{"type":"transition_applied","payload":{"type":"transition_applied","status":"running","residency_state":"resident"}}
{"type":"assistant_message","payload":{"text":"I read this as a read-only UX/copy review task — locate the supplied screenshot and its generating copy strings, then deliver the Korean target summary, blocking issues, and QA acceptance criteria. No edits.\n\n"}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"assistant_message","payload":{"text":"Two clipboard screenshots arrived minutes before this task spawned — reading them as the supplied screenshots.\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":"Image tokens are not supported here — extracting the screenshot text via OCR instead.\n\n"}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"assistant_message","payload":{"text":"`rapidocr-onnxruntime` is available — running OCR on both screenshots.\n\n"}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"assistant_message","payload":{"text":"First shot is mostly the bot/dev pane. Getting the full OCR of the second screenshot (output was truncated).\n\n"}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"assistant_message","payload":{"text":"OCR detected the dict-syntax leak (`{items: [], 'status': 'none'}`) in the second screenshot, but the Korean text in the Telegram pane got garbled. To improve the read results, I'll crop + upscale the Telegram pane and pinpoint the location of the generator code.\n\n"}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"assistant_message","payload":{"text":"Korean OCR remains poor with RapidOCR. I'm trying tesseract with the kor traineddata, and in parallel identifying the summary generation code within the evidence wheel.\n\n"}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"assistant_message","payload":{"text":"Found the generator code in evidence snapshots. Reading the latest candidate's copy logic.\n\n"}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"tool_execution","payload":{"tool":"read","is_error":false}}
{"type":"assistant_message","payload":{"text":"This file explains all four defects. Currently identifying the callers and the synthesis fixtures so we can reconstruct the exact inputs.\n\n"}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"assistant_message","payload":{"text":"What's running is the installed wheel (the `hermes-gateway-dualcoachtest.service` in the screenshot). To check the copy that was actually rendered, I'm comparing it against the reconciliation of installed-wheel-root.\n\n"}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"assistant_message","payload":{"text":"The deployed presenter is consistent with the evidence snapshot. Since the screenshot also includes body stats / smoking / drinking lines, I'm looking for the more complete renderer within the snapshot.\n\n"}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"assistant_message","payload":{"text":"No 흡연/음주 in the gateway code — those lines come from the LLM summary path. Recovering the exact synthetic inputs from fixtures/logs instead of guessing from noisy OCR.\n\n"}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"assistant_message","payload":{"text":"Sibling tasks (H1/H2/schema mapping) exist — checking their results for the exact synthetic inputs.\n\n"}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":true}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"assistant_message","payload":{"text":"I now have the exact synthetic inputs (`example_answer` set). One more targeted OCR pass on the message body to confirm the goal/activity lines before writing the target copy.\n\n"}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"tool_execution","payload":{"tool":"bash","is_error":false}}
