feat: wire learnings into all insight-producing skills

Adds LEARNINGS_SEARCH and/or LEARNINGS_LOG to 10 skill templates that
produce reusable insights but were previously disconnected from the
learning system:

- office-hours, plan-ceo-review, plan-eng-review: add LOG (had SEARCH)
- plan-design-review: add both SEARCH + LOG (had neither)
- design-review, design-consultation, cso, qa, qa-only: add both
- retro: add SEARCH (had LOG)

13 skills now fully participate in the learning loop (read + write).
Every review, QA, investigation, and design session both consults prior
learnings and contributes new ones.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Garry Tan
2026-03-29 21:06:36 -07:00
parent cade276c20
commit d6530583a8
20 changed files with 523 additions and 0 deletions

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@@ -1172,6 +1172,31 @@ plan's living status.
- Always place it as the very last section in the plan file. If it was found mid-file,
move it: delete the old location and append at the end.
## Capture Learnings
If you discovered a non-obvious pattern, pitfall, or architectural insight during
this session, log it for future sessions:
```bash
~/.claude/skills/gstack/bin/gstack-learnings-log '{"skill":"plan-eng-review","type":"TYPE","key":"SHORT_KEY","insight":"DESCRIPTION","confidence":N,"source":"SOURCE","files":["path/to/relevant/file"]}'
```
**Types:** `pattern` (reusable approach), `pitfall` (what NOT to do), `preference`
(user stated), `architecture` (structural decision), `tool` (library/framework insight),
`operational` (project environment/CLI/workflow knowledge).
**Sources:** `observed` (you found this in the code), `user-stated` (user told you),
`inferred` (AI deduction), `cross-model` (both Claude and Codex agree).
**Confidence:** 1-10. Be honest. An observed pattern you verified in the code is 8-9.
An inference you're not sure about is 4-5. A user preference they explicitly stated is 10.
**files:** Include the specific file paths this learning references. This enables
staleness detection: if those files are later deleted, the learning can be flagged.
**Only log genuine discoveries.** Don't log obvious things. Don't log things the user
already knows. A good test: would this insight save time in a future session? If yes, log it.
## Next Steps — Review Chaining
After displaying the Review Readiness Dashboard, check if additional reviews would be valuable. Read the dashboard output to see which reviews have already been run and whether they are stale.

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@@ -287,6 +287,8 @@ Substitute values from the Completion Summary:
{{PLAN_FILE_REVIEW_REPORT}}
{{LEARNINGS_LOG}}
## Next Steps — Review Chaining
After displaying the Review Readiness Dashboard, check if additional reviews would be valuable. Read the dashboard output to see which reviews have already been run and whether they are stale.