spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T04:16:50.603Z","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T04:16:50.609Z","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f"}4{"parentUuid":null,"isSidechain":false,"promptId":"7c63dae8-a509-4a57-a600-581bf60c9d13","type":"user","message":{"role":"user","content":"Begin"},"uuid":"f103a7ce-e99f-494a-bcac-e1c621d75a23","timestamp":"2026-10-30T04:16:50.613Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","shapeHash":"ef38d8a8bde17d36c01bd203bb6c76f380c9b7d1d1ecc47de8861e1d70009353","timestamp":"2026-10-30T04:16:50.628Z","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/RC0058\nDate: 2026-10-30\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n\nSEPARATE REVIEW (additional)\n- After the gate passes, a separate reviewer session that has not seen your work in progress will examine `TASK.md` and everything under `out/` and may return a list of issues (file `out/review/review.json`). You will then be resumed once to address them: fix what is justified, explain in `out/report.md` (Limitations and open issues) what you disagree with, re-run the gate, and update `out/claims.json` and `out/state/phases.json`. The reviewer has no reference answers; its role is to challenge unsupported claims, violated information rules, unverified computations, and inconsistencies between your report and your results.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. Never use words like \"complex\" or \"risk\" in the description - just describe what it does.\n\nSay what the command does in plain words: do not echo the command's text, its flags, or file paths - the user reads this description, often without seeing the command.\n\nFor simple commands (git, npm, standard CLI tools), keep it brief (5-10 words):\n- ls → \"List files in current directory\"\n- git status → \"Show working tree status\"\n- npm install → \"Install package dependencies\"\n\nFor commands that are harder to parse at a glance (piped commands, obscure flags, etc.), add enough context to clarify what it does:\n- find . -name \"*.tmp\" -exec rm {} \\; → \"Find and delete all .tmp files recursively\"\n- git reset --hard origin/main → \"Discard all local changes and match remote main\"\n- curl -s url | jq '.data[]' → \"Fetch JSON from URL and extract data array elements\"","type":"string"},"dangerouslyDisableSandbox":{"description":"Set this to true to dangerously override sandbox mode and run commands without sandboxing.","type":"boolean"}},"required":["command"],"additionalProperties":false}},{"name":"Edit","description":"modify file contents in place","input_schema":{"$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}}]}}6{"type":"api-request-blob","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","hash":"d43ab0e0b8ec9db790a6459cb2f4bc4ffce02825ca05063162023be60912ced9","message":{"role":"system","content":[],"output_config":{"effort":"medium"}}}7{"type":"api-request","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","id":"4ec8d8b7-98e6-4fb1-8a2a-1a96bb24dafc","timestamp":"2026-10-30T04:16:50.628Z","version":"2.1.314","querySource":"sdk","shapeHash":"ef38d8a8bde17d36c01bd203bb6c76f380c9b7d1d1ecc47de8861e1d70009353","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":"ea99a7472e02fdf4b9a60a5511e3e15cb48adb5bf6737e7fc244a25f6e59d312"}8{"parentUuid":"f103a7ce-e99f-494a-bcac-e1c621d75a23","isSidechain":false,"attachment":{"type":"session_context","context":{}},"type":"attachment","uuid":"e628768c-f368-4a0b-a8b5-05c0ae2dd711","timestamp":"2026-10-30T04:16:50.620Z","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}9{"parentUuid":"e628768c-f368-4a0b-a8b5-05c0ae2dd711","isSidechain":false,"attachment":{"type":"date","date":"2026-10-30"},"type":"attachment","uuid":"755f219e-7662-4e2e-ac55-7f53e4d24fd9","timestamp":"2026-10-30T04:16:50.620Z","rendered":[{"content":"<system-reminder>\nToday's date is 2026-10-30.\n</system-reminder>"}],"renderedRole":"system","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}10{"parentUuid":"755f219e-7662-4e2e-ac55-7f53e4d24fd9","isSidechain":false,"attachment":{"type":"remote_session_change","url":null,"commit":"Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>","pr":"🤖 Generated with [Claude Code](https://claude.com/claude-code)","sendUserFileHint":false,"managedCommit":false,"managedPr":false},"type":"attachment","uuid":"c45a26bc-26be-4bdb-836b-3e5006de39dc","timestamp":"2026-10-30T04:16:50.620Z","rendered":[{"content":"<system-reminder>\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 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f"}12{"parentUuid":"c45a26bc-26be-4bdb-836b-3e5006de39dc","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmvhk1U7kZRbjDYBxDg","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":2811,"cache_read_input_tokens":1504,"output_tokens":142,"output_tokens_details":{"thinking_tokens":19},"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":2811},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":616,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXmvhRfdmXDC7g9YZoSk","requestRef":"4ec8d8b7-98e6-4fb1-8a2a-1a96bb24dafc","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"782459ad-5c14-4059-92c6-3eedf075f34f","timestamp":"2026-10-30T04:16:52.143Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"782459ad-5c14-4059-92c6-3eedf075f34f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmvhk1U7kZRbjDYBxDg","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01EsZZcAhop45tNn6ttLSQ9Y","name":"Bash","input":{"command":"cat TASK.md; ls -R | head -50; ls out/state 2>/dev/null && cat out/state/log.md 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":2811,"cache_read_input_tokens":1504,"output_tokens":142,"output_tokens_details":{"thinking_tokens":19},"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":2811},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmvhRfdmXDC7g9YZoSk","requestRef":"4ec8d8b7-98e6-4fb1-8a2a-1a96bb24dafc","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"79069551-1da9-4fae-a51e-eb1a4cae81bf","timestamp":"2026-10-30T04:16:52.846Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}14{"parentUuid":"79069551-1da9-4fae-a51e-eb1a4cae81bf","isSidechain":false,"promptId":"7c63dae8-a509-4a57-a600-581bf60c9d13","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\n# F2-conf-03 — Monte Carlo coverage of OLS confidence intervals in a small heteroskedastic sample\n\n## Scientific question\nHow well do nominal 95% confidence intervals for an OLS slope perform in a small sample with heteroskedastic errors? Consider the fixed-design linear model y_i = α + β x_i + σ_i ε_i, i = 1, …, 30, with α = 1, β = 0.5, ε_i independent standard normal, and x_i and σ_i fixed at the values given in `data/design.csv` (the same 30 values in every replication; only ε is redrawn). The estimands are (a) the true coverage probability of the interval β̂ ± t_{0.975}(28) · se_conv, where se_conv is the conventional OLS standard error (residual variance with divisor n − 2); (b) the true coverage probability of the interval β̂ ± t_{0.975}(28) · se_HC1, where se_HC1 is the heteroskedasticity-robust HC1 standard error (White's HC0 sandwich multiplied by n/(n − 2)); (c) the exact finite-sample standard deviation of β̂ under this design; (d) the expected value of se_conv. Estimate (a), (b) and (d) by Monte Carlo with exactly R = 2000 replications and a fixed seed, compute (c) analytically, and conclude whether the conventional interval attains its nominal coverage.\n\n## Inputs\n- `data/design.csv` — the fixed design (a synthetic design generated for this exercise; not observed data). Columns: `i` (observation index 1–30), `x` (regressor value, dimensionless), `sigma` (standard deviation of the error for observation i, dimensionless). 30 rows.\n\n## Information-availability rules\n- Use only `data/design.csv`; the model constants (α = 1, β = 0.5, n = 30, R = 2000, nominal level 95%, t(28) critical value) are given above. Do not redraw x or σ.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | – | \"F2-conf-03\" |\n| `coverage_conventional` | number | proportion | share of the 2000 replications in which the conventional interval contains β = 0.5 — the naïve baseline |\n| `coverage_hc1` | number | proportion | share of the 2000 replications in which the HC1 interval contains β = 0.5 |\n| `sd_beta_exact` | number | – | exact standard deviation of β̂ under the design (analytical, no simulation) |\n| `sd_beta_mc` | number | – | sample standard deviation (divisor R − 1) of the 2000 simulated β̂ |\n| `mean_se_conventional` | number | – | average of se_conv over the 2000 replications |\n| `conventional_ci_undercovers` | boolean | – | true if and only if `coverage_conventional` is below 0.95 by more than three Monte Carlo standard errors (binomial standard error at R = 2000) |\n| `n_replications` | integer | – | number of replications (2000) |\n| `n_obs_per_replication` | integer | – | sample size per replication (30) |\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 | – | {\"design.csv\": rows read} |\n| `random_seed` | integer | – | the seed used for the simulation (required; the simulation must be reproducible) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty (include the Monte Carlo standard error of each coverage estimate); 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 (same seed, same numbers).\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 conventional interval attains its nominal coverage.\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; the simulation itself should take seconds.\nbundle.json\ndata\ngate_spec.json\nTASK.md\n\n./data:\ndesign.csv","is_error":true,"tool_use_id":"toolu_01EsZZcAhop45tNn6ttLSQ9Y"}]},"uuid":"1d204b4e-0797-4300-9d2d-3c82fd5a4ffc","timestamp":"2026-10-30T04:16:52.953Z","toolUseResult":"Error: Exit code 1\n# F2-conf-03 — Monte Carlo coverage of OLS confidence intervals in a small heteroskedastic sample\n\n## Scientific question\nHow well do nominal 95% confidence intervals for an OLS slope perform in a small sample with heteroskedastic errors? Consider the fixed-design linear model y_i = α + β x_i + σ_i ε_i, i = 1, …, 30, with α = 1, β = 0.5, ε_i independent standard normal, and x_i and σ_i fixed at the values given in `data/design.csv` (the same 30 values in every replication; only ε is redrawn). The estimands are (a) the true coverage probability of the interval β̂ ± t_{0.975}(28) · se_conv, where se_conv is the conventional OLS standard error (residual variance with divisor n − 2); (b) the true coverage probability of the interval β̂ ± t_{0.975}(28) · se_HC1, where se_HC1 is the heteroskedasticity-robust HC1 standard error (White's HC0 sandwich multiplied by n/(n − 2)); (c) the exact finite-sample standard deviation of β̂ under this design; (d) the expected value of se_conv. Estimate (a), (b) and (d) by Monte Carlo with exactly R = 2000 replications and a fixed seed, compute (c) analytically, and conclude whether the conventional interval attains its nominal coverage.\n\n## Inputs\n- `data/design.csv` — the fixed design (a synthetic design generated for this exercise; not observed data). Columns: `i` (observation index 1–30), `x` (regressor value, dimensionless), `sigma` (standard deviation of the error for observation i, dimensionless). 30 rows.\n\n## Information-availability rules\n- Use only `data/design.csv`; the model constants (α = 1, β = 0.5, n = 30, R = 2000, nominal level 95%, t(28) critical value) are given above. Do not redraw x or σ.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | – | \"F2-conf-03\" |\n| `coverage_conventional` | number | proportion | share of the 2000 replications in which the conventional interval contains β = 0.5 — the naïve baseline |\n| `coverage_hc1` | number | proportion | share of the 2000 replications in which the HC1 interval contains β = 0.5 |\n| `sd_beta_exact` | number | – | exact standard deviation of β̂ under the design (analytical, no simulation) |\n| `sd_beta_mc` | number | – | sample standard deviation (divisor R − 1) of the 2000 simulated β̂ |\n| `mean_se_conventional` | number | – | average of se_conv over the 2000 replications |\n| `conventional_ci_undercovers` | boolean | – | true if and only if `coverage_conventional` is below 0.95 by more than three Monte Carlo standard errors (binomial standard error at R = 2000) |\n| `n_replications` | integer | – | number of replications (2000) |\n| `n_obs_per_replication` | integer | – | sample size per replication (30) |\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 | – | {\"design.csv\": rows read} |\n| `random_seed` | integer | – | the seed used for the simulation (required; the simulation must be reproducible) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty (include the Monte Carlo standard error of each coverage estimate); 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 (same seed, same numbers).\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 conventional interval attains its nominal coverage.\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; the simulation itself should take seconds.