{"type":"assistant_message","payload":{"text":"All evidence gathered — the screenshot defects are fully traceable. Composing the review.\n\n---\n\n## Read-only UX/copy review — Telegram customer summary card (task st_01a00a93)\n\n### What I verified (no code edits, no live access)\n\n- **Screenshots**: `/tmp/sshclip-clipboard-20260816-2131{15,22}*.png` (Telegram Desktop, `dual_coach_pilot_test_bot`, 21:31–21:32). Image tokens unsupported by this model, so I OCR'd them (rapidocr, multiple crop/upscale passes). Independently confirmed on the card, confidence ≥ 0.86: the dict leak `{'items': [], 'status': 'none'} · {'items': [], 'status': 'none'} · {'items': [], 'status': 'none'}` on the 알레르기 line, and bullets `- 키: 180cm`, `- 체중: 80kg`. (Two low-confidence lines resembling 흡연/음주 bullets could not be resolved; not relied on below.)\n- **Exact synthetic inputs**: the card was generated from the canonical `example_answer` synthetic set — `profile-package/checkin_cli/nutrition_onboarding_contract.py:79-107` (DOB 1996-08-01, male, **height 180 / weight 80**, **`activity_category: moderate`**, rationale \"주 3회 근력 운동\", **`goal_type: maintain`**, all 7 structured fields `{\"status\":\"none\",\"items\":[]}`, **`pregnancy_breastfeeding: False`**, **`eating_disorder_risk: False`**, 기본 조리 가능, 보통, 3끼, schedule \"없음\"). Matches screenshot values 1:1.\n- **Causal chain** (evidence snapshots, `repository-runtime-closure/gateway/platforms/`):\n  - `nutrition_onboarding_reconciliation.py:62-68` — `value()` coerces any stored value with `str()` → dict repr leaks (D1).\n  - `:93, :96` — reads keys `goal` and `training_details` that **do not exist**; canonical keys are `goal_type` / `activity_rationale` (`QUESTION_FIELDS`, contract `:13-38`) → `목표: 미입력` (D2), activity ends `미입력` (D3).\n  - `:70-81` — health gate checks nonexistent `medical_conditions`/`pregnancy_lactation` (real: `conditions`/`pregnancy_breastfeeding`) and compares stringified values against `{\"없음\",\"해당 없음\",\"아니요\"}`; `False` → `\"False\"` and dict → repr both trip it → false `검토할 입력이 있습니다` (D4).\n  - `telegram_nutrition_onboarding_copy.py:100-147` — storage shapes that defeat the formatter: `{\"status\",\"items\"}` dicts, English enum codes (`moderate`, `maintain`), booleans.\n  - `nutrition_onboarding_reconciler.py:50-75` — the advisory copy model receives raw `json.dumps(answers)` with zero presentation rules.\n\n### H3 verdict\n\n**Confirmed.** There is no customer-facing presentation layer between stored answer shapes and rendered copy. Both authorities fail the same way: the deterministic formatter (`render_authoritative_customer_summary`) stringifies internal structures and reads wrong keys; the advisory model is handed raw JSON and only told \"list fields contain Korean strings.\" The output is technically truthful about the *store* and false about the *customer*. A presentation layer that maps canonical fields → Korean labels is the missing component.\n\n### Exact target summary (same synthetic inputs, Telegram-ready)\n\nFollows the product's existing copy system — header, `- ` bullets, `·` separators, and the disclaimer sentences reused **verbatim** from `nutrition_onboarding_reconciliation.py:131,147-148` and `telegram_nutrition_onboarding_runtime_publication.py:140-143`:\n\n```text\n입력 내용을 정리했어요\n\n- 목표: 유지\n- 활동: 보통 · 주 3회 근력 운동\n- 신체: 키 180cm · 체중 80kg\n- 식사: 하루 3끼\n- 조리·예산: 기본 조리 가능 · 보통\n- 식사 시간: 특별한 제약 없음\n- 알레르기·불내증·식품 제한: 해당 없음\n- 건강 관련 입력: 별도 검토 입력이 없습니다.\n\n이 요약은 참고용이며 안전 판정·승인·활성화를 대신하지 않습니다.\n내용이 맞으면 아래 확인 버튼을 눌러 주세요.\n```\n\nPost-confirmation footnote (unchanged): `아래 버튼을 누르면 입력 확인이 저장되고 운영자 검토로 넘어갑니다. 이 단계에서 영양 코칭이 시작되거나 활성화되지는 않습니다.`\n\nCopy rules encoded by this target: enum codes → customer labels (`maintain`→유지, `moderate`→보통); all-none structured fields collapse to one `해당 없음`; booleans → 아니오/해당 없음 semantics, never `False`; a genuinely empty optional field is **omitted or phrased `아직 입력되지 않았어요`** — the token `미입력` is banned customer-side.\n\n### Blocking CJK/copy issues\n\n1. **B1 — Internal dict repr in customer text.** `{'items': [], 'status': 'none'}` with ASCII braces/quotes inside Hangul sentences. Developer syntax visible to the customer; also mixes straight/curly quote glyphs. Blocking.