I read this as a debugging-and-implementation task - I’ll inspect the existing disposable harness and its tests, then complete or preserve its faithful failure based on runtime evidence.


I read this as implementation of the disposable NutriCoach lifecycle E2E - I’ll wire the real CLI-backed flow, then verify it with the required contract and runner.


I read this as diagnosing the first real lifecycle startup blocker - I’ll trace the adapter’s provider preflight and configure only the disposable profile surface needed for onboarding.


I read this as isolating whether the provider check can be satisfied without changing production behavior - I’ll inspect the installed resolver and the profile’s exact config-loading contract before altering the disposable fixture.


I read this as implementing the isolated startup configuration and compliant publication fault boundary - I’ll add a non-contacted loopback-only auxiliary route, move the sanitized transcript inside private evidence, and fault immediately after the real Bot API response but before its durable publication receipt.


I read this as advancing from static validation to the real disposable lifecycle - the focused contract suite passes, and I’ll now execute the loopback run with its evidence directory directly under `/tmp` to validate both startup and parent-permission handling.


I read this as investigating the first post-startup lifecycle failure - polling now reaches the loopback API, so I’ll inspect the private action and process evidence to trace why the `/start` update did not publish the consent card.


I read this as fixing a fake-transport fidelity gap, not a domain failure - the loopback payload omitted Telegram `bot_command` entities, so PTB never dispatched `/start`; I’ll add the protocol field and lock it with the fake API tests.


I read this as re-running the real lifecycle after restoring command-dispatch fidelity - the expanded fake API suite passes, and I’ll continue from the same `/tmp` evidence location to expose the next real boundary if any.


I read this as narrowing command dispatch versus handler failure - because the update is receipted without any reply, I’ll validate PTB’s real command filter against the exact synthetic payload before adding any diagnostic output.


I read this as correcting the harness’s deep-link construction - PTB dispatch is valid; the runner duplicated the `rc1_` prefix and therefore sent a non-matching invite token, so I’ll send the fixture’s canonical token verbatim.


I read this as continuing the E2E after fixing the canonical invite token - I’ll compile the changed runner and execute the full loopback lifecycle again.


I read this as fixing the harness’s first incorrect assumption about real adapter traffic - the consent card was published, but startup consumed message IDs before it; I’ll inspect the action log and make callback selection depend on the published card rather than a hard-coded ID.


I read this as removing a brittle adapter-ID assumption - the runner now resolves the consent card from its opaque callback protocol, so I’ll execute the lifecycle again through the real polling path.


I read this as identifying the actual consent callback protocol rather than guessing it - the real adapter did send both messages, but neither exposed the assumed `cc1:` token; I’ll trace the production onboarding renderer and callback grammar.


I read this as a Bot API JSON-body compatibility defect in the fake server - PTB serializes inline keyboards as nested JSON, which the fake discarded; I’ll preserve structured fields as JSON strings and add an HTTP-level regression test.


I read this as validating the repaired fake transport before resuming the lifecycle - I’ll run its focused tests and then re-execute the real child-process flow.


I read this as continuing the full disposable E2E after fake Bot API validation - 13 focused tests pass and the prohibited send helper is absent from harness fault code; I’ll run the lifecycle again.


I read this as reproducing the keyboard serialization at the exact PTB transport layer - I’ll send an inline keyboard through `python-telegram-bot` directly to the loopback fake to distinguish fake parsing from lifecycle state.


I read this as inspecting the actual adapter-produced card without exposing message content - I’ll reproduce only the real child `/start` exchange and report sanitized message metadata (callback counts and body digests) to establish whether the production coordinator emitted consent buttons.


I read this as making callback selection deterministic and protocol-aware - a consent card intentionally has two `cc1:` actions, so prefix-only lookup is ambiguous; I’ll add a bounded exact callback future keyed by prefix plus grant suffix and use it before triggering `/start`.


I read this as verifying the new exact callback synchronization primitive - I’ll run the fake transport and structural suites, then continue the full adapter/admin/domain lifecycle.


I read this as resuming the real E2E after deterministic callback selection - the focused suites remain green, so I’ll execute the lifecycle again.


I read this as diagnosing the first intended crash probe - the consent callback reached the adapter but did not cross the targeted registry-commit hook, so I’ll inspect its real API trace and bootstrap state evidence.


I read this as a protocol-level callback delivery failure rather than a registry hook failure - the child never polled a third update, so I’ll trace callback enqueueing and the fake API’s update serialization.


I read this as locating the fake Bot API’s offset bug - the production poll uses `offset=2`, which must return update ID 2, so I’ll inspect and correct its selection rule and regression coverage.


I read this as validating the callback payload against the actual PTB parser - the offset logic is correct, but malformed callback JSON can make PTB retry update 2 forever.


I read this as a confirmed fake-API defect - PTB rejects the callback because Telegram requires `chat_instance`; I’ll add that required field and lock it into the fake API contract.


I read this as verifying the focused protocol fix before rerunning lifecycle - I’ll check diagnostics and the fake API suite, then run the real disposable workflow once.


I read this as proceeding after the focused test passed - the LSP endpoint cannot access this worktree from its configured root, so I’ll run the harness’s documented entry point and inspect any next real boundary failure.


I read this as the callback now parsing but the intended registry probe still missing - I’ll inspect the new API and process evidence to distinguish a rejected callback from a wrong patch target.


I read this as a confirmed wrong patch binding - the real callback completed and advanced offset to 3, so I’ll identify the runtime’s injected domain object and intercept the exact bound production callable after its durable commit.


I read this as tracing module identity, not lifecycle behavior - the patch hits a `checkin_cli` module object different from the profile package resolved by runtime, so I’ll inspect the disposable package layout and resolver import path.


I read this as a real callback-authority rejection before the target commit - the second `answerCallbackQuery` is the runtime’s failure reply, so I’ll inspect the handoff validator and make the fake callback message match Telegram’s original card surface.


I read this as checking the two remaining evidence fields before changing the fault hook - I’ll verify PTB’s timestamp conversion and whether the profile resolver returns the monkey-patched module identity.


I read this as a timestamp-authority mismatch in the fake - the coordinator commits today’s consent, then the runtime safely rejects the fake card’s 1970 timestamp; I’ll confirm that registry behavior and propagate a deterministic current-KST card date through fake callback messages.


I read this as an unresolved runtime commit exception - the repeated consent write itself is valid, so I’ll add sanitized probe events around only the target production call to identify whether it returns or raises before the crash boundary.


I read this as executing the instrumented real path - the probe preserves the real callable and records only update ID and exception type, so I’ll rerun the lifecycle to capture the exact boundary outcome.


I read this as the probe still not observing the expected bound function - I’ll inspect its event log and then patch the exact module resolver path rather than relying on import-order assumptions.


I read this as the runtime rejecting at handoff binding, before any registry write - I’ll inspect `ConsentHandoff` validation and reproduce the bind with the exact PTB callback fields to isolate the missing authority datum.


I read this as a proven production blocker - the real published `cc1:...:g` callback is accepted by the coordinator, but runtime hard-requires incompatible `nutrition-consent:grant:g{generation}` data and rejects the handoff before registry persistence. I’ll remove temporary probe instrumentation and retain the protocol-fidelity fix with regression coverage.


I read this as finalizing blocker evidence - I’ll run the focused regression and structural contracts plus lint, then reproduce the same real-adapter blocker with the cleaned harness.


I read this as producing the final clean reproduction - focused tests, lint, and compilation pass, so I’ll rerun the disposable real-adapter lifecycle once to preserve blocker evidence without diagnostic instrumentation.


I read this as validating the final blocker record - I’ll confirm private cleanup evidence and capture the two incompatible callback contracts from production source for an exact, actionable report.


I read this as completing the evidence review - the blocker is isolated to incompatible production callback contracts, and I’ll capture the publisher’s exact token format before reporting the stopped result.


I read this as completing the disposable E2E - I fixed fake callback fidelity and stopped at a proven production blocker.

- Fixed fake Telegram callback payloads to include required `chat_instance`; PTB parser regression added.
- Verified: `13 passed`; Ruff and compileall pass.
- E2E remains blocked: production publishes `cc1:<token>:g`, but `bind_consent_handoff` requires `nutrition-consent:grant:g{generation}`. The real callback is rejected before registry commit, so the intended crash boundary cannot occur.

Clean private evidence is at `/tmp/dualcoach-v111-e2e-evidence` (`0700`/`0600`); cleanup is fully successful. No production code was changed.