\nbundle.json\ndata\ngate_spec.json\nTASK.md\n\n./data:\ndesign.csv","sandboxed":false,"sourceToolAssistantUUID":"79069551-1da9-4fae-a51e-eb1a4cae81bf","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}15{"type":"api-request-blob","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","hash":"3d802a42a1f81686add06003a99642ed9cfd1b9a4262eba4603529e121d2e106","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01EsZZcAhop45tNn6ttLSQ9Y","name":"Bash","input":{"command":"cat TASK.md; ls -R | head -50; ls out/state 2>/dev/null && cat out/state/log.md 2>/dev/null","description":"Read task and list workspace"}}]}}16{"type":"api-request","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","id":"4bcc6ac4-192f-4d26-af41-758049ea1df8","timestamp":"2026-10-30T04:16:52.958Z","version":"2.1.314","querySource":"sdk","shapeHash":"ef38d8a8bde17d36c01bd203bb6c76f380c9b7d1d1ecc47de8861e1d70009353","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":"4ec8d8b7-98e6-4fb1-8a2a-1a96bb24dafc","keep":2,"tail":["3d802a42a1f81686add06003a99642ed9cfd1b9a4262eba4603529e121d2e106","1aee9e27f51213ffb9055024e462858e9b77fd13aef166459c2a5c6368770374"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"ddd9ebf59816d10d7ad5bad64f37f35be4e87a3b85eb1ee3245df351172da03d"}17{"parentUuid":"1d204b4e-0797-4300-9d2d-3c82fd5a4ffc","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmvr3q8eX8QgGcaxRNA","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01RNbqnZXq6jyVizUqFW9xwY","name":"Bash","input":{"command":"cat data/design.csv; shasum -a 256 data/design.csv; cat gate_spec.json | head -80","description":"Inspect data and gate spec"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1767,"cache_read_input_tokens":4315,"output_tokens":113,"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":1767},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXmvqnSrdnThb7duRdbG","requestRef":"4bcc6ac4-192f-4d26-af41-758049ea1df8","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"7c61e820-a88e-4e45-888d-99d99e2f080c","timestamp":"2026-10-30T04:16:54.194Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}18{"parentUuid":"7c61e820-a88e-4e45-888d-99d99e2f080c","isSidechain":false,"promptId":"7c63dae8-a509-4a57-a600-581bf60c9d13","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RNbqnZXq6jyVizUqFW9xwY","type":"tool_result","content":"i,x,sigma\n1,-1.3942,1.1831\n2,-0.9737,0.8844\n3,-0.8373,0.8103\n4,-0.7367,0.7628\n5,-0.6908,0.7432\n6,-0.2796,0.6235\n7,-0.2431,0.6177\n8,-0.2367,0.6168\n9,-0.2175,0.6142\n10,-0.1756,0.6093\n11,-0.1682,0.6085\n12,-0.1062,0.6034\n13,0.1078,0.6035\n14,0.1216,0.6044\n15,0.2127,0.6136\n16,0.2496,0.6187\n17,0.3046,0.6278\n18,0.4577,0.6628\n19,0.5583,0.6935\n20,0.651,0.7271\n21,0.7123,0.7522\n22,0.7126,0.7523\n23,0.7832,0.784\n24,0.9147,0.851\n25,0.9582,0.8754\n26,1.2264,1.0512\n27,1.381,1.1721\n28,1.5008,1.2757\n29,1.6841,1.4509\n30,1.9919,1.7903\nb1a17a529973e7994bbedad5ec1bc73fe968c777301ca8745657ea300e3c74f0  data/design.csv\n{\n  \"task_id\": \"F2-conf-03\",\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    \"coverage_conventional\": {\"type\": \"number\", \"nullable\": false},\n    \"coverage_hc1\": {\"type\": \"number\", \"nullable\": false},\n    \"sd_beta_exact\": {\"type\": \"number\", \"nullable\": false},\n    \"sd_beta_mc\": {\"type\": \"number\", \"nullable\": false},\n    \"mean_se_conventional\": {\"type\": \"number\", \"nullable\": false},\n    \"conventional_ci_undercovers\": {\"type\": \"boolean\", \"nullable\": false},\n    \"n_replications\": {\"type\": \"integer\", \"nullable\": false},\n    \"n_obs_per_replication\": {\"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\": false}\n  },\n  \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n  \"family_checks\": [\"positive_se\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n  \"ci_pairs\": [],\n  \"positive_keys\": [\"sd_beta_exact\", \"sd_beta_mc\", \"mean_se_conventional\"],\n  \"chronology_pairs\": [],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 1e-6\n}","is_error":false}]},"uuid":"8e08ac42-d2ba-4704-a995-f0b018b401fe","timestamp":"2026-10-30T04:16:54.250Z","toolUseResult":{"stdout":"i,x,sigma\n1,-1.3942,1.1831\n2,-0.9737,0.8844\n3,-0.8373,0.8103\n4,-0.7367,0.7628\n5,-0.6908,0.7432\n6,-0.2796,0.6235\n7,-0.2431,0.6177\n8,-0.2367,0.6168\n9,-0.2175,0.6142\n10,-0.1756,0.6093\n11,-0.1682,0.6085\n12,-0.1062,0.6034\n13,0.1078,0.6035\n14,0.1216,0.6044\n15,0.2127,0.6136\n16,0.2496,0.6187\n17,0.3046,0.6278\n18,0.4577,0.6628\n19,0.5583,0.6935\n20,0.651,0.7271\n21,0.7123,0.7522\n22,0.7126,0.7523\n23,0.7832,0.784\n24,0.9147,0.851\n25,0.9582,0.8754\n26,1.2264,1.0512\n27,1.381,1.1721\n28,1.5008,1.2757\n29,1.6841,1.4509\n30,1.9919,1.7903\nb1a17a529973e7994bbedad5ec1bc73fe968c777301ca8745657ea300e3c74f0  data/design.csv\n{\n  \"task_id\": \"F2-conf-03\",\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    \"coverage_conventional\": {\"type\": \"number\", \"nullable\": false},\n    \"coverage_hc1\": {\"type\": \"number\", \"nullable\": false},\n    \"sd_beta_exact\": {\"type\": \"number\", \"nullable\": false},\n    \"sd_beta_mc\": {\"type\": \"number\", \"nullable\": false},\n    \"mean_se_conventional\": {\"type\": \"number\", \"nullable\": false},\n    \"conventional_ci_undercovers\": {\"type\": \"boolean\", \"nullable\": false},\n    \"n_replications\": {\"type\": \"integer\", \"nullable\": false},\n    \"n_obs_per_replication\": {\"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\": false}\n  },\n  \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n  \"family_checks\": [\"positive_se\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n  \"ci_pairs\": [],\n  \"positive_keys\": [\"sd_beta_exact\", \"sd_beta_mc\", \"mean_se_conventional\"],\n  \"chronology_pairs\": [],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 1e-6\n}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"7c61e820-a88e-4e45-888d-99d99e2f080c","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}19{"type":"api-request-blob","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","hash":"70bb88416e50c2ce96258e915cc2298b8648c2faa4b7163abd96a314c042fd87","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01RNbqnZXq6jyVizUqFW9xwY","name":"Bash","input":{"command":"cat data/design.csv; shasum -a 256 data/design.csv; cat gate_spec.json | head -80","description":"Inspect data and gate spec"}}]}}20{"type":"api-request","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","id":"4f0facfb-c583-4cbf-beb9-9972421cb211","timestamp":"2026-10-30T04:16:54.253Z","version":"2.1.314","querySource":"sdk","shapeHash":"ef38d8a8bde17d36c01bd203bb6c76f380c9b7d1d1ecc47de8861e1d70009353","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":"4bcc6ac4-192f-4d26-af41-758049ea1df8","keep":4,"tail":["70bb88416e50c2ce96258e915cc2298b8648c2faa4b7163abd96a314c042fd87","a9c2f569721960e89c2920c5ce15139f9774a53b0242d46ab10c514371df1a06"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"508d6fe06f36a804eec9be6a5e806d97be77a70330d5f26004ce2238cc8e5989"}21{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"8e08ac42-d2ba-4704-a995-f0b018b401fe","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f"}22{"type":"atis-latch","atis":"","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f"}23{"parentUuid":"8e08ac42-d2ba-4704-a995-f0b018b401fe","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmvwZU2e8oym6mWLshV","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQS4AgKEAgTGAI4AUIIdGhpbmtpbmcSDNePeP6IShxDQ83EfRoMiXYGC6gUEFzJAyLXIjDFAD7EWzfoTT0wOEa7UTn0ciAl9DMXOxOdFdLNfMdLEppMHLmLox0ItpUYDSvBSvQq/Qfb1uuMQkhtkL+exTpI6IIidfVa3pHCqtjsXF2vwcY1aBW/9zYhJDFjSdJnbhySh3UllTZqwIsIAgKakBXGVmskb0hKbJy48P4kIpRH8vwXOrSSiEGbOrVGvVZearb3mpH+ZTuEc26cOq0U8MLta2kV19tFTDdHvbahIW2US2Kqn//3mEZ5wVg2fJ4B84fy6EYHOGlTeiZTIZsyRk9xfyLHG/Cj9gaz5p0yKgNFKLV8qwvlA3z1YWTysrIawk/dYK8P2Gda8UztVUSURfRcqlfBtxadZYmoazaAoYovfDTg3AGvQFq9CLKscxaYTheEiDN/c08zPHo/yzhkRhQfwDuLA+U9xCk25piXe0KuiaeCleAJx/I2jMTFexKzhP/6MVHHLJFbe0dLpq3rGyGdHlWNkeLOYZ55AIkR0x0mFq1bzjQki92bvtcn+h3AVBXQnIXt/r77wqpdYfg9gboU5wv4BW0ICihJIbqvM5CWOr1MRRYLRdyNEAmcApm+ZCiRHtuDvPGWfIuKJqlzo5AGo1UzNN7bs7MVflOQ+omils91tLpMMTbIg2+nnyIzEw9j1mt/Qcg3lE2PAKJciGosJByVYhc72CYBzp06A36FsjKGPKtlev9TkqpEFGa9JIWITOHbDONcUzjOz3wLtFPgkDUQkpfhGhIzZEDOHf3Z+ep7oi+vxmOqiSPTvRMdxTcE6I1Gdu03tI6Y5PsMyKFkbSaDkf0T8HqZrtSFH5wxPwlsm7hHz/7SY6btJGtdNlx57UhpANFKMUPYticPxUWXkLt4WisyxWwMJVIEQeqovtbUSpyeKK6cfm46DBCBse7DNspjGqvVEOMH+YG9qe9N9oXcXYpTAgx+ba1AZTKQX4csiixo3DKfGB9t7iuji+U0SW7JGQCLAtmoOnpE/BNubmVX8MsrKOCHLh2WhB3F2uLx2mmwfGxLV3GHk7VqWhqWASGp2+qPYRYfPRDzgQEKt1D4ypz0yR8nL1D/1xs0qDthoJqL5EOGUd3dZsEpvu0QFLg+Z6K00hTMUCTAK5tMPfqpx9SHOOIQ18UlxutUklKUqeDANX8H0d5Mi1qCJSpOcKXDl02euMwp3GeQHvPmh5AMJJOyf9G9yfH/bqTZhvdtD1vuzQpaLYKdoBwuDK1TuEeSTXNYtgq1N55mrTcX4MwVNZN2oEwREkUWxCrwty7IKuC1dGV6vYE9j2beVJCY4uMuqKMgAasDOXL/ZxtND+drdoBxeu7u393JffhUZqu2ld2StG/E/Wzm1wGMLGpJWY2fbsdnVxahqOgxjq48jSCm/EiTRKJorDx7mXZiYCgOyIGuYakS/8Gti6gXiW3kIWd6zNzP1gpNp/axNRn8GAE="}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1168,"cache_read_input_tokens":6082,"output_tokens":1246,"output_tokens_details":{"thinking_tokens":172},"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":1168},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1602,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXmvwJqCXRGdYUUwkkSw","requestRef":"4f0facfb-c583-4cbf-beb9-9972421cb211","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"cc6aa0e2-8262-44fb-b28f-6bc2b63964b2","timestamp":"2026-10-30T04:16:56.848Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"cc6aa0e2-8262-44fb-b28f-6bc2b63964b2","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmvwZU2e8oym6mWLshV","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01CpZgukmMWw77SR8SZFYrEX","name":"Bash","input":{"command":"mkdir -p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract (P0)\n**Question.** In the fixed-design model y_i = 1 + 0.5 x_i + sigma_i eps_i (n=30, x and sigma from data/design.csv, eps iid N(0,1)), do nominal 95% t(28) intervals for the OLS slope achieve 95% coverage?\n\n**Estimands.**\n(a) P(beta in beta_hat ± t_.975(28) se_conv), se_conv = conventional OLS SE with s^2 = RSS/(n-2).\n(b) Same with se_HC1 = sqrt(n/(n-2) * HC0 sandwich).\n(c) Exact SD of beta_hat: sqrt(sum w_i^2 sigma_i^2), w_i = (x_i - xbar)/Sxx (analytic, valid since design fixed, errors independent).\n(d) E[se_conv], estimated by MC.\n(a),(b),(d) by Monte Carlo, R = 2000, fixed seed. Conclusion: undercovers iff coverage_conv < 0.95 - 3*MCSE, MCSE = binomial SE at R=2000 (decision: evaluated at nominal p=0.95, sqrt(.95*.05/2000); estimate-based SE also reported).\n\n**Outputs.** out/results.json keys: task_id, coverage_conventional, coverage_hc1, sd_beta_exact, sd_beta_mc, mean_se_conventional, conventional_ci_undercovers, n_replications, n_obs_per_replication, feasibility, feasibility_reason, data_issues, n_input_rows, random_seed. Also out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues), out/analysis.py, out/claims.json, out/exclusions.csv.\n\n**Information rules.** Only data/design.csv; constants from TASK.md; x and sigma never redrawn.\n\n**Invalidating conditions.** Design file not 30 rows / missing or nonpositive sigma / constant x; R != 2000; non-reproducible seed.\nEOF\ncat > log.md <<'EOF'\n# Log\n- 2026-10-30 P0: read TASK.md, wrote contract. Decision: MCSE for the undercoverage rule evaluated at nominal p=0.95.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF","description":"Write P0 contract, log, phase ledger"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1168,"cache_read_input_tokens":6082,"output_tokens":1246,"output_tokens_details":{"thinking_tokens":172},"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":1168},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmvwJqCXRGdYUUwkkSw","requestRef":"4f0facfb-c583-4cbf-beb9-9972421cb211","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a039d34c-e753-4333-b734-4beb4d212df3","timestamp":"2026-10-30T04:17:05.352Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}25{"parentUuid":"a039d34c-e753-4333-b734-4beb4d212df3","isSidechain":false,"promptId":"7c63dae8-a509-4a57-a600-581bf60c9d13","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01CpZgukmMWw77SR8SZFYrEX","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"2d66fd6b-710c-4616-8334-033e49ef30f6","timestamp":"2026-10-30T04:17:05.383Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"a039d34c-e753-4333-b734-4beb4d212df3","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058/out/state","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}26{"type":"api-request-blob","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","hash":"b5c4fd4ad55b3d55c865c524b882658043d8604fdcaab6d78c1db1240568b4be","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01CpZgukmMWw77SR8SZFYrEX","name":"Bash","input":{"command":"mkdir -p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract (P0)\n**Question.** In the fixed-design model y_i = 1 + 0.5 x_i + sigma_i eps_i (n=30, x and sigma from data/design.csv, eps iid N(0,1)), do nominal 95% t(28) intervals for the OLS slope achieve 95% coverage?\n\n**Estimands.