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feat: GStack Learns — per-project self-learning infrastructure (v0.13.4.0) (#622)

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* feat: learnings + confidence resolvers — cross-skill memory infrastructure

Three new resolvers for the self-learning system:
- LEARNINGS_SEARCH: tells skills to load prior learnings before analysis
- LEARNINGS_LOG: tells skills to capture discoveries after completing work
- CONFIDENCE_CALIBRATION: adds 1-10 confidence scoring to all review findings

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat: learnings bin scripts — append-only JSONL read/write

gstack-learnings-log: validates JSON, auto-injects timestamp, appends to
~/.gstack/projects/$SLUG/learnings.jsonl. Append-only (no mutation).

gstack-learnings-search: reads/filters/dedupes learnings with confidence
decay (observed/inferred lose 1pt/30d), cross-project discovery, and
"latest winner" resolution per key+type.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat: learnings count in preamble output

Every skill now prints "LEARNINGS: N entries loaded" during preamble,
making the compounding loop visible to the user.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat: integrate learnings + confidence into 9 skill templates

Add {{LEARNINGS_SEARCH}}, {{LEARNINGS_LOG}}, and {{CONFIDENCE_CALIBRATION}}
placeholders to review, ship, plan-eng-review, plan-ceo-review, office-hours,
investigate, retro, and cso templates. Regenerated all SKILL.md files.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat: /learn skill — manage project learnings

New skill for reviewing, searching, pruning, and exporting what gstack
has learned across sessions. Commands: /learn, /learn search, /learn prune,
/learn export, /learn stats, /learn add.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* docs: self-learning roadmap — 5-release design doc

Covers: R1 GStack Learns (v0.14), R2 Review Army (v0.15), R3 Smart Ceremony
(v0.16), R4 /autoship (v0.17), R5 Studio (v0.18). Inspired by Compound
Engineering, adapted to GStack's architecture.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* test: learnings bin script unit tests — 13 tests, free

Tests gstack-learnings-log (valid/invalid JSON, timestamp injection,
append-only) and gstack-learnings-search (dedup, type/query/limit filters,
confidence decay, user-stated no-decay, malformed JSONL skip).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* chore: bump version and changelog (v0.13.4.0)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* test: learnings resolver + bin script edge case tests — 21 new tests, free

Adds gen-skill-docs coverage for LEARNINGS_SEARCH, LEARNINGS_LOG, and
CONFIDENCE_CALIBRATION resolvers. Adds bin script edge cases: timestamp
preservation, special characters, files array, sort order, type grouping,
combined filtering, missing fields, confidence floor at 0.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: sync package.json version with VERSION file (0.13.4.0)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* chore: gitignore .factory/ — generated output, not source

Same pattern as .claude/skills/ and .agents/. These SKILL.md files are
generated from .tmpl templates by gen:skill-docs --host factory.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* test: /learn E2E — seed 3 learnings, verify agent surfaces them

Seeds N+1 query pattern, stale cache pitfall, and rubocop preference
into learnings.jsonl, then runs /learn and checks that at least 2/3
appear in the agent's output. Gate tier, ~$0.25/run.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Garry Tan
2026-03-29 17:02:01 -06:00
committed by GitHub
parent 66894601e3
commit ae0a9ad195
49 changed files with 2374 additions and 2 deletions
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retro/SKILL.md
+33
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@@ -59,6 +59,15 @@ for _PF in $(find ~/.gstack/analytics -maxdepth 1 -name '.pending-*' 2>/dev/null
fi
break
done
# Learnings count
eval "$(~/.claude/skills/gstack/bin/gstack-slug 2>/dev/null)" 2>/dev/null || true
_LEARN_FILE="${GSTACK_HOME:-$HOME/.gstack}/projects/${SLUG:-unknown}/learnings.jsonl"
if [ -f "$_LEARN_FILE" ]; then
_LEARN_COUNT=$(wc -l < "$_LEARN_FILE" 2>/dev/null | tr -d ' ')
echo "LEARNINGS: $_LEARN_COUNT entries loaded"
else
echo "LEARNINGS: 0"
fi
```
If `PROACTIVE` is `"false"`, do not proactively suggest gstack skills AND do not
@@ -621,6 +630,30 @@ For each contributor (including the current user), compute:
**If there are Co-Authored-By trailers:** Parse `Co-Authored-By:` lines in commit messages. Credit those authors for the commit alongside the primary author. Note AI co-authors (e.g., `noreply@anthropic.com`) but do not include them as team members — instead, track "AI-assisted commits" as a separate metric.
## 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":"retro","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).
**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.
### Step 10: Week-over-Week Trends (if window >= 14d)
If the time window is 14 days or more, split into weekly buckets and show trends:
retro/SKILL.md.tmpl
+2
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@@ -277,6 +277,8 @@ For each contributor (including the current user), compute:
**If there are Co-Authored-By trailers:** Parse `Co-Authored-By:` lines in commit messages. Credit those authors for the commit alongside the primary author. Note AI co-authors (e.g., `noreply@anthropic.com`) but do not include them as team members — instead, track "AI-assisted commits" as a separate metric.
{{LEARNINGS_LOG}}
### Step 10: Week-over-Week Trends (if window >= 14d)
If the time window is 14 days or more, split into weekly buckets and show trends: