{"task_id":"st_01a00a96","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":"2026-08-16T12:40:22.757Z","updated_at":"2026-08-18T15:58:05.577Z","notification":{"run_epoch":1,"notified_epoch":1},"name":"customer-summary-copy-fix","task_summary":"Fix Telegram customer summary schema and copy","description":"Fix Telegram customer summary schema and copy","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":"In /home/cube/projects/richard/hermes-agent, TDD-fix the confirmed customer-facing summary defect in gateway/platforms/nutrition_onboarding_reconciliation.py::render_authoritative_customer_summary. Runtime screenshot after the 22 canonical answers + one activity clarification showed: `목표: 미입력`, activity text followed by `미입력`, raw `{'items': [], 'status': 'none'}` structures in restrictions, and `건강 관련 입력: 검토할 입력이 있습니다` despite all safety inputs being none/false. Confirmed mechanism: renderer reads stale legacy keys (`goal`, `training_details`, `medical_conditions`, `pregnancy_lactation`, `religious_restrictions`) instead of current canonical keys (`goal_type`, `activity_rationale`, `conditions`, `pregnancy_breastfeeding`, `religious_ethical_exclusions`), and generic str() leaks structured items. First add the smallest failing test using the exact synthetic canonical answers from the real surface. The RED assertion must be machine-consumed semantic outputs, not pin broad prose: prove mapped goal/activity, none-structured items render as human values without braces/status tokens, and none/false safety fields classify no-review. Run RED and capture exact output. Then minimally fix the renderer using typed/exhaustive formatting appropriate to this existing codebase; map enum values to Korean customer copy, format StructuredItems status none/provided without developer syntax, combine all relevant restrictions/preferences clearly, and classify safety review only when conditions or medications status is provided, pregnancy_breastfeeding is true/unknown, or eating_disorder_risk is true/unknown. Do not alter advisory LLM output, state machine, authority, buttons, unrelated copy, or broader code. Also test provided-items and true/unknown safety branch. Run focused tests, all related onboarding tests, Ruff, format, compile, strict basedpyright on changed files, no-excuse audit, and a minimal driver rendering the exact synthetic summary. Do not mutate live profile/service/network/Telegram/provider/candidate/evidence/Git. Return RED/GREEN evidence, exact diff, file hashes, and the rendered target summary.\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":4082105,"final_response":"I read this as completed follow-up TDD delivery - exact legacy activity recovery is implemented and fully audited.\n\n## RED → GREEN\n\nRED evidence:\n\n- Parser returned the entire clarification sentence instead of `moderate`.\n- Schedule rewind retained the invalid enum and old rationale.\n- Real baseline attestation failed with Pydantic’s `activity_category` enum error.\n\nArtifacts:\n\n```text\n/tmp/st_01a00a96/followup/red-parse.txt\n/tmp/st_01a00a96/followup/red-recovery.txt\n```\n\nGREEN:\n\n- Focused recovery tests: **4 passed**\n- All related onboarding tests: **136 passed**\n\n## Behavior\n\n- Exact choices such as `보통` still map to `moderate`.\n- Only the exact shipped marker `활동 수준: <known label>. ` is recognized.\n- Arbitrary or unknown prose remains unchanged and therefore fails closed.\n- Customer-authorized cursor-21 rewind:\n  - converts the legacy category to `moderate`;\n  - moves all trailing text into `activity_rationale`;\n  - preserves prior answers;\n  - removes only `schedule_constraints`;\n  - clears reconciliation;\n  - returns collecting at cursor 21.\n- Resubmission produces a baseline-valid canonical answer set and corrected Korean summary.\n- No new LLM invocation, authority expansion, state transition, or direct migration path was added.\n\n## Supported live UI sequence\n\n1. Deploy/restart.\n2. Startup recovery calls `_publish` for waiting activation sessions.\n   - Changed rendered body digest: generation 24 advances to 25 and a replacement card is published.\n   - Identical body digest: generation 24 is reused; no duplicate card.\n   - This follow-up alone does not normalize persisted answers during startup.\n3. Customer presses the latest **수정** button.\n4. Existing authorized callback rewinds to `schedule_constraints`; exact legacy activity data is normalized during that rewind.\n5. Customer resubmits the schedule answer.\n6. Existing reconciliation/publication flow renders the corrected card.\n7. Customer confirms normally.\n\nThe fix adds no provider call. Tests and driver used no LLM or live systems.\n\n## Driver result\n\n```text\nparser=moderate\nbaseline_activity=moderate\nbaseline_rationale=주 3회, 회당 60분 정도의 중간 강도 근력운동을 희망합니다. 평소 걷기·이동은 하루 약 6,000~8,000보 수준입니다.\n- 목표: 유지\n- 활동: 보통 · 주 3회, 회당 60분 정도의 중간 강도 근력운동을 희망합니다. 평소 걷기·이동은 하루 약 6,000~8,000보 수준입니다.\n- 식사: 하루 3끼\n- 조리·예산: 기본 조리 가능 · 보통\n- 식사 시간: 없음\n- 알레르기·불내증·식품 제한: 알레르기 없음 · 불내증 없음 · 종교·윤리 제한 없음 · 비선호 식품 없음 · 식품 선호 없음\n- 건강 관련 입력: 별도 검토 입력이 없습니다.\n```\n\n## Verification\n\n- Ruff check: passed\n- Compileall: passed\n- `git diff --check`: passed\n- Related tests: 136 passed\n- Strict basedpyright: **237 baseline / 237 current**, zero regression\n- Ruff format status: identical baseline/current; same three pre-existing unformatted files\n- No live profile, service, network, Telegram, provider, candidate, evidence, or Git mutation\n\n## Hashes\n\n```text\n8afb45c5cca082fd85cf07ab43dc5d1062c34678fd268a5722e313ce35cffd8b  gateway/platforms/telegram_nutrition_onboarding_copy.py\n3c24d49d656c623edfb95fcfbc7fb0f92365fc123420df66f733f9dbe39d704f  gateway/platforms/telegram_nutrition_onboarding_runtime.py\ncc89f68a52c538b3730840b49d1afb0b9762745a64c3e2a7f8a7ead597d4d5f8  tests/gateway/test_telegram_nutrition_onboarding_copy.py\nce57d8240aef145ff9956f65b60b5e8b66316e2527f6845bdfa3a21510509321  tests/gateway/test_telegram_nutrition_onboarding.py\n```\n\nExact unified diff:\n\n```text\n/tmp/st_01a00a96/followup/exact.diff\nSHA-256 cb15bb8067e665eaaf5a5d0b0d70a8058e0986962a54917b708b9a5e5e9d9a87\n```","run_stats":{"runtime_ms":377761,"turns":28,"tool_calls":57,"output_tokens":16395,"total_tokens":3411901,"generation_ms":354452,"tokens_per_second":46,"cost_usd":2.5164200000000005,"cache_hit_rate_last":0.9875253929024546,"cache_hit_rate_run":0.9786111407254177}}