continuous-learning
The continuous-learning skill automatically extracts patterns from development sessions, scores their reliability and cross-project applicability, and converts high-confidence patterns into reusable skills in standardized format. Use it at the end of development sessions, after code reviews, or following debugging work to systematically capture and organize recurring practices into a shareable skill library.
git clone --depth 1 https://github.com/a5c-ai/babysitter /tmp/continuous-learning && cp -r /tmp/continuous-learning/library/methodologies/everything-claude-code/skills/continuous-learning ~/.claude/skills/continuous-learningSKILL.md
# 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 ## 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)
Review TypeScript code changes for consistency, type safety, and monorepo patterns across babysitter packages
Generate and validate documentation for @a5c-ai/babysitter-sdk CLI commands and exported APIs
Scaffold new babysitter process definitions following SDK patterns, proper structure, and best practices. Guides the 3-phase workflow from research to implementation.
Architect code review with DRY, YAGNI, abstraction, and test coverage principle enforcement