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merge: incorporate origin/main into community-mode branch

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Conflicts resolved:
- VERSION: keep 0.14.0.0 (our branch > main's 0.13.6.0)
- CHANGELOG.md: keep both entries, 0.14.0.0 above 0.13.6.0
- package.json: keep 0.14.0.0

Main brought in v0.13.6.0 "GStack Learns": project learnings system
with confidence calibration, /learn skill, cross-project discovery,
confidence decay, learnings count in preamble.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Garry Tan
2026-03-29 20:03:58 -07:00
parent 3074e6773a ae0a9ad195
commit d455e9661b
47 changed files with 2372 additions and 0 deletions
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ship/SKILL.md
+96
View File
@@ -60,6 +60,15 @@ 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: $_LEARN_COUNT entries loaded"
else
echo "LEARNINGS: 0"
fi
```
If `PROACTIVE` is `"false"`, do not proactively suggest gstack skills AND do not
@@ -1319,6 +1328,44 @@ Add a `## Verification Results` section to the PR body (Step 8):
- If verification ran: summary of results (N PASS, M FAIL, K SKIPPED)
- If skipped: reason for skipping (no plan, no server, no verification section)
## Prior Learnings
Search for relevant learnings from previous sessions:
```bash
_CROSS_PROJ=$(~/.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 3.5: Pre-Landing Review
@@ -1333,6 +1380,31 @@ Review the diff for structural issues that tests don't catch.
- **Pass 1 (CRITICAL):** SQL & Data Safety, LLM Output Trust Boundary
- **Pass 2 (INFORMATIONAL):** All remaining categories
## 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.
## Design Review (conditional, diff-scoped)
Check if the diff touches frontend files using `gstack-diff-scope`:
@@ -1600,6 +1672,30 @@ High-confidence findings (agreed on by multiple sources) should be prioritized f
---
## 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":"ship","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 4: Version bump (auto-decide)
1. Read the current `VERSION` file (4-digit format: `MAJOR.MINOR.PATCH.MICRO`)
ship/SKILL.md.tmpl
+6
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@@ -228,6 +228,8 @@ If multiple suites need to run, run them sequentially (each needs a test lane).
{{PLAN_VERIFICATION_EXEC}}
{{LEARNINGS_SEARCH}}
---
## Step 3.5: Pre-Landing Review
@@ -242,6 +244,8 @@ Review the diff for structural issues that tests don't catch.
- **Pass 1 (CRITICAL):** SQL & Data Safety, LLM Output Trust Boundary
- **Pass 2 (INFORMATIONAL):** All remaining categories
{{CONFIDENCE_CALIBRATION}}
{{DESIGN_REVIEW_LITE}}
Include any design findings alongside the code review findings. They follow the same Fix-First flow below.
@@ -318,6 +322,8 @@ For each classified comment:
{{ADVERSARIAL_STEP}}
{{LEARNINGS_LOG}}
## Step 4: Version bump (auto-decide)
1. Read the current `VERSION` file (4-digit format: `MAJOR.MINOR.PATCH.MICRO`)