I read this as resuming the real disposable lifecycle after the production callback fix - I’ll reread the updated consent contract and tests, then rerun the clean loopback E2E through its remaining crash/restart boundaries.


I read this as context complete - the shared callback contract is present and the harness still drives only real adapter ingress, so I’ll run the clean disposable lifecycle now.


I read this as progress past all three intended restart probes into the real attestation stage - I’ll inspect the private trace and the attestation callback handler to determine why the customer confirmation was not published.


I read this as an attestation callback that reaches the handler but fails before publication - the two callback acknowledgements show a validation/persistence rejection, so I’ll trace the exact callback decision path and evidence requirements.


I read this as a stale-publication validation failure before membership or domain mutation - no post-callback `getChatMember` occurred, so I’ll inspect the publication receipt matcher and callback payload against the card it validates.


I read this as requiring exact state observation rather than a speculative fix - I’ll inspect the existing publication-recovery tests and add a temporary, sanitized harness-only probe for the callback’s generation/message/receipt match if no existing test exposes this path.


I read this as needing one non-mutating diagnostic reproduction - I’ll run the same real E2E with only cleanup deferred in-process, inspect sanitized publication/receipt invariants from its private temporary profile, then delete those diagnostics.


I read this as having a preserved private reproduction with the same failure - I’ll compare its current publication record and outbox receipt against the selected callback route without exposing answers, tokens, or identifiers.


I read this as a domain transition rejection after publication and membership validation - I’ll inspect only the workflow’s structural state, reconciliation state, and consumed-update sequence to identify the rejecting invariant.


I read this as a valid attestation state whose final baseline construction rejects the fixture data - I’ll trace the baseline builder’s field requirements against the disposable answers without invoking a state-changing service method.


I read this as correcting the diagnostic import only - I’ll invoke the pure baseline builder on the already persisted canonical answers, which does not alter lifecycle state, to obtain the precise validation result.


I read this as ruling out baseline construction - I’ll now validate the persisted reconciliation against the current answer digest, the only remaining pure precondition before the state-changing write.


I read this as checking the final unverified guard before adding instrumentation - the attestation trace reaches `getChatMember`, and the fake’s membership response may be rejecting the legitimate private customer callback.


I read this as a loopback fake fidelity defect, not a production rejection - the fake reports both the configured private customer and staff owner as `left`, whereas real callback membership checks require them present on their own routes. I’ll model those two authorized memberships and add contract coverage.


I read this as validating the focused fake-route correction before resuming E2E - I’ll run the targeted contract suite, lint, and compilation in parallel, then remove the private diagnostic profile and evidence.


I read this as resuming the real lifecycle after the verified loopback membership correction - I’ll rerun the clean disposable E2E from the first consent crash through activation and final staff-membership checks.


I read this as a remaining transition failure after membership correction - I’ll compare the updated callback trace and, if the domain still rejects, instrument only the real attestation call to record its sanitized exception type and message.


I read this as an unhandled exception after successful membership authorization - I’ll add an environment-gated, sanitized observation wrapper around the real `attest_baseline` call, reproduce once, and remove the wrapper before the final run.


I read this as executing the bounded diagnostic observation - it leaves all lifecycle decisions and fault timing untouched, so I’ll compile it and rerun the real E2E with recording enabled.


I read this as the diagnostic run completing with the same real failure - I’ll read its sanitized exception record to identify the production defect or remaining fake API contract gap.


I read this as a real handler stall, not an exception - polling never resumes after the successful membership response, so I’ll replace the ineffective method wrapper with a bounded faulthandler stack capture in the disposable child and reproduce once.


I read this as capturing the precise live stall location - the diagnostic is private, bounded to ten seconds, and does not change handler control flow, so I’ll reproduce once with it enabled.


I read this as the bounded stack capture being available - I’ll inspect it to identify the exact blocking call, then remove the diagnostic hook before applying any focused harness correction.


WebSocket error


I read this as needing asyncio task-level evidence rather than thread stacks - the event loop is idle while the gated callback has not completed, so I’ll capture every live task’s Python stack after the same bounded interval.


I read this as obtaining the live callback await point - I’ll compile and run one final diagnostic reproduction with the bounded task dump enabled.


I read this as the diagnostic reproduction completing - I’ll inspect the task dump to locate the blocked await and apply the smallest protocol-faithful correction.


