1{"type":"atis-latch","atis":"","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T04:39:12.437Z","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T04:39:12.443Z","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50"}4{"parentUuid":null,"isSidechain":false,"promptId":"ba181d7f-0518-4f86-adea-4e9a9411ddae","type":"user","message":{"role":"user","content":"Begin"},"uuid":"1d7c73ef-c10f-4d54-8296-3618655abe74","timestamp":"2026-10-30T04:39:12.447Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0134","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","shapeHash":"4f38d1ccef87ea41d9618e8bcfacc99aeaef70086be77f5818b0603952dbe3ff","timestamp":"2026-10-30T04:39:12.459Z","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/RC0134\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"}],"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":"46b84cfa-0362-44e5-bb04-387e7e660e50","hash":"d43ab0e0b8ec9db790a6459cb2f4bc4ffce02825ca05063162023be60912ced9","message":{"role":"system","content":[],"output_config":{"effort":"medium"}}}7{"type":"api-request","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","id":"56801fbf-f253-4f8e-92c8-8635771bc0e1","timestamp":"2026-10-30T04:39:12.459Z","version":"2.1.314","querySource":"sdk","shapeHash":"4f38d1ccef87ea41d9618e8bcfacc99aeaef70086be77f5818b0603952dbe3ff","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":"61c7c7b74737b6a6fd8da7ab1eecfb7dae9227f6b1e415f0ca4a2dc1fbf18c0d"}8{"parentUuid":"1d7c73ef-c10f-4d54-8296-3618655abe74","isSidechain":false,"attachment":{"type":"session_context","context":{}},"type":"attachment","uuid":"3df1b5a4-0011-448d-a1a6-52291ce0d5e0","timestamp":"2026-10-30T04:39:12.452Z","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0134","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","version":"2.1.314","gitBranch":"HEAD"}9{"parentUuid":"3df1b5a4-0011-448d-a1a6-52291ce0d5e0","isSidechain":false,"attachment":{"type":"date","date":"2026-10-30"},"type":"attachment","uuid":"33b137ac-db7c-41f5-b2e1-c6794ad0be2f","timestamp":"2026-10-30T04:39:12.452Z","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/RC0134","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","version":"2.1.314","gitBranch":"HEAD"}10{"parentUuid":"33b137ac-db7c-41f5-b2e1-c6794ad0be2f","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":"dee6257a-bc02-41d1-ae8b-fe418907cdca","timestamp":"2026-10-30T04:39:12.452Z","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/RC0134","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50"}12{"parentUuid":"dee6257a-bc02-41d1-ae8b-fe418907cdca","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXodbWiRMDo81uLrjHSs","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":2209,"cache_read_input_tokens":1504,"output_tokens":129,"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":2209},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":397,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXodbE6iKkMzyMHwSURK","requestRef":"56801fbf-f253-4f8e-92c8-8635771bc0e1","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"876ebcbc-3122-41d2-b219-a69245331548","timestamp":"2026-10-30T04:39:13.915Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0134","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"876ebcbc-3122-41d2-b219-a69245331548","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXodbWiRMDo81uLrjHSs","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01CPkdJ1F4ujfSSyXqVfG5NN","name":"Bash","input":{"command":"cat TASK.md; ls -la; ls -R data out 2>/dev/null | head -50","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":2209,"cache_read_input_tokens":1504,"output_tokens":129,"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":2209},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXodbE6iKkMzyMHwSURK","requestRef":"56801fbf-f253-4f8e-92c8-8635771bc0e1","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ee2f467c-43c4-4a50-8608-59e4298eb053","timestamp":"2026-10-30T04:39:14.574Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0134","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","version":"2.1.314","gitBranch":"HEAD"}14{"parentUuid":"ee2f467c-43c4-4a50-8608-59e4298eb053","isSidechain":false,"promptId":"ba181d7f-0518-4f86-adea-4e9a9411ddae","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01CPkdJ1F4ujfSSyXqVfG5NN","type":"tool_result","content":"# F6-conf-02 — Effect of the Bank of Canada's March-2020 emergency rate cuts on the 5-year government bond yield\n\n## Scientific question\nOn 2020-03-04 the Bank of Canada announced the first of its emergency 50-basis-point policy-rate reductions of the COVID-19 period. This mini-project asks what happened to the Government of Canada 5-year benchmark bond yield afterwards: the estimand is the average one-day change of the 5-year yield (basis points) on trading days flagged `post_cut = 1` minus the average one-day change on the 120 trading days immediately preceding the first flagged day, with a 95% interval that allows unequal variances in the two windows. The chain required is: question → data audit → descriptive baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report.\n\n## Inputs\n- `data/gc5y_daily.csv` — observed data, Bank of Canada Valet series BD.CDN.5YR.DQ.YLD (Government of Canada benchmark bond yield, 5-year, business daily, percent); provenance, URL, retrieval parameters, terms and SHA-256 in `data/README.md`. Columns: `date` (YYYY-MM-DD), `yield_5y` (percent), `post_cut` (integer indicator, 1 for dates on or after 2020-03-04, 0 otherwise).\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- One-day changes are 100·(y_t − y_{t−1}) in basis points between consecutive rows of the file, dated t.\n- Treatment status is given by `post_cut` exactly as provided; do not redefine it.\n- Use only the file listed above. Audit the sample (coverage, dates, missing values, treatment status) before estimating anything, and report what you find in `data_issues`.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `sample_start`, `sample_end` | string | ISO date | first and last `date` in the file |\n| `n_treated` | integer | days | rows with `post_cut = 1` |\n| `baseline_metric` | number | basis points | descriptive baseline: mean one-day change over the whole file |\n| `model_metric` | number or null | basis points | RMSE of the two-window (pre/post) mean model over the two windows; null if not estimable |\n| `effect_estimate` | number or null | basis points | post-cut mean change minus pre-window mean change; null if not estimable |\n| `effect_ci_low`, `effect_ci_high` | number or null | basis points | 95% interval for the effect; null if not estimable |\n| `conclusion` | string | — | one of the codes below |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\n(`n_analysed` = observations used in the effect estimate; null if none.)\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — time plot of the 5-year yield over the file's sample, produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per calendar year present in the file with columns `year`, `n_days`, `mean_yield`, `mean_change_bp` (header + at least two data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. This task is designed to be completed in well under 20 minutes of work; prefer a short, correct chain over an elaborate one.\ntotal 32\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:42 .\ndrwxr-xr-x 85 simon-pierreboucher staff 2720 oct. 30 00:39 ..\n-rw-r--r-- 1 simon-pierreboucher staff 514 oct. 29 23:42 bundle.json\ndrwxr-xr-x 4 simon-pierreboucher staff 128 oct. 29 23:27 data\n-rw-r--r-- 1 simon-pierreboucher staff 1975 oct. 29 23:35 gate_spec.json\n-rw-r--r-- 1 simon-pierreboucher staff 5195 oct. 29 23:35 TASK.md\ndata:\ngc5y_daily.csv\nREADME.md","is_error":false}]},"uuid":"0f93ed32-c397-4c19-8b2e-3dbabf36a7a8","timestamp":"2026-10-30T04:39:14.653Z","toolUseResult":{"stdout":"# F6-conf-02 — Effect of the Bank of Canada's March-2020 emergency rate cuts on the 5-year government bond yield\n\n## Scientific question\nOn 2020-03-04 the Bank of Canada announced the first of its emergency 50-basis-point policy-rate reductions of the COVID-19 period. This mini-project asks what happened to the Government of Canada 5-year benchmark bond yield afterwards: the estimand is the average one-day change of the 5-year yield (basis points) on trading days flagged `post_cut = 1` minus the average one-day change on the 120 trading days immediately preceding the first flagged day, with a 95% interval that allows unequal variances in the two windows. The chain required is: question → data audit → descriptive baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report.\n\n## Inputs\n- `data/gc5y_daily.csv` — observed data, Bank of Canada Valet series BD.CDN.5YR.DQ.YLD (Government of Canada benchmark bond yield, 5-year, business daily, percent); provenance, URL, retrieval parameters, terms and SHA-256 in `data/README.md`. Columns: `date` (YYYY-MM-DD), `yield_5y` (percent), `post_cut` (integer indicator, 1 for dates on or after 2020-03-04, 0 otherwise).