continuous-learning

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

AI & Automation 814 stars 53 forks Updated today MIT

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Quality Score: 93/100

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100
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70
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50
License 10%
100
Description 5%
100

Skill Content

# Continuous Learning ## Overview Continuous learning pipeline adapted from the Everything Claude Code methodology. Automatically extracts patterns from development sessions, evaluates them with confidence scoring, and converts high-quality patterns into reusable skills. ## Learning Pipeline ### 1. Pattern Extraction - 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 ## Strateg...

Details

Author
a5c-ai
Repository
a5c-ai/babysitter
Created
4 months ago
Last Updated
today
Language
JavaScript
License
MIT

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