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continuous-learning

Pattern extraction, confidence-scored evaluation, skill creation, organization, versioning, and cross-project export pipeline.

Quellinformationen

Repository
a5c-ai/babysitter
Letzte Quellaktivität
1. Juni 2026 um 07:46
Erkannte Sprache von SKILL.md
Englisch
Sterne
1.832
Forks
113

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Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

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SKILL.md wird angezeigt

SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
name
continuous-learning
description
Pattern extraction, confidence-scored evaluation, skill creation, organization, versioning, and cross-project export pipeline.
allowed-tools
Read, Write, Edit, Bash, Grep, Glob
graph
{"domains":["domain:software-engineering"],"skillAreas":["skill-area:agentic-loops","skill-area:orchestration-loop"],"workflows":["workflow:feature-development"],"topics":["topic:developer-experience"],"roles":["role:tech-lead","role:backend-engineer"]}
- Analyze code changes and implementation approaches - Identify recurring patterns and conventions - Extract architectural decisions with rationale - Capture error resolution strategies - Record tool usage patterns - Assign initial confidence scores (0-100) ### 2. Pattern Evaluation - Score generalizability (0-100): cross-project applicability - Score reliability (0-100): validation frequency - Score impact (0-100): outcome improvement - Composite: generalizability * 0.3 + reliability * 0.4 + impact * 0.3 - Filter below confidence threshold (default: 75) - Merge similar patterns ### 3. Skill Creation - Convert high-confidence patterns to SKILL.md format - Write clear instructions with phases - Include when-to-use and when-not-to-use sections - Add usage examples and agent references - Follow kebab-case naming convention ### 4. Organization - Categorize: language-specific, domain, business, meta - Resolve naming conflicts - Update indexes and manifests - Create dependency graphs ### 5. Version and Export - Assign semantic versions by maturity - Create portable export bundles - Include usage examples and test cases - Generate import instructions ## Strategic Compaction - Analyze context token usage - Identify low-value context for compression - Archive completed phases to memory files - Calculate token savings per suggestion ## When to Use - End of development sessions - After significant code reviews - After debugging sessions - Periodically during long sessions ## Agents Used - `continuous-learning` (custom agent for this skill) - `context-engineering` (compaction analysis)
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