Can I manually trigger a learning session?

Yes, you can use the /learn command or ask Claude to 'save this as a skill' or 'extract a skill from this' at any time during a conversation.

Where are the extracted skills stored?

Skills are saved as .mdc files in either .claude/rules/ or .claude/skills/ directories depending on your project's skiller configuration.

How does the learn skill decide what knowledge to save?

It evaluates findings based on reusability, specificity, and whether the solution was non-obvious or required significant discovery rather than just a documentation lookup.

Does it integrate with external documentation?

Yes, the system can perform web searches to validate findings against current best practices and official documentation before finalizing a new skill.

Autonomous Skill Learning

Name: Autonomous Skill Learning
Author: udecode

byudecode

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1,136

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Learning & Documentation

Extracts and codifies development insights into reusable Claude Code skills to preserve technical knowledge and patterns.

The Learn skill acts as an intelligent memory layer for Claude Code, systematically identifying non-obvious solutions, debugging workarounds, and project-specific patterns encountered during development. By applying a TDD-inspired approach to knowledge extraction, it transforms fleeting session discoveries into permanent, searchable .mdc rule files. This ensures that once a complex problem is solved or a specific architectural pattern is established, the knowledge is preserved for future sessions, preventing repetitive investigation and accelerating development speed through autonomous self-improvement.

Key Features

011,136 GitHub stars

02Web-integrated best practice validation for technology-specific insights

03Automatic creation of structured .mdc skill files with metadata

04Retrospective session analysis via the /learn command

05TDD-based knowledge extraction mapping (Red-Green-Refactor for skills)

06Semantic discovery optimization for future AI skill retrieval

Use Cases

01Preserving complex debugging workarounds for obscure legacy system errors

02Documenting project-specific architectural patterns and team conventions

03Capturing tool-specific gotchas and API quirks discovered through trial and error

Key Features

011,136 GitHub stars

02Web-integrated best practice validation for technology-specific insights

03Automatic creation of structured .mdc skill files with metadata

04Retrospective session analysis via the /learn command

05TDD-based knowledge extraction mapping (Red-Green-Refactor for skills)

06Semantic discovery optimization for future AI skill retrieval

Use Cases

01Preserving complex debugging workarounds for obscure legacy system errors

02Documenting project-specific architectural patterns and team conventions

03Capturing tool-specific gotchas and API quirks discovered through trial and error