I read this as a completed-or-crashed handler whose receipt gate never advances - I’ll inspect the adapter’s PTB error/receipt integration and preserve one private post-run state snapshot to determine whether the attestation mutation occurred.


I read this as a PTB-dispatched callback exception that is already terminally gated - I’ll record the production error handler’s exception class and stack-frame coordinates (no callback or user data) in the private event log, then reproduce once.


I read this as running the final narrow diagnostic - the wrapper records only exception type and source coordinates while leaving the real adapter, callback, and receipt fault flow unchanged.


I read this as the exception telemetry now being available - I’ll read the sanitized production stack coordinates to isolate the offending adapter call.


I read this as a loopback Bot API fidelity gap, not a production blocker - the real callback reaches `editMessageReplyMarkup` to retire the attestation keyboard, so I’ll add that official API method to the fake and cover it in the focused contract test.


I read this as implementing the missing official Bot API operation - I’ll support reply-markup retirement with Telegram’s not-modified behavior and add a direct contract test, then remove all temporary diagnostic instrumentation.


I read this as validating the fake API correction before rerunning the lifecycle - I’ll run the focused Bot API and E2E contract suite plus Ruff and compilation.


I read this as the focused contract repair passing - I’ll run the complete clean disposable E2E with no diagnostic hooks, exercising the actual adapter, all three crash/restart boundaries, admin activation, and final membership verification.


I read this as a later lifecycle failure after attestation now succeeds - I’ll inspect the sanitized operation trace and the real bootstrap creation/approval path to determine whether the missing ledger is a production contract defect or an incorrect disposable fixture assertion.


I read this as a stale harness storage assertion: the real owner approval completed and was receipted before `_bootstrap_state` read the old ledger path - I’ll resolve the canonical bootstrap-store location from the current adapter configuration and update only the harness helper.


I read this as confirming the canonical store moved to `telegram-customer-bootstrap-v1` - I’ll derive the ledger path through production’s `room_bootstrap_state_dir` helper so the harness follows the active adapter contract rather than retaining a duplicated path.


I read this as validating the harness’s canonical-path correction - I’ll run its focused contract suite, Ruff, and compilation before the next full lifecycle execution.


I read this as the harness correction passing its focused checks - I’ll rerun the entire disposable lifecycle from the three real crashes through real activation and post-lifecycle staff verification.


I read this as a post-activation membership-preflight failure - I’ll inspect the sanitized Bot API calls and real admin finalization contract to identify the exact required authorized route or missing fake API operation.


I read this as a failure before the post-lifecycle CLI reaches Bot API - I’ll preserve one otherwise-clean disposable profile, replay that exact final CLI against a new loopback fake, print only its terminal exception class/message, and remove all diagnostic directories afterward.


I read this as the diagnostic setup needing real `mkdtemp` semantics - I’ll recreate the two private directories before invoking the unchanged lifecycle, then rerun the exact final CLI and clean them deterministically.


I read this as a source-proven bootstrap-state defect after successful activation - the exact post-lifecycle CLI rejects the persisted `ACTIVE` session as having invalid authority before any Bot API call, so I’ll trace that validation rule and the activation writer to identify the incompatible fields.


I read this as a deterministic production persistence invariant failure - I’ll verify whether the consent-handoff digest includes mutable session state/generation while activation preserves that digest, then stop at the source-proven blocker without touching concurrent production code.


I read this as a proven production blocker after a successful real activation - I’ll remove the obsolete private stack dump from prior diagnostics, then run final scoped diagnostics and tests to leave the harness changes verified and the failure evidence clean.


I read this as completing final validation around the blocked run - I’ll check diagnostics across every harness/test change in parallel with the focused suite and determine the project’s defined build command rather than assuming one.


I read this as final build verification for the scoped harness work - the language-server bridge cannot address this sibling worktree, while Ruff and compilation are clean, so I’ll run the project’s standard setuptools wheel build into a private temporary directory.


I read this as the optional `build` frontend being absent from the project environment - I’ll use pip’s standard wheel build frontend with dependencies disabled, still writing only to a private temporary directory.


I read this as a minimal virtual environment without either build frontend - I’ll invoke the declared setuptools backend directly from an isolated temporary source copy, preventing generated artifacts from touching the concurrent worktree.


