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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
co-authored by Claude Opus 4.6
parent 66894601e3
commit ae0a9ad195
49 changed files with 2374 additions and 2 deletions
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review/SKILL.md
+96
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@@ -62,6 +62,15 @@ 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: $_LEARN_COUNT entries loaded"
else
echo "LEARNINGS: 0"
fi
```
If `PROACTIVE` is `"false"`, do not proactively suggest gstack skills AND do not
@@ -582,6 +591,44 @@ Run `git diff origin/<base>` to get the full diff. This includes both committed
---
## Prior Learnings
Search for relevant learnings from previous sessions:
```bash
_CROSS_PROJ=$(~/.claude/skills/gstack/bin/gstack-config get cross_project_learnings 2>/dev/null || echo "unset")
echo "CROSS_PROJECT: $_CROSS_PROJ"
if [ "$_CROSS_PROJ" = "true" ]; then
~/.claude/skills/gstack/bin/gstack-learnings-search --limit 10 --cross-project 2>/dev/null || true
else
~/.claude/skills/gstack/bin/gstack-learnings-search --limit 10 2>/dev/null || true
fi
```
If `CROSS_PROJECT` is `unset` (first time): Use AskUserQuestion:
> gstack can search learnings from your other projects on this machine to find
> patterns that might apply here. This stays local (no data leaves your machine).
> Recommended for solo developers. Skip if you work on multiple client codebases
> where cross-contamination would be a concern.
Options:
- A) Enable cross-project learnings (recommended)
- B) Keep learnings project-scoped only
If A: run `~/.claude/skills/gstack/bin/gstack-config set cross_project_learnings true`
If B: run `~/.claude/skills/gstack/bin/gstack-config set cross_project_learnings false`
Then re-run the search with the appropriate flag.
If learnings are found, incorporate them into your analysis. When a review finding
matches a past learning, display:
**"Prior learning applied: [key] (confidence N/10, from [date])"**
This makes the compounding visible. The user should see that gstack is getting
smarter on their codebase over time.
## Step 4: Two-pass review
Apply the checklist against the diff in two passes:
@@ -600,6 +647,31 @@ Takes seconds, prevents recommending outdated patterns. If WebSearch is unavaila
Follow the output format specified in the checklist. Respect the suppressions — do NOT flag items listed in the "DO NOT flag" section.
## Confidence Calibration
Every finding MUST include a confidence score (1-10):
| Score | Meaning | Display rule |
|-------|---------|-------------|
| 9-10 | Verified by reading specific code. Concrete bug or exploit demonstrated. | Show normally |
| 7-8 | High confidence pattern match. Very likely correct. | Show normally |
| 5-6 | Moderate. Could be a false positive. | Show with caveat: "Medium confidence, verify this is actually an issue" |
| 3-4 | Low confidence. Pattern is suspicious but may be fine. | Suppress from main report. Include in appendix only. |
| 1-2 | Speculation. | Only report if severity would be P0. |
**Finding format:**
\`[SEVERITY] (confidence: N/10) file:line — description\`
Example:
\`[P1] (confidence: 9/10) app/models/user.rb:42 — SQL injection via string interpolation in where clause\`
\`[P2] (confidence: 5/10) app/controllers/api/v1/users_controller.rb:18 — Possible N+1 query, verify with production logs\`
**Calibration learning:** If you report a finding with confidence < 7 and the user
confirms it IS a real issue, that is a calibration event. Your initial confidence was
too low. Log the corrected pattern as a learning so future reviews catch it with
higher confidence.
---
## Step 4.5: Design Review (conditional)
@@ -1127,6 +1199,30 @@ Substitute:
- `informational` = remaining unresolved informational findings
- `COMMIT` = output of `git rev-parse --short HEAD`
## 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":"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).
**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.
If the review exits early before a real review completes (for example, no diff against the base branch), do **not** write this entry.
## Important Rules
review/SKILL.md.tmpl
+6
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@@ -104,6 +104,8 @@ Run `git diff origin/<base>` to get the full diff. This includes both committed
---
{{LEARNINGS_SEARCH}}
## Step 4: Two-pass review
Apply the checklist against the diff in two passes:
@@ -122,6 +124,8 @@ Takes seconds, prevents recommending outdated patterns. If WebSearch is unavaila
Follow the output format specified in the checklist. Respect the suppressions — do NOT flag items listed in the "DO NOT flag" section.
{{CONFIDENCE_CALIBRATION}}
---
## Step 4.5: Design Review (conditional)
@@ -273,6 +277,8 @@ Substitute:
- `informational` = remaining unresolved informational findings
- `COMMIT` = output of `git rev-parse --short HEAD`
{{LEARNINGS_LOG}}
If the review exits early before a real review completes (for example, no diff against the base branch), do **not** write this entry.
## Important Rules