**\n(a) P(beta in beta_hat ± t_.975(28) se_conv), se_conv = conventional OLS SE with s^2 = RSS/(n-2).\n(b) Same with se_HC1 = sqrt(n/(n-2) * HC0 sandwich).\n(c) Exact SD of beta_hat: sqrt(sum w_i^2 sigma_i^2), w_i = (x_i - xbar)/Sxx (analytic, valid since design fixed, errors independent).\n(d) E[se_conv], estimated by MC.\n(a),(b),(d) by Monte Carlo, R = 2000, fixed seed. Conclusion: undercovers iff coverage_conv < 0.95 - 3*MCSE, MCSE = binomial SE at R=2000 (decision: evaluated at nominal p=0.95, sqrt(.95*.05/2000); estimate-based SE also reported).\n\n**Outputs.** out/results.json keys: task_id, coverage_conventional, coverage_hc1, sd_beta_exact, sd_beta_mc, mean_se_conventional, conventional_ci_undercovers, n_replications, n_obs_per_replication, feasibility, feasibility_reason, data_issues, n_input_rows, random_seed. Also out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues), out/analysis.py, out/claims.json, out/exclusions.csv.\n\n**Information rules.** Only data/design.csv; constants from TASK.md; x and sigma never redrawn.\n\n**Invalidating conditions.** Design file not 30 rows / missing or nonpositive sigma / constant x; R != 2000; non-reproducible seed.\nEOF\ncat > log.md <<'EOF'\n# Log\n- 2026-10-30 P0: read TASK.md, wrote contract. Decision: MCSE for the undercoverage rule evaluated at nominal p=0.95.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF","description":"Write P0 contract, log, phase ledger"}}]}}27{"type":"api-request","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","id":"1039e8a1-c7d7-46bb-a045-45d4945ec6c9","timestamp":"2026-10-30T04:17:05.386Z","version":"2.1.314","querySource":"sdk","shapeHash":"ef38d8a8bde17d36c01bd203bb6c76f380c9b7d1d1ecc47de8861e1d70009353","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":"4f0facfb-c583-4cbf-beb9-9972421cb211","keep":6,"tail":["b5c4fd4ad55b3d55c865c524b882658043d8604fdcaab6d78c1db1240568b4be","4007b3fd64d1553dfff29e73a70008e5410434f31b24d83ed4295f65eccd1cf4"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"5157c0a330bdeb1d895563abfca1ece249df53cd3c50f1fa79ec69e56f671f06"}28{"parentUuid":"2d66fd6b-710c-4616-8334-033e49ef30f6","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmwmBfcnBjKoPHmdgwJ","type":"message","role":"assistant","content":[{"type":"text","text":"Now the analysis script (includes audit checks)."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1261,"cache_read_input_tokens":7250,"output_tokens":2005,"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":1261},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXmwkuY8Y3nSsCPRVrR5","requestRef":"1039e8a1-c7d7-46bb-a045-45d4945ec6c9","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6f30fcb7-c3ba-4e28-87b4-880e3cbc9670","timestamp":"2026-10-30T04:17:06.115Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058/out/state","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"6f30fcb7-c3ba-4e28-87b4-880e3cbc9670","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmwmBfcnBjKoPHmdgwJ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01C3fKv56hWR9mJ7Bght83Jo","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F2-conf-03: Monte Carlo coverage of OLS CIs under heteroskedasticity.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\n\"\"\"\nimport json, os, csv\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\n\nALPHA, BETA, N, R, LEVEL = 1.0, 0.5, 30, 2000, 0.95\nSEED = 20261030\n\nd = pd.read_csv(\"data/design.csv\")\nn_raw = len(d)\nissues = []\n# audit checks (no filtering needed if all pass)\nassert list(d.columns) == [\"i\", \"x\", \"sigma\"]\nif d.isna().any().any():\n    issues.append({\"issue\": \"missing values\", \"handling\": \"none dropped; would abort\"})\nassert not d.isna().any().any()\nassert n_raw == N, f\"expected {N} rows, got {n_raw}\"\nassert (d[\"sigma\"] > 0).all()\nassert d[\"i\"].tolist() == list(range(1, N + 1))\nassert d[\"x\"].std() > 0\nd = d.sort_values(\"i\").reset_index(drop=True)\nx = d[\"x\"].to_numpy(float); sig = d[\"sigma\"].to_numpy(float)\n\nos.makedirs(\"out\", exist_ok=True)\nwith open(\"out/exclusions.csv\", \"w\", newline=\"\") as f:\n    w = csv.writer(f)\n    w.writerow([\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"])\n    w.writerow([\"1\", \"drop rows with missing/invalid x or sigma\", n_raw, n_raw, 0,\n                \"audit found no missing, duplicate or non-positive values; nothing removed\"])\n\nX = np.column_stack([np.ones(N), x])\nXtX_inv = np.linalg.inv(X.T @ X)\nxc = x - x.mean(); Sxx = xc @ xc\nwts = xc / Sxx                     # beta_hat - beta = sum w_i sigma_i eps_i\nsd_beta_exact = float(np.sqrt(np.sum(wts**2 * sig**2)))\ntcrit = stats.t.ppf(0.5 + LEVEL / 2, N - 2)\n\nrng = np.random.default_rng(SEED)\neps = rng.standard_normal((R, N))           # one row per replication\nY = ALPHA + BETA * x[None, :] + sig[None, :] * eps\nB = Y @ (X @ XtX_inv)                       # (R,2) coefficient estimates\nresid = Y - B @ X.T\ns2 = (resid**2).sum(1) / (N - 2)\nse_conv = np.sqrt(s2 * XtX_inv[1, 1])\n# HC0 sandwich for slope: [ (X'X)^-1 X' diag(e^2) X (X'X)^-1 ]_{11}\nA = X @ XtX_inv                              # rows a_i; slope weight a_i[1]\nhc0 = (resid**2) @ (A[:, 1]**2)\nse_hc1 = np.sqrt(hc0 * N / (N - 2))\nbhat = B[:, 1]\ncov_conv = float(np.mean(np.abs(bhat - BETA) <= tcrit * se_conv))\ncov_hc1 = float(np.mean(np.abs(bhat - BETA) <= tcrit * se_hc1))\nmcse_nominal = float(np.sqrt(LEVEL * (1 - LEVEL) / R))\nundercovers = bool(cov_conv < LEVEL - 3 * mcse_nominal)\n\nres = {\n    \"task_id\": \"F2-conf-03\",\n    \"coverage_conventional\": cov_conv,\n    \"coverage_hc1\": cov_hc1,\n    \"sd_beta_exact\": sd_beta_exact,\n    \"sd_beta_mc\": float(np.std(bhat, ddof=1)),\n    \"mean_se_conventional\": float(se_conv.mean()),\n    \"conventional_ci_undercovers\": undercovers,\n    \"n_replications\": R,\n    \"n_obs_per_replication\": N,\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": issues,\n    \"n_input_rows\": {\"design.csv\": int(n_raw)},\n    \"random_seed\": SEED,\n    # supplementary\n    \"t_critical_28\": float(tcrit),\n    \"mcse_coverage_at_nominal\": mcse_nominal,\n    \"undercoverage_threshold\": float(LEVEL - 3 * mcse_nominal),\n    \"mcse_coverage_conventional\": float(np.sqrt(cov_conv * (1 - cov_conv) / R)),\n    \"mcse_coverage_hc1\": float(np.sqrt(cov_hc1 * (1 - cov_hc1) / R)),\n    \"sd_beta_conventional_formula\": float(np.sqrt(np.mean(sig**2) * XtX_inv[1, 1])),\n    \"mean_se_hc1\": float(se_hc1.mean()),\n    \"mean_beta_hat\": float(bhat.mean()),\n    \"mcse_mean_beta_hat\": float(np.std(bhat, ddof=1) / np.sqrt(R)),\n    \"mcse_mean_se_conventional\": float(np.std(se_conv, ddof=1) / np.sqrt(R)),\n}\nwith open(\"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","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":1261,"cache_read_input_tokens":7250,"output_tokens":2005,"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":1261},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmwkuY8Y3nSsCPRVrR5","requestRef":"1039e8a1-c7d7-46bb-a045-45d4945ec6c9","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"aaa05a6e-1049-4c9f-9730-3aa97c5e37d1","timestamp":"2026-10-30T04:17:20.568Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058/out/state","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}30{"parentUuid":"aaa05a6e-1049-4c9f-9730-3aa97c5e37d1","isSidechain":false,"promptId":"7c63dae8-a509-4a57-a600-581bf60c9d13","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 2\n(eval):1: no such file or directory: out/analysis.py\n/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/simon-pierreboucher/wp29/runs/RC0058/out/state/out/analysis.py': [Errno 2] No such file or directory","is_error":true,"tool_use_id":"toolu_01C3fKv56hWR9mJ7Bght83Jo"}]},"uuid":"99cd551d-1b13-49c2-ac67-99f18448d464","timestamp":"2026-10-30T04:17:20.621Z","toolUseResult":"Error: Exit code 2\n(eval):1: no such file or directory: out/analysis.py\n/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/simon-pierreboucher/wp29/runs/RC0058/out/state/out/analysis.py': [Errno 2] No such file or directory","sandboxed":false,"sourceToolAssistantUUID":"aaa05a6e-1049-4c9f-9730-3aa97c5e37d1","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058/out/state","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}31{"type":"api-request-blob","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","hash":"e1fd5a2623443608fa4cddeebf42cd2b515843349b91afa502f344de0ef613b6","message":{"role":"assistant","content":[{"type":"text","text":"Now the analysis script (includes audit checks)."},{"type":"tool_use","id":"toolu_01C3fKv56hWR9mJ7Bght83Jo","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F2-conf-03: Monte Carlo coverage of OLS CIs under heteroskedasticity.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\n\"\"\"\nimport json, os, csv\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\n\nALPHA, BETA, N, R, LEVEL = 1.0, 0.5, 30, 2000, 0.95\nSEED = 20261030\n\nd = pd.read_csv(\"data/design.csv\")\nn_raw = len(d)\nissues = []\n# audit checks (no filtering needed if all pass)\nassert list(d.columns) == [\"i\", \"x\", \"sigma\"]\nif d.isna().any().any():\n    issues.append({\"issue\": \"missing values\", \"handling\": \"none dropped; would abort\"})\nassert not d.isna().any().any()\nassert n_raw == N, f\"expected {N} rows, got {n_raw}\"\nassert (d[\"sigma\"] > 0).all()\nassert d[\"i\"].tolist() == list(range(1, N + 1))\nassert d[\"x\"].std() > 0\nd = d.sort_values(\"i\").reset_index(drop=True)\nx = d[\"x\"].to_numpy(float); sig = d[\"sigma\"].to_numpy(float)\n\nos.makedirs(\"out\", exist_ok=True)\nwith open(\"out/exclusions.csv\", \"w\", newline=\"\") as f:\n    w = csv.writer(f)\n    w.writerow([\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"])\n    w.writerow([\"1\", \"drop rows with missing/invalid x or sigma\", n_raw, n_raw, 0,\n                \"audit found no missing, duplicate or non-positive values; nothing removed\"])\n\nX = np.column_stack([np.ones(N), x])\nXtX_inv = np.linalg.inv(X.T @ X)\nxc = x - x.mean(); Sxx = xc @ xc\nwts = xc / Sxx                     # beta_hat - beta = sum w_i sigma_i eps_i\nsd_beta_exact = float(np.sqrt(np.sum(wts**2 * sig**2)))\ntcrit = stats.t.ppf(0.5 + LEVEL / 2, N - 2)\n\nrng = np.random.default_rng(SEED)\neps = rng.standard_normal((R, N))           # one row per replication\nY = ALPHA + BETA * x[None, :] + sig[None, :] * eps\nB = Y @ (X @ XtX_inv)                       # (R,2) coefficient estimates\nresid = Y - B @ X.T\ns2 = (resid**2).sum(1) / (N - 2)\nse_conv = np.sqrt(s2 * XtX_inv[1, 1])\n# HC0 sandwich for slope: [ (X'X)^-1 X' diag(e^2) X (X'X)^-1 ]_{11}\nA = X @ XtX_inv                              # rows a_i; slope weight a_i[1]\nhc0 = (resid**2) @ (A[:, 1]**2)\nse_hc1 = np.sqrt(hc0 * N / (N - 2))\nbhat = B[:, 1]\ncov_conv = float(np.mean(np.abs(bhat - BETA) <= tcrit * se_conv))\ncov_hc1 = float(np.mean(np.abs(bhat - BETA) <= tcrit * se_hc1))\nmcse_nominal = float(np.sqrt(LEVEL * (1 - LEVEL) / R))\nundercovers = bool(cov_conv < LEVEL - 3 * mcse_nominal)\n\nres = {\n    \"task_id\": \"F2-conf-03\",\n    \"coverage_conventional\": cov_conv,\n    \"coverage_hc1\": cov_hc1,\n    \"sd_beta_exact\": sd_beta_exact,\n    \"sd_beta_mc\": float(np.std(bhat, ddof=1)),\n    \"mean_se_conventional\": float(se_conv.mean()),\n    \"conventional_ci_undercovers\": undercovers,\n    \"n_replications\": R,\n    \"n_obs_per_replication\": N,\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": issues,\n    \"n_input_rows\": {\"design.csv\": int(n_raw)},\n    \"random_seed\": SEED,\n    # supplementary\n    \"t_critical_28\": float(tcrit),\n    \"mcse_coverage_at_nominal\": mcse_nominal,\n    \"undercoverage_threshold\": float(LEVEL - 3 * mcse_nominal),\n    \"mcse_coverage_conventional\": float(np.sqrt(cov_conv * (1 - cov_conv) / R)),\n    \"mcse_coverage_hc1\": float(np.sqrt(cov_hc1 * (1 - cov_hc1) / R)),\n    \"sd_beta_conventional_formula\": float(np.sqrt(np.mean(sig**2) * XtX_inv[1, 1])),\n    \"mean_se_hc1\": float(se_hc1.mean()),\n    \"mean_beta_hat\": float(bhat.mean()),\n    \"mcse_mean_beta_hat\": float(np.std(bhat, ddof=1) / np.sqrt(R)),\n    \"mcse_mean_se_conventional\": float(np.std(se_conv, ddof=1) / np.sqrt(R)),\n}\nwith open(\"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","description":"Write and run analysis script"}}]}}32{"type":"api-request","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","id":"7a71fcad-5d5f-48ee-9674-1937236fd3ab","timestamp":"2026-10-30T04:17:20.626Z","version":"2.1.314","querySource":"sdk","shapeHash":"ef38d8a8bde17d36c01bd203bb6c76f380c9b7d1d1ecc47de8861e1d70009353","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":"1039e8a1-c7d7-46bb-a045-45d4945ec6c9","keep":8,"tail":["e1fd5a2623443608fa4cddeebf42cd2b515843349b91afa502f344de0ef613b6","7be5ef9dc0a35f8a3f660b0435e87259fc41879a0f5d8882fa2458d6c0b5ca4e"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"728d6f93f8330d3e359cb174b1abc4456935974b9d00bfb8bcca648599fedfee"}33{"parentUuid":"99cd551d-1b13-49c2-ac67-99f18448d464","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmxtLoGs5ex3y7SLxUT","type":"message","role":"assistant","content":[{"type":"text","text":"The shell was still in out/state; rerunning from the root."