{"task_id":"st_01a02bef","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":"2026-08-23T00:05:08.856Z","updated_at":"2026-08-25T02:47:24.914Z","notification":{"run_epoch":0,"notified_epoch":0},"name":"finalization-data-hypothesis","task_summary":"Trace live data values against finalization validators","description":"Trace live data boundary rejection","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":"Investigate hypothesis H1 read-only: the live customer's real date/profile values violate a finalization boundary absent from the Golden Path. Worktree: /home/cube/projects/richard/.worktrees/nutricoach-v111-impl. Live profile: /home/cube/.hermes/profiles/dualcoachtest. Inspect code and live JSON only; do not edit files, run git, or mutate live state. Trace every ValueError reachable from NutritionOnboardingService.finalize for the current finalizing workflow. Return the most likely exact exception with file:line evidence and the observed live value that triggers it. Stop when each reachable validation is classified pass/fail.\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":1082531,"final_response":"I read this as a read-only root-cause investigation - I traced the live finalization path and classified each validation through the first failure.\n\n## Finding: H1 confirmed\n\nThe most likely exact exception is:\n\n```text\nUnsafeGoalTrajectoryError: unsafe goal trajectory\n```\n\nRaised at:\n\n- `dualcoach/profile/checkin_cli/nutrition_onboarding_calculations.py:126-127`\n- Called by `generate_initial_plan()` at `nutrition_onboarding_calculations.py:159`\n- Reached from `finalize_onboarding()` at `nutrition_onboarding_finalization.py:161`\n\n### Triggering live values\n\nFrom `baseline-candidate.json:1`:\n\n- `goal_type = \"loss\"`\n- `weight_kg = 82.3`\n- `target_weight_kg = 75`\n- `target_date = 2026-10-22`\n\nFrom `telegram-customer-bootstrap-v1/ledger.json:1`:\n\n- `starts_on = 2026-08-21`\n\nThe boundary computes:\n\n```text\ndays = 62\nweekly change = (75 - 82.3) / (62 / 7)\n              = -0.8241935484 kg/week\nrate = abs(-0.8241935484 / 82.3)\n     = 0.0100145024\nlimit = 0.01\n```\n\nBecause `0.0100145024 > 0.01`, line 126 rejects it. The minimum safe target over this interval is approximately `75.01057 kg`; the requested `75 kg` misses the boundary by only about `0.01057 kg`.\n\n## Validation classification in execution order\n\n| Boundary | Result | Evidence |\n|---|---|---|\n| Storage/path ownership | Pass | All relevant directories are owner-controlled `0700`, files `0600`, with no symlinks. |\n| Current authority supplied | Pass | Runtime supplies `authority` at `telegram_nutrition_onboarding_runtime.py:277-285`. |\n| Current registry authority | Pass | Customer exists uniquely, is disabled, has granted `privacy-v1` consent, and current customer/owner routes match the registry. |\n| Workflow state | Pass | Live `workflow.json:1` has `\"state\":\"finalizing\"`; checked at `nutrition_onboarding_finalize_service.py:32-34`. |\n| KST timestamp | Pass | Runtime uses `datetime.now(ZoneInfo(\"Asia/Seoul\"))` at runtime line 279; validated at finalization lines 79-80. |\n| Privacy digest | Pass | Live value is lowercase SHA-256 `9f6a...10f1`; boundary at lines 81-86. |\n| Feature epoch digest | Pass | Feature-epoch file is absent, so runtime generates the canonical disabled-epoch SHA-256; boundary at lines 81-86. |\n| Input reconciliation digest | Pass | `95261e...90f0` is a valid lowercase SHA-256; lines 88-95. |\n| Baseline candidate digest | Pass | Recomputed digest exactly matches `f68370...fb2b`; lines 96-102. |\n| Baseline model validation | Pass | Age 21, male basis, 183 cm, 82.3 kg, loss target fields and structured values all satisfy the model. |\n| Restriction KB source template | Pass | Present and valid; effective `2026-08-01` through `2027-07-31`; lines 106-120. |\n| Clinical receipt validation | Not taken | `clinical-review.json` is absent. |\n| Owner-risk history | Pass | Receipt context matches customer, baseline and reconciliation; stored receipt digest recomputes exactly to `4700c0...63ed`; lines 142-152. |\n| Medical/restriction gate | Pass via owner acceptance | Conditions and medications require review, but valid owner-risk history satisfies lines 154-160. |\n| Equation basis | Pass | `\"male\"` produces BMR `1867`; decline exception at calculation lines 153-154 is not reachable. |\n| Goal direction and positive duration | Pass | Loss direction is correct and duration is 62 days. |\n| Weekly trajectory rate | **Fail** | `1.00145024% > 1%`; calculation lines 119-127. |\n\nAll later `ValueError` sites—macro reconciliation, generated-plan model checks, readiness audit, and registry projection—are unreachable because execution stops at the trajectory exception.\n\n## Why the Golden Path misses it\n\nThe Golden Path answers declare a maintenance goal and explicitly omit target weight/date in `source_golden_path.py:47-69`. For maintenance, `_safe_weekly_change()` returns immediately at `nutrition_onboarding_calculations.py:109-111`, so the rate boundary at lines 119-127 is never exercised.\n\nNo files were edited and no live state was mutated.","run_stats":{"runtime_ms":152663,"turns":11,"tool_calls":42,"output_tokens":6617,"total_tokens":1044432,"generation_ms":145682,"tokens_per_second":45,"cost_usd":1.2749449999999998,"cache_hit_rate_last":0.9834119085035795,"cache_hit_rate_run":0.8806193782128799}}