Skill Learning Patterns

关于

Facilitates the continuous evolution of shared AI knowledge by teaching agents to recognize valuable insights during their workflows and translate them into communal skill improvements. This meta-skill provides a structured framework for distinguishing between project-specific fixes and generalizable patterns, ensuring that lessons learned from intensive debugging or repeated problem-solving are captured through a rigorous validation and pull request process. By focusing on high-quality, battle-tested contributions, it maintains a living knowledge base that prevents information fragmentation and ensures agents across different contexts benefit from collective learning.

主要功能

• Detailed guide to avoiding common pitfalls like the specificity trap and over-generalization.
• Rigorous validation criteria to ensure knowledge is generalizable and superior to official documentation.
• Standardized pull request workflow and templates for high-quality knowledge contributions.
• Structured decision logic for updating existing skills versus creating new domain-specific patterns.
• 16 GitHub stars
• Pattern recognition framework based on time investment, repetition, and correction signals.

使用场景

• Capturing non-obvious solutions to recurring API errors that aren't addressed in standard documentation.
• Extracting a general-purpose architectural pattern from a specific project to benefit the broader development collective.
• Refining and clarifying existing skill instructions that proved ambiguous or incomplete during implementation.