Skill Analysis

Skrills analyzes skills for token usage, dependencies, and optimization opportunities. This helps you understand the context window impact of your skills and identify candidates for optimization.

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

Understanding Token Counts

Token counts are estimates based on the skill content. They help you:

1. Budget context window usage: Large skills consume more of the available context.
2. Identify optimization targets: Skills with high token counts may benefit from refactoring.
3. Compare alternatives: Choose between skill variants based on efficiency.

Optimization Suggestions

When using --suggestions, skrills provides recommendations such as:

• Split large skills: Skills over 2000 tokens may benefit from modular decomposition.
• Remove redundant content: Duplicate information across skills.
• Simplify examples: Verbose examples can often be condensed.
• Use references: Link to external docs instead of embedding large content.

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

1. Set token budgets: Establish team guidelines for maximum skill token counts.
2. Review regularly: Run analysis after adding new skills or updating existing ones.
3. Prioritize high-impact skills: Focus optimization efforts on frequently-used skills.
4. Test after optimization: Ensure skills still work correctly after reducing content.