\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- One-day changes are 100·(y_t − y_{t−1}) in basis points between consecutive rows of the file, dated t.\n- Treatment status is given by `post_cut` exactly as provided; do not redefine it.\n- Use only the file listed above. Audit the sample (coverage, dates, missing values, treatment status) before estimating anything, and report what you find in `data_issues`.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `sample_start`, `sample_end` | string | ISO date | first and last `date` in the file |\n| `n_treated` | integer | days | rows with `post_cut = 1` |\n| `baseline_metric` | number | basis points | descriptive baseline: mean one-day change over the whole file |\n| `model_metric` | number or null | basis points | RMSE of the two-window (pre/post) mean model over the two windows; null if not estimable |\n| `effect_estimate` | number or null | basis points | post-cut mean change minus pre-window mean change; null if not estimable |\n| `effect_ci_low`, `effect_ci_high` | number or null | basis points | 95% interval for the effect; null if not estimable |\n| `conclusion` | string | — | one of the codes below |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\n(`n_analysed` = observations used in the effect estimate; null if none.)\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — time plot of the 5-year yield over the file's sample, produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per calendar year present in the file with columns `year`, `n_days`, `mean_yield`, `mean_change_bp` (header + at least two data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. This task is designed to be completed in well under 20 minutes of work; prefer a short, correct chain over an elaborate one.\ntotal 32\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:42 .\ndrwxr-xr-x 85 simon-pierreboucher staff 2720 oct. 30 00:39 ..\n-rw-r--r-- 1 simon-pierreboucher staff 514 oct. 29 23:42 bundle.json\ndrwxr-xr-x 4 simon-pierreboucher staff 128 oct. 29 23:27 data\n-rw-r--r-- 1 simon-pierreboucher staff 1975 oct. 29 23:35 gate_spec.json\n-rw-r--r-- 1 simon-pierreboucher staff 5195 oct. 29 23:35 TASK.md\ndata:\ngc5y_daily.csv\nREADME.md","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"ee2f467c-43c4-4a50-8608-59e4298eb053","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0134","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","version":"2.1.314","gitBranch":"HEAD"}15{"type":"api-request-blob","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","hash":"bf2345c6915e189c25f9345d2b68373c34af674052e26432f90dad008211fb5b","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01CPkdJ1F4ujfSSyXqVfG5NN","name":"Bash","input":{"command":"cat TASK.md; ls -la; ls -R data out 2>/dev/null | head -50","description":"Read task and list workspace"}}]}}16{"type":"api-request","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","id":"6c8a4fd7-14a7-4691-954c-e94442c86778","timestamp":"2026-10-30T04:39:14.659Z","version":"2.1.314","querySource":"sdk","shapeHash":"4f38d1ccef87ea41d9618e8bcfacc99aeaef70086be77f5818b0603952dbe3ff","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":"56801fbf-f253-4f8e-92c8-8635771bc0e1","keep":2,"tail":["bf2345c6915e189c25f9345d2b68373c34af674052e26432f90dad008211fb5b","d0a8e884fb1197dadf11a4aa609af5ffd0553c4d09e623e39807593eb17e8119"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"a416c5186aa1458c1f36344cc922041b9cb24fbd229ca9363d1720346703d662"}17{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"0f93ed32-c397-4c19-8b2e-3dbabf36a7a8","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50"}18{"type":"atis-latch","atis":"","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50"}19{"parentUuid":"0f93ed32-c397-4c19-8b2e-3dbabf36a7a8","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXodksnCoi2yCW6V8diH","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01EhSUaJqWD2t48w9WBUA98J","name":"Bash","input":{"command":"cat data/README.md; shasum -a 256 data/*; wc -l data/gc5y_daily.csv; head -5 data/gc5y_daily.csv; tail -5 data/gc5y_daily.csv; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/gc5y_daily.csv');print(d.describe(include='all'));print(d.isna().sum());print(d.date.duplicated().sum());print(d.post_cut.value_counts())\nd['date']=pd.to_datetime(d.date);print(d.date.is_monotonic_increasing);g=d.date.diff().dt.days;print(g.value_counts().head(10));print(d[g>4])\nprint(d[(d.date>='2020-02-20')&(d.date<='2020-03-15')])\n\"","description":"Audit data file"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2294,"cache_read_input_tokens":3713,"output_tokens":348,"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":2294},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXodkXx3Vp8QMs5NtZVh","requestRef":"6c8a4fd7-14a7-4691-954c-e94442c86778","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8f871681-d17b-4e6e-9c61-6202f58a9e48","timestamp":"2026-10-30T04:39:18.136Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0134","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","version":"2.1.314","gitBranch":"HEAD"}20{"parentUuid":"8f871681-d17b-4e6e-9c61-6202f58a9e48","isSidechain":false,"promptId":"ba181d7f-0518-4f86-adea-4e9a9411ddae","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01EhSUaJqWD2t48w9WBUA98J","type":"tool_result","content":"# Data provenance — F6-conf-02\n\n## `gc5y_daily.csv` (2498 rows)\nObserved data: Government of Canada benchmark bond yield, 5-year (Bank of Canada Valet series `BD.CDN.5YR.DQ.YLD`, business-daily, percent), taken from the Valet group `bond_yields_benchmark`.\n- URL: `https://www.bankofcanada.ca/valet/observations/group/bond_yields_benchmark/json?start_date=2010-01-01&end_date=2019-12-31` (raw response SHA-256 `ccd9ec57b87ffd1194fe248cec327acf08e90608d2419b435955563d5824928e`), retrieved 2026-10-30T03:23:28Z. Only the 5-year series was kept from the response.\n- Columns: `date` (YYYY-MM-DD, business days on which the Bank published a yield), `yield_5y` (percent, as published), `post_cut` (integer indicator added by the task author: 1 for dates on or after 2020-03-04, the first business day after the Bank of Canada's 50-basis-point emergency reduction of the policy rate announced on 2020-03-04, and 0 otherwise).\n- Terms: Bank of Canada Terms of Use, https://www.bankofcanada.ca/terms/ (reproduction permitted for non-commercial purposes with attribution; source: Bank of Canada).\n- SHA-256 of `gc5y_daily.csv`: `2cdda6ccb2147c8861997166cb0d32cf85ff2744e1e7c0f47eded0f354c2db51`\n2cdda6ccb2147c8861997166cb0d32cf85ff2744e1e7c0f47eded0f354c2db51 data/gc5y_daily.csv\n7716f8b58d8f4729504a7713a8a3d651cd2f7298f53f668b377215a5b2af1e34 data/README.md\n 2499 data/gc5y_daily.csv\ndate,yield_5y,post_cut\n2010-01-04,2.75,0\n2010-01-05,2.70,0\n2010-01-06,2.74,0\n2010-01-07,2.75,0\n2019-12-23,1.65,0\n2019-12-24,1.64,0\n2019-12-27,1.62,0\n2019-12-30,1.66,0\n2019-12-31,1.68,0\n date yield_5y post_cut\ncount 2498 2498.000000 2498.0\nunique 2498 NaN NaN\ntop 2010-01-04 NaN NaN\nfreq 1 NaN NaN\nmean NaN 1.574496 0.0\nstd NaN 0.582884 0.0\nmin NaN 0.480000 0.0\n25% NaN 1.180000 0.0\n50% NaN 1.520000 0.0\n75% NaN 1.980000 0.0\nmax NaN 3.200000 0.0\ndate 0\nyield_5y 0\npost_cut 0\ndtype: int64\n0\npost_cut\n0 2498\nName: count, dtype: int64\nTrue\ndate\n1.0 1962\n3.0 440\n4.0 79\n2.0 10\n5.0 6\nName: count, dtype: int64\n date yield_5y post_cut\n247 2010-12-29 2.45 0\n496 2011-12-28 1.28 0\n1246 2014-12-29 1.38 0\n1496 2015-12-29 0.75 0\n1746 2016-12-28 1.16 0\n1995 2017-12-27 1.82 0\nEmpty DataFrame\nColumns: [date, yield_5y, post_cut]\nIndex: []","is_error":false}]},"uuid":"75617924-6ce4-4011-a9da-784b2e51070b","timestamp":"2026-10-30T04:39:18.445Z","toolUseResult":{"stdout":"# Data provenance — F6-conf-02\n\n## `gc5y_daily.csv` (2498 rows)\nObserved data: Government of Canada benchmark bond yield, 5-year (Bank of Canada Valet series `BD.CDN.5YR.DQ.YLD`, business-daily, percent), taken from the Valet group `bond_yields_benchmark`.\n- URL: `https://www.bankofcanada.ca/valet/observations/group/bond_yields_benchmark/json?start_date=2010-01-01&end_date=2019-12-31` (raw response SHA-256 `ccd9ec57b87ffd1194fe248cec327acf08e90608d2419b435955563d5824928e`), retrieved 2026-10-30T03:23:28Z. Only the 5-year series was kept from the response.\n- Columns: `date` (YYYY-MM-DD, business days on which the Bank published a yield), `yield_5y` (percent, as published), `post_cut` (integer indicator added by the task author: 1 for dates on or after 2020-03-04, the first business day after the Bank of Canada's 50-basis-point emergency reduction of the policy rate announced on 2020-03-04, and 0 otherwise).\n- Terms: Bank of Canada Terms of Use, https://www.bankofcanada.ca/terms/ (reproduction permitted for non-commercial purposes with attribution; source: Bank of Canada).