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2167,"cache_read_input_tokens":8511,"output_tokens":124,"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":2167},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXmxt4vY1ppgV8wfNJg9","requestRef":"7a71fcad-5d5f-48ee-9674-1937236fd3ab","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"fa852a36-f232-4c12-b40b-6310576b1b9c","timestamp":"2026-10-30T04:17:21.592Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058/out/state","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}34{"parentUuid":"fa852a36-f232-4c12-b40b-6310576b1b9c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmxtLoGs5ex3y7SLxUT","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Hhc7iEMG57mAErUHU6CGA7","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0058 && pwd","description":"Return to workspace root"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2167,"cache_read_input_tokens":8511,"output_tokens":124,"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":2167},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmxt4vY1ppgV8wfNJg9","requestRef":"7a71fcad-5d5f-48ee-9674-1937236fd3ab","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"df7091d0-a6d5-47a9-9b8f-35e86e289480","timestamp":"2026-10-30T04:17:22.141Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058/out/state","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}35{"parentUuid":"df7091d0-a6d5-47a9-9b8f-35e86e289480","isSidechain":false,"promptId":"7c63dae8-a509-4a57-a600-581bf60c9d13","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Hhc7iEMG57mAErUHU6CGA7","type":"tool_result","content":"/Users/simon-pierreboucher/wp29/runs/RC0058","is_error":false}]},"uuid":"b01ff5f0-5673-4824-97bf-e3b1b65d9851","timestamp":"2026-10-30T04:17:22.165Z","toolUseResult":{"stdout":"/Users/simon-pierreboucher/wp29/runs/RC0058","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"df7091d0-a6d5-47a9-9b8f-35e86e289480","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}36{"type":"api-request-blob","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","hash":"44a799315f6acc8968dcf1dcc85f0bfd99e6e6df6740ca759670fee0da01b17d","message":{"role":"assistant","content":[{"type":"text","text":"The shell was still in out/state; rerunning from the root."},{"type":"tool_use","id":"toolu_01Hhc7iEMG57mAErUHU6CGA7","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0058 && pwd","description":"Return to workspace root"}}]}}37{"type":"api-request","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","id":"8f249d2f-4ff2-436e-b575-c0780217c412","timestamp":"2026-10-30T04:17:22.170Z","version":"2.1.314","querySource":"sdk","shapeHash":"ef38d8a8bde17d36c01bd203bb6c76f380c9b7d1d1ecc47de8861e1d70009353","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":"7a71fcad-5d5f-48ee-9674-1937236fd3ab","keep":10,"tail":["44a799315f6acc8968dcf1dcc85f0bfd99e6e6df6740ca759670fee0da01b17d","c0096cba325c5880b2d8664c798ca74d5e1aa3d41328a97662b7d18ff0804c30"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"df24627a1ebe50d442f6fd7f930f89b1fd8100b62c8aef04b6647290c1fe44c6"}38{"parentUuid":"b01ff5f0-5673-4824-97bf-e3b1b65d9851","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmxzwvSrTqBFGCN6yUB","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":154,"cache_read_input_tokens":10678,"output_tokens":1943,"output_tokens_details":{"thinking_tokens":23},"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":154},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":406,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXmxzfZD7LzMFWYMkd2Q","requestRef":"8f249d2f-4ff2-436e-b575-c0780217c412","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"aa606884-a32f-445f-81a1-2b41af8a5182","timestamp":"2026-10-30T04:17:23.627Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}39{"parentUuid":"aa606884-a32f-445f-81a1-2b41af8a5182","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmxzwvSrTqBFGCN6yUB","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01TsE1p3NwYqF5EAfQx8UwBq","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F2-conf-03: Monte Carlo coverage of OLS CIs under heteroskedasticity.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\n\"\"\"\nimport json, os, csv\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\n\nALPHA, BETA, N, R, LEVEL = 1.0, 0.5, 30, 2000, 0.95\nSEED = 20261030\n\nd = pd.read_csv(\"data/design.csv\")\nn_raw = len(d)\nissues = []\n# audit checks (no filtering needed if all pass)\nassert list(d.columns) == [\"i\", \"x\", \"sigma\"]\nassert not d.isna().any().any(), \"missing values in design\"\nassert n_raw == N, f\"expected {N} rows, got {n_raw}\"\nassert (d[\"sigma\"] > 0).all()\nassert d[\"i\"].tolist() == list(range(1, N + 1))\nassert d[\"x\"].std() > 0\nx = d[\"x\"].to_numpy(float); sig = d[\"sigma\"].to_numpy(float)\n\nos.makedirs(\"out\", exist_ok=True)\nwith open(\"out/exclusions.csv\", \"w\", newline=\"\") as f:\n    w = csv.writer(f)\n    w.writerow([\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"])\n    w.writerow([\"1\", \"drop rows with missing/invalid x or sigma\", n_raw, n_raw, 0,\n                \"audit found no missing, duplicate or non-positive values; nothing removed\"])\n\nX = np.column_stack([np.ones(N), x])\nXtX_inv = np.linalg.inv(X.T @ X)\nxc = x - x.mean(); Sxx = xc @ xc\nwts = xc / Sxx                     # beta_hat - beta = sum w_i sigma_i eps_i\nsd_beta_exact = float(np.sqrt(np.sum(wts**2 * sig**2)))\ntcrit = stats.t.ppf(0.5 + LEVEL / 2, N - 2)\n\nrng = np.random.default_rng(SEED)\neps = rng.standard_normal((R, N))           # one row per replication\nY = ALPHA + BETA * x[None, :] + sig[None, :] * eps\nA = X @ XtX_inv                             # (N,2): beta_hat = Y @ A\nB = Y @ A\nresid = Y - B @ X.T\ns2 = (resid**2).sum(1) / (N - 2)\nse_conv = np.sqrt(s2 * XtX_inv[1, 1])\n# HC0 slope variance: sum_i a_i1^2 e_i^2  (= [(X'X)^-1 X' diag(e^2) X (X'X)^-1]_{11})\nhc0 = (resid**2) @ (A[:, 1]**2)\nse_hc1 = np.sqrt(hc0 * N / (N - 2))\nbhat = B[:, 1]\ncov_conv = float(np.mean(np.abs(bhat - BETA) <= tcrit * se_conv))\ncov_hc1 = float(np.mean(np.abs(bhat - BETA) <= tcrit * se_hc1))\nmcse_nominal = float(np.sqrt(LEVEL * (1 - LEVEL) / R))\nundercovers = bool(cov_conv < LEVEL - 3 * mcse_nominal)\n\nres = {\n    \"task_id\": \"F2-conf-03\",\n    \"coverage_conventional\": cov_conv,\n    \"coverage_hc1\": cov_hc1,\n    \"sd_beta_exact\": sd_beta_exact,\n    \"sd_beta_mc\": float(np.std(bhat, ddof=1)),\n    \"mean_se_conventional\": float(se_conv.mean()),\n    \"conventional_ci_undercovers\": undercovers,\n    \"n_replications\": R,\n    \"n_obs_per_replication\": N,\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": issues,\n    \"n_input_rows\": {\"design.csv\": int(n_raw)},\n    \"random_seed\": SEED,\n    # supplementary keys\n    \"t_critical_28\": float(tcrit),\n    \"mcse_coverage_at_nominal\": mcse_nominal,\n    \"undercoverage_threshold\": float(LEVEL - 3 * mcse_nominal),\n    \"mcse_coverage_conventional\": float(np.sqrt(cov_conv * (1 - cov_conv) / R)),\n    \"mcse_coverage_hc1\": float(np.sqrt(cov_hc1 * (1 - cov_hc1) / R)),\n    \"sd_beta_homoskedastic_formula\": float(np.sqrt(np.mean(sig**2) / Sxx)),\n    \"mean_se_hc1\": float(se_hc1.mean()),\n    \"mean_beta_hat\": float(bhat.mean()),\n    \"mcse_mean_beta_hat\": float(np.std(bhat, ddof=1) / np.sqrt(R)),\n    \"mcse_mean_se_conventional\": float(np.std(se_conv, ddof=1) / np.sqrt(R)),\n}\nwith open(\"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","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":154,"cache_read_input_tokens":10678,"output_tokens":1943,"output_tokens_details":{"thinking_tokens":23},"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":154},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmxzfZD7LzMFWYMkd2Q","requestRef":"8f249d2f-4ff2-436e-b575-c0780217c412","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5a7486da-18b3-4fd0-b362-b0d2d1d3921c","timestamp":"2026-10-30T04:17:35.531Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}40{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"5a7486da-18b3-4fd0-b362-b0d2d1d3921c","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f"}41{"type":"atis-latch","atis":"","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f"}42{"parentUuid":"5a7486da-18b3-4fd0-b362-b0d2d1d3921c","isSidechain":false,"promptId":"7c63dae8-a509-4a57-a600-581bf60c9d13","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TsE1p3NwYqF5EAfQx8UwBq","type":"tool_result","content":"{\n  \"task_id\": \"F2-conf-03\",\n  \"coverage_conventional\": 0.844,\n  \"coverage_hc1\": 0.902,\n  \"sd_beta_exact\": 0.26798059283028336,\n  \"sd_beta_mc\": 0.2696473708309126,\n  \"mean_se_conventional\": 0.1902623101215266,\n  \"conventional_ci_undercovers\": true,\n  \"n_replications\": 2000,\n  \"n_obs_per_replication\": 30,\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [],\n  \"n_input_rows\": {\n    \"design.csv\": 30\n  },\n  \"random_seed\": 20261030,\n  \"t_critical_28\": 2.048407141795244,\n  \"mcse_coverage_at_nominal\": 0.004873397172404485,\n  \"undercoverage_threshold\": 0.9353798084827865,\n  \"mcse_coverage_conventional\": 0.00811369213120636,\n  \"mcse_coverage_hc1\": 0.006648157639526908,\n  \"sd_beta_homoskedastic_formula\": 0.19623471734704753,\n  \"mean_se_hc1\": 0.24080977065103962,\n  \"mean_beta_hat\": 0.4961965063446238,\n  \"mcse_mean_beta_hat\": 0.006029498511320145,\n  \"mcse_mean_se_conventional\": 0.0006978794961917418\n}","is_error":false}]},"uuid":"a2dce578-0557-4ad6-bd69-b03b32fddbc7","timestamp":"2026-10-30T04:17:36.289Z","toolUseResult":{"stdout":"{\n  \"task_id\": \"F2-conf-03\",\n  \"coverage_conventional\": 0.844,\n  \"coverage_hc1\": 0.902,\n  \"sd_beta_exact\": 0.26798059283028336,\n  \"sd_beta_mc\": 0.2696473708309126,\n  \"mean_se_conventional\": 0.1902623101215266,\n  \"conventional_ci_undercovers\": true,\n  \"n_replications\": 2000,\n  \"n_obs_per_replication\": 30,\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [],\n  \"n_input_rows\": {\n    \"design.csv\": 30\n  },\n  \"random_seed\": 20261030,\n  \"t_critical_28\": 2.048407141795244,\n  \"mcse_coverage_at_nominal\": 0.004873397172404485,\n  \"undercoverage_threshold\": 0.9353798084827865,\n  \"mcse_coverage_conventional\": 0.00811369213120636,\n  \"mcse_coverage_hc1\": 0.006648157639526908,\n  \"sd_beta_homoskedastic_formula\": 0.19623471734704753,\n  \"mean_se_hc1\": 0.24080977065103962,\n  \"mean_beta_hat\": 0.4961965063446238,\n  \"mcse_mean_beta_hat\": 0.006029498511320145,\n  \"mcse_mean_se_conventional\": 0.0006978794961917418\n}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"5a7486da-18b3-4fd0-b362-b0d2d1d3921c","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}43{"type":"api-request-blob","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","hash":"6561be8ab4bd89463cab40dd9e5b9e29c65dbd3e5b91a28dc55cce56dbc45612","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01TsE1p3NwYqF5EAfQx8UwBq","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F2-conf-03: Monte Carlo coverage of OLS CIs under heteroskedasticity.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\n\"\"\"\nimport json, os, csv\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\n\nALPHA, BETA, N, R, LEVEL = 1.0, 0.5, 30, 2000, 0.95\nSEED = 20261030\n\nd = pd.read_csv(\"data/design.csv\")\nn_raw = len(d)\nissues = []\n# audit checks (no filtering needed if all pass)\nassert list(d.columns) == [\"i\", \"x\", \"sigma\"]\nassert not d.isna().any().any(), \"missing values in design\"\nassert n_raw == N, f\"expected {N} rows, got {n_raw}\"\nassert (d[\"sigma\"] > 0).all()\nassert d[\"i\"].tolist() == list(range(1, N + 1))\nassert d[\"x\"].std() > 0\nx = d[\"x\"].to_numpy(float); sig = d[\"sigma\"].to_numpy(float)\n\nos.makedirs(\"out\", exist_ok=True)\nwith open(\"out/exclusions.csv\", \"w\", newline=\"\") as f:\n    w = csv.writer(f)\n    w.writerow([\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"])\n    w.writerow([\"1\", \"drop rows with missing/invalid x or sigma\", n_raw, n_raw, 0,\n                \"audit found no missing, duplicate or non-positive values; nothing removed\"])\n\nX = np.column_stack([np.ones(N), x])\nXtX_inv = np.linalg.inv(X.T @ X)\nxc = x - x.mean(); Sxx = xc @ xc\nwts = xc / Sxx                     # beta_hat - beta = sum w_i sigma_i eps_i\nsd_beta_exact = float(np.sqrt(np.sum(wts**2 * sig**2)))\ntcrit = stats.t.ppf(0.5 + LEVEL / 2, N - 2)\n\nrng = np.random.default_rng(SEED)\neps = rng.standard_normal((R, N))           # one row per replication\nY = ALPHA + BETA * x[None, :] + sig[None, :] * eps\nA = X @ XtX_inv                             # (N,2): beta_hat = Y @ A\nB = Y @ A\nresid = Y - B @ X.T\ns2 = (resid**2).sum(1) / (N - 2)\nse_conv = np.sqrt(s2 * XtX_inv[1, 1])\n# HC0 slope variance: sum_i a_i1^2 e_i^2  (= [(X'X)^-1 X' diag(e^2) X (X'X)^-1]_{11})\nhc0 = (resid**2) @ (A[:, 1]**2)\nse_hc1 = np.sqrt(hc0 * N / (N - 2))\nbhat = B[:, 1]\ncov_conv = float(np.mean(np.abs(bhat - BETA) <= tcrit * se_conv))\ncov_hc1 = float(np.mean(np.abs(bhat - BETA) <= tcrit * se_hc1))\nmcse_nominal = float(np.sqrt(LEVEL * (1 - LEVEL) / R))\nundercovers = bool(cov_conv < LEVEL - 3 * mcse_nominal)\n\nres = {\n    \"task_id\": \"F2-conf-03\",\n    \"coverage_conventional\": cov_conv,\n    \"coverage_hc1\": cov_hc1,\n    \"sd_beta_exact\": sd_beta_exact,\n    \"sd_beta_mc\": float(np.std(bhat, ddof=1)),\n    \"mean_se_conventional\": float(se_conv.mean()),\n    \"conventional_ci_undercovers\": undercovers,\n    \"n_replications\": R,\n    \"n_obs_per_replication\": N,\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": issues,\n    \"n_input_rows\": {\"design.csv\": int(n_raw)},\n    \"random_seed\": SEED,\n    # supplementary keys\n    \"t_critical_28\": float(tcrit),\n    \"mcse_coverage_at_nominal\": mcse_nominal,\n    \"undercoverage_threshold\": float(LEVEL - 3 * mcse_nominal),\n    \"mcse_coverage_conventional\": float(np.sqrt(cov_conv * (1 - cov_conv) / R)),\n    \"mcse_coverage_hc1\": float(np.sqrt(cov_hc1 * (1 - cov_hc1) / R)),\n    \"sd_beta_homoskedastic_formula\": float(np.sqrt(np.mean(sig**2) / Sxx)),\n    \"mean_se_hc1\": float(se_hc1.mean()),\n    \"mean_beta_hat\": float(bhat.mean()),\n    \"mcse_mean_beta_hat\": float(np.std(bhat, ddof=1) / np.sqrt(R)),\n    \"mcse_mean_se_conventional\": float(np.std(se_conv, ddof=1) / np.sqrt(R)),\n}\nwith open(\"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","description":"Write and run analysis script"}}]}}44{"type":"api-request","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","id":"9ae0d088-23ee-417d-968c-abecdb217dfc","timestamp":"2026-10-30T04:17:36.293Z","version":"2.1.314","querySource":"sdk","shapeHash":"ef38d8a8bde17d36c01bd203bb6c76f380c9b7d1d1ecc47de8861e1d70009353","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":"8f249d2f-4ff2-436e-b575-c0780217c412","keep":12,"tail":["6561be8ab4bd89463cab40dd9e5b9e29c65dbd3e5b91a28dc55cce56dbc45612","597f285a267a7d6ec15bb723e55704e0d29e8542dd99ea5a06a68e9533af1f5d"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"462d0c88feeb7d4fa06113543d151b10f134f10a57a5326e1f3fc47485b81b49"}45{"parentUuid":"a2dce578-0557-4ad6-bd69-b03b32fddbc7","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmz3L4sGfYmjEgjxkQj","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":2426,"cache_read_input_tokens":10832,"output_tokens":817,"output_tokens_details":{"thinking_tokens":65},"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":2426},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":486,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXmz34gzttKhWXEPLU8E","requestRef":"9ae0d088-23ee-417d-968c-abecdb217dfc","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"973857be-c0a3-4763-a2cf-8e7efa4e18ad","timestamp":"2026-10-30T04:17:37.913Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}46{"parentUuid":"973857be-c0a3-4763-a2cf-8e7efa4e18ad","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmz3L4sGfYmjEgjxkQj","type":"message","role":"assistant","content":[{"type":"text","text":"Cross-checking SEs against statsmodels and running a reproducibility check:"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2426,"cache_read_input_tokens":10832,"output_tokens":817,"output_tokens_details":{"thinking_tokens":65},"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":2426},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmz34gzttKhWXEPLU8E","requestRef":"9ae0d088-23ee-417d-968c-abecdb217dfc","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4dc0ea4f-ceda-4b1a-bd08-0cd09e1ad061","timestamp":"2026-10-30T04:17:37.914Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}47{"parentUuid":"4dc0ea4f-ceda-4b1a-bd08-0cd09e1ad061","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmz3L4sGfYmjEgjxkQj","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01HFaKJX4Yg5rnwBVAXN3b7f","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp /tmp/r1.json out/results.json && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport numpy as np, pandas as pd, statsmodels.api as sm\nd=pd.read_csv(\"data/design.csv\"); x=d.x.values; s=d.sigma.values\nrng=np.random.default_rng(20261030); eps=rng.standard_normal((2000,30))\nX=sm.add_constant(x); B=[];maxd=0\nfor r in range(5):\n    y=1+0.5*x+s*eps[r]\n    m=sm.OLS(y,X).fit(); h=sm.OLS(y,X).fit(cov_type=\"HC1\")\n    print(r, m.params[1], m.bse[1], h.bse[1])\nprint(\"dups\",d.duplicated().sum(), d.describe().to_string())\nprint(\"corr(|x-xbar|,sigma)\", np.corrcoef(abs(x-x.mean()),s)[0,1])\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport numpy as np, pandas as pd\nd=pd.read_csv(\"data/design.csv\"); x=d.x.values; s=d.sigma.values\nX=np.column_stack([np.ones(30),x]); A=X@np.linalg.inv(X.T@X)\nrng=np.random.default_rng(20261030); eps=rng.standard_normal((2000,30))\nfor r in range(5):\n    y=1+0.5*x+s*eps[r]; b=y@A; e=y-X@b\n    print(r,b[1],np.sqrt(e@e/28*np.linalg.inv(X.T@X)[1,1]),np.sqrt((e**2)@(A[:,1]**2)*30/28))\nEOF","description":"Reproducibility and statsmodels cross-check"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2426,"cache_read_input_tokens":10832,"output_tokens":817,"output_tokens_details":{"thinking_tokens":65},"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":2426},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXmz34gzttKhWXEPLU8E","requestRef":"9ae0d088-23ee-417d-968c-abecdb217dfc","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e189fdef-0adf-4122-a732-5432c6f6382e","timestamp":"2026-10-30T04:17:43.194Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}48{"parentUuid":"e189fdef-0adf-4122-a732-5432c6f6382e","isSidechain":false,"promptId":"7c63dae8-a509-4a57-a600-581bf60c9d13","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HFaKJX4Yg5rnwBVAXN3b7f","type":"tool_result","content":"IDENTICAL\n0 0.32082849576917916 0.15933017823041823 0.15393737845216712\n1 0.34507414720363233 0.15730567175237073 0.20668177665947274\n2 0.16096917516217452 0.2337755408328807 0.38529616665190514\n3 0.5350214490068953 0.1552414829769103 0.1845619517814559\n4 0.9141517194363276 0.1887230522558592 0.25230115021851696\ndups 0                i          x      sigma\ncount  30.000000  30.000000  30.000000\nmean   15.500000   0.282297   0.819457\nstd     8.803408   0.821180   0.290442\nmin     1.000000  -1.394200   0.603400\n25%     8.250000  -0.231900   0.617025\n50%    15.500000   0.231150   0.735150\n75%    22.750000   0.765550   0.869300\nmax    30.000000   1.991900   1.790300\ncorr(|x-xbar|,sigma) 0.8371553430740877\n0 0.3208284957691791 0.1593301782304182 0.1539373784521671\n1 0.3450741472036324 0.15730567175237076 0.20668177665947274\n2 0.16096917516217454 0.23377554083288074 0.38529616665190514\n3 0.5350214490068953 0.1552414829769103 0.1845619517814559\n4 0.9141517194363276 0.1887230522558592 0.25230115021851696","is_error":false}]},"uuid":"886c8f3f-6104-4f6e-8730-993a46bf3583","timestamp":"2026-10-30T04:17:45.090Z","toolUseResult":{"stdout":"IDENTICAL\n0 0.32082849576917916 0.15933017823041823 0.15393737845216712\n1 0.34507414720363233 0.15730567175237073 0.20668177665947274\n2 0.16096917516217452 0.2337755408328807 0.38529616665190514\n3 0.5350214490068953 0.1552414829769103 0.1845619517814559\n4 0.9141517194363276 0.1887230522558592 0.25230115021851696\ndups 0                i          x      sigma\ncount  30.000000  30.000000  30.000000\nmean   15.500000   0.282297   0.819457\nstd     8.803408   0.821180   0.290442\nmin     1.000000  -1.394200   0.603400\n25%     8.250000  -0.231900   0.617025\n50%    15.500000   0.231150   0.735150\n75%    22.750000   0.765550   0.869300\nmax    30.000000   1.991900   1.790300\ncorr(|x-xbar|,sigma) 0.8371553430740877\n0 0.3208284957691791 0.1593301782304182 0.1539373784521671\n1 0.3450741472036324 0.15730567175237076 0.20668177665947274\n2 0.16096917516217454 0.23377554083288074 0.38529616665190514\n3 0.5350214490068953 0.1552414829769103 0.1845619517814559\n4 0.9141517194363276 0.1887230522558592 0.25230115021851696","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"e189fdef-0adf-4122-a732-5432c6f6382e","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}49{"type":"api-request-blob","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","hash":"64dce0efd0241590b8a5b5d994e611bb68cb7b20632b887fc59d2395b624ed36","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"Cross-checking SEs against statsmodels and running a reproducibility check:"},{"type":"tool_use","id":"toolu_01HFaKJX4Yg5rnwBVAXN3b7f","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp /tmp/r1.json out/results.json && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport numpy as np, pandas as pd, statsmodels.api as sm\nd=pd.read_csv(\"data/design.csv\"); x=d.x.values; s=d.sigma.values\nrng=np.random.default_rng(20261030); eps=rng.standard_normal((2000,30))\nX=sm.add_constant(x); B=[];maxd=0\nfor r in range(5):\n    y=1+0.5*x+s*eps[r]\n    m=sm.OLS(y,X).fit(); h=sm.OLS(y,X).fit(cov_type=\"HC1\")\n    print(r, m.params[1], m.bse[1], h.bse[1])\nprint(\"dups\",d.duplicated().sum(), d.describe().to_string())\nprint(\"corr(|x-xbar|,sigma)\", np.corrcoef(abs(x-x.mean()),s)[0,1])\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport numpy as np, pandas as pd\nd=pd.read_csv(\"data/design.csv\"); x=d.x.values; s=d.sigma.values\nX=np.column_stack([np.ones(30),x]); A=X@np.linalg.inv(X.T@X)\nrng=np.random.default_rng(20261030); eps=rng.standard_normal((2000,30))\nfor r in range(5):\n    y=1+0.5*x+s*eps[r]; b=y@A; e=y-X@b\n    print(r,b[1],np.sqrt(e@e/28*np.linalg.inv(X.T@X)[1,1]),np.sqrt((e**2)@(A[:,1]**2)*30/28))\nEOF","description":"Reproducibility and statsmodels cross-check"}}]}}50{"type":"api-request","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","id":"1c55b6e0-69be-412b-b7b6-931ee68eb957","timestamp":"2026-10-30T04:17:45.093Z","version":"2.1.314","querySource":"sdk","shapeHash":"ef38d8a8bde17d36c01bd203bb6c76f380c9b7d1d1ecc47de8861e1d70009353","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":"9ae0d088-23ee-417d-968c-abecdb217dfc","keep":14,"tail":["64dce0efd0241590b8a5b5d994e611bb68cb7b20632b887fc59d2395b624ed36","a661524404fabadd816638bf474e525a6087f2073e5270ca132942435f328bbc"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"f9505934b86e2c10fc2b327d829e3c41e5fbae2c982eb45f0d0f830917791c3e"}51{"parentUuid":"886c8f3f-6104-4f6e-8730-993a46bf3583","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmzgwzq7Ae9JV7CVUkw","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":1299,"cache_read_input_tokens":13258,"output_tokens":1781,"output_tokens_details":{"thinking_tokens":43},"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":1299},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":490,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXmzgg8k9krM4RqFfwB1","requestRef":"1c55b6e0-69be-412b-b7b6-931ee68eb957","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f854f584-8596-4016-8891-dcdd142022a1","timestamp":"2026-10-30T04:17:46.481Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}52{"parentUuid":"f854f584-8596-4016-8891-dcdd142022a1","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmzgwzq7Ae9JV7CVUkw","type":"message","role":"assistant","content":[{"type":"text","text":"Matches statsmodels exactly. Writing audit, checks, and ledgers, then running the gate."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1299,"cache_read_input_tokens":13258,"output_tokens":1781,"output_tokens_details":{"thinking_tokens":43},"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":1299},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmzgg8k9krM4RqFfwB1","requestRef":"1c55b6e0-69be-412b-b7b6-931ee68eb957","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6d6c5d26-07ce-42a3-af0c-27350ed9e963","timestamp":"2026-10-30T04:17:46.482Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}53{"parentUuid":"6d6c5d26-07ce-42a3-af0c-27350ed9e963","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmzgwzq7Ae9JV7CVUkw","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01FR6GZb863HZsQZwN1mwWnF","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n| file | sha256 | rows | cols |\n|---|---|---|---|\n| data/design.csv | b1a17a529973e7994bbedad5ec1bc73fe968c777301ca8745657ea300e3c74f0 | 30 | 3 (i, x, sigma) |\n\n- Schema: i int 1..30 contiguous, sorted; x float (dimensionless) in [-1.3942, 1.9919], mean 0.2823, sd 0.8212; sigma float (dimensionless) in [0.6034, 1.7903], mean 0.8195, all > 0.\n- Missing values: 0. Duplicate rows: 0. No dates (synthetic fixed design).\n- Anomalies: none. Note (design feature, not a defect): sigma rises with |x - xbar| (corr 0.837), i.e. error variance largest at high-leverage points -> conventional SE expected to understate the slope SD.\n- Information-availability: the only input is the allowed design file; no violations. Nothing excluded (out/exclusions.csv: 30 -> 30).\nEOF\ncat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n1. Re-ran out/analysis.py from scratch; results.json byte-identical (cmp) -> PASS.\n2. Cross-check vs statsmodels OLS (nonrobust and cov_type=HC1) for replications 1-5 using same eps draws: beta_hat, se_conv, se_HC1 agree to ~1e-16 -> PASS.\n3. Exclusion ledger: 30 raw - 0 removed = 30 analysed = n_input_rows -> PASS.\n4. Positive SDs/SEs: sd_beta_exact=0.2680, sd_beta_mc=0.2696, mean_se_conventional=0.1903 > 0 -> PASS.\n5. sd_beta_mc vs sd_beta_exact: rel diff 0.6%; the MC SE of a sample SD at R=2000 is ~sd/sqrt(2(R-1)) ~ 0.0042, diff 0.0017 -> consistent, PASS.\n6. mean_beta_hat=0.4962, |0.4962-0.5| = 0.0038 < 2*mcse 0.012 -> unbiasedness consistent, PASS.\n7. mean_se_conventional (0.190) close to homoskedastic-formula SD sqrt(mean sigma^2/Sxx)=0.196 (below it, as expected from E[sqrt]<sqrt E and the leverage-weighting of residual variance) and well below sd_beta_exact 0.268 -> explains undercoverage, PASS.\n8. Undercoverage rule: threshold 0.95 - 3*0.004873 = 0.9354; coverage_conventional 0.844 < threshold -> true. Also robust to using estimate-based MCSE (0.95-3*0.0081=0.926) -> same decision, PASS.\n9. coverage values in [0,1]; n_replications=2000, n_obs=30, seed recorded -> PASS.\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-30 P1 passed: audit clean, no exclusions.\n- 2026-10-30 P2 passed: baseline = conventional-interval coverage (coverage_conventional), as TASK names it the naive baseline.\n- 2026-10-30 P3 passed: analysis.py written, seed 20261030, vectorised simulation (eps drawn as 2000x30 matrix, row = replication).\n- 2026-10-30 P4: checks 1-9 passed; running gate.\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open(\"out/state/phases.json\"))\np[\"P1\"]={\"status\":\"passed\",\"checks\":[\"sha256 recorded\",\"schema/missing/duplicates/ranges checked\",\"exclusion ledger written (0 removed)\",\"no availability violations\"],\"notes\":\"see data_audit.md\"}\np[\"P2\"]={\"status\":\"passed\",\"checks\":[\"baseline coverage_conventional computed with same 2000-replication evaluation rule\"],\"notes\":\"coverage_conventional=0.844\"}\np[\"P3\"]={\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, fixed seed 20261030\",\"writes results.json\"],\"notes\":\"\"}\np[\"P4\"]={\"status\":\"running\",\"checks\":[\"clean re-run identical (cmp)\",\"statsmodels cross-check passed\",\"consistency checks 1-9 in checks.md passed\"],\"notes\":\"\"}\njson.dump(p,open(\"out/state/phases.json\",\"w\"),indent=1)\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Write audit/checks, update ledger, 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":1299,"cache_read_input_tokens":13258,"output_tokens":1781,"output_tokens_details":{"thinking_tokens":43},"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":1299},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXmzgg8k9krM4RqFfwB1","requestRef":"1c55b6e0-69be-412b-b7b6-931ee68eb957","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ee74e603-40bf-4978-a7a0-9300ee299dc8","timestamp":"2026-10-30T04:18:00.447Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}54{"parentUuid":"ee74e603-40bf-4978-a7a0-9300ee299dc8","isSidechain":false,"promptId":"7c63dae8-a509-4a57-a600-581bf60c9d13","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nPGAR GATE pgar_gate/1.0 — FAILED (10 of 32 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:coverage_conventional: ok\n  [ok] key:coverage_hc1: ok\n  [ok] key:sd_beta_exact: ok\n  [ok] key:sd_beta_mc: ok\n  [ok] key:mean_se_conventional: ok\n  [ok] key:conventional_ci_undercovers: ok\n  [ok] key:n_replications: ok\n  [ok] key:n_obs_per_replication: 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: 1 steps consistent\n  [ok] positive:sd_beta_exact: 0.26798059283028336\n  [ok] positive:sd_beta_mc: 0.2696473708309126\n  [ok] positive:mean_se_conventional: 0.1902623101215266\n  [ok] reproducibility: regenerated in 0.8s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0058/out/state/gate_receipts/20261030T041801.310146Z.json","is_error":true,"tool_use_id":"toolu_01FR6GZb863HZsQZwN1mwWnF"}]},"uuid":"df48ebbe-14cb-4e55-b4ea-e070124dec75","timestamp":"2026-10-30T04:18:01.317Z","toolUseResult":"Error: Exit code 1\nPGAR GATE pgar_gate/1.0 — FAILED (10 of 32 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:coverage_conventional: ok\n  [ok] key:coverage_hc1: ok\n  [ok] key:sd_beta_exact: ok\n  [ok] key:sd_beta_mc: ok\n  [ok] key:mean_se_conventional: ok\n  [ok] key:conventional_ci_undercovers: ok\n  [ok] key:n_replications: ok\n  [ok] key:n_obs_per_replication: 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: 1 steps consistent\n  [ok] positive:sd_beta_exact: 0.26798059283028336\n  [ok] positive:sd_beta_mc: 0.2696473708309126\n  [ok] positive:mean_se_conventional: 0.1902623101215266\n  [ok] reproducibility: regenerated in 0.8s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0058/out/state/gate_receipts/20261030T041801.310146Z.json","sandboxed":false,"sourceToolAssistantUUID":"ee74e603-40bf-4978-a7a0-9300ee299dc8","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}55{"type":"api-request-blob","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","hash":"dcd0f1a54c2212e1a808a4163e59e450b1937c6a8493408e715204d1d5691ab8","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"Matches statsmodels exactly. Writing audit, checks, and ledgers, then running the gate."},{"type":"tool_use","id":"toolu_01FR6GZb863HZsQZwN1mwWnF","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n| file | sha256 | rows | cols |\n|---|---|---|---|\n| data/design.csv | b1a17a529973e7994bbedad5ec1bc73fe968c777301ca8745657ea300e3c74f0 | 30 | 3 (i, x, sigma) |\n\n- Schema: i int 1..30 contiguous, sorted; x float (dimensionless) in [-1.3942, 1.9919], mean 0.2823, sd 0.8212; sigma float (dimensionless) in [0.6034, 1.7903], mean 0.8195, all > 0.\n- Missing values: 0. Duplicate rows: 0. No dates (synthetic fixed design).\n- Anomalies: none. Note (design feature, not a defect): sigma rises with |x - xbar| (corr 0.837), i.e. error variance largest at high-leverage points -> conventional SE expected to understate the slope SD.\n- Information-availability: the only input is the allowed design file; no violations. Nothing excluded (out/exclusions.csv: 30 -> 30).\nEOF\ncat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n1. Re-ran out/analysis.py from scratch; results.json byte-identical (cmp) -> PASS.\n2. Cross-check vs statsmodels OLS (nonrobust and cov_type=HC1) for replications 1-5 using same eps draws: beta_hat, se_conv, se_HC1 agree to ~1e-16 -> PASS.\n3. Exclusion ledger: 30 raw - 0 removed = 30 analysed = n_input_rows -> PASS.\n4. Positive SDs/SEs: sd_beta_exact=0.2680, sd_beta_mc=0.2696, mean_se_conventional=0.1903 > 0 -> PASS.\n5. sd_beta_mc vs sd_beta_exact: rel diff 0.6%; the MC SE of a sample SD at R=2000 is ~sd/sqrt(2(R-1)) ~ 0.0042, diff 0.0017 -> consistent, PASS.\n6. mean_beta_hat=0.4962, |0.4962-0.5| = 0.0038 < 2*mcse 0.012 -> unbiasedness consistent, PASS.\n7. mean_se_conventional (0.190) close to homoskedastic-formula SD sqrt(mean sigma^2/Sxx)=0.196 (below it, as expected from E[sqrt]<sqrt E and the leverage-weighting of residual variance) and well below sd_beta_exact 0.268 -> explains undercoverage, PASS.\n8. Undercoverage rule: threshold 0.95 - 3*0.004873 = 0.9354; coverage_conventional 0.844 < threshold -> true. Also robust to using estimate-based MCSE (0.95-3*0.0081=0.926) -> same decision, PASS.\n9. coverage values in [0,1]; n_replications=2000, n_obs=30, seed recorded -> PASS.\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-30 P1 passed: audit clean, no exclusions.\n- 2026-10-30 P2 passed: baseline = conventional-interval coverage (coverage_conventional), as TASK names it the naive baseline.\n- 2026-10-30 P3 passed: analysis.py written, seed 20261030, vectorised simulation (eps drawn as 2000x30 matrix, row = replication).\n- 2026-10-30 P4: checks 1-9 passed; running gate.\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open(\"out/state/phases.json\"))\np[\"P1\"]={\"status\":\"passed\",\"checks\":[\"sha256 recorded\",\"schema/missing/duplicates/ranges checked\",\"exclusion ledger written (0 removed)\",\"no availability violations\"],\"notes\":\"see data_audit.md\"}\np[\"P2\"]={\"status\":\"passed\",\"checks\":[\"baseline coverage_conventional computed with same 2000-replication evaluation rule\"],\"notes\":\"coverage_conventional=0.844\"}\np[\"P3\"]={\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, fixed seed 20261030\",\"writes results.json\"],\"notes\":\"\"}\np[\"P4\"]={\"status\":\"running\",\"checks\":[\"clean re-run identical (cmp)\",\"statsmodels cross-check passed\",\"consistency checks 1-9 in checks.md passed\"],\"notes\":\"\"}\njson.dump(p,open(\"out/state/phases.json\",\"w\"),indent=1)\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Write audit/checks, update ledger, run gate"}}]}}56{"type":"api-request","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","id":"737cb36a-baa6-4092-8c8e-071fc0758cf5","timestamp":"2026-10-30T04:18:01.320Z","version":"2.1.314","querySource":"sdk","shapeHash":"ef38d8a8bde17d36c01bd203bb6c76f380c9b7d1d1ecc47de8861e1d70009353","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":"1c55b6e0-69be-412b-b7b6-931ee68eb957","keep":16,"tail":["dcd0f1a54c2212e1a808a4163e59e450b1937c6a8493408e715204d1d5691ab8","d5a7e73b260c7d5f34724ef312bdf4baa5b9e04b9bd14db311711c4c8d3ab4f7"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",17,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"4faf0c2ef7fee5a3714e9d4385e20dd885d30fbfb0701943a9f1a433b866b918"}57{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"df48ebbe-14cb-4e55-b4ea-e070124dec75","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f"}58{"type":"atis-latch","atis":"","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f"}59{"parentUuid":"df48ebbe-14cb-4e55-b4ea-e070124dec75","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXn1tK9GzvRcBLaxFwfN","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":2525,"cache_read_input_tokens":14557,"output_tokens":3212,"output_tokens_details":{"thinking_tokens":59},"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":2525},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":649,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXn1t41ppf4FKWVd2Qbk","requestRef":"737cb36a-baa6-4092-8c8e-071fc0758cf5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"44bd598a-43d4-422a-9e50-d351c3907d8f","timestamp":"2026-10-30T04:18:02.862Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}60{"parentUuid":"44bd598a-43d4-422a-9e50-d351c3907d8f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXn1tK9GzvRcBLaxFwfN","type":"message","role":"assistant","content":[{"type":"text","text":"The P4-relevant gate checks (results contract, reproducibility, exclusion ledger) pass; the failures are the P5 deliverables. Writing the report and claims."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2525,"cache_read_input_tokens":14557,"output_tokens":3212,"output_tokens_details":{"thinking_tokens":59},"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":2525},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXn1t41ppf4FKWVd2Qbk","requestRef":"737cb36a-baa6-4092-8c8e-071fc0758cf5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8e1f94ab-7a75-4d7e-85f5-1b3c5d413359","timestamp":"2026-10-30T04:18:02.862Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}61{"parentUuid":"8e1f94ab-7a75-4d7e-85f5-1b3c5d413359","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXn1tK9GzvRcBLaxFwfN","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018to9txL7QG4uBwZiqjFLxR","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F2-conf-03 — Coverage of OLS slope CIs in a small heteroskedastic fixed design\n\n## Question\nIn y_i = 1 + 0.5 x_i + σ_i ε_i (n = 30; x_i, σ_i fixed from `data/design.csv`; ε_i iid N(0,1)), does the nominal 95% interval β̂ ± t₀.₉₇₅(28)·se reach 95% coverage, using (a) the conventional OLS SE and (b) the HC1 robust SE? Also: (c) the exact SD of β̂ and (d) E[se_conv].\n\n## Data and exclusions\nOne input, `data/design.csv` (30 rows, columns i, x, sigma; `n_input_rows`). The audit (`out/state/data_audit.md`) found no missing values, duplicates or non-positive σ; `data_issues` is empty and no rows were excluded (`out/exclusions.csv`: 30 → 30). Note on the design: σ_i increases with |x_i − x̄| (correlation 0.84, computed during the audit), so the error variance is largest at the high-leverage points.\n\n## Method\n- Exact SD (no simulation): β̂ − β = Σ w_i σ_i ε_i with w_i = (x_i − x̄)/S_xx, so SD(β̂) = sqrt(Σ w_i² σ_i²) (`sd_beta_exact`).\n- Monte Carlo: R = 2000 replications (`n_replications`), n = 30 (`n_obs_per_replication`), numpy `default_rng` with seed 20261030 (`random_seed`). Only ε is redrawn. For each replication: OLS fit, se_conv = sqrt(s²·[(X'X)⁻¹]₂₂) with s² = RSS/(n−2), se_HC1 = sqrt(n/(n−2)·Σ a_i² e_i²) (HC0 sandwich × n/(n−2)), critical value t₀.₉₇₅(28) = 2.0484 (`t_critical_28`). Coverage = share of replications whose interval contains 0.5.\n- The baseline is the conventional interval coverage (`coverage_conventional`), which the task names as the naïve baseline.\n- Decision rule: the conventional CI undercovers iff coverage_conventional < 0.95 − 3·MCSE, with MCSE = sqrt(0.95·0.05/2000) = 0.00487 (`mcse_coverage_at_nominal`), giving a threshold of 0.9354 (`undercoverage_threshold`). I evaluated the binomial SE at the nominal 0.95 because the rule tests against that value.