feat(analysis): integrate native learning review into standard reports - #68
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phodal merged 1 commit intoAug 5, 2026
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Route Task Episode learning candidates through the canonical source/brief/ report chain: episodes emit native learning review packets, verified decisions bind to packet and source digests, and confirmed candidates surface in learningCaptureDiagnostics. Keep review create/apply behind strict owner validation, refuse review-alias/evidence fabrication, and stay fully backward compatible when no candidates exist. Spec: docs/specs/2026-08-02-native-learning-report-integration.md Test: focused report-source-review/cli/contract 98 passed; learning-loop and task-loop suites 93 passed; doc-link 6/6; full suite 1266 passed with 1 existing Windows symlink skip; pack verification npm 450 / runtime zip 472. Co-authored-by: Codex (GPT 5.6 Sol) <codex@openai.com>
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The Implementation Evidence section of the LC-05 report-integration spec still carried pre-revision numbers: a full regression of 1089 passed with 4 failed and 6 skipped, and package verification of 385 npm / 408 runtime ZIP entries. Both contradicted the state actually merged in #68, and a spec claiming four failing tests misleads later traceability review. Re-verified on the merged main: npm test reports 1267 passed, 0 failed, 0 skipped on macOS, and npm run pack:verify reports 450 npm entries and 472 runtime ZIP entries. CI for #68 passed on ubuntu (Node 22 and 24), macOS, and Windows, so the earlier Windows EPERM symlink failures are recorded as environment restrictions rather than open defects. Docs-only change; no behavior, contract, or generated artifact is affected. Test: node --test test/doc-link-graph.test.mjs 6/6; npm test 1267 passed; npm run pack:verify passed; git diff --check passed. Co-authored-by: QoderAI (Qwen 3.8 Max) <qoder_ai@qoder.com>
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Summary
Integrate the native Learning Capture review pipeline into the standard report chain so regular Task Episodes surface confirmed learning candidates in
learningCaptureDiagnostics.Task Episodes now emit bounded, privacy-safe native learning review packets; lead decisions must bind to the exact packet and source digests; source apply projects only validated matches into canonical Learning Capture diagnostics and reader evidence. Adds a
source-review createCLI route behind strict owner validation, with fail-closed--helphandling.This is the second half of the LC-05 slice: the candidate-mining engine (#48) was previously only exercised by tests — the standard
task-loop-sourceanalysis path still used the legacybuildLearningLoopReview(). This PR wires the new capability into the user-facing report path.Why
Issue/Story: No linked issue; this completes roadmap item LC-05 (learning capture).
User or maintainer outcome: repeated repair behavior becomes a reviewable learning opportunity visible in ordinary reports, instead of an engine that exists only behind tests.
Traceability and Scope
docs/specs/2026-08-02-native-learning-report-integration.mdChange Type
Test and Review Evidence
node --test test/report-source-review.test.mjsnode --test test/doc-link-graph.test.mjsnpm run pack:verifynpm testgit diff --checkRisk and Recovery
Compatibility and cross-platform impact: zero-candidate output is byte-identical to previous behavior; provider/legacy flat learning signals remain supported; missing booleans retain unavailable/false/true semantics. Uses platform-neutral Node.js APIs.
Package, plugin, schema, or generated-file impact: Learning Loop and report-source modules are verified in both the npm package and runtime ZIP; the generated doc-link graph changed only by the new spec and reference pages.
Rollback or recovery path: revert commit
ccbf03986861388480e97f2502194130ea13851f.Residual risk or unverified boundary: one symlink-dependent full-suite case cannot run under the current Windows account; native review is deliberately limited to the recurring-correction pipeline.
AI Involvement
Level: Assisted.
Human review and validation: automated evidence and two independent counterexample-review rounds are complete.
Checklist
🤖 Generated with Claude Code