\n- SHA-256 of `gc5y_daily.csv`: `2cdda6ccb2147c8861997166cb0d32cf85ff2744e1e7c0f47eded0f354c2db51`\n2cdda6ccb2147c8861997166cb0d32cf85ff2744e1e7c0f47eded0f354c2db51 data/gc5y_daily.csv\n7716f8b58d8f4729504a7713a8a3d651cd2f7298f53f668b377215a5b2af1e34 data/README.md\n 2499 data/gc5y_daily.csv\ndate,yield_5y,post_cut\n2010-01-04,2.75,0\n2010-01-05,2.70,0\n2010-01-06,2.74,0\n2010-01-07,2.75,0\n2019-12-23,1.65,0\n2019-12-24,1.64,0\n2019-12-27,1.62,0\n2019-12-30,1.66,0\n2019-12-31,1.68,0\n date yield_5y post_cut\ncount 2498 2498.000000 2498.0\nunique 2498 NaN NaN\ntop 2010-01-04 NaN NaN\nfreq 1 NaN NaN\nmean NaN 1.574496 0.0\nstd NaN 0.582884 0.0\nmin NaN 0.480000 0.0\n25% NaN 1.180000 0.0\n50% NaN 1.520000 0.0\n75% NaN 1.980000 0.0\nmax NaN 3.200000 0.0\ndate 0\nyield_5y 0\npost_cut 0\ndtype: int64\n0\npost_cut\n0 2498\nName: count, dtype: int64\nTrue\ndate\n1.0 1962\n3.0 440\n4.0 79\n2.0 10\n5.0 6\nName: count, dtype: int64\n date yield_5y post_cut\n247 2010-12-29 2.45 0\n496 2011-12-28 1.28 0\n1246 2014-12-29 1.38 0\n1496 2015-12-29 0.75 0\n1746 2016-12-28 1.16 0\n1995 2017-12-27 1.82 0\nEmpty DataFrame\nColumns: [date, yield_5y, post_cut]\nIndex: []","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"8f871681-d17b-4e6e-9c61-6202f58a9e48","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0134","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","version":"2.1.314","gitBranch":"HEAD"}21{"type":"api-request-blob","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","hash":"998fe2d5af7dc76947f660a30deac84596cf1d973e4542763308056515d9a0e8","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01EhSUaJqWD2t48w9WBUA98J","name":"Bash","input":{"command":"cat data/README.md; shasum -a 256 data/*; wc -l data/gc5y_daily.csv; head -5 data/gc5y_daily.csv; tail -5 data/gc5y_daily.csv; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/gc5y_daily.csv');print(d.describe(include='all'));print(d.isna().sum());print(d.date.duplicated().sum());print(d.post_cut.value_counts())\nd['date']=pd.to_datetime(d.date);print(d.date.is_monotonic_increasing);g=d.date.diff().dt.days;print(g.value_counts().head(10));print(d[g>4])\nprint(d[(d.date>='2020-02-20')&(d.date<='2020-03-15')])\n\"","description":"Audit data file"}}]}}22{"type":"api-request","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","id":"78bf44cc-f701-420f-b4dc-90feba679937","timestamp":"2026-10-30T04:39:18.448Z","version":"2.1.314","querySource":"sdk","shapeHash":"4f38d1ccef87ea41d9618e8bcfacc99aeaef70086be77f5818b0603952dbe3ff","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":"6c8a4fd7-14a7-4691-954c-e94442c86778","keep":4,"tail":["998fe2d5af7dc76947f660a30deac84596cf1d973e4542763308056515d9a0e8","f50560bbaa423870c02095e12cc3bd61e52586258830d96244b2b2ae0948f6af"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"e6384a0e752bafd61b037f9b96aeb27cd1514d0434dc7fdd5f2ceb9bb406b27c"}23{"parentUuid":"75617924-6ce4-4011-a9da-784b2e51070b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoe2yuLqC3z9kXV58tf","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":1573,"cache_read_input_tokens":6007,"output_tokens":3017,"output_tokens_details":{"thinking_tokens":155},"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":1573},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1771,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXoe2jX8KcUFT6dTqc1i","requestRef":"78bf44cc-f701-420f-b4dc-90feba679937","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"87ee1841-d698-4787-91b5-8600769034e7","timestamp":"2026-10-30T04:39:22.564Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0134","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"87ee1841-d698-4787-91b5-8600769034e7","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoe2yuLqC3z9kXV58tf","type":"message","role":"assistant","content":[{"type":"text","text":"The file ends on 2019-12-31 and has no rows with `post_cut = 1`, so the estimand can't be computed (premise invalid). I'll record that and write all the deliverables to match."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1573,"cache_read_input_tokens":6007,"output_tokens":3017,"output_tokens_details":{"thinking_tokens":155},"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":1573},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXoe2jX8KcUFT6dTqc1i","requestRef":"78bf44cc-f701-420f-b4dc-90feba679937","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2f10d3c3-dd3d-43b6-9956-03171013246e","timestamp":"2026-10-30T04:39:22.565Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0134","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","version":"2.1.314","gitBranch":"HEAD"}25{"parentUuid":"2f10d3c3-dd3d-43b6-9956-03171013246e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoe2yuLqC3z9kXV58tf","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Hxsc39wQox98x643VxrKhZ","name":"Bash","input":{"command":"mkdir -p out/state && cat > out/analysis.py <<'EOF'\n\"\"\"Replication entry point for F6-conf-02. Run from workspace root:\n ~/wp29/venv/bin/python out/analysis.py\nReads only data/gc5y_daily.csv; writes out/results.json, out/figure1.png,\nout/table1.csv, out/exclusions.csv. No randomness is used.\"\"\"\nimport json, os, hashlib\nimport numpy as np, pandas as pd\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\nfrom scipy import stats\n\nDATA = \"data/gc5y_daily.csv\"; OUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nraw = pd.read_csv(DATA)\nn_raw = len(raw)\nsha = hashlib.sha256(open(DATA, \"rb\").read()).hexdigest()\nd = raw.copy(); d[\"date\"] = pd.to_datetime(d[\"date\"])\n\n# ---- audit ----\nissues = []\nn_treated = int((d[\"post_cut\"] == 1).sum())\nn_na = int(d[\"yield_5y\"].isna().sum()); n_dup = int(d[\"date\"].duplicated().sum())\nsorted_ok = bool(d[\"date\"].is_monotonic_increasing)\nbad_flag = int(((d[\"date\"] >= \"2020-03-04\").astype(int) != d[\"post_cut\"]).sum())\nif n_treated == 0:\n issues.append({\"issue\": f\"No rows with post_cut = 1: the file covers {raw['date'].iloc[0]} to {raw['date'].iloc[-1]} \"\n \"(README retrieval used end_date=2019-12-31), entirely before the 2020-03-04 cut, so the post-cut window is empty.\",\n \"handling\": \"Estimand declared not computable (premise_invalid); effect, CI, model_metric and n_analysed set to null. No proxy or substitute data used.\"})\nif n_na: issues.append({\"issue\": f\"{n_na} missing yields\", \"handling\": \"Changes involving missing values dropped.\"})\nif n_dup: issues.append({\"issue\": f\"{n_dup} duplicate dates\", \"handling\": \"Reported; not removed.\"})\nif not sorted_ok: issues.append({\"issue\": \"Dates not sorted\", \"handling\": \"Sorted by date before differencing.\"})\nif bad_flag: issues.append({\"issue\": f\"{bad_flag} rows where post_cut disagrees with date>=2020-03-04\", \"handling\": \"post_cut used as provided per task rules.\"})\ngaps = d[\"date\"].diff().dt.days\nissues.append({\"issue\": f\"Calendar gaps between consecutive rows up to {int(gaps.max())} days (weekends/holidays; max gaps around year-end).\",\n \"handling\": \"Per task rules, one-day change is defined between consecutive rows regardless of calendar gap; no adjustment.\"})\nd = d.sort_values(\"date\").reset_index(drop=True)\n\n# ---- changes and baseline ----\nd[\"chg_bp\"] = 100 * d[\"yield_5y\"].diff()\nexcl = [(\"1\", \"drop first row (no prior row for one-day change)\", n_raw, n_raw - 1, 1,\n \"Change undefined for first observation\")]\nchg = d.dropna(subset=[\"chg_bp\"])\nbaseline = float(chg[\"chg_bp\"].mean())\n\n# ---- main estimate ----\neffect = lo = hi = rmse = None; n_an = None\nif n_treated > 0:\n first = d.index[d[\"post_cut\"] == 1][0]\n post = d.loc[d[\"post_cut\"] == 1, \"chg_bp\"].dropna()\n pre = d.loc[max(first - 120, 1):first - 1, \"chg_bp\"].dropna()\n if len(pre) >= 2 and len(post) >= 2:\n effect = float(post.mean() - pre.mean())\n v1, v0 = post.var(ddof=1) / len(post), pre.var(ddof=1) / len(pre)\n se = np.sqrt(v1 + v0)\n df = (v1 + v0) ** 2 / (v1 ** 2 / (len(post) - 1) + v0 ** 2 / (len(pre) - 1))\n t = stats.t.ppf(0.975, df); lo, hi = effect - t * se, effect + t * se\n resid = np.concatenate([post - post.mean(), pre - pre.mean()])\n rmse = float(np.sqrt(np.mean(resid ** 2))); n_an = int(len(post) + len(pre))\nif effect is None:\n concl, feas = \"premise_invalid\", \"infeasible\"\n reason = (\"The file contains no rows with post_cut = 1 (sample ends 2019-12-31, before the 2020-03-04 cut), \"\n \"so neither the post-cut window nor the '120 trading days before the first flagged day' window exists; \"\n \"the effect, its interval and the two-window RMSE cannot be computed.