\n2. **B2 — `목표: 미입력` despite a maintain goal.** Factually wrong statement about the customer's own answer (root cause: key mismatch `goal` ≠ `goal_type`). Blocking.\n3. **B3 — Activity line ends `미입력`** and leaks the English enum `moderate` (key mismatch `training_details` ≠ `activity_rationale`). Blocking.\n4. **B4 — False safety signal.** `건강 관련 입력: 검토할 입력이 있습니다.` when every safety input is none/false. Safety-adjacent copy lying in the alarming direction; trains customers to ignore the warning when it is real. Blocking.\n5. **B5 — Field-name contract drift (root cause of B2–B4).** Formatter keys (`goal`, `training_details`, `medical_conditions`, `pregnancy_lactation`, `religious_restrictions`) do not match `QUESTION_FIELDS` (`goal_type`, `activity_rationale`, `conditions`, `pregnancy_breastfeeding`, `religious_ethical_exclusions`). Every mismatched key silently renders as missing data. Blocking.\n6. **B6 — English enum values in customer copy.** Any fix must map `sedentary/light/moderate/very_active/extra_active` and `loss/maintain/gain` to 좌식/가벼움/보통/매우 활동적/최고 활동 and 감량/유지/증량 (labels already exist in `telegram_nutrition_onboarding_copy.py:117-129`). Blocking for the customer surface.\n7. **B7 — Stringified-value health gate.** The allowlist `{없음, 해당 없음, 아니요}` evaluated on `str(value)` misclassifies `False`, `None`, `[]`, and dict shapes. Must evaluate semantics (structured `status == \"none\"`, booleans false), not strings. Blocking.\n\nNon-blocking: `없음 · 없음 · 없음 · 없음`-style repetition when multiple restriction fields are empty (collapse to one phrase); `- 식사 시간: 없음` reads curt (use `특별한 제약 없음`).\n\n### Acceptance criteria for fresh screenshot QA\n\n**Capture**: Telegram Desktop, customer DM topic, full card visible without \"더 보기\" truncation, bot name + timestamp in frame.\n\n**Negative checks (must have zero occurrences in the card text):**\n- `{`, `}`, `'items'`, `'status'`, `repr`-style quotes around Korean text\n- `미입력` on any field that has a stored value; ideally no `미입력` at all\n- standalone English enums: `moderate|sedentary|light|very_active|extra_active|maintain|loss|gain|False|True|None`\n- `검토할 입력이 있습니다` when all safety fields are none/false\n- keys or phrasing: `goal_type`, `training_details`, `medical_conditions`, `pregnancy_lactation`\n\n**Positive checks for the all-none synthetic input (this case):**\n- `- 목표: 유지` present; activity line contains `보통` and `주 3회 근력 운동`, ends without `미입력`\n- restrictions line reads exactly `해당 없음`\n- health line reads exactly `별도 검토 입력이 없습니다.`\n- both disclaimer sentences verbatim; `- ` bullet style and `·` separators consistent; every line ≤ ~60 chars; total well under Telegram's 4096 limit\n\n**Regression pairs (second screenshot each):**\n- *provided-items case* (e.g., allergies `{\"status\":\"provided\",\"items\":[\"유당\"]}`, conditions provided): items render as comma-joined Korean text, no dict syntax, and the health line flips to `검토할 입력이 있습니다.` **only** when a safety field is genuinely provided/true.\n- *genuinely-empty optional case*: empty optional renders as omitted line or `아직 입력되지 않았어요`, never `미입력`.\n\n**Unit seam**: pin these as tests against `render_authoritative_customer_summary` with the `example_answer` set as input — the all-none fixture must produce the exact target card above (deterministic, no LLM in the loop).\n\nNo files were modified; no live system, service, or Telegram surface was touched."}}
{"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_01a00a93/sessions/st_01a00a93/2026-08-16T12-34-55-696Z_01a00a91-6450-778f-81ed-50f02b35c05c.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_01a00a93/sessions/st_01a00a93/2026-08-16T12-34-55-696Z_01a00a91-6450-778f-81ed-50f02b35c05c.jsonl"}}
{"type":"transition_applied","payload":{"type":"transition_applied","status":"completed","residency_state":"evicted"}}
{"type":"evicted","payload":{"cause":"evict"}}