{"type":"transition_applied","payload":{"type":"transition_applied","status":"running","residency_state":"resident"}}
{"type":"assistant_message","payload":{"text":"\n\n"}}
{"type":"tool_execution","payload":{"tool":"grep","is_error":false}}
{"type":"tool_execution","payload":{"tool":"grep","is_error":false}}
{"type":"tool_execution","payload":{"tool":"ls","is_error":false}}
{"type":"assistant_message","payload":{"text":"\n\n"}}
{"type":"tool_execution","payload":{"tool":"grep","is_error":false}}
{"type":"tool_execution","payload":{"tool":"ls","is_error":false}}
{"type":"tool_execution","payload":{"tool":"grep","is_error":false}}
{"type":"assistant_message","payload":{"text":"\n\n"}}
{"type":"tool_execution","payload":{"tool":"grep","is_error":false}}
{"type":"tool_execution","payload":{"tool":"read","is_error":false}}
{"type":"tool_execution","payload":{"tool":"grep","is_error":false}}
{"type":"assistant_message","payload":{"text":"The canonical schema lives in `checkin_cli/nutrition_onboarding_contract.py`. Let me read it plus the runtime collection logic to answer the bulk-vs-single-message question.\n\n"}}
{"type":"tool_execution","payload":{"tool":"read","is_error":true}}
{"type":"tool_execution","payload":{"tool":"read","is_error":false}}
{"type":"assistant_message","payload":{"text":"\n\n"}}
{"type":"tool_execution","payload":{"tool":"find","is_error":false}}
{"type":"tool_execution","payload":{"tool":"find","is_error":false}}
{"type":"assistant_message","payload":{"text":"`checkin_cli` is not in this repo. It's an external installed package. Let me locate it and find the exact schema plus the skip/optional-default and submit flow.\n\n"}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":true}}
{"type":"tool_execution","payload":{"tool":"grep","is_error":false}}
{"type":"tool_execution","payload":{"tool":"grep","is_error":false}}
{"type":"assistant_message","payload":{"text":"\n\n"}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":true}}
{"type":"tool_execution","payload":{"tool":"grep","is_error":false}}
{"type":"assistant_message","payload":{"text":"\n\n"}}
{"type":"tool_execution","payload":{"tool":"read","is_error":false}}
{"type":"tool_execution","payload":{"tool":"find","is_error":false}}
{"type":"assistant_message","payload":{"text":"The canonical `checkin_cli` contract isn't in the working tree as source (it's a shipped package), but exact copies exist in the evidence snapshots. Let me read the contract there plus the service-side submit/domain logic in `nutrition_coaching.py`.\n\n"}}
{"type":"tool_execution","payload":{"tool":"read","is_error":true}}
{"type":"assistant_message","payload":{"text":"\n\n"}}
{"type":"tool_execution","payload":{"tool":"read","is_error":true}}
{"type":"tool_execution","payload":{"tool":"ls","is_error":false}}
{"type":"assistant_message","payload":{"text":"The v3 path exists (ls succeeded). Let me read the contract file directly.\n\n"}}
{"type":"tool_execution","payload":{"tool":"read","is_error":false}}
{"type":"assistant_message","payload":{"text":"I have the canonical field order and Korean copy. Now I need the validation ranges/constraints and conditional branching from the domain logic. Let me dig into the checkin_cli package.\n\n"}}
{"type":"tool_execution","payload":{"tool":"grep","is_error":true}}
{"type":"tool_execution","payload":{"tool":"grep","is_error":true}}
{"type":"assistant_message","payload":{"text":"\n\n"}}
{"type":"tool_execution","payload":{"tool":"grep","is_error":false}}
{"type":"tool_execution","payload":{"tool":"grep","is_error":false}}
