurationWithoutRetries":94559,"totalToolDuration":4178,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":98831,"startTime":1793334950594,"modelUsage":{"claude-opus-5-5":{"inputTokens":24,"outputTokens":11162,"thinkingTokens":658,"cacheReadInputTokens":115400,"cacheCreationInputTokens":18459,"cacheCreationEphemeral5mInputTokens":18459,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.338711}},"hasUnknownModelCost":false}