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feat(analysis): integrate native learning review into standard reports - #68

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phodal merged 1 commit into
QoderAI:mainfrom
U1oSun:feat/native-learning-report-integration
Aug 5, 2026
Merged

feat(analysis): integrate native learning review into standard reports#68
phodal merged 1 commit into
QoderAI:mainfrom
U1oSun:feat/native-learning-report-integration

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@U1oSun

@U1oSun U1oSun commented Aug 5, 2026

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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 create CLI route behind strict owner validation, with fail-closed --help handling.

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-source analysis path still used the legacy buildLearningLoopReview(). 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

  • Spec/ADR: docs/specs/2026-08-02-native-learning-report-integration.md
  • Acceptance criteria addressed: AC-1 through AC-5.
  • Canonical owners changed: Task Episode normalization, Learning Loop candidate construction, report-source review create/apply, CLI registry, evidence brief.
  • Explicit non-goals: no model auto-invocation, no automatic Rule/Skill/Memory creation, no effectiveness claim, no new detection mode, no second candidate-mining pattern.

Change Type

  • Feature
  • Bug fix
  • Tests only
  • Documentation/community
  • Refactor with no intended behavior change
  • Dependency, packaging, or infrastructure

Test and Review Evidence

Check Result
node --test test/report-source-review.test.mjs 98/98 passed
learning-loop + task-loop suites 93/93 passed
node --test test/doc-link-graph.test.mjs 6/6 passed
npm run pack:verify Passed; 450 npm entries and 472 runtime ZIP entries
npm test 1266 passed, 0 failed, 1 skipped (existing Windows symlink case outside changed modules)
git diff --check Passed
Independent counterexample review No remaining P1/P2 findings after two revision rounds
Manual or visual evidence Not applicable; no UI surface

Risk 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

  • I followed AGENTS.md, CONTRIBUTING.md, and the relevant canonical-owner guidance.
  • The change is focused and does not include unrelated local or generated state.
  • Tests and documentation match the behavior actually delivered.
  • Markdown links were checked when documentation moved or changed.
  • Cross-platform behavior was considered for Windows, macOS, and Linux.
  • Package/runtime verification was run when shipped files or dependencies changed.
  • User-facing or compatibility changes are recorded in CHANGELOG.md.
  • I have the right to contribute this work under the repository's MIT License.

🤖 Generated with Claude Code

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>
@phodal
phodal merged commit 1114519 into QoderAI:main Aug 5, 2026
4 checks passed
phodal added a commit that referenced this pull request Aug 5, 2026
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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2 participants