Analyze conversation history and usage feedback to enhance existing skills.
npx skills add https://github.com/BaqiF2/Synapse-Agent --skill skill-enhance
Please help me install this Agent Skill. Skill: skill-enhance Source: https://github.com/BaqiF2/Synapse-Agent/tree/main/src/resource/meta-skill/skill-enhance Command: npx skills add https://github.com/BaqiF2/Synapse-Agent --skill skill-enhance
This skill helps teams and agents improve and update existing skills by incorporating new patterns, correcting errors, and tightening content based on conversation analysis and usage feedback.
Analyze the current SKILL.md to identify gaps and implement a targeted refinement following the Enhancement Process.
High-intent search queries and answers about installing and using this skill.
Skill enhancement improves existing skills by analyzing conversation history and usage feedback to identify gaps and implement targeted refinements. It updates patterns, examples, and references to keep content actionable.
The best way to refine skill content is through a systematic enhancement workflow that prioritizes case-driven updates based on real usage signals. This applies content consolidation and language consistency to maintain precise guidance.
Yes, you can use conversation history to identify gaps in your current skills. Analyzing this history reveals outdated content and opportunities for workflow refinement, ensuring updated skills maintain coherence and concise guidance.
Yes, skill enhancement enforces strict requirements on frontmatter scope, content density, and structured guidance. This ensures that updated skills maintain coherence and concise, actionable guidance throughout the refinement process.
To update outdated references and examples, analyze usage feedback and apply merge and consolidation techniques. This content quality control process tightens content density and ensures structured guidance remains accurate and actionable.
Prioritizing updates based on real usage signals ensures that skill enhancements target actual gaps and edge cases. This case-driven approach corrects errors and tightens content based on actual conversation analysis rather than assumptions.
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