I read this as setuptools exposing only its PEP 517 backend API in this environment - I’ll call that declared backend’s `build_wheel` entry point directly in the same isolated copy.


I read this as concluding the real lifecycle verification - it is blocked by a production persistence invariant.

- Consent reached registry commit; all three real crash/restart probes passed. Attestation, owner approval, and admin activation also completed.
- The post-activation `staff-membership-finalize` CLI fails before Bot API access: `BootstrapError: customer bootstrap session authority is invalid`.
- Cause: consent provenance binds `session.generation`, but `activate_bound_customer` increments generation without re-binding the persisted consent handoff; subsequent ledger parsing recomputes a different digest.

No production code was changed. I corrected loopback fidelity for `editMessageReplyMarkup` and updated the harness to use the canonical bootstrap-store path.

Verified:
- Focused tests: `15 passed`
- Ruff, compileall, diff check: passed
- PEP 517 wheel build: passed
- Evidence is private at `/tmp/dualcoach-v111-e2e-evidence`; cleanup confirms all temporary resources were released.


I read this as resuming the real disposable E2E after the immutable consent-handoff repair - I’ll rerun it cleanly through staff finalization, then inspect and extend only the harness for the requested first-check-in delivery/restart duplicate proof.


I read this as the activation lifecycle now passing - I’ll map the real post-activation customer ingress and operator-delivery paths, plus existing duplicate-recovery contracts, before adding the smallest end-to-end continuation.


I read this as narrowing the post-activation surface to the real customer text handler - I’ll inspect its exact trigger and publication/restart semantics, along with the adapter’s routing and existing recovery tests.


I read this as needing the exact active check-in protocol before exercising it - I’ll inspect the profile bridge’s customer start/finalization sequence and the adapter’s operator-card/delivery callbacks so the runner drives only real Telegram messages and callbacks.


I read this as identifying the actual delivery crash seam and customer wizard inputs - I’ll trace the coordinator’s bridge construction and generation flow, then add a fault only after the real customer send reaches its durable transport-receipt boundary.


I read this as the bridge being resolved indirectly rather than through the profile text search - I’ll locate its concrete construction and read the callback/render contracts needed to drive the full customer-to-operator sequence without guessing inputs.


I read this as the final protocol detail needed for a non-simulated check-in - I’ll inspect the bridge’s launcher/callback encoding and prompt choices so the harness selects official opaque callbacks from the fake surface rather than constructing or bypassing them.


I read this as validating whether the disposable profile can drive a real draft to delivery without an external model - I’ll inspect the wizard’s accepted text transitions and the generation worker/provider preflight before changing the lifecycle harness.


I read this as mapping the exact real check-in and draft path - I’ll read the completion/card routing and the disposable runner’s existing process and evidence interfaces so the extension uses only queued Telegram updates and bounded fake-API events.


I read this as confirming the harness already has the right synchronization primitives - I’ll inspect the existing restart sequence and production delivery callbacks to determine whether the loopback-only provider can produce an approvable draft or is an explicit blocker.


I read this as deciding whether a real delivery proof is executable under the stated no-simulation constraint - I’ll inspect the configured provider resolution and local runtime availability, since the current fixture deliberately points draft generation at an unreachable loopback endpoint.


I read this as finding a non-simulated execution path - a real local Ollama server is available on loopback, so I’ll verify its model/API contract and trace the activated route configuration before wiring it into the disposable fixture.


I read this as validating the actual local model route before exercising lifecycle delivery - I’ll inspect the required JSON response shape and the post-activation operator route, then make one bounded loopback provider contract call rather than introducing a simulated generator.


I read this as diagnosing the local provider contract probe - the server responded but did not yield JSON content under the first generic request, so I’ll inspect its response metadata and retry with the production local-request options before selecting a model.


I read this as selecting a local model that can actually complete within the production 256-token ceiling - the two reasoning models exhaust their budget before emitting content, so I’ll test non-reasoning local models with the same OpenAI-wire JSON mode.


I read this as preparing the smallest faithful harness extension - I’ll add only fake-Telegram callback selection/synchronization and a real local-provider fixture route; all check-in, approval, delivery, crash, and replay transitions will remain production adapter calls.


I read this as verifying the launcher trigger before coding against it - I’ll inspect the bridge’s no-session text behavior and then update the disposable-only fixture, fake callback selectors, and fault boundary in one focused patch.


