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Garry Tan
1b64d492b9
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>
97 lines
3.9 KiB
TypeScript
97 lines
3.9 KiB
TypeScript
/**
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* Learnings resolver — cross-skill institutional memory
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*
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* Learnings are stored per-project at ~/.gstack/projects/{slug}/learnings.jsonl.
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* Each entry is a JSONL line with: ts, skill, type, key, insight, confidence,
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* source, branch, commit, files[].
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*
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* Storage is append-only. Duplicates (same key+type) are resolved at read time
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* by gstack-learnings-search ("latest winner" per key+type).
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*
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* Cross-project discovery is opt-in. The resolver asks the user once via
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* AskUserQuestion and persists the preference via gstack-config.
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*/
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import type { TemplateContext } from './types';
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export function generateLearningsSearch(ctx: TemplateContext): string {
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if (ctx.host === 'codex') {
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// Codex: simpler version, no cross-project, uses $GSTACK_BIN
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return `## Prior Learnings
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Search for relevant learnings from previous sessions on this project:
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\`\`\`bash
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$GSTACK_BIN/gstack-learnings-search --limit 10 2>/dev/null || true
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\`\`\`
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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, note it: "Prior learning applied: [key] (confidence N, from [date])"`;
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}
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return `## Prior Learnings
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Search for relevant learnings from previous sessions:
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\`\`\`bash
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_CROSS_PROJ=$(${ctx.paths.binDir}/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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${ctx.paths.binDir}/gstack-learnings-search --limit 10 --cross-project 2>/dev/null || true
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else
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${ctx.paths.binDir}/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 \`${ctx.paths.binDir}/gstack-config set cross_project_learnings true\`
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If B: run \`${ctx.paths.binDir}/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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export function generateLearningsLog(ctx: TemplateContext): string {
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const binDir = ctx.host === 'codex' ? '$GSTACK_BIN' : ctx.paths.binDir;
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return `## Capture Learnings
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If you discovered a non-obvious pattern, pitfall, or architectural insight during
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this session, log it for future sessions:
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\`\`\`bash
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${binDir}/gstack-learnings-log '{"skill":"${ctx.skillName}","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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}
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