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
- 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)
Coordinate Crew (persistent) and Polecat (transient) agents using Gas Town's hook-based work distribution and GUPP principle.
System and API design guidance covering component boundaries, data flow, integration patterns, and scalability considerations.
This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.
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This skill should be used when the user asks to "babysit issues", "work on assigned issues", "check a5c-agent issues", "process babysitter issues", or wants to find and work on open GitHub issues assigned to a5c-agent in the babysitter repo.