{"type":"assistant_message","payload":{"text":"\n\n"}}
{"type":"tool_execution","payload":{"tool":"read","is_error":false}}
{"type":"tool_execution","payload":{"tool":"read","is_error":false}}
{"type":"assistant_message","payload":{"text":"Now let me confirm the per-question transitions, the safety-hold branching, and how messages/prompts are published.\n\n"}}
{"type":"tool_execution","payload":{"tool":"read","is_error":false}}
{"type":"tool_execution","payload":{"tool":"read","is_error":false}}
{"type":"assistant_message","payload":{"text":"Let me confirm the safety-hold branching trigger.\n\n"}}
{"type":"tool_execution","payload":{"tool":"grep","is_error":false}}
{"type":"tool_execution","payload":{"tool":"grep","is_error":false}}
{"type":"assistant_message","payload":{"text":"\n\n"}}
{"type":"tool_execution","payload":{"tool":"read","is_error":false}}
{"type":"assistant_message","payload":{"text":"Let me pin exact line numbers for citations and confirm the parse/unions precisely.\n\n"}}
{"type":"tool_execution","payload":{"tool":"grep","is_error":true}}
{"type":"tool_execution","payload":{"tool":"grep","is_error":true}}
{"type":"tool_execution","payload":{"tool":"grep","is_error":false}}
{"type":"tool_execution","payload":{"tool":"grep","is_error":false}}
{"type":"assistant_message","payload":{"text":"\n\n"}}
{"type":"tool_execution","payload":{"tool":"grep","is_error":true}}
{"type":"tool_execution","payload":{"tool":"grep","is_error":true}}
{"type":"assistant_message","payload":{"text":"\n\n"}}
{"type":"tool_execution","payload":{"tool":"read","is_error":false}}
{"type":"assistant_message","payload":{"text":"\n\n"}}
{"type":"tool_execution","payload":{"tool":"read","is_error":false}}
{"type":"tool_execution","payload":{"tool":"read","is_error":false}}
{"type":"assistant_message","payload":{"text":"\n\n"}}
{"type":"tool_execution","payload":{"tool":"read","is_error":false}}
{"type":"assistant_message","payload":{"text":"\n\n"}}
{"type":"tool_execution","payload":{"tool":"grep","is_error":false}}
{"type":"assistant_message","payload":{"text":"<analysis>\n**Literal Request**: Inspect the exact production onboarding schema for the dualcoach Telegram customer flow (currently at `date_of_birth`, question 1/22) and return, in runtime order, each question key, prompt summary, input type, accepted format/choices/ranges, conditional branching rules, one consistent synthetic adult answer per field, plus whether replies must be one-message-at-a-time or bulk.\n\n**Actual Need**: A caller who must drive this live flow through the Telegram UI step by step (e.g. an automated/demo customer) needs an authoritative, line-cited questionnaire so they can answer all 22 fields correctly and pass validation in one pass.\n\n**Success Looks Like**: A complete, ordered, internally consistent 22-field answer sheet with exact Korean prompt, allowed formats/ranges/choices, every condition that changes later questions or state, and the one-per-message rule confirmed by code — all with source paths+lines.\n</analysis>\n\nThe production schema is a split design: the ordered 22-field contract and value-models live in the **shipped `checkin_cli` package** (not present as source in this repo; an exact copy is snapshot under `.omo/evidence/.../snapshot/profile-package/checkin_cli/`), while the Telegram display copy and per-answer parsing live in `gateway/platforms/telegram_nutrition_onboarding_copy.py`. The runtime that drives 1/22 is `gateway/platforms/telegram_nutrition_onboarding_runtime_collection.py`.