\")\nelse:\n feas, reason = \"complete\", None\n concl = \"positive_effect\" if lo > 0 else (\"negative_effect\" if hi < 0 else \"no_detectable_effect\")\nexcl.append((\"2\", \"main-estimate windows (post_cut=1 rows + 120 preceding rows)\", n_raw - 1,\n 0 if n_an is None else n_an, (n_raw - 1) - (0 if n_an is None else n_an),\n \"No post_cut=1 rows exist; window empty\" if n_an is None else \"Restrict to estimand windows\"))\npd.DataFrame(excl, columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\n# ---- table and figure ----\nd[\"year\"] = d[\"date\"].dt.year\ntab = d.groupby(\"year\").agg(n_days=(\"yield_5y\", \"size\"), mean_yield=(\"yield_5y\", \"mean\"),\n mean_change_bp=(\"chg_bp\", \"mean\")).reset_index().round(4)\ntab.to_csv(f\"{OUT}/table1.csv\", index=False)\nfig, ax = plt.subplots(figsize=(9, 4))\nax.plot(d[\"date\"], d[\"yield_5y\"], lw=0.8)\nax.set_title(\"Government of Canada 5-year benchmark yield (BD.CDN.5YR.DQ.YLD)\\n\"\n \"No post-cut (2020-03-04+) observations in file\")\nax.set_ylabel(\"Yield (%)\"); ax.set_xlabel(\"Date\"); fig.tight_layout()\nfig.savefig(f\"{OUT}/figure1.png\", dpi=120); plt.close(fig)\n\nr = lambda x: None if x is None else round(float(x), 6)\nres = {\"task_id\": \"F6-conf-02\", \"sample_start\": raw[\"date\"].iloc[0], \"sample_end\": raw[\"date\"].iloc[-1],\n \"n_treated\": n_treated, \"baseline_metric\": r(baseline), \"model_metric\": r(rmse),\n \"effect_estimate\": r(effect), \"effect_ci_low\": r(lo), \"effect_ci_high\": r(hi),\n \"conclusion\": concl, \"feasibility\": feas, \"feasibility_reason\": reason,\n \"data_issues\": issues, \"n_input_rows\": {\"gc5y_daily.csv\": int(n_raw)},\n \"n_analysed\": n_an, \"random_seed\": None,\n \"audit_n_changes\": int(len(chg)), \"audit_sd_change_bp\": r(chg[\"chg_bp\"].std(ddof=1)),\n \"audit_input_sha256\": sha}\njson.dump(res, open(f\"{OUT}/results.json\", \"w\"), indent=2)\nprint(f\"rows={n_raw} treated={n_treated} baseline={baseline:.4f} bp conclusion={concl}\")\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/table1.csv out/exclusions.csv","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1573,"cache_read_input_tokens":6007,"output_tokens":3017,"output_tokens_details":{"thinking_tokens":155},"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":1573},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXoe2jX8KcUFT6dTqc1i","requestRef":"78bf44cc-f701-420f-b4dc-90feba679937","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"80f168a4-d486-4c14-ad9d-008bbbdb53da","timestamp":"2026-10-30T04:39:43.419Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0134","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","version":"2.1.314","gitBranch":"HEAD"}26{"parentUuid":"80f168a4-d486-4c14-ad9d-008bbbdb53da","isSidechain":false,"promptId":"ba181d7f-0518-4f86-adea-4e9a9411ddae","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Hxsc39wQox98x643VxrKhZ","type":"tool_result","content":"rows=2498 treated=0 baseline=-0.0429 bp conclusion=premise_invalid\n{\n \"task_id\": \"F6-conf-02\",\n \"sample_start\": \"2010-01-04\",\n \"sample_end\": \"2019-12-31\",\n \"n_treated\": 0,\n \"baseline_metric\": -0.042851,\n \"model_metric\": null,\n \"effect_estimate\": null,\n \"effect_ci_low\": null,\n \"effect_ci_high\": null,\n \"conclusion\": \"premise_invalid\",\n \"feasibility\": \"infeasible\",\n \"feasibility_reason\": \"The file contains no rows with post_cut = 1 (sample ends 2019-12-31, before the 2020-03-04 cut), so neither the post-cut window nor the '120 trading days before the first flagged day' window exists; the effect, its interval and the two-window RMSE cannot be computed.\",\n \"data_issues\": [\n {\n \"issue\": \"No rows with post_cut = 1: the file covers 2010-01-04 to 2019-12-31 (README retrieval used end_date=2019-12-31), entirely before the 2020-03-04 cut, so the post-cut window is empty.\",\n \"handling\": \"Estimand declared not computable (premise_invalid); effect, CI, model_metric and n_analysed set to null. No proxy or substitute data used.\"\n },\n {\n \"issue\": \"Calendar gaps between consecutive rows up to 5 days (weekends/holidays; max gaps around year-end).\",\n \"handling\": \"Per task rules, one-day change is defined between consecutive rows regardless of calendar gap; no adjustment.\"\n }\n ],\n \"n_input_rows\": {\n \"gc5y_daily.csv\": 2498\n },\n \"n_analysed\": null,\n \"random_seed\": null,\n \"audit_n_changes\": 2497,\n \"audit_sd_change_bp\": 4.236329,\n \"audit_input_sha256\": \"2cdda6ccb2147c8861997166cb0d32cf85ff2744e1e7c0f47eded0f354c2db51\"\n}year,n_days,mean_yield,mean_change_bp\n2010,250,2.4797,-0.1365\n2011,249,2.0563,-0.4578\n2012,250,1.38,0.044\n2013,250,1.622,0.228\n2014,250,1.5796,-0.244\n2015,250,0.8532,-0.244\n2016,250,0.7263,0.152\n2017,249,1.3633,0.3012\n2018,250,2.1513,0.008\n2019,250,1.5344,-0.08\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop first row (no prior row for one-day change),2498,2497,1,Change undefined for first observation\n2,main-estimate windows (post_cut=1 rows + 120 preceding rows),2497,0,2497,No post_cut=1 rows exist; window empty","is_error":false}]},"uuid":"fccb1e47-516b-4b4b-be73-1bb004190322","timestamp":"2026-10-30T04:39:44.450Z","toolUseResult":{"stdout":"rows=2498 treated=0 baseline=-0.0429 bp conclusion=premise_invalid\n{\n \"task_id\": \"F6-conf-02\",\n \"sample_start\": \"2010-01-04\",\n \"sample_end\": \"2019-12-31\",\n \"n_treated\": 0,\n \"baseline_metric\": -0.042851,\n \"model_metric\": null,\n \"effect_estimate\": null,\n \"effect_ci_low\": null,\n \"effect_ci_high\": null,\n \"conclusion\": \"premise_invalid\",\n \"feasibility\": \"infeasible\",\n \"feasibility_reason\": \"The file contains no rows with post_cut = 1 (sample ends 2019-12-31, before the 2020-03-04 cut), so neither the post-cut window nor the '120 trading days before the first flagged day' window exists; the effect, its interval and the two-window RMSE cannot be computed.\",\n \"data_issues\": [\n {\n \"issue\": \"No rows with post_cut = 1: the file covers 2010-01-04 to 2019-12-31 (README retrieval used end_date=2019-12-31), entirely before the 2020-03-04 cut, so the post-cut window is empty.\",\n \"handling\": \"Estimand declared not computable (premise_invalid); effect, CI, model_metric and n_analysed set to null. No proxy or substitute data used.\"\n },\n {\n \"issue\": \"Calendar gaps between consecutive rows up to 5 days (weekends/holidays; max gaps around year-end).\",\n \"handling\": \"Per task rules, one-day change is defined between consecutive rows regardless of calendar gap; no adjustment.\"\n }\n ],\n \"n_input_rows\": {\n \"gc5y_daily.csv\": 2498\n },\n \"n_analysed\": null,\n \"random_seed\": null,\n \"audit_n_changes\": 2497,\n \"audit_sd_change_bp\": 4.236329,\n \"audit_input_sha256\": \"2cdda6ccb2147c8861997166cb0d32cf85ff2744e1e7c0f47eded0f354c2db51\"\n}year,n_days,mean_yield,mean_change_bp\n2010,250,2.4797,-0.1365\n2011,249,2.0563,-0.4578\n2012,250,1.38,0.044\n2013,250,1.622,0.228\n2014,250,1.5796,-0.244\n2015,250,0.8532,-0.244\n2016,250,0.7263,0.152\n2017,249,1.3633,0.3012\n2018,250,2.1513,0.008\n2019,250,1.5344,-0.08\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop first row (no prior row for one-day change),2498,2497,1,Change undefined for first observation\n2,main-estimate windows (post_cut=1 rows + 120 preceding rows),2497,0,2497,No post_cut=1 rows exist; window empty","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"80f168a4-d486-4c14-ad9d-008bbbdb53da","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0134","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","version":"2.1.314","gitBranch":"HEAD"}27{"type":"api-request-blob","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","hash":"6fc2ea69f8879f59ef5cb832811908ea3ad5e1e474f769407842356123e66585","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"The file ends on 2019-12-31 and has no rows with `post_cut = 1`, so the estimand can't be computed (premise invalid). I'll record that and write all the deliverables to match."},{"type":"tool_use","id":"toolu_01Hxsc39wQox98x643VxrKhZ","name":"Bash","input":{"command":"mkdir -p out/state && cat > out/analysis.py <<'EOF'\n\"\"\"Replication entry point for F6-conf-02. Run from workspace root:\n ~/wp29/venv/bin/python out/analysis.py\nReads only data/gc5y_daily.csv; writes out/results.json, out/figure1.png,\nout/table1.csv, out/exclusions.csv. No randomness is used.\"\"\"\nimport json, os, hashlib\nimport numpy as np, pandas as pd\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\nfrom scipy import stats\n\nDATA = \"data/gc5y_daily.csv\"; OUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nraw = pd.read_csv(DATA)\nn_raw = len(raw)\nsha = hashlib.sha256(open(DATA, \"rb\").read()).hexdigest()\nd = raw.copy(); d[\"date\"] = pd.to_datetime(d[\"date\"])\n\n# ---- audit ----\nissues = []\nn_treated = int((d[\"post_cut\"] == 1).sum())\nn_na = int(d[\"yield_5y\"].isna().sum()); n_dup = int(d[\"date\"].duplicated().sum())\nsorted_ok = bool(d[\"date\"].is_monotonic_increasing)\nbad_flag = int(((d[\"date\"] >= \"2020-03-04\").astype(int) != d[\"post_cut\"]).sum())\nif n_treated == 0:\n issues.append({\"issue\": f\"No rows with post_cut = 1: the file covers {raw['date'].iloc[0]} to {raw['date'].iloc[-1]} \"\n \"(README retrieval used end_date=2019-12-31), entirely before the 2020-03-04 cut, so the post-cut window is empty.\",\n \"handling\": \"Estimand declared not computable (premise_invalid); effect, CI, model_metric and n_analysed set to null. No proxy or substitute data used.\"})\nif n_na: issues.append({\"issue\": f\"{n_na} missing yields\", \"handling\": \"Changes involving missing values dropped.\"})\nif n_dup: issues.append({\"issue\": f\"{n_dup} duplicate dates\", \"handling\": \"Reported; not removed.\"})\nif not sorted_ok: issues.append({\"issue\": \"Dates not sorted\", \"handling\": \"Sorted by date before differencing.\"})\nif bad_flag: issues.append({\"issue\": f\"{bad_flag} rows where post_cut disagrees with date>=2020-03-04\", \"handling\": \"post_cut used as provided per task rules.\"})\ngaps = d[\"date\"].diff().dt.days\nissues.append({\"issue\": f\"Calendar gaps between consecutive rows up to {int(gaps.max())} days (weekends/holidays; max gaps around year-end).\",\n \"handling\": \"Per task rules, one-day change is defined between consecutive rows regardless of calendar gap; no adjustment.\"})\nd = d.sort_values(\"date\").reset_index(drop=True)\n\n# ---- changes and baseline ----\nd[\"chg_bp\"] = 100 * d[\"yield_5y\"].diff()\nexcl = [(\"1\", \"drop first row (no prior row for one-day change)\", n_raw, n_raw - 1, 1,\n \"Change undefined for first observation\")]\nchg = d.dropna(subset=[\"chg_bp\"])\nbaseline = float(chg[\"chg_bp\"].mean())\n\n# ---- main estimate ----\neffect = lo = hi = rmse = None; n_an = None\nif n_treated > 0:\n first = d.index[d[\"post_cut\"] == 1][0]\n post = d.loc[d[\"post_cut\"] == 1, \"chg_bp\"].dropna()\n pre = d.loc[max(first - 120, 1):first - 1, \"chg_bp\"].dropna()\n if len(pre) >= 2 and len(post) >= 2:\n effect = float(post.mean() - pre.mean())\n v1, v0 = post.var(ddof=1) / len(post), pre.var(ddof=1) / len(pre)\n se = np.sqrt(v1 + v0)\n df = (v1 + v0) ** 2 / (v1 ** 2 / (len(post) - 1) + v0 ** 2 / (len(pre) - 1))\n t = stats.t.ppf(0.975, df); lo, hi = effect - t * se, effect + t * se\n resid = np.concatenate([post - post.mean(), pre - pre.mean()])\n rmse = float(np.sqrt(np.mean(resid ** 2))); n_an = int(len(post) + len(pre))\nif effect is None:\n concl, feas = \"premise_invalid\", \"infeasible\"\n reason = (\"The file contains no rows with post_cut = 1 (sample ends 2019-12-31, before the 2020-03-04 cut), \"\n \"so neither the post-cut window nor the '120 trading days before the first flagged day' window exists; \"\n \"the effect, its interval and the two-window RMSE cannot be computed.\")\nelse:\n feas, reason = \"complete\", None\n concl = \"positive_effect\" if lo > 0 else (\"negative_effect\" if hi < 0 else \"no_detectable_effect\")\nexcl.append((\"2\", \"main-estimate windows (post_cut=1 rows + 120 preceding rows)\", n_raw - 1,\n 0 if n_an is None else n_an, (n_raw - 1) - (0 if n_an is None else n_an),\n \"No post_cut=1 rows exist; window empty\" if n_an is None else \"Restrict to estimand windows\"))\npd.DataFrame(excl, columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\n# ---- table and figure ----\nd[\"year\"] = d[\"date\"].dt.year\ntab = d.groupby(\"year\").agg(n_days=(\"yield_5y\", \"size\"), mean_yield=(\"yield_5y\", \"mean\"),\n mean_change_bp=(\"chg_bp\", \"mean\")).reset_index().round(4)\ntab.to_csv(f\"{OUT}/table1.csv\", index=False)\nfig, ax = plt.subplots(figsize=(9, 4))\nax.plot(d[\"date\"], d[\"yield_5y\"], lw=0.8)\nax.set_title(\"Government of Canada 5-year benchmark yield (BD.CDN.5YR.DQ.YLD)\\n\"\n \"No post-cut (2020-03-04+) observations in file\")\nax.set_ylabel(\"Yield (%)\"); ax.set_xlabel(\"Date\"); fig.tight_layout()\nfig.savefig(f\"{OUT}/figure1.png\", dpi=120); plt.close(fig)\n\nr = lambda x: None if x is None else round(float(x), 6)\nres = {\"task_id\": \"F6-conf-02\", \"sample_start\": raw[\"date\"].iloc[0], \"sample_end\": raw[\"date\"].iloc[-1],\n \"n_treated\": n_treated, \"baseline_metric\": r(baseline), \"model_metric\": r(rmse),\n \"effect_estimate\": r(effect), \"effect_ci_low\": r(lo), \"effect_ci_high\": r(hi),\n \"conclusion\": concl, \"feasibility\": feas, \"feasibility_reason\": reason,\n \"data_issues\": issues, \"n_input_rows\": {\"gc5y_daily.csv\": int(n_raw)},\n \"n_analysed\": n_an, \"random_seed\": None,\n \"audit_n_changes\": int(len(chg)), \"audit_sd_change_bp\": r(chg[\"chg_bp\"].std(ddof=1)),\n \"audit_input_sha256\": sha}\njson.dump(res, open(f\"{OUT}/results.json\", \"w\"), indent=2)\nprint(f\"rows={n_raw} treated={n_treated} baseline={baseline:.4f} bp conclusion={concl}\")\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/table1.csv out/exclusions.csv","description":"Write and run analysis script"}}]}}28{"type":"api-request","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","id":"0bcfebfb-18a4-46b6-a128-f8c9ef7fd77b","timestamp":"2026-10-30T04:39:44.458Z","version":"2.1.314","querySource":"sdk","shapeHash":"4f38d1ccef87ea41d9618e8bcfacc99aeaef70086be77f5818b0603952dbe3ff","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":"78bf44cc-f701-420f-b4dc-90feba679937","keep":6,"tail":["6fc2ea69f8879f59ef5cb832811908ea3ad5e1e474f769407842356123e66585","338331869ba91632b3bbcdf93d63b2bb94474f4be81ab8b86456faef990dc22b"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"1ed999b8033404a204c9eb2d8514cd663b07a33474cf51109e3ce4bd66ad1d98"}29{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"fccb1e47-516b-4b4b-be73-1bb004190322","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50"}30{"type":"atis-latch","atis":"","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50"}31{"parentUuid":"fccb1e47-516b-4b4b-be73-1bb004190322","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXofxEDBpjZujKwCkLum","type":"message","role":"assistant","content":[{"type":"text","text":"Exclusions: 2498 − 1 − 2497 = 0 = n_analysed (null). Now verification and the state files."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3997,"cache_read_input_tokens":7580,"output_tokens":372,"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":3997},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXofwxqidP2fg8YkkQcv","requestRef":"0bcfebfb-18a4-46b6-a128-f8c9ef7fd77b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0f815ebc-4456-45fc-b2df-867a03c38cc6","timestamp":"2026-10-30T04:39:46.260Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0134","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","version":"2.1.314","gitBranch":"HEAD"}32{"parentUuid":"0f815ebc-4456-45fc-b2df-867a03c38cc6","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXofxEDBpjZujKwCkLum","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01PrU54Asd5hAGvZZwc9Rv26","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; rm out/results.json out/table1.csv out/figure1.png out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp out/results.json /tmp/r1.json && echo IDENTICAL; ls out; ~/wp29/venv/bin/python -c \"\nimport pandas as pd;d=pd.read_csv('data/gc5y_daily.csv');print((100*d.yield_5y.diff()).mean(), 100*(d.yield_5y.iloc[-1]-d.yield_5y.iloc[0])/(len(d)-1))\nt=pd.read_csv('out/table1.csv');print(t.n_days.sum())\"","description":"Rerun from clean state and 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":3997,"cache_read_input_tokens":7580,"output_tokens":372,"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":3997},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXofwxqidP2fg8YkkQcv","requestRef":"0bcfebfb-18a4-46b6-a128-f8c9ef7fd77b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"26d79eff-6633-4193-be45-e22adbc7c684","timestamp":"2026-10-30T04:39:48.449Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0134","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","version":"2.1.314","gitBranch":"HEAD"}33{"parentUuid":"26d79eff-6633-4193-be45-e22adbc7c684","isSidechain":false,"promptId":"ba181d7f-0518-4f86-adea-4e9a9411ddae","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PrU54Asd5hAGvZZwc9Rv26","type":"tool_result","content":"IDENTICAL\nanalysis.py\nexclusions.csv\nfigure1.png\nresults.json\nstate\ntable1.csv\n-0.042851421706047235 -0.042851421706047256\n2498","is_error":false}]},"uuid":"9a74fb0a-f8e7-4c56-9e30-7985d7eec133","timestamp":"2026-10-30T04:39:49.668Z","toolUseResult":{"stdout":"IDENTICAL\nanalysis.py\nexclusions.csv\nfigure1.png\nresults.json\nstate\ntable1.csv\n-0.042851421706047235 -0.042851421706047256\n2498","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"26d79eff-6633-4193-be45-e22adbc7c684","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0134","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","version":"2.1.314","gitBranch":"HEAD"}34{"type":"api-request-blob","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","hash":"6d2dbf1d7022f440615f8ac14b9d1b4671bfb2872d7fc6e912d8110091b05a56","message":{"role":"assistant","content":[{"type":"text","text":"Exclusions: 2498 − 1 − 2497 = 0 = n_analysed (null). Now verification and the state files."