I read this as locating the public customer entry point - I’ll inspect the adapter’s active-customer text routing now, then apply the harness patch with that exact command and no hidden lifecycle calls.


I read this as implementing the disposable harness prerequisites - I’ll add bounded callback-action futures that also resolve on card edits, point only this fixture at the real loopback Ollama model, and add a fault exactly between customer transport and its durable receipt.


I read this as finishing the real interaction wiring - I’ll expose a read-only selector for one action on an already-rendered opaque keyboard, then extend the runner to drive the active customer wizard, approve its real draft, crash after the actual customer send, and verify replay does not resend.


I read this as selecting valid production inputs before running the path - I’ll inspect the profile wizard’s nutrition-field validators so the live E2E advances deterministically rather than relying on permissive text parsing.


I read this as applying the verified interaction sequence - the profile accepts space-delimited macros and text scores, so I’ll drive those exact values and reserve callbacks only for the fields that require them.


I read this as reviewing the just-added synchronization surfaces before composing the lifecycle sequence - I’ll verify the exact runner/test text, then add the post-activation scenario at the existing activation boundary.


I read this as composing the scenario at the correct seam - I’ll add one helper that uses only fake-API updates and receipts to complete the active check-in, then leave restart orchestration in the existing top-level lifecycle.


I read this as implementing the complete real customer path - I’ll add a bounded adapter-only helper that opens the production card, completes the wizard, and waits for the model-produced draft’s real approval callback.


I read this as adding the real fault/replay proof - I’ll approve the generated card, restart at the exact post-transport/pre-receipt boundary, and assert the replayed Telegram update cannot create a second customer send.


I read this as validating the harness changes before the full run - I’ll check diagnostics and execute the focused fake-API/structural tests, fixing only issues introduced by this extension.


I read this as proceeding to the live proof - focused tests pass; the system PATH lacks Ruff, so I’ll use the project virtualenv’s validator and then run one clean, private full E2E against the real local Ollama route.


I read this as diagnosing a real E2E failure before changing behavior - the activated customer-start message produced no `nc1` callback, so I’ll inspect the sanitized evidence and the production transport gate that controls customer-card publication.


I read this as correcting the evidence path and tracing the live gate - I’ll inspect the actual recorded calls and the complete `customer_transport_allowed` condition to distinguish a missing card from a callback protocol mismatch.


I read this as identifying a protocol mismatch, not a transport-gate failure - the live adapter sent both expected customer messages, so I’ll locate the coordinator’s emitted start-callback prefix and update only the harness selector.


I read this as a focused harness correction - the production launcher is `cs1:`, and I also found the replay count must be measured from the pre-delivery baseline rather than all prior customer publications.


I read this as validating the narrowed correction before exercising the real path - the focused tests pass; I’ll resolve the lint path anomaly, inspect the execution environment, then run the full disposable lifecycle once.


I read this as the real end-to-end confirmation step - lint, compilation, and focused tests are clean, so I’ll execute one private loopback lifecycle run and retain its evidence for verification.


I read this as the next harness protocol mismatch - the `cs1:` fix advanced through digestion; I’ll inspect the emitted wizard transitions and the retained sanitized evidence to select the actual training action without touching production code.


I read this as a source-proven wizard sequencing fix - `training_summary` is text input immediately after appetite/stress; the next callback is the optional-note skip, so I’ll remove only the nonexistent training-entry click.


I read this as validating the corrected input sequence - I’ll run the focused harness checks again, then retry the real loopback lifecycle once from a fresh private evidence directory.


I read this as the final real-path retry - the corrected sequence and focused suite are clean, so I’ll run the disposable lifecycle with a fresh `0700` evidence directory.


I read this as a generation-stage investigation, not a delivery failure - the live wizard completed and emitted the operator card, but no `n3:a` approval callback appeared within the bounded model window; I’ll inspect the retained evidence and the generation path before making any change.


I read this as tracing a confirmed generation failure - the Bot API shows the operator card was edited twice but never gained `n3:a`, so I’ll inspect provider resolution and the worker’s terminal-failure behavior to isolate whether configuration or response validation is responsible.


I read this as checking the real provider boundary itself - configuration resolves a named loopback client, so I’ll issue one bounded, schema-constrained request to the configured Ollama model to distinguish slow generation from an invalid provider route.


I read this as correcting the harness’s bound to the production retry contract - the real local provider is healthy, while draft generation can make two 90-second schema attempts; I’ll allow that bounded worst case plus transport overhead.


I read this as validating the retry-bound adjustment before another expensive run - I’ll re-run lint, compilation, and focused tests, then execute one fresh private E2E lifecycle.


I read this as the real verification run - all focused checks remain clean, so I’ll execute the full lifecycle once more with the retry-aware bounded wait.


I read this as a bounded-provider behavior investigation - extending the wait did not produce a terminal card, so I’ll inspect the exact token parameter sent to Ollama and the failed run’s terminal transport trace before changing model behavior.


I read this as a terminal-failure observability gap in the harness - Ollama completed two real requests in about seven seconds each, so I’ll add an opaque selector for either approved or retryable operator-card actions and read only the persisted failure code when generation is rejected.


I read this as making the harness react to the actual terminal event - the real provider finishes quickly but publishes a retry-only card; I’ll add a protocol-faithful terminal-action future and a focused fake-API regression test, then report the persisted failure code instead of timing out.


I read this as validating the new terminal-card synchronization - it observes only opaque callback action codes and reads only the persisted failure category; I’ll run the affected fake API and lifecycle-contract tests now.


I read this as obtaining the source-level generation diagnosis - the synchronization test and focused suite pass, so I’ll run the lifecycle once to capture the durable failure category from the real model’s terminal card.


I read this as isolating strict-schema rejection details - the real model completes but both outputs are invalid, so I’ll expose only bounded validation codes and JSON paths from the durable ledger, never model or customer text.


I read this as validating the bounded diagnostic change - I’ll re-run static checks and the focused suite, then reproduce the model failure once to obtain the exact structural rejection.


I read this as the source-diagnostic execution - focused checks pass, so I’ll run one fresh lifecycle to capture only the validation categories from the real model’s rejected output.


I read this as choosing a real local model that honors the production JSON-schema wire contract - `llama3.1` returns non-JSON for the full schema despite passing a simple JSON-object probe, so I’ll compare available local non-reasoning models against the actual request format using synthetic, non-customer data.


I read this as a real-provider selection adjustment - `llama3.1` truncates the full validated response at the production 256-token cap, while the installed `llama3:8b` emits compact JSON on the same schema route; I’ll switch only the disposable fixture and exercise the full lifecycle.


I read this as validating the fixture-only provider change - I’ll run lint, compilation, and the focused contract suite, then perform a fresh end-to-end run with `llama3:8b`.


I read this as the real lifecycle verification with the compact local model - focused checks are clean, so I’ll run the full loopback activation, generation, delivery-fault, and replay path once.


I read this as diagnosing the remaining real-model incompatibility - the compact model also produces a non-JSON full response, so I’ll inspect the child adapter boundary and add a temporary-profile-safe, opt-in raw-output capture only if the existing harness can pass a private evidence path to its child.


I read this as a bounded temporary diagnostic - the child already inherits runner environment, so I’ll add an opt-in wrapper around the existing adapter call that writes its raw result only to a caller-supplied private log, then run one failing lifecycle and remove the wrapper afterward.


I read this as executing the opt-in capture - it wraps the production call without changing its request or result; I’ll compile it and run one isolated lifecycle with the capture file constrained to the private evidence directory.


I read this as inspecting the captured real-provider response - the lifecycle reproduced the same durable rejection, so I’ll read the private capture to determine whether it is truncation, markup, or an upstream error payload.


I read this as selecting the provider from evidence - `llama3:8b` emits placeholder symbols, commentary, and truncation on the real request, so I’ll inventory installed local models and choose a non-reasoning instruction model that can satisfy strict JSON within 256 tokens.


I read this as screening the strongest installed non-reasoning local model before another lifecycle run - I’ll send `exaone3.5:32b-64k` the actual schema route with non-customer synthetic input and inspect JSON validity and completion length.


I read this as applying the evidence-backed model choice - `exaone3.5:32b-64k` returns valid compact Korean JSON on the production schema route, so I’ll select it in the disposable fixture and run the complete lifecycle with the private diagnostic capture still enabled.


I read this as the decisive real lifecycle execution - the fixture now uses the schema-capable local model; I’ll run activation through delivery crash/replay and retain capture only if it again reaches a terminal retry card.