\n\n<results>\n<files>\n- /home/cube/projects/richard/hermes-agent/.omo/evidence/task26/task26-owner-customer-v1-candidate-v3/snapshot/profile-package/checkin_cli/nutrition_onboarding_contract.py - Canonical `QUESTION_FIELDS` ordered tuple (22 fields, lines 13-38) and `example_answer` (lines 79-101). Imported at runtime as `checkin_cli.nutrition_onboarding_contract` (e.g. gateway/platforms/telegram_nutrition_onboarding_preview_flow.py:6).\n- /home/cube/projects/richard/hermes-agent/.omo/evidence/task26/task26-owner-customer-v1-candidate-v3/snapshot/profile-package/checkin_cli/nutrition_onboarding_models.py - Value model: adult_age/height/weight/target/meal_count ranges (lines 127-147), `validate_goal` goal-target branching (174-180), `derive_adult_age` 18-120 enforcement (189-195), free-text 500-char cap (163-...).\n- /home/cube/projects/richard/hermes-agent/.omo/evidence/task26/task26-owner-customer-v1-candidate-v3/snapshot/profile-package/checkin_cli/nutrition_onboarding_authority.py - `requires_safety_hold` trigger (lines 83-88).\n- /home/cube/projects/richard/hermes-agent/.omo/evidence/task26/task26-owner-customer-v1-candidate-v3/snapshot/profile-package/checkin_cli/nutrition_onboarding.py - `submit_answer` enforces one-field-at-a-time (cursor must equal current `QUESTION_FIELDS[cursor]`, lines ~157-196); `attest_baseline` builds baseline and routes to SAFETY_HOLD vs OWNER_REVIEW (~250-281).\n- /home/cube/projects/richard/hermes-agent/.omo/evidence/task26/task26-owner-customer-v1-candidate-v3/snapshot/profile-package/checkin_cli/nutrition_onboarding_session.py - `build_status` derives `next_field=QUESTION_FIELDS[cursor]` (line 37).\n- /home/cube/projects/richard/hermes-agent/gateway/platforms/telegram_nutrition_onboarding_copy.py - Korean `PROMPTS` dict (lines 49-71), `card()` display routing (73-96), `parse_answer` per-field parsing (99-149).\n- /home/cube/projects/richard/hermes-agent/gateway/platforms/telegram_nutrition_onboarding.py - `OPTIONAL_QUESTION_DEFAULTS` skip set (lines 31-35).\n- /home/cube/projects/richard/hermes-agent/gateway/platforms/telegram_nutrition_onboarding_runtime_collection.py - `handle_text` one-answer-per-message ingestion (line 53), skip handling (237-243).\n- /home/cube/projects/richard/hermes-agent/gateway/platforms/telegram_nutrition_onboarding_runtime_publication.py - `[n/22]` header and forced-reply question prompt (lines ~58-67).\n</files>\n\n<answer>\nThe flow displays each question as a forced-reply card \"`[n/22] <Korean prompt>`\" and advances by stored cursor. Answers are validated for format at parse time and for range at final baseline build. Ranges not enforced at the parse layer are enforced when `build_onboarding_baseline` runs (after attestation), so a value passing parse can still hard-fail finalization.\n\n**One-message-at-a-time — YES, bulk/multiline is NOT supported.** `handle_text` (runtime_collection.py:53) ingests a single Telegram message, parses exactly the current `status.next_field` (one field), and calls `submit_answer` for that one field; `submit_answer` rejects any message whose field isn't the current cursor question (nutrition_onboarding.py:185-195). Nothing splits answers across multiple questions. \"Multiline\" only exists *inside* a single field: comma-separated items for the 7 structured list fields (allergies, etc., parsed in telegram_nutrition_onboarding_copy.py:100-107). Each of the 22 questions must be answered in its own message before the next card is published.\n\n**Skip escape (`건너뛰기`)**: only for 4 optional free-text fields (OPTIONAL_QUESTION_DEFAULTS, telegram_nutrition_onboarding.py:31-35; enforced at runtime_collection.py:237-243). These map to \"\". The prompt footer advertises skip only for these (runtime_publication.py:65-67).\n\n**The 22 questions in exact runtime order** (CONTRACT: nutrition_onboarding_contract.py:13-38; PROMPTS Korean copy at telegram_nutrition_onboarding_copy.py:49-71; parse at :99-149; range/model facts from nutrition_onboarding_models.py):\n\n1. `date_of_birth` — \"생년월일을 YYYY-MM-DD 형식으로 입력해 주세요.\" Input: ISO date string `YYYY-MM-DD`. Format enforced at final build via `date.fromisoformat` + `derive_adult_age`: derived age must be 18-120 as-of build date (models.py:189-195; baseline `adult_age` ge=18 le=120 at :127). Parse returns raw string (no parse-time check). Answer: `1996-08-01` (age 30).\n\n2. `equation_sex_basis` — \"계산 기준을 입력해 주세요: 남성 / 여성 / 선택 안 함\". Choices: 남성→`male`, 여성→`female`, 선택 안 함→`decline` (enum EquationSexBasis; parse mapping copy.py:111-114; enum models.py validates at :95). Answer: `남성`.\n\n3. `height_cm` — \"키를 cm 단위 숫자로 입력해 주세요.\" Numeric with unit: regex `\\d+(\\.\\d+)?\\s*cm` (parse copy.py:23-28 via `_parse_measurement`, unit table :6-8). Range 120-250 cm (models.py:129). Answer: `180cm`.\n\n4. `weight_kg` — \"현재 체중을 kg 단위 숫자로 입력해 주세요.\" Numeric `\\d+(\\.\\d+)?\\s*kg`. Range 35-300 kg (models.py:130). Answer: `80kg`.\n\n5. `activity_category` — \"활동 수준: 좌식 / 가벼움 / 보통 / 매우 활동적 / 최고 활동\". Choices: 좌식→`sedentary`, 가벼움→`light`, 보통→`moderate`, 매우 활동적→`very_active`, 최고 활동→`extra_active` (parse mapping copy.py:116-119; enum models.py:99-105). Answer: `보통`.\n\n6. `activity_rationale` — \"주간 운동 횟수와 활동 근거를 적어 주세요.\" Free text, **optional/skippable** (건너뛰기 → \"\"). ≤500 chars at build (models.py:163-182). Answer: `주 3회 근력 운동`.\n\n7. `goal_type` — \"목표를 입력해 주세요: 감량 / 유지 / 증량\". Choices: 감량→`loss`, 유지→`maintain`, 증량→`gain` (copy.py:120-122; enum models.py:107-113). **Primary branch**: if `maintain`, target_weight_kg AND target_date must both be absent (`None`); if `loss`/`gain`, both are REQUIRED (validate_goal, models.py:174-180). Answer: `유지` (maintain).\n\n8. `target_weight_kg` — \"목표 체중을 kg으로 입력해 주세요. 유지라면 '없음'.\" Branch-dependent: `없음`/`none` → `None` (copy.py:133-134); else kg measure, range 35-300 (models.py:134). For a maintain goal must be None. Answer: `없음`.\n\n9. `target_date` — \"목표 날짜를 YYYY-MM-DD로 입력해 주세요. 유지라면 '없음'.\" Branch-dependent: `없음`/`none` → `None` (copy.py:133-134); else ISO date. For maintain goal must be None. Answer: `없음`.\n\n10. `allergies` — \"알레르기를 쉼표로 구분해 입력해 주세요. 없으면 '없음'.\" Structured: `없음`/`none` → `{\"status\":\"none\",\"items\":[]}`; else comma-split → `{\"status\":\"provided\",\"items\":[...]}` (copy.py:100-107). Items ≤200 chars, ≤40 items (models.py StructuredItems :55-75). Not safety-triggering. Answer: `없음`.\n\n11. `intolerances` — \"불내증을 쉼표로 구분해 입력해 주세요. 없으면 '없음'.\" Same structured format as #10. Answer: `없음`.\n\n12. `religious_ethical_exclusions` — \"종교·윤리상 제외 식품을 입력해 주세요. 없으면 '없음'.\" Same structured format. Answer: `없음`.\n\n13. `disliked_foods` — \"싫어하는 식품을 입력해 주세요. 없으면 '없음'.\" Same structured format. Answer: `없음`.\n\n14. `dietary_preferences` — \"선호 식품을 입력해 주세요. 없으면 '없음'.\" Same structured format. Answer: `없음`.\n\n15. `conditions` — \"의학적 고려사항을 입력해 주세요. 없으면 '없음'.\" Structured format, **branching**: `status:provided` (any item) forces SAFETY_HOLD at attestation (authority.py:88). Answer: `없음` (none, avoids hold).\n\n16. `medications` — \"복용 약·보충제를 입력해 주세요. 없으면 '없음'.\" Structured format, **branching**: `status:provided` also forces SAFETY_HOLD (authority.py:88). Answer: `없음`.\n\n17. `pregnancy_breastfeeding` — \"임신·수유 중인가요? 