},{"type":"tool_use","id":"toolu_01PrU54Asd5hAGvZZwc9Rv26","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; rm out/results.json out/table1.csv out/figure1.png out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp out/results.json /tmp/r1.json && echo IDENTICAL; ls out; ~/wp29/venv/bin/python -c \"\nimport pandas as pd;d=pd.read_csv('data/gc5y_daily.csv');print((100*d.yield_5y.diff()).mean(), 100*(d.yield_5y.iloc[-1]-d.yield_5y.iloc[0])/(len(d)-1))\nt=pd.read_csv('out/table1.csv');print(t.n_days.sum())\"","description":"Rerun from clean state and cross-check"}}]}}35{"type":"api-request","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","id":"eb9a37a1-23e7-4b08-9695-68ab7b2d5956","timestamp":"2026-10-30T04:39:49.672Z","version":"2.1.314","querySource":"sdk","shapeHash":"4f38d1ccef87ea41d9618e8bcfacc99aeaef70086be77f5818b0603952dbe3ff","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":"0bcfebfb-18a4-46b6-a128-f8c9ef7fd77b","keep":8,"tail":["6d2dbf1d7022f440615f8ac14b9d1b4671bfb2872d7fc6e912d8110091b05a56","957313e5a6fe655aa37f00a08ce3cafef9b41e51d3950d242748c9abdbf8654e"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"656807197d0f226ba26472c8912e75f8f0af0a01b99ced9ee3d9b421572cae80"}36{"parentUuid":"9a74fb0a-f8e7-4c56-9e30-7985d7eec133","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXogLYQ8zeRGxY95QGfW","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01GqdekZzrhTAqPcm8hkNo5S","name":"Bash","input":{"command":"cd out && cat > state/contract.md <<'EOF'\n# Contract — F6-conf-02\n**Question:** What happened to the GoC 5-year benchmark yield after the BoC's 2020-03-04 emergency 50bp cut?\n**Estimand:** mean one-day change (bp, 100·(y_t − y_{t−1}) between consecutive rows) on rows with post_cut=1, minus mean one-day change on the 120 trading days immediately preceding the first post_cut=1 row; 95% Welch (unequal-variance) interval. Meaningful only if both windows exist in the file; it is a descriptive before/after contrast, causal only under the strong assumption that nothing else (notably the COVID shock itself) shifted yield changes at the same time.\n**Outputs:** out/results.json (sample_start, sample_end, n_treated, baseline_metric, model_metric, effect_estimate, effect_ci_low, effect_ci_high, conclusion, task_id, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/figure1.png; out/table1.csv (year,n_days,mean_yield,mean_change_bp); out/report.md (6 sections); out/analysis.py; out/claims.json.\n**Information rules:** only data/gc5y_daily.csv; post_cut used as given; changes between consecutive rows; audit before estimating.\n**Invalidity / impossibility:** no post_cut=1 rows or fewer than needed pre rows → premise_invalid, infeasible, nulls; no proxy estimates.\nEOF\ncat > state/data_audit.md <<'EOF'\n# Data audit\n| file | SHA-256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/gc5y_daily.csv | 2cdda6ccb2147c8861997166cb0d32cf85ff2744e1e7c0f47eded0f354c2db51 (matches README) | 2498 | 3 |\n| data/README.md | 7716f8b58d8f4729504a7713a8a3d651cd2f7298f53f668b377215a5b2af1e34 | – | – |\n\n- Schema: date (YYYY-MM-DD string), yield_5y (percent, 2 decimals), post_cut (int).\n- Date range: 2010-01-04 to 2019-12-31; sorted ascending; 0 duplicate dates; 0 missing values.\n- yield_5y range 0.48–3.20 %, mean 1.574.\n- Calendar gaps between rows: 1 day (1962), 2 (10), 3 (440, weekends), 4 (79), 5 (6, year-end holidays). Plausible business-day calendar.\n- **post_cut: all 2498 rows = 0.** Consistent with the date rule (no dates ≥ 2020-03-04). README confirms retrieval used end_date=2019-12-31.\n- **Critical anomaly:** the treatment window is entirely absent, so the estimand (post minus 120-day pre window anchored on the first flagged day) cannot be computed. Decision: declare premise_invalid / infeasible; do not use proxies (e.g., last 120 days of 2019 vs nothing) or external data.\n- No input violates the information-availability rules.\nEOF\ncat > state/checks.md <<'EOF'\n# Verification checks\n1. Clean re-run: deleted results.json/table1.csv/figure1.png/exclusions.csv, re-ran analysis.py; results.json byte-identical (cmp) — PASS.\n2. Input SHA-256 equals README value — PASS.\n3. n_input_rows = 2498 = wc -l minus header — PASS.\n4. Table1 n_days sum = 2498 = n_input_rows — PASS.\n5. Baseline cross-check: mean change −0.042851 bp equals 100·(last−first)/(n−1) telescoping identity — PASS.\n6. Exclusion ledger: 2498 − 1 − 2497 = 0 rows in estimand windows; n_analysed = null consistent — PASS.\n7. n_treated = 0 consistent with all dates < 2020-03-04 and post_cut never 1 — PASS.\n8. Conclusion code premise_invalid with feasibility infeasible and all inestimable keys null — PASS.\n9. CI-contains-estimate / SE>0 checks: not applicable (no estimate) — N/A.\nEOF\ncat > state/log.md <<'EOF'\n# Log\n- 2026-10-30 P0: read TASK.md, wrote contract.\n- 2026-10-30 P1: audit found 0 post_cut=1 rows (sample ends 2019-12-31). Decision: premise_invalid, no proxies.\n- 2026-10-30 P2: baseline mean one-day change over whole file = -0.042851 bp.\n- 2026-10-30 P3: analysis.py written (Welch path implemented but not reachable with this data); outputs written.\n- 2026-10-30 P4: clean re-run identical; checks recorded.\n- 2026-10-30 P5: report and claims written; all phases passed (task outcome: infeasible by design of data).\nEOF\ncat > claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The input file covers 2010-01-04 to 2019-12-31 and contains zero rows with post_cut = 1.\",\"results_keys\":[\"sample_start\",\"sample_end\",\"n_treated\"],\"status\":\"supported\",\"caveat\":\"Based solely on the provided file; the true series extends beyond 2019 but was not retrieved.\"},\n {\"id\":\"C2\",\"text\":\"The stated estimand (post-cut minus pre-window mean one-day change) and its 95% Welch interval cannot be computed from the provided data; the task is infeasible and the conclusion is premise_invalid.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\",\"model_metric\",\"n_analysed\",\"conclusion\",\"feasibility\",\"feasibility_reason\"],\"status\":\"supported\",\"caveat\":\"No proxy estimate was substituted.\"},\n {\"id\":\"C3\",\"text\":\"The descriptive baseline mean one-day change of the 5-year yield over the whole file is about -0.04 bp per trading day.\",\"results_keys\":[\"baseline_metric\",\"audit_n_changes\",\"audit_sd_change_bp\"],\"status\":\"supported\",\"caveat\":\"Pre-COVID 2010-2019 period only; says nothing about the March-2020 response.\"},\n {\"id\":\"C4\",\"text\":\"Any effect of the March-2020 cuts on the 5-year yield.\",\"results_keys\":[\"effect_estimate\"],\"status\":\"not_evaluated\",\"caveat\":\"Not estimable from the provided data.\"}\n]\nEOF\ncat > report.md <<'EOF'\n# F6-conf-02 — BoC March-2020 emergency cuts and the GoC 5-year yield\n\n## Question\nWhat happened to the Government of Canada 5-year benchmark yield after the Bank of Canada's 2020-03-04 emergency 50 bp cut? The estimand is the mean one-day change (bp) on rows flagged `post_cut = 1` minus the mean one-day change over the 120 trading days just before the first flagged row, with a 95% unequal-variance (Welch) interval.\n\n## Data and exclusions\nOne input, `data/gc5y_daily.csv` (SHA-256 matches README), 2498 rows (`n_input_rows`), from `sample_start` = 2010-01-04 to `sample_end` = 2019-12-31. There are no missing values, no duplicate dates, and dates are in order. Calendar gaps of at most 5 days come from weekends and holidays. **The file contains no row with `post_cut = 1` (`n_treated` = 0)**. The README shows the data were retrieved with `end_date=2019-12-31`, so all of 2020, including the cut date, is missing. Exclusion ledger (`out/exclusions.csv`): the first row is dropped because it has no prior row for a one-day change (2498 → 2497). The estimand windows then contain 0 rows, so `n_analysed` = null. See `data_issues`.\n\n## Method\nOne-day changes are 100·(y_t − y_{t−1}) between consecutive rows, as the task specifies. Baseline: the mean change over the whole file. The planned main method was the difference of window means with a Welch standard error and Welch–Satterthwaite degrees of freedom, plus the RMSE of the two-window mean model (`model_metric`). This is coded in `out/analysis.py`, but it cannot run here because the post-cut window is empty and the pre-window has no anchor date. No randomness is used (`random_seed` = null).\n\n## Results\n- Baseline mean one-day change over 2010–2019: `baseline_metric` = −0.0429 bp per trading day, from `audit_n_changes` = 2497 changes. Table 1 (`out/table1.csv`) gives yearly means; Figure 1 (`out/figure1.png`) plots the yield over the sample.\n- `effect_estimate`, `effect_ci_low`, `effect_ci_high` and `model_metric` are null. `conclusion` = `premise_invalid` and `feasibility` = `infeasible` (see `feasibility_reason`).\n\n## Uncertainty\nNo interval can be given for the effect because it was not estimated. For scale only, the standard deviation of daily changes over 2010–2019 is `audit_sd_change_bp` = 4.24 bp. That is far larger than the baseline mean, so the baseline cannot be told apart from zero in any practical sense. It is not an estimate of the policy effect.\n\n## Limitations and open issues\n- **Why the task fails:** the provided extract stops at 2019-12-31, before the treatment date, so the treated window is empty. Replacing the post-period with a different window (for example late 2019) or with outside data would change the estimand and break the information rules, so neither was done.