{"task_id":"st_01a01302","status":"completed","residency_state":"evicted","parent_session_id":"01a00387-aaf8-7f2f-89e3-e24c1af24859","root_session_id":"01a00387-aaf8-7f2f-89e3-e24c1af24859","depth":1,"execution_mode":"in-process","model":"openai-codex/gpt-5.6-sol","notify_on_terminal":true,"created_at":"2026-08-18T03:55:38.961Z","updated_at":"2026-08-19T11:03:08.933Z","notification":{"run_epoch":0,"notified_epoch":0},"name":"strict-sex-basis-normalization-fix","task_summary":"Fix sex basis attestation normalization","description":"Fix sex basis attestation normalization","category":"deep","requested_model":{"provider":"openai-codex","model_id":"gpt-5.6-sol","display":"openai-codex/gpt-5.6-sol","source":"category","variant":"medium","reasoning_effort":"medium"},"fallback_models":[{"provider":"clinepass","model_id":"cline-pass/deepseek-v4-pro","display":"clinepass/cline-pass/deepseek-v4-pro","source":"category","variant":"medium","reasoning_effort":"medium"},{"provider":"clinepass","model_id":"cline-pass/glm-5.2","display":"clinepass/cline-pass/glm-5.2","source":"category","variant":"medium","reasoning_effort":"medium"}],"resolved_model":{"provider":"openai-codex","model_id":"gpt-5.6-sol","display":"GPT-5.6 Sol","source":"category","variant":"medium","reasoning_effort":"medium"},"spawn_spec":{"version":1,"cwd":"/home/cube/projects/richard/traning coach","prompt":"Goal: test-first fix the confirmed current v6 product defect in the profile package source: customer answered polite Korean `넵 남성입니다`; reconciliation interpreted male/resolved but workflow answers retained raw string, so `attest_baseline` -> `build_onboarding_baseline` Pydantic enum validation rejected and current updates629525165/166 logged `transition_rejected`, owner card never published. User also correctly identified the redundant physiological clarification as bad UX.\n\nLocate authoritative profile repo/source (same source used to build preserved `physique_checkin_cli` wheel). Write failing tests using exact live phrase and flow: parse/collect 22 answers, reconciliation (including redundant equation-sex clarification if current reconciler produces it), resolve, attest baseline. Assert stored authoritative answer is canonical `male`, baseline succeeds, owner_review state reached, and no redundant clarification is presented for an already unambiguous male/female/decline selection. Preserve genuinely ambiguous/contradictory clarification behavior. Implement smallest boundary normalization for common exact Korean/English affirmative forms prompted by UI, not unsafe substring guessing; ensure clarification resolution cannot leave a non-enum source value. Audit the three allowed enum values and tests. Run ruff, basedpyright/ty per repo, compileall, focused tests, full profile suite. No live profile/evidence/service/Git/plan/todo changes. Return RED/GREEN, files, exact normalization contract, counts. Observable stop source ready to reseal, not deployed.\n\n<Category_Context name=\"deep\">\nYou are operating in DEEP mode. This is the category reserved for goal-oriented autonomous work on hairy problems that reward thorough exploration and comprehensive solutions.\n\nThe orchestrator chose this category because the task benefits from depth over speed. You should feel empowered to spend the time needed: five to fifteen minutes of silent exploration before the first edit is normal and correct. Rushing to implementation on a deep task is a failure mode, not a feature.\n\n# How deep mode adjusts the base behavior\n\n**Exploration budget: generous.** Read the files you need, trace dependencies both directions, fire 2-5 explore/librarian sub-agents in parallel for broader questions. Build a complete mental model before the first `apply_patch`. Exploration here is an investment, not overhead.\n\n**Goal, not plan.** You receive a GOAL describing the desired outcome. You figure out HOW to achieve it. The orchestrator deliberately did not hand you a step-by-step plan; producing one and asking for approval is not what was asked. Execute.\n\n**Atomic task treatment.