예 / 아니오 / 확실하지 않음\". Choices: 예→`True`, 아니오→`False`, 확실하지 않음→`None` (copy.py:126-127). **Branching**: any non-`False` value (True or unsure/None) forces SAFETY_HOLD (authority.py:87). Answer: `아니오`.\n\n18. `eating_disorder_risk` — \"섭식장애 위험·과거력이 있나요? 예 / 아니오 / 확실하지 않음\". Choices: 예→`True`, 아니오→`False`, 확실하지 않음→`None`. **Branching**: non-`False` forces SAFETY_HOLD (authority.py:88). Answer: `아니오`.\n\n19. `cooking_access` — \"사용 가능한 조리 환경을 적어 주세요.\" Free text, **optional/skippable** (→ \"\"). ≤500 chars. Answer: `기본 조리 가능`.\n\n20. `budget_band` — \"식비 범위를 적어 주세요.\" Free text, **optional/skippable** (→ \"\"). ≤500 chars. Answer: `보통`.\n\n21. `meal_count` — \"하루 식사 횟수를 2~6 숫자로 입력해 주세요.\" Numeric integer; parse accepts plain int, `하루 N끼/회`, or Korean numerals 한...열 (copy.py:31-40). Range enforced 2-6 at build (models.py:147). Answer: `3`.\n\n22. `schedule_constraints` — \"식사 시간 제약을 적어 주세요. 없으면 '없음'.\" Free text, **optional/skippable** (→ \"\"). ≤500 chars. Answer: `없음`.\n\n**Post-22-questions state machine (conditional, not per-question)**: after field 22, state becomes `customer_attestation` (nutrition_onboarding.py:190-193); customer attests → `build_onboarding_baseline` runs (all ranges/age/format enforced, models.py + build fn line 211) → state routes to `SAFETY_HOLD` if ANY of conditions/medications provided, or pregnancy/ED not False (authority.py:83-88), else `OWNER_REVIEW` (nutrition_onboarding.py:274-279). Safety hold requires external clinical clearance to reach owner review; owner may approve (→ finalizing → ready), revise (re-opens at a chosen field, keeping earlier answers), reject, or hold (nutrition_onboarding.py:295-357).\n\nThe internally consistent synthetic adult run above: a 30-year-old maintain-goal male, no allergies/intolerances/conditions/medications, not pregnant, no ED risk — passes all build checks and routes to OWNER_REVIEW (not SAFETY_HOLD) so the flow completes without clinical intervention.\n</answer>\n\n<next_steps>\nDrive the flow by replying to each forced card (one message per question) using the 22 answers above in order; reply `유지` at Q7 and `없음` for Q8/Q9 to keep the maintain-goal consistent. For a loss/gain goal instead, Q8/Q9 must be real values (35-300 kg and a YYYY-MM-DD date). To make the run reach `ready` rather than owner review, keep Q15/Q16 as `없음` and Q17/Q18 as `아니오`. No follow-up needed — the schema, branching, and answer sheet are complete and all facts are line-cited.\n</next_steps>\n</results>"}}
{"type":"transition_applied","payload":{"type":"transition_applied","status":"completed","residency_state":"resident"}}
{"type":"transition_applied","payload":{"type":"transition_applied","status":"completed","residency_state":"persisted_only"}}
{"type":"suspended","payload":{"reason":"reload"}}
{"type":"reconcile_reattached","payload":{"session_path":"/home/cube/projects/richard/traning coach/.omo/senpi-task/children/st_01a00a87/sessions/st_01a00a87/2026-08-16T12-24-08-413Z_01a00a87-83dd-79db-a9c3-b8efd11502da.jsonl"}}
{"type":"transition_applied","payload":{"type":"transition_applied","status":"completed","residency_state":"persisted_only"}}
{"type":"suspended","payload":{"reason":"reload"}}
{"type":"reconcile_reattached","payload":{"session_path":"/home/cube/projects/richard/traning coach/.omo/senpi-task/children/st_01a00a87/sessions/st_01a00a87/2026-08-16T12-24-08-413Z_01a00a87-83dd-79db-a9c3-b8efd11502da.jsonl"}}
{"type":"transition_applied","payload":{"type":"transition_applied","status":"completed","residency_state":"evicted"}}
{"type":"evicted","payload":{"cause":"evict"}}