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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
co-authored by
Claude Opus 4.6
Claude Opus 4.6
parent
66894601e3
commit
ae0a9ad195
@@ -63,6 +63,15 @@ 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: $_LEARN_COUNT entries loaded"
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else
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echo "LEARNINGS: 0"
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fi
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```
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If `PROACTIVE` is `"false"`, do not proactively suggest gstack skills AND do not
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@@ -485,6 +494,44 @@ Always work through the full interactive review: one section at a time (Architec
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## Review Sections (after scope is agreed)
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## Prior Learnings
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Search for relevant learnings from previous sessions:
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```bash
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_CROSS_PROJ=$(~/.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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### 1. Architecture review
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Evaluate:
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* Overall system design and component boundaries.
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@@ -498,6 +545,31 @@ Evaluate:
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**STOP.** For each issue found in this section, call AskUserQuestion individually. One issue per call. Present options, state your recommendation, explain WHY. Do NOT batch multiple issues into one AskUserQuestion. Only proceed to the next section after ALL issues in this section are resolved.
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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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### 2. Code quality review
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Evaluate:
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* Code organization and module structure.
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@@ -110,6 +110,8 @@ Always work through the full interactive review: one section at a time (Architec
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## Review Sections (after scope is agreed)
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{{LEARNINGS_SEARCH}}
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### 1. Architecture review
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Evaluate:
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* Overall system design and component boundaries.
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@@ -123,6 +125,8 @@ Evaluate:
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**STOP.** For each issue found in this section, call AskUserQuestion individually. One issue per call. Present options, state your recommendation, explain WHY. Do NOT batch multiple issues into one AskUserQuestion. Only proceed to the next section after ALL issues in this section are resolved.
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{{CONFIDENCE_CALIBRATION}}
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### 2. Code quality review
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Evaluate:
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* Code organization and module structure.
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