** When the goal contains numbered steps or phases, treat them as sub-steps of ONE task and execute them all in this turn. Splitting them across turns is wrong unless they reveal an architectural blocker that requires the user's input. If the \"steps\" turn out to be genuinely independent tasks that should have been separate delegations, flag that in your final message and refuse the ones beyond scope.\n\n**Root cause bias.** Prefer root-cause fixes over symptom fixes. A null check around `foo()` is a symptom fix; fixing whatever causes `foo()` to return unexpected values is the root fix. Trace at least two levels up before settling on an answer. In deep mode, you have permission (and the expectation) to do the deeper fix.\n\n**Ambition scaled to context.** For brand-new greenfield work, be ambitious. Choose strong defaults, avoid AI-slop aesthetics, produce something you would be proud to hand to another senior engineer. For changes in an existing codebase, be surgical and respect the existing patterns; depth does not mean invasiveness.\n\n**Completion bar: full delivery.** \"Simplified version\", \"proof of concept\", and \"you can extend this later\" are not acceptable deliveries for a deep task. The orchestrator routed here specifically for a complete solution. If you hit a genuine blocker (missing secret, design decision only the user can make, three materially different attempts all failed), document it and return; otherwise, finish the task.\n\n**Status cadence: sparse.** The user is not on the other side of this conversation; the orchestrator is, and they will synthesize your progress. Send commentary only at meaningful phase transitions (starting exploration, starting implementation, starting verification, hitting a genuine blocker). Do not narrate every tool call; silence during focused work is expected.\n</Category_Context>"},"host_pid":1761519,"final_response":"I read this as completing the test-first profile-source fix - the authoritative source is fixed, fully verified, and ready to reseal without deployment.\n\n## Result\n\n**RED**\n- Exact flow failed at collection: `canonical_equation_sex_basis` was absent, reproducing the missing boundary normalization.\n- Original preserved wheel: `fb48a1931828ee60ab3812faf795550bf4e672a54c56f78cd10dcf674c45560d`.\n\n**GREEN**\n- Focused tests: **51 passed**\n- Full profile suite: **705 passed**\n- Ruff: **75 Python files, all checks passed**\n- basedpyright on changed source: **0 errors, 0 warnings**\n- compileall: **PASS**\n- Added coverage: **22 parameterized cases**\n\n## Changed files\n\nUnder `/home/cube/.cache/task26-strict-successor-1786976146/src-p`:\n\n- `checkin_cli/nutrition_onboarding.py`\n- `checkin_cli/nutrition_onboarding_models.py`\n- `tests/test_nutrition_onboarding.py`\n- `tests/test_nutrition_onboarding_models.py`\n\n## Exact normalization contract\n\nNormalization applies NFC, whitespace trim/collapse, and case-folding, followed by an **exact 32-entry lookup**—never substring matching.\n\nAccepted outputs remain exactly:\n\n- `male`\n- `female`\n- `decline`\n\nAccepted forms include:\n\n- Direct Korean: `남성`, `남성입니다`, `여성`, `여성입니다`, `선택 안 함`, `선택 안 합니다`, `선택하지 않겠습니다`\n- Exact Korean affirmatives using `네`, `넵`, or `예`, with optional comma, followed by `남성입니다` or `여성입니다`\n- English: canonical values; exact `yes`/`yep` male/female forms with optional comma\n- Decline: `prefer not to say`, `I prefer not to say`\n\nUnsupported or compound text is not guessed. It remains available for genuine reconciliation during initial collection, but an equation-sex clarification cannot resolve until its answer maps exactly to one of the three enum values.\n\nCanonical male, female, and decline selections suppress redundant equation-sex clarification. Noncanonical inputs preserve both ambiguity and contradiction clarification behavior.\n\nNo live profile, evidence, service, Git, plan, or todo state was changed. Source is ready to reseal; it was not built or deployed.","run_stats":{"runtime_ms":410858,"turns":21,"tool_calls":64,"output_tokens":13893,"total_tokens":2344856,"generation_ms":289959,"tokens_per_second":48,"cost_usd":2.371765,"cache_hit_rate_last":0.9937643260296256,"cache_hit_rate_run":0.9247336830314338}}