\n- Validation: results matched statsmodels (OLS nonrobust and HC1) for the first 5 replications to machine precision. A clean re-run reproduced the results exactly (`out/state/checks.md`).\n\n## Results\n| quantity | value | key |\n|---|---|---|\n| Coverage, conventional CI | 0.844 | `coverage_conventional` |\n| Coverage, HC1 CI | 0.902 | `coverage_hc1` |\n| Exact SD of β̂ | 0.2680 | `sd_beta_exact` |\n| MC SD of β̂ | 0.2696 | `sd_beta_mc` |\n| Mean se_conv | 0.1903 | `mean_se_conventional` |\n| Mean se_HC1 | 0.2408 | `mean_se_hc1` |\n| Mean β̂ | 0.4962 | `mean_beta_hat` |\n| Conventional CI undercovers | true | `conventional_ci_undercovers` |\n\nThe conventional interval does **not** reach its nominal 95% coverage. Its coverage of 0.844 (`coverage_conventional`) is far below the threshold of 0.9354 (`undercoverage_threshold`). The cause is that se_conv is too small on average: 0.190 (`mean_se_conventional`), compared with the true SD of 0.268 (`sd_beta_exact`), about 29% too small. The homoskedastic formula ignores that the noisiest observations are also the high-leverage ones. The HC1 interval does better at 0.902 (`coverage_hc1`) but still falls short of 95%, because its mean SE of 0.241 (`mean_se_hc1`) is still below 0.268 at n = 30. The MC SD of β̂ agrees with the exact value (0.2696 vs 0.2680), and the mean of β̂ is consistent with 0.5. This is a check that the simulation is correct.\n\n## Uncertainty\n- Monte Carlo SE of coverage_conventional, from the estimated proportion: 0.0081 (`mcse_coverage_conventional`). Approximate 95% MC interval: 0.828–0.860.\n- Monte Carlo SE of coverage_hc1: 0.0066 (`mcse_coverage_hc1`). Approximate 95% MC interval: 0.889–0.915.\n- MCSE at the nominal level used in the decision rule: 0.00487 (`mcse_coverage_at_nominal`). The decision does not change if the estimate-based MCSE is used instead: 0.95 − 3·0.0081 = 0.926 > 0.844.\n- MC SE of mean se_conv: 0.0007 (`mcse_mean_se_conventional`). MC SE of mean β̂: 0.0060 (`mcse_mean_beta_hat`).\n- `sd_beta_exact` is analytic and has no MC error.\n\n## Limitations and open issues\n- The results apply only to this one fixed design, with normal errors and R = 2000. Coverage estimates carry the MC error stated above. Other seeds would move them by about ±0.016 (2 MCSE).\n- The task does not fix how the \"binomial standard error\" in the undercoverage rule should be computed. I used the value at the nominal p = 0.95, and the conclusion is the same under the alternative.\n- The HC1 shortfall below 95% is a descriptive finding. I did not test it formally against a threshold, although 0.902 is more than 7 MCSE below 0.95.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The conventional 95% OLS interval does not attain nominal coverage: estimated coverage 0.844, below the 0.95 - 3 MCSE threshold of 0.935.\",\"results_keys\":[\"coverage_conventional\",\"conventional_ci_undercovers\",\"undercoverage_threshold\",\"mcse_coverage_at_nominal\"],\"status\":\"supported\",\"caveat\":\"Single fixed design, normal errors, R=2000; MCSE ~0.008.\"},\n {\"id\":\"C2\",\"text\":\"Undercoverage arises because the mean conventional SE (0.190) understates the exact SD of the slope estimator (0.268).\",\"results_keys\":[\"mean_se_conventional\",\"sd_beta_exact\"],\"status\":\"supported\",\"caveat\":\"Mechanism attributed to sigma rising with |x - xbar| (leverage); correlation from the audit.\"},\n {\"id\":\"C3\",\"text\":\"The HC1 interval improves coverage to 0.902 but still falls short of 95% at n=30.\",\"results_keys\":[\"coverage_hc1\",\"mcse_coverage_hc1\",\"mean_se_hc1\"],\"status\":\"supported\",\"caveat\":\"Shortfall not formally tested with the 3-MCSE rule; descriptive.\"},\n {\"id\":\"C4\",\"text\":\"The Monte Carlo SD of the slope estimator (0.2696) agrees with the analytic exact SD (0.2680), validating the simulation.\",\"results_keys\":[\"sd_beta_mc\",\"sd_beta_exact\"],\"status\":\"supported\",\"caveat\":\"Agreement judged against approx MC SE of a sample SD (~0.004).\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open(\"out/state/phases.json\"))\np[\"P4\"][\"status\"]=\"passed\"\np[\"P4\"][\"checks\"].append(\"gate run 20261030T041801.310146Z: results contract, exclusion ledger, positivity and reproducibility checks ok; remaining failures were P5 deliverables (report/claims) not yet written\")\np[\"P4\"][\"notes\"]=\"receipt out/state/gate_receipts/20261030T041801.310146Z.json\"\np[\"P5\"]={\"status\":\"passed\",\"checks\":[\"report.md with 6 required sections, every number cited to a results key\",\"claims.json written (C1-C4), C1 on nominal coverage\",\"final gate run (see receipts)\"],\"notes\":\"\"}\njson.dump(p,open(\"out/state/phases.json\",\"w\"),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate receipt 20261030T041801); P5 report+claims written; running final gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Write report, claims, finalize ledger, rerun gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2525,"cache_read_input_tokens":14557,"output_tokens":3212,"output_tokens_details":{"thinking_tokens":59},"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":2525},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXn1t41ppf4FKWVd2Qbk","requestRef":"737cb36a-baa6-4092-8c8e-071fc0758cf5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1cd54e18-0db5-4eb5-a517-c26cfd784a09","timestamp":"2026-10-30T04:18:27.654Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}62{"parentUuid":"1cd54e18-0db5-4eb5-a517-c26cfd784a09","isSidechain":false,"promptId":"7c63dae8-a509-4a57-a600-581bf60c9d13","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018to9txL7QG4uBwZiqjFLxR","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 32 checks failed)\n  [ok] file:out/results.json: present\n  [ok] file:out/report.md: present\n  [ok] file:out/analysis.py: present\n  [ok] file:out/claims.json: present\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:coverage_conventional: ok\n  [ok] key:coverage_hc1: ok\n  [ok] key:sd_beta_exact: ok\n  [ok] key:sd_beta_mc: ok\n  [ok] key:mean_se_conventional: ok\n  [ok] key:conventional_ci_undercovers: ok\n  [ok] key:n_replications: ok\n  [ok] key:n_obs_per_replication: 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  [ok] section:Question: present\n  [ok] section:Data and exclusions: present\n  [ok] section:Method: present\n  [ok] section:Results: present\n  [ok] section:Uncertainty: present\n  [ok] section:Limitations and open issues: present\n  [ok] claims_json: 4 claims, all keys resolve\n  [ok] phase_ledger: consistent\n  [ok] exclusion_ledger: 1 steps consistent\n  [ok] positive:sd_beta_exact: 0.26798059283028336\n  [ok] positive:sd_beta_mc: 0.2696473708309126\n  [ok] positive:mean_se_conventional: 0.1902623101215266\n  [ok] reproducibility: regenerated in 0.7s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0058/out/state/gate_receipts/20261030T041828.420491Z.json","is_error":false}]},"uuid":"64d6973c-8312-478b-bb49-4810f4cc0797","timestamp":"2026-10-30T04:18:28.429Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 32 checks failed)\n  [ok] file:out/results.json: present\n  [ok] file:out/report.md: present\n  [ok] file:out/analysis.py: present\n  [ok] file:out/claims.json: present\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:coverage_conventional: ok\n  [ok] key:coverage_hc1: ok\n  [ok] key:sd_beta_exact: ok\n  [ok] key:sd_beta_mc: ok\n  [ok] key:mean_se_conventional: ok\n  [ok] key:conventional_ci_undercovers: ok\n  [ok] key:n_replications: ok\n  [ok] key:n_obs_per_replication: 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  [ok] section:Question: present\n  [ok] section:Data and exclusions: present\n  [ok] section:Method: present\n  [ok] section:Results: present\n  [ok] section:Uncertainty: present\n  [ok] section:Limitations and open issues: present\n  [ok] claims_json: 4 claims, all keys resolve\n  [ok] phase_ledger: consistent\n  [ok] exclusion_ledger: 1 steps consistent\n  [ok] positive:sd_beta_exact: 0.26798059283028336\n  [ok] positive:sd_beta_mc: 0.2696473708309126\n  [ok] positive:mean_se_conventional: 0.1902623101215266\n  [ok] reproducibility: regenerated in 0.7s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0058/out/state/gate_receipts/20261030T041828.420491Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"1cd54e18-0db5-4eb5-a517-c26cfd784a09","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}63{"type":"api-request-blob","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","hash":"9f8af5d36ad0cffdb4083b2778b0da2f227b7276d7a08042bade14e4f1d7cc37","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"The P4-relevant gate checks (results contract, reproducibility, exclusion ledger) pass; the failures are the P5 deliverables. Writing the report and claims."},{"type":"tool_use","id":"toolu_018to9txL7QG4uBwZiqjFLxR","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F2-conf-03 — Coverage of OLS slope CIs in a small heteroskedastic fixed design\n\n## Question\nIn y_i = 1 + 0.5 x_i + σ_i ε_i (n = 30; x_i, σ_i fixed from `data/design.csv`; ε_i iid N(0,1)), does the nominal 95% interval β̂ ± t₀.₉₇₅(28)·se reach 95% coverage, using (a) the conventional OLS SE and (b) the HC1 robust SE? Also: (c) the exact SD of β̂ and (d) E[se_conv].\n\n## Data and exclusions\nOne input, `data/design.csv` (30 rows, columns i, x, sigma; `n_input_rows`). The audit (`out/state/data_audit.md`) found no missing values, duplicates or non-positive σ; `data_issues` is empty and no rows were excluded (`out/exclusions.csv`: 30 → 30). Note on the design: σ_i increases with |x_i − x̄| (correlation 0.84, computed during the audit), so the error variance is largest at the high-leverage points.\n\n## Method\n- Exact SD (no simulation): β̂ − β = Σ w_i σ_i ε_i with w_i = (x_i − x̄)/S_xx, so SD(β̂) = sqrt(Σ w_i² σ_i²) (`sd_beta_exact`).\n- Monte Carlo: R = 2000 replications (`n_replications`), n = 30 (`n_obs_per_replication`), numpy `default_rng` with seed 20261030 (`random_seed`). Only ε is redrawn. For each replication: OLS fit, se_conv = sqrt(s²·[(X'X)⁻¹]₂₂) with s² = RSS/(n−2), se_HC1 = sqrt(n/(n−2)·Σ a_i² e_i²) (HC0 sandwich × n/(n−2)), critical value t₀.₉₇₅(28) = 2.0484 (`t_critical_28`). Coverage = share of replications whose interval contains 0.5.\n- The baseline is the conventional interval coverage (`coverage_conventional`), which the task names as the naïve baseline.\n- Decision rule: the conventional CI undercovers iff coverage_conventional < 0.95 − 3·MCSE, with MCSE = sqrt(0.95·0.05/2000) = 0.00487 (`mcse_coverage_at_nominal`), giving a threshold of 0.9354 (`undercoverage_threshold`). I evaluated the binomial SE at the nominal 0.95 because the rule tests against that value.\n- Validation: results matched statsmodels (OLS nonrobust and HC1) for the first 5 replications to machine precision. A clean re-run reproduced the results exactly (`out/state/checks.md`).\n\n## Results\n| quantity | value | key |\n|---|---|---|\n| Coverage, conventional CI | 0.844 | `coverage_conventional` |\n| Coverage, HC1 CI | 0.902 | `coverage_hc1` |\n| Exact SD of β̂ | 0.2680 | `sd_beta_exact` |\n| MC SD of β̂ | 0.2696 | `sd_beta_mc` |\n| Mean se_conv | 0.1903 | `mean_se_conventional` |\n| Mean se_HC1 | 0.2408 | `mean_se_hc1` |\n| Mean β̂ | 0.4962 | `mean_beta_hat` |\n| Conventional CI undercovers | true | `conventional_ci_undercovers` |\n\nThe conventional interval does **not** reach its nominal 95% coverage. Its coverage of 0.844 (`coverage_conventional`) is far below the threshold of 0.9354 (`undercoverage_threshold`). The cause is that se_conv is too small on average: 0.190 (`mean_se_conventional`), compared with the true SD of 0.268 (`sd_beta_exact`), about 29% too small. The homoskedastic formula ignores that the noisiest observations are also the high-leverage ones. The HC1 interval does better at 0.902 (`coverage_hc1`) but still falls short of 95%, because its mean SE of 0.241 (`mean_se_hc1`) is still below 0.268 at n = 30. The MC SD of β̂ agrees with the exact value (0.2696 vs 0.2680), and the mean of β̂ is consistent with 0.5. This is a check that the simulation is correct.\n\n## Uncertainty\n- Monte Carlo SE of coverage_conventional, from the estimated proportion: 0.0081 (`mcse_coverage_conventional`). Approximate 95% MC interval: 0.828–0.860.\n- Monte Carlo SE of coverage_hc1: 0.0066 (`mcse_coverage_hc1`). Approximate 95% MC interval: 0.889–0.915.\n- MCSE at the nominal level used in the decision rule: 0.00487 (`mcse_coverage_at_nominal`). The decision does not change if the estimate-based MCSE is used instead: 0.95 − 3·0.0081 = 0.926 > 0.844.\n- MC SE of mean se_conv: 0.0007 (`mcse_mean_se_conventional`). MC SE of mean β̂: 0.0060 (`mcse_mean_beta_hat`).\n- `sd_beta_exact` is analytic and has no MC error.\n\n## Limitations and open issues\n- The results apply only to this one fixed design, with normal errors and R = 2000. Coverage estimates carry the MC error stated above. Other seeds would move them by about ±0.016 (2 MCSE).\n- The task does not fix how the \"binomial standard error\" in the undercoverage rule should be computed. I used the value at the nominal p = 0.95, and the conclusion is the same under the alternative.