Evaluating the Effectiveness of Skills vs. CLAUDE.md in AI Assistants
The article discusses the performance of skills vs. CLAUDE.md in AI assistants like Claude Code. It presents research showing that CLAUDE.md outperforms skills in general knowledge tests, but skills can be effective in specific workflows when properly invoked.
Why it matters
This research provides insights into the strengths and limitations of skills vs. CLAUDE.md in AI assistants, which can inform the design and implementation of such systems.
Key Points
- 1Vercel's research found AGENTS.md outperformed skills in single-shot evaluations
- 2Skills depend on context to be invoked, which often fails in 34-94% of cases
- 3CLAUDE.md is always in context and reaches the model, while skills have an 'activation gap'
- 4Superpowers tool works well by bypassing the skill system and approximating CLAUDE.md
Details
The article delves into the underlying mechanics of how skills work in Claude Code. It explains that at session initialization, only the name and description of skills are presented to the model, not the full content. The model then has to decide whether to invoke a skill based on this limited information, which often fails. In contrast, CLAUDE.md content is always available to the model. The author conducted multi-turn evaluations and found that when skills are successfully invoked, they perform just as well as CLAUDE.md. The conclusion is that skills are best suited for specific, on-demand workflows, while CLAUDE.md is more effective for general best practices and guidelines.
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