I read this as checking why the schema-capable model still failed under real grounding - the terminal state is again a retry card, so I’ll inspect the private capture to identify the exact difference from the synthetic schema probe.


I read this as a token-budget diagnosis - `exaone` produces valid JSON for a short request but exhausts the 256-token production cap on verbose real grounding; I’ll inspect the production Coach prompt and local model configuration boundary before selecting the remaining instruction model rather than widening production limits.


I read this as trying the only remaining high-capability instruction model - the cap is intentional production behavior and cannot be widened in the disposable harness, so I’ll switch the fixture to installed `llama3.3:latest` and verify its real grounded response end-to-end.


I read this as the full local-provider proof with `llama3.3` - I’ll run the disposable lifecycle once under the same loopback and fault/replay constraints, with a longer process timeout only to accommodate the 42 GB local model.


I read this as determining the `llama3.3` transport failure before changing any timeout - I’ll inspect its opt-in capture and current Ollama state to distinguish a provider timeout from a malformed completed response.


I read this as measuring `llama3.3` directly - the adapter received no content at all, so I’ll make one synthetic production-format request with a 300-second provider timeout to determine whether the model can complete on this workstation.


I read this as correcting the harness route rather than production - the fixture’s named custom provider bypasses the production local-route controls, including `enable_thinking: false`; I’ll probe the configured fallback route with `qwen3.6` so the same real loopback provider receives the intended local request contract.


I read this as testing the alternative local template - `qwen3.6` exposes no final content through the current Ollama OpenAI route even with the production local-route flag, so I’ll screen the installed Qwen coder model on that identical fallback request before rejecting the route.


I read this as selecting the verified local fallback route - `qwen3-coder:30b` returns complete Korean JSON through the production local request path, including the thinking-disable control; I’ll configure only that loopback fallback and run the real lifecycle.


I read this as the first real lifecycle attempt on the verified fallback model - I’ll run it with opt-in private output capture to distinguish any semantic validation failure from transport or JSON-contract failure.


I read this as diagnosing an unexpected pre-generation startup failure - no model request has occurred, so I’ll inspect the private process/fake-API evidence and child status before attributing it to the fallback configuration.


I read this as obtaining the hidden child startup error - the adapter exited before `connect()` became ready and made no Bot API call, so I’ll inspect the exact `connect()` false paths and the launcher’s wait behavior to expose the real preflight cause rather than retry blindly.


I read this as confirming the startup gate - an empty draft provider violates the nutrition preflight before networking, so I’ll read that gate and its accepted local-provider configuration to preserve startup while retaining the real local request route.


I read this as finding a configuration-only split between startup validation and request transport - I’ll inspect the generation-provider preflight implementation and resolver behavior to see whether the authenticated-looking local fixture can pass startup while the actual stage falls through to the controlled loopback path.


I read this as testing the only startup-compatible direct route - the startup preflight requires a non-empty resolved provider, so I’ll send `qwen3-coder` the same schema via the named custom-provider route before changing the fixture again.


I read this as applying the startup-compatible real-provider route - `qwen3-coder` returns complete JSON without reasoning on the named local provider, so I’ll restore the required preflight configuration with that model and run the full lifecycle.


I read this as the real end-to-end validation of the selected direct local provider - I’ll run the complete activation and delivery-replay path with the private diagnostic capture still enabled only for this selection run.


I read this as inspecting the selected model’s grounded output - startup and provider routing are correct but the terminal retry is structural, so I’ll read the capture to determine whether compactness, invalid numeric fields, or trailing content is the remaining blocker.


I read this as applying a model-configuration correction, not changing production limits - the real Qwen coder response is valid until its verbose text reaches the hard cap, so I’ll create a disposable Ollama derivative of the same installed model with a compact-schema system directive and test it through the unchanged provider route.


I read this as testing the compact derivative in the real harness - the derivative reuses the installed Qwen weights and only sets response concision/temperature; I’ll point the fixture at it and run the full lifecycle with capture.


I read this as the real lifecycle trial of the compact local configuration - I’ll execute it through the same activation, approval, transport-fault, and replay flow, preserving raw results only in the private diagnostic evidence for this run.


I read this as checking the derivative’s effective behavior - it still reaches a retry card, so I’ll inspect the output and the base model template to verify whether an explicit request system message overrides the derivative’s `SYSTEM` directive.


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