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feat: GStack Learns — per-project self-learning infrastructure (v0.13.4.0) (#622)
* 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
@@ -60,6 +60,15 @@ for _PF in $(find ~/.gstack/analytics -maxdepth 1 -name '.pending-*' 2>/dev/null
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fi
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break
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done
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# Learnings count
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eval "$(~/.claude/skills/gstack/bin/gstack-slug 2>/dev/null)" 2>/dev/null || true
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_LEARN_FILE="${GSTACK_HOME:-$HOME/.gstack}/projects/${SLUG:-unknown}/learnings.jsonl"
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if [ -f "$_LEARN_FILE" ]; then
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_LEARN_COUNT=$(wc -l < "$_LEARN_FILE" 2>/dev/null | tr -d ' ')
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echo "LEARNINGS: $_LEARN_COUNT entries loaded"
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else
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echo "LEARNINGS: 0"
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fi
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```
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If `PROACTIVE` is `"false"`, do not proactively suggest gstack skills AND do not
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@@ -1318,6 +1327,44 @@ Add a `## Verification Results` section to the PR body (Step 8):
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- If verification ran: summary of results (N PASS, M FAIL, K SKIPPED)
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- If skipped: reason for skipping (no plan, no server, no verification section)
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## Prior Learnings
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Search for relevant learnings from previous sessions:
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```bash
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_CROSS_PROJ=$(~/.claude/skills/gstack/bin/gstack-config get cross_project_learnings 2>/dev/null || echo "unset")
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echo "CROSS_PROJECT: $_CROSS_PROJ"
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if [ "$_CROSS_PROJ" = "true" ]; then
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~/.claude/skills/gstack/bin/gstack-learnings-search --limit 10 --cross-project 2>/dev/null || true
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else
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~/.claude/skills/gstack/bin/gstack-learnings-search --limit 10 2>/dev/null || true
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fi
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```
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If `CROSS_PROJECT` is `unset` (first time): Use AskUserQuestion:
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> gstack can search learnings from your other projects on this machine to find
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> patterns that might apply here. This stays local (no data leaves your machine).
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> Recommended for solo developers. Skip if you work on multiple client codebases
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> where cross-contamination would be a concern.
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Options:
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- A) Enable cross-project learnings (recommended)
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- B) Keep learnings project-scoped only
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If A: run `~/.claude/skills/gstack/bin/gstack-config set cross_project_learnings true`
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If B: run `~/.claude/skills/gstack/bin/gstack-config set cross_project_learnings false`
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Then re-run the search with the appropriate flag.
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If learnings are found, incorporate them into your analysis. When a review finding
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matches a past learning, display:
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**"Prior learning applied: [key] (confidence N/10, from [date])"**
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This makes the compounding visible. The user should see that gstack is getting
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smarter on their codebase over time.
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---
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## Step 3.5: Pre-Landing Review
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@@ -1332,6 +1379,31 @@ Review the diff for structural issues that tests don't catch.
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- **Pass 1 (CRITICAL):** SQL & Data Safety, LLM Output Trust Boundary
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- **Pass 2 (INFORMATIONAL):** All remaining categories
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## Confidence Calibration
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Every finding MUST include a confidence score (1-10):
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| Score | Meaning | Display rule |
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|-------|---------|-------------|
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| 9-10 | Verified by reading specific code. Concrete bug or exploit demonstrated. | Show normally |
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| 7-8 | High confidence pattern match. Very likely correct. | Show normally |
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| 5-6 | Moderate. Could be a false positive. | Show with caveat: "Medium confidence, verify this is actually an issue" |
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| 3-4 | Low confidence. Pattern is suspicious but may be fine. | Suppress from main report. Include in appendix only. |
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| 1-2 | Speculation. | Only report if severity would be P0. |
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**Finding format:**
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\`[SEVERITY] (confidence: N/10) file:line — description\`
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Example:
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\`[P1] (confidence: 9/10) app/models/user.rb:42 — SQL injection via string interpolation in where clause\`
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\`[P2] (confidence: 5/10) app/controllers/api/v1/users_controller.rb:18 — Possible N+1 query, verify with production logs\`
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**Calibration learning:** If you report a finding with confidence < 7 and the user
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confirms it IS a real issue, that is a calibration event. Your initial confidence was
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too low. Log the corrected pattern as a learning so future reviews catch it with
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higher confidence.
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## Design Review (conditional, diff-scoped)
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Check if the diff touches frontend files using `gstack-diff-scope`:
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@@ -1599,6 +1671,30 @@ High-confidence findings (agreed on by multiple sources) should be prioritized f
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---
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## Capture Learnings
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If you discovered a non-obvious pattern, pitfall, or architectural insight during
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this session, log it for future sessions:
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```bash
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~/.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"]}'
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```
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**Types:** `pattern` (reusable approach), `pitfall` (what NOT to do), `preference`
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(user stated), `architecture` (structural decision), `tool` (library/framework insight).
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**Sources:** `observed` (you found this in the code), `user-stated` (user told you),
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`inferred` (AI deduction), `cross-model` (both Claude and Codex agree).
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**Confidence:** 1-10. Be honest. An observed pattern you verified in the code is 8-9.
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An inference you're not sure about is 4-5. A user preference they explicitly stated is 10.
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**files:** Include the specific file paths this learning references. This enables
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staleness detection: if those files are later deleted, the learning can be flagged.
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**Only log genuine discoveries.** Don't log obvious things. Don't log things the user
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already knows. A good test: would this insight save time in a future session? If yes, log it.
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## Step 4: Version bump (auto-decide)
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1. Read the current `VERSION` file (4-digit format: `MAJOR.MINOR.PATCH.MICRO`)
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@@ -227,6 +227,8 @@ If multiple suites need to run, run them sequentially (each needs a test lane).
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{{PLAN_VERIFICATION_EXEC}}
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{{LEARNINGS_SEARCH}}
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---
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## Step 3.5: Pre-Landing Review
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@@ -241,6 +243,8 @@ Review the diff for structural issues that tests don't catch.
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- **Pass 1 (CRITICAL):** SQL & Data Safety, LLM Output Trust Boundary
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- **Pass 2 (INFORMATIONAL):** All remaining categories
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{{CONFIDENCE_CALIBRATION}}
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{{DESIGN_REVIEW_LITE}}
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Include any design findings alongside the code review findings. They follow the same Fix-First flow below.
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@@ -317,6 +321,8 @@ For each classified comment:
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{{ADVERSARIAL_STEP}}
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{{LEARNINGS_LOG}}
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## Step 4: Version bump (auto-decide)
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1. Read the current `VERSION` file (4-digit format: `MAJOR.MINOR.PATCH.MICRO`)
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