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* feat: add a restricted and supervised Claude Code runner Preserve configured authentication and models while enforcing tool access, strict completion JSON, bounded output and process cleanup. Cover argv, failure handling, session metadata and Windows process containment. * feat: route outside reviews by harness and migrate wrapper installs Use Claude Code from Codex and Codex from other supported hosts, with shared invocation rendering, positive gate validation and per-phase provenance. Rename /claude to /claude-code, repair managed shared and copied installations safely, and generate native Kiro skills. Add installed-workflow, failure-injection and live cross-harness regression coverage. * test: recognize CEO mode labels without terminal spacing The paid workflow rendered SCOPEEXPANSION at option 4, but its driver required a literal space. Match the leading mode title without cursor-spacing artifacts and ignore adjacent preview text. Preserve missing-target failures and downstream posture assertions. * test: isolate plan-count fixtures before starting review workflows Seed the complete test plan in a private git repository before launching Claude, so a bare slash command cannot review the live workspace while a delayed fixture message remains queued. Preserve count thresholds, parsers and budgets. Add initial-context and installed-discovery tests, and retain startup/terminal diagnostics on failed evaluations. * test: stabilize review fixtures and Claude eval startup Preserve source boundaries in workflow judge inputs, isolate CEO mode plans, and wait for interactive trust input readiness. Keep startup failure evidence and retain existing models, budgets, and assertions. Co-Authored-By: OpenAI Codex <noreply@openai.com> * test: classify collapsed review modes and isolate seeded findings Keep review questions out of the setup count when terminal cursor positioning removes spaces. State existing webhook safeguards so the five-finding control measures its seeded defects without accidental extra security and concurrency gaps. Preserve question bands and the paired control. Co-Authored-By: OpenAI Codex <noreply@openai.com> * test: isolate browser daemon state across free shards Co-Authored-By: OpenAI Codex <noreply@openai.com> * test: stabilize native review counting and interactive navigation Co-Authored-By: OpenAI Codex <noreply@openai.com> * chore: prepare v1.82.0.0 release Co-Authored-By: OpenAI Codex <noreply@openai.com> * fix: eliminate browser and process-cleanup test flakes Pin every CI surface to Bun 1.4.0 to avoid extra-stdio finalizers closing reused live sockets. Add an isolated GC/listener regression that fails on Bun 1.3.13, and prevent coordinated rollback to an affected CI runtime. Check renderer cleanup against the render's own staging directory so concurrent renders cannot invalidate the assertion. Make the no-pgrep process-tree walk tolerate disappearing /proc entries, and synchronize its test fixture through child readiness and pipe EOF instead of sleeps. Validation: 9,157 passed, 31 skipped, zero failures across 556 files with retries disabled. Build, all-host generation freshness, and skill checks passed. All three races have failing-before/passing-after regressions. * fix: count completed native review questions in evals * fix: drive review navigation from confirmed native choices * fix: require complete section-loading eval reports * test: isolate telemetry HTTP transport from local assertions * fix: keep review input on the active native question * test: let tunnel revocation daemon choose an available port * test: allocate available ports for pairing and watchdog fixtures * fix: stabilize planning eval navigation and phase reporting * test: isolate installed runtime paths in planning evals * test: stabilize review evidence and concurrent refresh fixtures * fix: resolve design findings before editing the plan * fix: honor and persist disabled outside plan reviews * fix: preserve planning decisions and terminal evidence Load installed host reviews at autoplan phase entry and wait for completed reviewers and saved artifacts. Reuse approved remedies while preserving individual finding decisions. Drive interactive evals from the current terminal viewport, bind native questions across scrolling, and require complete native report evidence. Cover captured stale menus, permission lifecycles, setup classification, and disabled-review tool availability with deterministic regressions. Advance release metadata and the upgrade migration to the unclaimed 1.83.0.0 slot. * fix: drive native review questions and preserve current plans Use the native single-choice keyboard protocol and current terminal viewport, with per-question navigation inside packets and completed-call coverage. Keep permissions, multi-select menus, and Submit controls distinct. Send Autoplan reviewers the amended implementation plan, keep its review record separate, and supply retained application contracts in the chain fixture. Clarify individual DevEx decisions and complete CEO fix options; use one active plan destination for the section-loading report. * fix: preserve complete plan-review decisions * fix: recognize native plan dialogs and reviewer controls * fix: preserve review decisions and phase completion * fix: recognize completed reviews without losing findings * fix: preserve review continuity and native eval completion * test: fix native review completion and eval retry isolation * test: handle native review menus and complete eval fixtures * test: fix native review setup, completion, and isolation failures * test: limit native skill discovery to runtime assets * fix: bind Autoplan reviews to full ordered phase inputs * test: fix planning eval routing, counting, and timeout handling * chore: advance queued release to v1.84.0.0 * fix: preserve complete review inputs and planning decisions * fix: reconcile review approvals and preserve phase obligations * fix: preserve review obligations and unblock eval permissions Carry recorded Autoplan requirements into blind phase inputs, require Eng review approvals before exit, and exercise combined asynchronous flows in CEO reviews. Correct native finding and handoff classification and unblock repeated report edits using scoped request identities. * fix: retain plan requirements and complete native review dialogs * fix: complete native review prompts and retain plan references * fix: preserve review inputs and classify native eval evidence * fix: check competing completion orders in CEO reviews * fix: recognize review decisions and require phase methodology Require the current phase methodology before Autoplan snapshots. Correct substantive decision, closed handoff, and cache-finding classification, and honor the recommended implementation approach in native review dialogs. Add captured-transcript regressions without changing review thresholds, provider models, retries, or deadlines. * test: bind native review decisions and close completed handoffs * fix: complete review dialogs and verify methodology delivery * fix: preserve review evidence and unblock native eval prompts * fix: handle native review question completions * fix: recognize native review narration and controls * fix: count native review decisions and isolate eval fixtures * test: verify seeded review coverage and current artifact permissions * test: isolate model and brain-aware skill renders * fix: repair native workflow evaluation and clarify review steps * fix: stabilize workflow eval evidence and review guidance * test: repair native workflow observation and fixture isolation * fix: recognize completed workflow evidence and owned skill reads * test: repair seeded workflow delivery and completion evidence * test: recognize current review evidence across native forms * test: handle native review variants and permission redraws * fix: honor review preferences and recognize native eval evidence * test: recognize completed review decisions and queued permissions * test: match current review contracts and partial-line edits * test: recognize completed workflow evidence and bounded human waits * fix: preserve review entry gates and native eval interactions * fix: recognize native workflow evidence and preserve review gates * test: recognize current review evidence and preconfigure workflow fixtures * test: recognize completed review findings and scoped artifact permissions * fix: stabilize native workflow review and permission evidence * fix: recognize current review evidence and scoped edit confirmations Clarify Design and engineering review entry instructions and Design scoring. Recognize required legacy coverage and public Autoplan completion recaps. Bind the pending Edit confirmation to its exact file, ordered digest, and one-request approval when a preceding command display remains visible. Keep reviews within their existing size limits and preserve scope gates when extracting workflow fixtures from either supported preamble header. Keep failure outcomes, review thresholds, provider choices, and eval budgets. * fix: recover review workflow progress and eval evidence * fix: recognize valid review evidence and scope selection * test: fix review evidence parsing and repeated artifact prompts * test: recognize valid review decisions and pending native cards * fix(plan-eng-review): keep final navigation consistent with approved tasks * test: recognize valid review evidence and bind legacy diff requests * fix: stabilize review eval evidence and harness repair guidance * docs: update project documentation for v1.85.0.0 Co-Authored-By: OpenAI Codex <noreply@openai.com> * test: fix Windows CI fixtures and credential scan Rebase captured JSON values and filesystem evidence using the appropriate path convention. Compile native fake CLIs on Windows and synchronize pipe holder readiness, with cleanup retained when assertions fail. Assemble synthetic credential fixtures at runtime so the added-line scan keeps enforcing the same gate without flagging its own rejection controls. Discover generated skills directly for the empty-find regression check, avoiding a recursive scan through saved evaluation artifacts and dependencies. * fix: preserve source renders on Windows Compare canonical generator paths using native separators so an output sidecar pointing at the source cannot overwrite its skill or metadata. Keep the regression fixture isolated from the real checkout and expose freshness diagnostics before asserting subprocess status. Detach Windows drain-test pipe holders from the fake provider's automatic child cleanup while preserving the enclosing runner job and its assertions. * fix: clarify outside review fallback and CEO decisions Render one applicable own-harness fallback path and retain native review, disabled policy, and missing-coverage semantics. Align report field names and mode labels, and make the existing per-cut scope approval explicit. Regenerate skill outputs and keep the workflow judge's model, thresholds, and retry policy unchanged. * chore: move release to free version slot (v1.86.0.0) PR #2852 now claims v1.85.0.0. Align the release metadata and rename migration so upgrades from that version still receive it. Co-Authored-By: OpenAI Codex <noreply@openai.com> * fix: include engineering review prerequisites and restore branch context * fix: recognize coverage diagrams and clarify design review instructions * fix: preserve file identities and join Windows test processes --------- Co-authored-by: OpenAI Codex <noreply@openai.com>
139 lines
18 KiB
JSON
139 lines
18 KiB
JSON
{
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"observationSha256": "1fbba18c11a5b46ef64cb72e39f8b74f3b9b7953274fe58eed2a93f0dd594135",
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"reportSha256": "370461dbf4820f9811843682539845b8043cb8d72b610868473241a02577283c",
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"calls": [
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{
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"sessionId": "26447393-d025-40f1-a80a-e6a34fcddf1a",
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"toolUseId": "toolu_01NjPQmJ7CTTEgsvqYRCT3Bs",
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"questions": [
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{
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"question": "D1 \u2014 Reduce the class inventory before building?\nProject/branch/task: main \u2014 Multi-tenant Auth Refactor plan review, Step 0 scope challenge.\nELI10: The plan adds five new classes (AuthBroker, SessionMint, AuthCache, TokenStore, RequestPolicy), and three of them are ways of holding the same cached tokens the existing adapter already holds. Every extra class is a place for bugs to hide and a thing the next engineer must learn. The question is whether the two real services can use the existing cache adapter directly through a narrow interface.\nStakes if we pick wrong: over-reduce and you re-add a class mid-build; under-reduce and you maintain three caches and 12 files for a change whose goal is not yet written down.\nRecommendation: A because the AuthCache facade adds no rule or serialization (PLAN.md:11-13) and TokenStore has no stated responsibility.\nNote: options differ in kind, not coverage \u2014 no completeness score.\nNet: a possible re-add later versus three overlapping abstractions now.",
