Skill Analysis

Skrills analyzes skills for token usage, dependencies, and optimization opportunities.

Token Analysis

Basic Analysis

Analyze all discovered skills:

skrills analyze

Filter by Token Count

Show only skills exceeding a token threshold:

skrills analyze --min-tokens 1000

Include Optimization Suggestions

Get actionable recommendations for reducing token usage:

skrills analyze --suggestions

Output Formats

Get machine-readable output:

skrills analyze --format json

Analyze Specific Directories

Override default discovery paths:

skrills analyze --skill-dir ~/my-skills

Token counts provide an estimate of a skill’s impact on your context window. This helps you budget available context, identify candidates for refactoring, and compare efficient alternatives. Large skills can displace other important context, so keeping them lean is critical for performance.

Optimization Suggestions

The --suggestions flag identifies potential issues that bloat your context usage. It looks for skills exceeding 2000 tokens that might benefit from modular decomposition or removal of redundant content. It also flags verbose examples that could be simplified and suggests linking to external documentation instead of embedding large blocks of text.

MCP Tool

When running as an MCP server (skrills serve), the analyze-skills tool provides the same functionality:

{
  "name": "analyze-skills",
  "arguments": {
    "min_tokens": 1000,
    "suggestions": true
  }
}

Best Practices

Establish a budget for maximum skill token counts and review them regularly, especially after major updates. Focus your optimization efforts on frequently used skills where the savings will have the most impact. Always verify that skills still function correctly after any refactoring to reduce size.

skrills Book