{"task_id":"st_019fd7ca","status":"cancelled","residency_state":"disposed","parent_session_id":"019fd78b-bc20-7f7e-baca-b4bb0dc6b301","root_session_id":"019fd78b-bc20-7f7e-baca-b4bb0dc6b301","depth":1,"execution_mode":"in-process","model":"openai-codex/gpt-5.6-sol","notify_on_terminal":true,"created_at":"2026-08-06T15:56:50.461Z","updated_at":"2026-08-06T15:59:14.669Z","notification":{"run_epoch":1,"notified_epoch":0},"name":"dualcoach-addresses-executor","task_summary":"Typed customer/staff address boundary를 TDD로 구현·검증","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"},"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":"Execute plan task `4. Introduce explicit customer and staff addresses` end-to-end in `/home/cube/projects/richard/hermes-agent`.\n\nRead first:\n- `/home/cube/projects/richard/traning coach/.omo/plans/dualcoach-production-readiness.md` Specification/Todo 4\n- immutable manifest JSON and Golden Path contract\n- repo `AGENTS.md`\n- relevant current Telegram nutrition onboarding authority/address, room bootstrap configuration/ledger projection, customer registry, activation readiness, and tests.\n\nDirty-worktree safety:\n- Before editing, capture `git status --porcelain=v1 --untracked-files=all` and targeted diffs for every file you may touch.\n- Preserve all unrelated user/agent changes. Never run commit, push, reset, stash, clean, checkout, or restore.\n- Use direct apply_patch for all edits. If your environment lacks a direct apply_patch tool, stop and return the exact proposed patch rather than using shell/heredoc/script file writes.\n\nBehavior/TDD goal:\n1. Introduce distinct strict typed concepts for customer private-delivery address and trainer/owner/operator review addresses, matching existing project style and keeping modules under the programming skill's 250-LOC guideline.\n2. Parse and validate at configuration boundaries, not deep in business logic.\n3. Customer delivery address MUST be private chat and normalize any DM topic/thread representation to canonical `0`; group/supergroup/topic delivery must fail with a typed actionable error.\n4. Staff review address must be staff-only; readiness must reject activation when the customer is a member of the configured staff group.\n5. Existing shared-topic configuration/test data must be recognized as legacy and fail production readiness with an actionable migration message to configure a customer DM and remove customer staff-group membership.\n6. Preserve separate trainer/owner/operator review authority. Do not collapse roles to raw chat/thread tuples or strings.\n7. Wire the smallest correct configuration/room-bootstrap ledger/customer-registry/activation-readiness seams required for this task; do not migrate all onboarding message delivery yet (task 5 owns that).\n8. Do not add compatibility shims except explicit legacy detection for persisted/shared-topic data.\n\nTest-first discipline:\n- Add the smallest regression tests at the actual parse/readiness seams BEFORE implementation.\n- Run them and capture the failing command/output showing the right missing behavior.\n- Implement the minimum source change to pass.\n- Tests must cover: private DM acceptance; DM topic normalization to 0; group/topic customer delivery rejection; separate trainer/owner/operator address parsing; customer membership in staff group blocks activation; legacy shared-topic data yields actionable migration error; malformed/role-confused input; untrusted metadata cannot change role.\n- No sleeps/polling/time luck.\n\nVerification:\n- Run targeted pytest for all changed/address/readiness tests once and make it reliable.\n- Run Ruff and BasedPyright (or the repository's exact configured equivalents) covering changed Python files; do not suppress errors.\n- Run adjacent existing room-bootstrap/activation/onboarding authority tests affected by the seam.\n- Manual QA gate: a minimal real module driver imports the new types/parser/readiness API and executes happy path plus bad group/topic, staff-membership, legacy migration, and malformed-role cases; exact invocation/output required.\n- Adversarially address all nine ultraqa classes; mark truly inapplicable ones with reasons.\n- Cleanup: remove temp scripts/caches/processes; retain only intentional source/test changes. No customer, credential, gateway, or runtime state changes.\n\nReturn a strict DoneClaim with task, baseline/failing-first evidence, changed_files, tests exact commands/results, diagnostics, manual_qa, adversarial coverage, cleanup receipt, residual risks, and ending git status for touched paths. PASS is forbidden unless observable behavior satisfies every acceptance item.\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":489237,"error_message":"Revived seam-map request remained live across repeated goal turns without returning; replace with bounded file-specific output-only implementation workers.","run_stats":{"runtime_ms":72446,"turns":0,"tool_calls":0}}