\n- The HC1 shortfall below 95% is a descriptive finding. I did not test it formally against a threshold, although 0.902 is more than 7 MCSE below 0.95.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The conventional 95% OLS interval does not attain nominal coverage: estimated coverage 0.844, below the 0.95 - 3 MCSE threshold of 0.935.\",\"results_keys\":[\"coverage_conventional\",\"conventional_ci_undercovers\",\"undercoverage_threshold\",\"mcse_coverage_at_nominal\"],\"status\":\"supported\",\"caveat\":\"Single fixed design, normal errors, R=2000; MCSE ~0.008.\"},\n {\"id\":\"C2\",\"text\":\"Undercoverage arises because the mean conventional SE (0.190) understates the exact SD of the slope estimator (0.268).\",\"results_keys\":[\"mean_se_conventional\",\"sd_beta_exact\"],\"status\":\"supported\",\"caveat\":\"Mechanism attributed to sigma rising with |x - xbar| (leverage); correlation from the audit.\"},\n {\"id\":\"C3\",\"text\":\"The HC1 interval improves coverage to 0.902 but still falls short of 95% at n=30.\",\"results_keys\":[\"coverage_hc1\",\"mcse_coverage_hc1\",\"mean_se_hc1\"],\"status\":\"supported\",\"caveat\":\"Shortfall not formally tested with the 3-MCSE rule; descriptive.\"},\n {\"id\":\"C4\",\"text\":\"The Monte Carlo SD of the slope estimator (0.2696) agrees with the analytic exact SD (0.2680), validating the simulation.\",\"results_keys\":[\"sd_beta_mc\",\"sd_beta_exact\"],\"status\":\"supported\",\"caveat\":\"Agreement judged against approx MC SE of a sample SD (~0.004).\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open(\"out/state/phases.json\"))\np[\"P4\"][\"status\"]=\"passed\"\np[\"P4\"][\"checks\"].append(\"gate run 20261030T041801.310146Z: results contract, exclusion ledger, positivity and reproducibility checks ok; remaining failures were P5 deliverables (report/claims) not yet written\")\np[\"P4\"][\"notes\"]=\"receipt out/state/gate_receipts/20261030T041801.310146Z.json\"\np[\"P5\"]={\"status\":\"passed\",\"checks\":[\"report.md with 6 required sections, every number cited to a results key\",\"claims.json written (C1-C4), C1 on nominal coverage\",\"final gate run (see receipts)\"],\"notes\":\"\"}\njson.dump(p,open(\"out/state/phases.json\",\"w\"),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate receipt 20261030T041801); P5 report+claims written; running final gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Write report, claims, finalize ledger, rerun gate"}}]}}64{"type":"api-request","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","id":"5dee182f-c482-4c5a-8dcb-8f20330c376d","timestamp":"2026-10-30T04:18:28.433Z","version":"2.1.314","querySource":"sdk","shapeHash":"ef38d8a8bde17d36c01bd203bb6c76f380c9b7d1d1ecc47de8861e1d70009353","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":"737cb36a-baa6-4092-8c8e-071fc0758cf5","keep":18,"tail":["9f8af5d36ad0cffdb4083b2778b0da2f227b7276d7a08042bade14e4f1d7cc37","abb6d229b8bb9a8eadef912df70c3a7a11bf5ac4328f935443e0a96051110166"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"fee10332aa73e73d0be9530d1f3c7b8dfadcdf8afd090986714fb161a6c83b70"}65{"parentUuid":"64d6973c-8312-478b-bb49-4810f4cc0797","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXn3tKwHD9ZWkTph6XLE","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01FeVSjCb44CHCNgNDV73WFv","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 20261030T041828.420491Z: PASSED 32/32\"\np[\"P5\"][\"notes\"]=\"receipt out/state/gate_receipts/20261030T041828.420491Z.json\"\njson.dump(p,open(\"out/state/phases.json\",\"w\"),indent=1)\nEOF\necho \"- 2026-10-30 Final gate PASSED (20261030T041828). Task complete.\" >> out/state/log.md","description":"Record final gate receipt"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3887,"cache_read_input_tokens":17082,"output_tokens":274,"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":3887},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXn3syNCiZTSwwAYmUak","requestRef":"5dee182f-c482-4c5a-8dcb-8f20330c376d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9939c526-480d-4535-94a1-bcb540dce86f","timestamp":"2026-10-30T04:18:30.976Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}66{"parentUuid":"9939c526-480d-4535-94a1-bcb540dce86f","isSidechain":false,"promptId":"7c63dae8-a509-4a57-a600-581bf60c9d13","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01FeVSjCb44CHCNgNDV73WFv","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"1260ecb0-8438-44fa-85a6-3f263cd0709d","timestamp":"2026-10-30T04:18:31.022Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"9939c526-480d-4535-94a1-bcb540dce86f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}67{"type":"api-request-blob","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","hash":"62fc99503005c19e4f37b38d7566c63dd47a1471e61e06c0d5324db0fcc5c89d","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01FeVSjCb44CHCNgNDV73WFv","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 20261030T041828.420491Z: PASSED 32/32\"\np[\"P5\"][\"notes\"]=\"receipt out/state/gate_receipts/20261030T041828.420491Z.json\"\njson.dump(p,open(\"out/state/phases.json\",\"w\"),indent=1)\nEOF\necho \"- 2026-10-30 Final gate PASSED (20261030T041828). Task complete.\" >> out/state/log.md","description":"Record final gate receipt"}}]}}68{"type":"api-request","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","id":"48dd51e6-b6e0-428d-a503-23d911c8f503","timestamp":"2026-10-30T04:18:31.025Z","version":"2.1.314","querySource":"sdk","shapeHash":"ef38d8a8bde17d36c01bd203bb6c76f380c9b7d1d1ecc47de8861e1d70009353","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":"5dee182f-c482-4c5a-8dcb-8f20330c376d","keep":20,"tail":["62fc99503005c19e4f37b38d7566c63dd47a1471e61e06c0d5324db0fcc5c89d","2ed3593b13b74815e2859c4821f265852335391b37ccba1f9f43568752fe4972"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",21,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"4fdb5445775e3c993eee82f01cd1e74c057f546c480e1e230c543a1e9966a10b"}69{"parentUuid":"1260ecb0-8438-44fa-85a6-3f263cd0709d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXn45ND8tTy4dHrMw3Mz","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":289,"cache_read_input_tokens":20969,"output_tokens":202,"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":289},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":853,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXn454cCqvsejYahisBg","requestRef":"48dd51e6-b6e0-428d-a503-23d911c8f503","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9f41abca-17b8-4277-963a-b100ff1734cc","timestamp":"2026-10-30T04:18:33.126Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}70{"parentUuid":"9f41abca-17b8-4277-963a-b100ff1734cc","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXn45ND8tTy4dHrMw3Mz","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0143YwuCSAMGJHWtF352oqTy","name":"Bash","input":{"command":"~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | head -1","description":"Confirm gate still passes"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":289,"cache_read_input_tokens":20969,"output_tokens":202,"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":289},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXn454cCqvsejYahisBg","requestRef":"48dd51e6-b6e0-428d-a503-23d911c8f503","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"798e464b-b7ce-4706-963b-6ef87869ab8f","timestamp":"2026-10-30T04:18:33.637Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}71{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"798e464b-b7ce-4706-963b-6ef87869ab8f","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f"}72{"type":"atis-latch","atis":"","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f"}73{"parentUuid":"798e464b-b7ce-4706-963b-6ef87869ab8f","isSidechain":false,"promptId":"7c63dae8-a509-4a57-a600-581bf60c9d13","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0143YwuCSAMGJHWtF352oqTy","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 32 checks failed)","is_error":false}]},"uuid":"3ff8834e-cd69-448f-99cc-0808c4f450da","timestamp":"2026-10-30T04:18:34.388Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 32 checks failed)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"798e464b-b7ce-4706-963b-6ef87869ab8f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}74{"type":"api-request-blob","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","hash":"f0515ddf9c764f0a0dd760af9d7435debd8e1a4158f3152131f7f2bc2d222531","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_0143YwuCSAMGJHWtF352oqTy","name":"Bash","input":{"command":"~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | head -1","description":"Confirm gate still passes"}}]}}75{"type":"api-request","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","id":"1615390b-0d64-41f6-99ed-e8ef1b0da99a","timestamp":"2026-10-30T04:18:34.391Z","version":"2.1.314","querySource":"sdk","shapeHash":"ef38d8a8bde17d36c01bd203bb6c76f380c9b7d1d1ecc47de8861e1d70009353","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":"48dd51e6-b6e0-428d-a503-23d911c8f503","keep":22,"tail":["f0515ddf9c764f0a0dd760af9d7435debd8e1a4158f3152131f7f2bc2d222531","6b674bb9204d24dd8974e65af5cd95d731a76a99345d9cc6a2a565fd3d2b84bf"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",23,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"bd9cbe4aca012a94975aee933fa0fc2cfc16f9ad892214e531cdc2042f0b6eca"}76{"parentUuid":"3ff8834e-cd69-448f-99cc-0808c4f450da","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXn4KmPkMBhcpQCetzGN","type":"message","role":"assistant","content":[{"type":"text","text":"The conventional 95% interval for the slope does not reach its nominal coverage: it contains β = 0.5 in only **84.4%** of the 2000 replications. All phases P0–P5 are complete, and the final gate passes all 32 checks, including re-running the script and getting the same results.\n\n| Estimand | Result |\n|---|---|\n| (a) Coverage, conventional interval | **0.844** (Monte Carlo standard error 0.008) |\n| (b) Coverage, HC1 interval | **0.902** (Monte Carlo standard error 0.007) |\n| (c) Exact SD of β̂ (analytic) | **0.2680** |\n| SD of the simulated β̂ (check on (c)) | 0.2696 |\n| (d) Average conventional SE | **0.1903** |\n\n- **Conventional interval:** 0.844 is far below the cutoff for undercoverage, 0.95 − 3 × 0.0049 = 0.935, so `conventional_ci_undercovers` is `true`.\n- **Why it undercovers:** the conventional SE averages about 29% less than the true SD of β̂. In this design σ rises with the distance of x from its mean (correlation 0.84), so the noisiest observations are the ones that pull hardest on the slope, and the conventional formula ignores this.\n- **HC1 interval:** it does better but still falls short of 95% at n = 30, because its SE averages 0.241, still below 0.268.\n- **Checks:** the simulated SD of β̂ agrees with the exact value, and the average β̂ (0.4962) is consistent with the true 0.5. The per-replication estimates and SEs matched statsmodels exactly for the first five replications.\n\nThe design file was clean: no missing values, duplicates or non-positive σ, and no rows excluded.\n\n**Limitations:**\n- The results hold only for this one fixed design, with normal errors, R = 2000 and seed 20261030. A different seed would move the coverage estimates by roughly ±0.016.\n- The task doesn't say at which proportion to compute the binomial standard error in the undercoverage rule. I used the nominal 0.95; using the estimated 0.844 instead gives the same conclusion.\n- The HC1 shortfall is descriptive only. I didn't apply a formal test to it, though 0.902 is more than 7 standard errors below 0.95.\n\nFiles are in `out/`:\n- `results.json`\n- `report.md`\n- `analysis.py`\n- `claims.json`\n- `exclusions.csv`\n- `state/` (contract, data audit, checks, phase ledger, log, gate receipts)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":242,"cache_read_input_tokens":21258,"output_tokens":861,"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":242},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXn4KTYcfDDB7dLEES4m","requestRef":"1615390b-0d64-41f6-99ed-e8ef1b0da99a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"541d0714-516a-456f-a2a5-122defdc90b8","timestamp":"2026-10-30T04:18:42.151Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0058","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","version":"2.1.314","gitBranch":"HEAD"}77{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"541d0714-516a-456f-a2a5-122defdc90b8","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f"}78{"type":"cost-state","sessionId":"f5f0b467-16ab-4b96-bf12-634f9029069f","totalCostUSD":0.38174319999999995,"totalAPIDuration":106234,"totalAPIDurationWithoutRetries":106226,"totalToolDuration":5276,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":111703,"startTime":1793333810457,"modelUsage":{"claude-opus-5-5":{"inputTokens":26,"outputTokens":12720,"thinkingTokens":471,"cacheReadInputTokens":136296,"cacheCreationInputTokens":19996,"cacheCreationEphemeral5mInputTokens":19996,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.38174319999999995}},"hasUnknownModelCost":false}79