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"header": "D1 scope",
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"multiSelect": false,
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"options": [
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{
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"label": "A) Reduce to 2-3 classes (recommended)",
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"description": "\u2705 Same service-facing boundary with zero new runtime code to test or debug (human: saves ~2 days / CC: saves ~20min). \u2705 Removes the double/triple-caching memory and consistency question entirely. \u274c If TokenStore had a real hidden purpose it returns later as an unplanned change."
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},
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{
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"label": "B) Proceed with 5 classes",
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"description": "\u2705 No re-planning; keeps whatever design intent the author had for TokenStore and RequestPolicy. \u2705 Forces a one-sentence responsibility per class into the plan, useful either way. \u274c 12 files and three cache-shaped types before the goal of the refactor is stated."
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},
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{
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"label": "C) Hold \u2014 state the goal first",
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"description": "\u2705 The plan has no problem statement; writing it may settle D1 on its own. \u2705 Cheapest possible step before any code (human: ~30min / CC: ~5min). \u274c Delays every other decision until the goal is written."
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}
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]
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}
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],
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"answered": true,
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"failed": false,
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"answers": {
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"D1 \u2014 Reduce the class inventory before building?\nProject/branch/task: main \u2014 Multi-tenant Auth Refactor plan review, Step 0 scope challenge.\nELI10: The plan adds five new classes (AuthBroker, SessionMint, AuthCache, TokenStore, RequestPolicy), and three of them are ways of holding the same cached tokens the existing adapter already holds. Every extra class is a place for bugs to hide and a thing the next engineer must learn. The question is whether the two real services can use the existing cache adapter directly through a narrow interface.\nStakes if we pick wrong: over-reduce and you re-add a class mid-build; under-reduce and you maintain three caches and 12 files for a change whose goal is not yet written down.\nRecommendation: A because the AuthCache facade adds no rule or serialization (PLAN.md:11-13) and TokenStore has no stated responsibility.\nNote: options differ in kind, not coverage \u2014 no completeness score.\nNet: a possible re-add later versus three overlapping abstractions now.": "A) Reduce to 2-3 classes (recommended)"
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},
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"unansweredQuestionIndices": [],
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"answeredAt": "2026-09-10T09:44:08.579Z"
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},
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{
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"sessionId": "26447393-d025-40f1-a80a-e6a34fcddf1a",
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"toolUseId": "toolu_01Qs552AuhXqYDwihpYWqTaG",
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"questions": [
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{
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"question": "D2 \u2014 Module-level AuthCache singleton \u2192 constructor injection with a single writer?\nProject/branch/task: main \u2014 Multi-tenant Auth Refactor, Architecture finding A1 (PLAN.md:19-20: \"share a global mutable AuthCache instance via module-level export. Both services mutate it\").\nELI10: Right now both services grab the same global cache object from a module import and both write to it. Nobody can tell who owns a write, and tests share state through that global. Building the cache once at app startup and handing it to each service makes the dependency visible and gives every test its own instance. With D1 answered, the injected thing is the existing adapter behind a narrow interface.\nStakes if we pick wrong: flaky or order-dependent tests, and a production bundle with two \"singletons\" where an invalidation clears the wrong one.\nRecommendation: A because it is the standard Layer 1 fix (composition root + DI) and costs minutes with CC; it maps to your explicit-over-clever preference.\nCompleteness: A=9/10, B=6/10, C=2/10\nNet: explicit dependencies now versus hidden coupling you debug later.",
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"header": "D2 DI",
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"multiSelect": false,
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"options": [
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{
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"label": "A) Composition-root injection, single writer (recommended)",
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"description": "\u2705 Test files construct their own fake cache; no cross-file state, parallel workers safe (human: ~1 day / CC: ~15min). \u2705 SessionMint is the only session writer and AuthBroker gets a read-only port, enforced by types not convention. \u274c Touches the bootstrap file and both service constructors."
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},
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{
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"label": "B) Keep module export, add serializing proxy",
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"description": "\u2705 Smaller diff; interleaved writes on one key get serialized (human: ~0.5 day / CC: ~10min). \u2705 No bootstrap changes. \u274c Still a hidden global; test isolation and duplicate-bundle problems remain."
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},
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{
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"label": "C) Do nothing",
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"description": "\u2705 Zero effort. \u2705 Matches the plan as written. \u274c Both A1 failure scenarios stay live and the plan's own test coverage cannot isolate state."
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}
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]
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}
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],
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"answered": true,
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"failed": false,
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"answers": {
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"D2 \u2014 Module-level AuthCache singleton \u2192 constructor injection with a single writer?\nProject/branch/task: main \u2014 Multi-tenant Auth Refactor, Architecture finding A1 (PLAN.md:19-20: \"share a global mutable AuthCache instance via module-level export. Both services mutate it\").\nELI10: Right now both services grab the same global cache object from a module import and both write to it. Nobody can tell who owns a write, and tests share state through that global. Building the cache once at app startup and handing it to each service makes the dependency visible and gives every test its own instance. With D1 answered, the injected thing is the existing adapter behind a narrow interface.\nStakes if we pick wrong: flaky or order-dependent tests, and a production bundle with two \"singletons\" where an invalidation clears the wrong one.\nRecommendation: A because it is the standard Layer 1 fix (composition root + DI) and costs minutes with CC; it maps to your explicit-over-clever preference.\nCompleteness: A=9/10, B=6/10, C=2/10\nNet: explicit dependencies now versus hidden coupling you debug later.": "A) Composition-root injection, single writer (recommended)"
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},
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"unansweredQuestionIndices": [],
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"answeredAt": "2026-09-10T09:44:39.717Z"
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},
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{
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"sessionId": "26447393-d025-40f1-a80a-e6a34fcddf1a",
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"toolUseId": "toolu_01BohFzt1fQdwGkAf2KTWQfm",
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"questions": [
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{
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"question": "D5 \u2014 Split validateAndDispatch() and stop swallowing errors?\nProject/branch/task: main \u2014 Multi-tenant Auth Refactor, Code quality finding C1 (PLAN.md:23-24: \"60 lines with three nested try/catch blocks; each catch swallows a different error class\").\nELI10: The function catches three kinds of errors and quietly keeps going. In an auth path, a swallowed error is an authorization decision made by accident: an IDP 503 can fall through to dispatch with partial claims. Splitting validation from dispatch and returning typed errors makes every outcome named, logged, and testable.\nStakes if we pick wrong: requests proceed on partial claims, or fail with no log line. This is the second critical gap.\nRecommendation: A because explicit over clever is your stated preference and each branch gets its own test; B logs a bug you keep.\nCompleteness: A=9/10, B=5/10, C=1/10\nNet: a real refactor with tests versus logging a bug you keep.",
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"header": "D5 errors",
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"multiSelect": false,
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"options": [
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{
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"label": "A) Split + typed AuthError taxonomy + rethrow unknown (recommended)",
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"description": "\u2705 Every error path becomes a named, tested outcome (IdpUnavailable, TokenInvalid, PolicyDenied, Unknown) (human: ~1 day / CC: ~20min). \u2705 60 lines with 3 nested try/catch become two ~20-line functions with one boundary try. \u274c Callers that relied on silent continuation will surface; that is the point, but it is work."
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},
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{
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"label": "B) Keep structure, add a log line per catch",
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"description": "\u2705 Trivial diff (human: ~1h / CC: ~3min). \u2705 Failures become visible in logs. \u274c Behavior is still wrong and still untestable per branch."
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},
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{
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"label": "C) Leave as-is",
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"description": "\u2705 Zero effort. \u2705 No behavior change for existing callers. \u274c Critical gap stays open: silent allow on IDP failure."
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}
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]
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}
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],
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"answered": true,
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"failed": false,
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"answers": {
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"D5 \u2014 Split validateAndDispatch() and stop swallowing errors?\nProject/branch/task: main \u2014 Multi-tenant Auth Refactor, Code quality finding C1 (PLAN.md:23-24: \"60 lines with three nested try/catch blocks; each catch swallows a different error class\").\nELI10: The function catches three kinds of errors and quietly keeps going. In an auth path, a swallowed error is an authorization decision made by accident: an IDP 503 can fall through to dispatch with partial claims. Splitting validation from dispatch and returning typed errors makes every outcome named, logged, and testable.\nStakes if we pick wrong: requests proceed on partial claims, or fail with no log line. This is the second critical gap.\nRecommendation: A because explicit over clever is your stated preference and each branch gets its own test; B logs a bug you keep.\nCompleteness: A=9/10, B=5/10, C=1/10\nNet: a real refactor with tests versus logging a bug you keep.": "A) Split + typed AuthError taxonomy + rethrow unknown (recommended)"
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},
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"unansweredQuestionIndices": [],
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"answeredAt": "2026-09-10T09:45:43.007Z"
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},
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{
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"sessionId": "26447393-d025-40f1-a80a-e6a34fcddf1a",
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"toolUseId": "toolu_016459fhN82mqXuCRYoktSvN",
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"questions": [
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{
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"question": "D9 \u2014 Parallelize IDP calls with per-call timeouts, shared abort, and JWKS/discovery caching?\nProject/branch/task: main \u2014 Multi-tenant Auth Refactor, Performance finding P1 (PLAN.md:31-32: \"5 sequential API calls to the IDP; they could be parallelized via Promise.all trivially\").\nELI10: Running the five calls at once is faster, but Promise.all on its own still hangs the whole validation if one call hangs, and a failure leaves the other four in flight burning IDP quota. Two of the five (discovery document, JWKS) almost never change and should be cached, so the real win is making fewer calls, not just faster ones.\nStakes if we pick wrong: validation hangs on one slow IDP call, or a cold cache after deploy hits IDP rate limits with 5\u00d7 the calls.\nRecommendation: A because \"trivially Promise.all\" is the incomplete version; timeouts and caching are minutes with CC and remove two failure modes.\nCompleteness: A=9/10, B=6/10, C=2/10\nNet: a cache with one edge case (kid miss) versus unbounded latency and IDP quota burn.",
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"header": "D9 perf",
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"multiSelect": false,
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"options": [
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{
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"label": "A) Promise.all + per-call timeout + shared abort + JWKS/discovery cache (recommended)",
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"description": "\u2705 Cold validation drops from 5 calls to 2-3; warm cache hit is 0 IDP calls (human: ~1 day / CC: ~15min). \u2705 Worst-case latency is bounded; a failure aborts siblings instead of leaking them. \u274c JWKS cache needs kid-miss refresh logic and its own test (R5 covers it)."
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},
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{
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"label": "B) Promise.all only, as the plan says",
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"description": "\u2705 Immediate latency win with a minimal diff (human: ~1h / CC: ~3min). \u2705 No new cache to reason about. \u274c One hung call still hangs validation; 5\u00d7 IDP load on every cold validation."
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},
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{
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"label": "C) Keep sequential",
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"description": "\u2705 No change; lowest IDP concurrency. \u2705 Simplest failure semantics. \u274c 5\u00d7 serial latency on every cold validation."
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}
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]
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}
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],
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"answered": true,
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"failed": false,
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"answers": {
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"D9 \u2014 Parallelize IDP calls with per-call timeouts, shared abort, and JWKS/discovery caching?\nProject/branch/task: main \u2014 Multi-tenant Auth Refactor, Performance finding P1 (PLAN.md:31-32: \"5 sequential API calls to the IDP; they could be parallelized via Promise.all trivially\").\nELI10: Running the five calls at once is faster, but Promise.all on its own still hangs the whole validation if one call hangs, and a failure leaves the other four in flight burning IDP quota. Two of the five (discovery document, JWKS) almost never change and should be cached, so the real win is making fewer calls, not just faster ones.\nStakes if we pick wrong: validation hangs on one slow IDP call, or a cold cache after deploy hits IDP rate limits with 5\u00d7 the calls.\nRecommendation: A because \"trivially Promise.all\" is the incomplete version; timeouts and caching are minutes with CC and remove two failure modes.\nCompleteness: A=9/10, B=6/10, C=2/10\nNet: a cache with one edge case (kid miss) versus unbounded latency and IDP quota burn.": "A) Promise.all + per-call timeout + shared abort + JWKS/discovery cache (recommended)"
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},
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"unansweredQuestionIndices": [],
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"answeredAt": "2026-09-10T09:47:14.009Z"
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}
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],
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"declaration": "### REGRESSION (mandatory, authorized by the coverage-audit regression rule \u2014 no question asked)\n\n`legacyAuthFlow()` is existing behavior being rewritten (PLAN.md:27-28) with the plan\nexplicitly disclaiming compatibility coverage (PLAN.md:14-16). This is a regression by\ndefinition. **CRITICAL requirement added to the plan:**\n\n- `auth/legacyAuthFlow.characterization.test.ts` \u2014 record current outputs for: valid\n token, expired token, revoked token, wrong audience, wrong issuer, suspended tenant,\n missing tenant header, IDP 5xx, IDP timeout, malformed JWT. Assert the new path\n (behind the flag) produces identical decisions and equivalent error surfaces. These\n tests are written BEFORE any rewrite (T1) and stay green through cut-over.\n\n",
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"tasks": "## Implementation Tasks\nSynthesized from this review's findings. Each task derives from a specific\nfinding above. Run with Claude Code or Codex; checkbox as you ship.\n\n- [ ] **T1 (P1, human: ~1 day / CC: ~20min)** \u2014 auth/ \u2014 Write characterization tests for `legacyAuthFlow()` before any rewrite\n - Surfaced by: Test review \u2014 REGRESSION rule; PLAN.md:27-28 \"no regression test for the prior behavior is planned\"\n - Files: `auth/legacyAuthFlow.characterization.test.ts`\n - Verify: suite green on current main; re-run after each later task\n",
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"review": "## GSTACK REVIEW REPORT\n\n| Review | Trigger | Why | Runs | Status | Findings |\n|--------|---------|-----|------|--------|----------|\n| CEO Review | `/plan-ceo-review` | Scope & strategy | 0 | \u2014 | \u2014 |\n",
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"compact": "### REGRESSION (mandatory, authorized by the coverage-audit regression rule \u2014 no question asked)\n\n`legacyAuthFlow()` is existing behavior being rewritten (PLAN.md:27-28) with the plan\nexplicitly disclaiming compatibility coverage (PLAN.md:14-16). This is a regression by\ndefinition. **CRITICAL requirement added to the plan:**\n\n- `auth/legacyAuthFlow.characterization.test.ts` \u2014 record current outputs for: valid\n token, expired token, revoked token, wrong audience, wrong issuer, suspended tenant,\n missing tenant header, IDP 5xx, IDP timeout, malformed JWT. Assert the new path\n (behind the flag) produces identical decisions and equivalent error surfaces. These\n tests are written BEFORE any rewrite (T1) and stay green through cut-over.\n\n\n## Implementation Tasks\nSynthesized from this review's findings. Each task derives from a specific\nfinding above. Run with Claude Code or Codex; checkbox as you ship.\n\n- [ ] **T1 (P1, human: ~1 day / CC: ~20min)** \u2014 auth/ \u2014 Write characterization tests for `legacyAuthFlow()` before any rewrite\n - Surfaced by: Test review \u2014 REGRESSION rule; PLAN.md:27-28 \"no regression test for the prior behavior is planned\"\n - Files: `auth/legacyAuthFlow.characterization.test.ts`\n - Verify: suite green on current main; re-run after each later task\n\n## GSTACK REVIEW REPORT\n\n| Review | Trigger | Why | Runs | Status | Findings |\n|--------|---------|-----|------|--------|----------|\n| CEO Review | `/plan-ceo-review` | Scope & strategy | 0 | \u2014 | \u2014 |\n"
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}
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