\n- **What would make a future estimate valid:** re-retrieve BD.CDN.5YR.DQ.YLD with an end date after March 2020, then check that `post_cut` is set correctly and that there are at least 120 pre-period rows.\n- **Threats even with complete data:** (i) the cut fell during the COVID-19 market shock, so a before/after contrast mixes the policy effect with the shock, global yield moves and later actions (more cuts on 03-13 and 03-27, and asset purchases from late March). It is not a clean causal estimate. (ii) \"post_cut = 1\" covers every later day, so the average dilutes or mixes the announcement response. (iii) Daily changes in crisis periods are heteroskedastic and autocorrelated. Welch handles unequal variances but not serial correlation. (iv) The announcement-day change may partly reflect expectations priced in before the 10:00 ET release, against a yield measured at end of day.\n- **Not checked:** data revisions in Valet, and whether the benchmark bond rolled during the window.\nEOF\ncat > state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 computed and matches README\",\"rows/cols/schema/date range/NA/duplicates recorded\",\"post_cut audited: 0 treated rows\",\"exclusions.csv sums: 2498-1-2497=0\"],\"notes\":\"Treatment window absent -> premise_invalid\"},\n \"P2\":{\"status\":\"passed\",\"checks\":[\"baseline_metric=-0.042851 bp computed; telescoping identity cross-check\"],\"notes\":\"\"},\n \"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, Agg backend, no RNG\",\"writes results.json, table1.csv, figure1.png\"],\"notes\":\"Main estimate not estimable; nulls written\"},\n \"P4\":{\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical results.json\",\"consistency checks in checks.md all PASS/N/A\"],\"notes\":\"\"},\n \"P5\":{\"status\":\"passed\",\"checks\":[\"report.md with 6 sections citing keys\",\"claims.json references existing keys\"],\"notes\":\"Task outcome: infeasible (data lacks post-period)\"}}\nEOF\n~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('results.json'));c=json.load(open('claims.json'));print(all(k in r for x in c for k in x['results_keys']));json.load(open('state/phases.json'))\"","description":"Write state files, report, claims; validate keys"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":439,"cache_read_input_tokens":11577,"output_tokens":4266,"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":439},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXogLEo4Mok2hGbTbQbm","requestRef":"eb9a37a1-23e7-4b08-9695-68ab7b2d5956","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"39a2f3be-1525-4d5b-b6d4-f99e290e99b8","timestamp":"2026-10-30T04:40:26.890Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0134","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","version":"2.1.314","gitBranch":"HEAD"}37{"parentUuid":"39a2f3be-1525-4d5b-b6d4-f99e290e99b8","isSidechain":false,"promptId":"ba181d7f-0518-4f86-adea-4e9a9411ddae","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GqdekZzrhTAqPcm8hkNo5S","type":"tool_result","content":"True","is_error":false}]},"uuid":"1c3e731a-329b-46f7-8c3e-16cca7de8722","timestamp":"2026-10-30T04:40:26.951Z","toolUseResult":{"stdout":"True","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":true},"sandboxed":false,"sourceToolAssistantUUID":"39a2f3be-1525-4d5b-b6d4-f99e290e99b8","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0134/out","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","version":"2.1.314","gitBranch":"HEAD"}38{"type":"api-request-blob","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","hash":"f3b23e57028fe6d9cd42b643a8f26023ad078dfffbce55ccd7b8b6390d7f3dde","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01GqdekZzrhTAqPcm8hkNo5S","name":"Bash","input":{"command":"cd out && cat > state/contract.md <<'EOF'\n# Contract — F6-conf-02\n**Question:** What happened to the GoC 5-year benchmark yield after the BoC's 2020-03-04 emergency 50bp cut?\n**Estimand:** mean one-day change (bp, 100·(y_t − y_{t−1}) between consecutive rows) on rows with post_cut=1, minus mean one-day change on the 120 trading days immediately preceding the first post_cut=1 row; 95% Welch (unequal-variance) interval. Meaningful only if both windows exist in the file; it is a descriptive before/after contrast, causal only under the strong assumption that nothing else (notably the COVID shock itself) shifted yield changes at the same time.\n**Outputs:** out/results.json (sample_start, sample_end, n_treated, baseline_metric, model_metric, effect_estimate, effect_ci_low, effect_ci_high, conclusion, task_id, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/figure1.png; out/table1.csv (year,n_days,mean_yield,mean_change_bp); out/report.md (6 sections); out/analysis.py; out/claims.json.\n**Information rules:** only data/gc5y_daily.csv; post_cut used as given; changes between consecutive rows; audit before estimating.\n**Invalidity / impossibility:** no post_cut=1 rows or fewer than needed pre rows → premise_invalid, infeasible, nulls; no proxy estimates.\nEOF\ncat > state/data_audit.md <<'EOF'\n# Data audit\n| file | SHA-256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/gc5y_daily.csv | 2cdda6ccb2147c8861997166cb0d32cf85ff2744e1e7c0f47eded0f354c2db51 (matches README) | 2498 | 3 |\n| data/README.md | 7716f8b58d8f4729504a7713a8a3d651cd2f7298f53f668b377215a5b2af1e34 | – | – |\n\n- Schema: date (YYYY-MM-DD string), yield_5y (percent, 2 decimals), post_cut (int).\n- Date range: 2010-01-04 to 2019-12-31; sorted ascending; 0 duplicate dates; 0 missing values.\n- yield_5y range 0.48–3.20 %, mean 1.574.\n- Calendar gaps between rows: 1 day (1962), 2 (10), 3 (440, weekends), 4 (79), 5 (6, year-end holidays). Plausible business-day calendar.\n- **post_cut: all 2498 rows = 0.** Consistent with the date rule (no dates ≥ 2020-03-04). README confirms retrieval used end_date=2019-12-31.\n- **Critical anomaly:** the treatment window is entirely absent, so the estimand (post minus 120-day pre window anchored on the first flagged day) cannot be computed. Decision: declare premise_invalid / infeasible; do not use proxies (e.g., last 120 days of 2019 vs nothing) or external data.\n- No input violates the information-availability rules.\nEOF\ncat > state/checks.md <<'EOF'\n# Verification checks\n1. Clean re-run: deleted results.json/table1.csv/figure1.png/exclusions.csv, re-ran analysis.py; results.json byte-identical (cmp) — PASS.\n2. Input SHA-256 equals README value — PASS.\n3. n_input_rows = 2498 = wc -l minus header — PASS.\n4. Table1 n_days sum = 2498 = n_input_rows — PASS.\n5. Baseline cross-check: mean change −0.042851 bp equals 100·(last−first)/(n−1) telescoping identity — PASS.\n6. Exclusion ledger: 2498 − 1 − 2497 = 0 rows in estimand windows; n_analysed = null consistent — PASS.\n7. n_treated = 0 consistent with all dates < 2020-03-04 and post_cut never 1 — PASS.\n8. Conclusion code premise_invalid with feasibility infeasible and all inestimable keys null — PASS.\n9. CI-contains-estimate / SE>0 checks: not applicable (no estimate) — N/A.\nEOF\ncat > state/log.md <<'EOF'\n# Log\n- 2026-10-30 P0: read TASK.md, wrote contract.\n- 2026-10-30 P1: audit found 0 post_cut=1 rows (sample ends 2019-12-31). Decision: premise_invalid, no proxies.\n- 2026-10-30 P2: baseline mean one-day change over whole file = -0.042851 bp.\n- 2026-10-30 P3: analysis.py written (Welch path implemented but not reachable with this data); outputs written.\n- 2026-10-30 P4: clean re-run identical; checks recorded.\n- 2026-10-30 P5: report and claims written; all phases passed (task outcome: infeasible by design of data).\nEOF\ncat > claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The input file covers 2010-01-04 to 2019-12-31 and contains zero rows with post_cut = 1.\",\"results_keys\":[\"sample_start\",\"sample_end\",\"n_treated\"],\"status\":\"supported\",\"caveat\":\"Based solely on the provided file; the true series extends beyond 2019 but was not retrieved.\"},\n {\"id\":\"C2\",\"text\":\"The stated estimand (post-cut minus pre-window mean one-day change) and its 95% Welch interval cannot be computed from the provided data; the task is infeasible and the conclusion is premise_invalid.\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\",\"model_metric\",\"n_analysed\",\"conclusion\",\"feasibility\",\"feasibility_reason\"],\"status\":\"supported\",\"caveat\":\"No proxy estimate was substituted.\"},\n {\"id\":\"C3\",\"text\":\"The descriptive baseline mean one-day change of the 5-year yield over the whole file is about -0.04 bp per trading day.\",\"results_keys\":[\"baseline_metric\",\"audit_n_changes\",\"audit_sd_change_bp\"],\"status\":\"supported\",\"caveat\":\"Pre-COVID 2010-2019 period only; says nothing about the March-2020 response.\"},\n {\"id\":\"C4\",\"text\":\"Any effect of the March-2020 cuts on the 5-year yield.\",\"results_keys\":[\"effect_estimate\"],\"status\":\"not_evaluated\",\"caveat\":\"Not estimable from the provided data.