Agent Skills: continuous-learning

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

UncategorizedID: a5c-ai/babysitter/continuous-learning

Install this agent skill to your local

pnpm dlx add-skill https://github.com/a5c-ai/babysitter/tree/HEAD/library/methodologies/everything-claude-code/skills/continuous-learning

Skill Files

Browse the full folder contents for continuous-learning.

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library/methodologies/everything-claude-code/skills/continuous-learning/SKILL.md

Skill Metadata

Name
continuous-learning
Description
Pattern extraction, confidence-scored evaluation, skill creation, organization, versioning, and cross-project export pipeline.
  • 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)