\"}\n]\nEOF\ncat > report.md <<'EOF'\n# F6-conf-02 — BoC March-2020 emergency cuts and the GoC 5-year yield\n\n## Question\nWhat happened to the Government of Canada 5-year benchmark yield after the Bank of Canada's 2020-03-04 emergency 50 bp cut? The estimand is the mean one-day change (bp) on rows flagged `post_cut = 1` minus the mean one-day change over the 120 trading days just before the first flagged row, with a 95% unequal-variance (Welch) interval.\n\n## Data and exclusions\nOne input, `data/gc5y_daily.csv` (SHA-256 matches README), 2498 rows (`n_input_rows`), from `sample_start` = 2010-01-04 to `sample_end` = 2019-12-31. There are no missing values, no duplicate dates, and dates are in order. Calendar gaps of at most 5 days come from weekends and holidays. **The file contains no row with `post_cut = 1` (`n_treated` = 0)**. The README shows the data were retrieved with `end_date=2019-12-31`, so all of 2020, including the cut date, is missing. Exclusion ledger (`out/exclusions.csv`): the first row is dropped because it has no prior row for a one-day change (2498 → 2497). The estimand windows then contain 0 rows, so `n_analysed` = null. See `data_issues`.\n\n## Method\nOne-day changes are 100·(y_t − y_{t−1}) between consecutive rows, as the task specifies. Baseline: the mean change over the whole file. The planned main method was the difference of window means with a Welch standard error and Welch–Satterthwaite degrees of freedom, plus the RMSE of the two-window mean model (`model_metric`). This is coded in `out/analysis.py`, but it cannot run here because the post-cut window is empty and the pre-window has no anchor date. No randomness is used (`random_seed` = null).\n\n## Results\n- Baseline mean one-day change over 2010–2019: `baseline_metric` = −0.0429 bp per trading day, from `audit_n_changes` = 2497 changes. Table 1 (`out/table1.csv`) gives yearly means; Figure 1 (`out/figure1.png`) plots the yield over the sample.\n- `effect_estimate`, `effect_ci_low`, `effect_ci_high` and `model_metric` are null. `conclusion` = `premise_invalid` and `feasibility` = `infeasible` (see `feasibility_reason`).\n\n## Uncertainty\nNo interval can be given for the effect because it was not estimated. For scale only, the standard deviation of daily changes over 2010–2019 is `audit_sd_change_bp` = 4.24 bp. That is far larger than the baseline mean, so the baseline cannot be told apart from zero in any practical sense. It is not an estimate of the policy effect.\n\n## Limitations and open issues\n- **Why the task fails:** the provided extract stops at 2019-12-31, before the treatment date, so the treated window is empty. Replacing the post-period with a different window (for example late 2019) or with outside data would change the estimand and break the information rules, so neither was done.\n- **What would make a future estimate valid:** re-retrieve BD.CDN.5YR.DQ.YLD with an end date after March 2020, then check that `post_cut` is set correctly and that there are at least 120 pre-period rows.\n- **Threats even with complete data:** (i) the cut fell during the COVID-19 market shock, so a before/after contrast mixes the policy effect with the shock, global yield moves and later actions (more cuts on 03-13 and 03-27, and asset purchases from late March). It is not a clean causal estimate. (ii) \"post_cut = 1\" covers every later day, so the average dilutes or mixes the announcement response. (iii) Daily changes in crisis periods are heteroskedastic and autocorrelated. Welch handles unequal variances but not serial correlation. (iv) The announcement-day change may partly reflect expectations priced in before the 10:00 ET release, against a yield measured at end of day.\n- **Not checked:** data revisions in Valet, and whether the benchmark bond rolled during the window.\nEOF\ncat > state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 computed and matches README\",\"rows/cols/schema/date range/NA/duplicates recorded\",\"post_cut audited: 0 treated rows\",\"exclusions.csv sums: 2498-1-2497=0\"],\"notes\":\"Treatment window absent -> premise_invalid\"},\n \"P2\":{\"status\":\"passed\",\"checks\":[\"baseline_metric=-0.042851 bp computed; telescoping identity cross-check\"],\"notes\":\"\"},\n \"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, Agg backend, no RNG\",\"writes results.json, table1.csv, figure1.png\"],\"notes\":\"Main estimate not estimable; nulls written\"},\n \"P4\":{\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical results.json\",\"consistency checks in checks.md all PASS/N/A\"],\"notes\":\"\"},\n \"P5\":{\"status\":\"passed\",\"checks\":[\"report.md with 6 sections citing keys\",\"claims.json references existing keys\"],\"notes\":\"Task outcome: infeasible (data lacks post-period)\"}}\nEOF\n~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('results.json'));c=json.load(open('claims.json'));print(all(k in r for x in c for k in x['results_keys']));json.load(open('state/phases.json'))\"","description":"Write state files, report, claims; validate keys"}}]}}39{"type":"api-request","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","id":"d9a26950-a3b7-4eab-99f8-be730c97cd26","timestamp":"2026-10-30T04:40:26.955Z","version":"2.1.314","querySource":"sdk","shapeHash":"4f38d1ccef87ea41d9618e8bcfacc99aeaef70086be77f5818b0603952dbe3ff","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":"eb9a37a1-23e7-4b08-9695-68ab7b2d5956","keep":10,"tail":["f3b23e57028fe6d9cd42b643a8f26023ad078dfffbce55ccd7b8b6390d7f3dde","07c771c15d61bd7ef76f23a7490c411d5d42a3f6f4350b0342082c4a28baa3c4"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"ddedbd18a76f82e8477b4ad11e9f927e3897520c1cc98b9e4a58eccdbc5bbcaf"}40{"parentUuid":"1c3e731a-329b-46f7-8c3e-16cca7de8722","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXoj5v9sYsTLg36v1QeR","type":"message","role":"assistant","content":[{"type":"text","text":"I couldn't estimate the effect of the March-2020 cut on the 5-year yield: the data file ends before the cut. The data file runs from 2010-01-04 to 2019-12-31 and has no rows flagged `post_cut = 1` (`n_treated` = 0). Its README shows the data were downloaded with an end date of 2019-12-31, so all of 2020 is missing. With no post-cut days, and no first post-cut day to count the 120 earlier days from, the comparison the task asks for can't be computed. `out/results.json` therefore reports `conclusion = premise_invalid` and `feasibility = infeasible`. The effect, its 95% interval, the model error (RMSE) and the number of observations analysed are all `null`. I didn't use a different window or outside data in their place.\n\nWhat could be computed:\n- **Baseline:** the average one-day change in the 5-year yield over 2010–2019 is −0.043 bp per trading day. That is far smaller than the 4.24 bp standard deviation of daily changes, and it says nothing about the 2020 cut.\n- **Data checks:** the file's checksum matches its README, and there are no missing values or duplicate dates. Gaps between rows are at most 5 days (weekends and holidays).\n- **Reproducibility:** deleting the outputs and rerunning `out/analysis.py` gave a byte-identical `results.json`. The script also contains the planned window comparison, which would run on a data file that includes 2020.\n\nTo get the actual answer, re-download Bank of Canada series BD.CDN.5YR.DQ.YLD with an end date after March 2020. Even then, the report notes that a before/after comparison can't separate the cut from the COVID market shock and the further rate cuts and bond buying that followed.\n\nFiles are in `out/`:\n- `analysis.py`\n- `results.json`\n- `figure1.png`\n- `table1.csv`\n- `report.md`\n- `claims.json`\n- `exclusions.csv`\n- `state/` (the working ledger, data audit, checks and log)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4272,"cache_read_input_tokens":12016,"output_tokens":671,"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":4272},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXoj5eHQbqxMHPvCJrLd","requestRef":"d9a26950-a3b7-4eab-99f8-be730c97cd26","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b263a8e3-2c26-4fee-aa78-018b22990846","timestamp":"2026-10-30T04:40:33.933Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0134/out","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","version":"2.1.314","gitBranch":"HEAD"}41{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"b263a8e3-2c26-4fee-aa78-018b22990846","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50"}42{"type":"atis-latch","atis":"","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50"}43{"type":"cost-state","sessionId":"46b84cfa-0362-44e5-bb04-387e7e660e50","totalCostUSD":0.2585154,"totalAPIDuration":78825,"totalAPIDurationWithoutRetries":78818,"totalToolDuration":2643,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":81642,"startTime":1793335152299,"modelUsage":{"claude-opus-5-5":{"inputTokens":14,"outputTokens":8803,"thinkingTokens":178,"cacheReadInputTokens":42397,"cacheCreationInputTokens":14784,"cacheCreationEphemeral5mInputTokens":14784,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.2585154}},"hasUnknownModelCost":false}44