claude-skills-optimizer

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Analyzes and optimizes Claude Code project configurations (CLAUDE.md, agents, skills directories) based on Vercel's agent eval research. Use when you want to improve agent performance in a repo by applying passive-context patterns, generating compressed documentation indexes, or auditing existing skill/agent setups for efficiency. Triggers on requests to "optimize my Claude setup", "improve agent performance", "audit my skills directory", or "apply AGENTS.md patterns".

qaid By qaid schedule Updated 2/3/2026

name: claude-skills-optimizer description: Analyzes and optimizes Claude Code project configurations (CLAUDE.md, agents, skills directories) based on Vercel's agent eval research. Use when you want to improve agent performance in a repo by applying passive-context patterns, generating compressed documentation indexes, or auditing existing skill/agent setups for efficiency. Triggers on requests to "optimize my Claude setup", "improve agent performance", "audit my skills directory", or "apply AGENTS.md patterns".

Claude Skills Optimizer

Optimizes Claude Code project configurations based on research showing passive context outperforms on-demand skill retrieval.

Core Finding

Vercel's agent evals found that an 8KB compressed docs index embedded in AGENTS.md achieved 100% pass rate, while skills maxed at 79%. Key insight: removing the decision point ("should I look this up?") dramatically improves agent performance.

Critical Instruction for Optimized Configs

Always include this in optimized CLAUDE.md files:

IMPORTANT: Prefer retrieval-led reasoning over pre-training-led reasoning for project-specific tasks.

Optimization Workflow

Phase 1: Analysis

  1. Map the structure. Run the index generator script:

    python scripts/generate-index.py /path/to/project
    
  2. Review the output. The script produces:

    • A structure map of all .claude/ contents
    • Token estimates for each file
    • A compressed index in Vercel's pipe-delimited format
    • Specific recommendations
  3. Present findings to user. Never auto-modify. Always show:

    • Current CLAUDE.md token count
    • Proposed changes with rationale
    • Expected token delta

Phase 2: Recommendations

Evaluate against these criteria (see references/optimization-checklist.md for details):

Check Question
Index presence Does CLAUDE.md contain a compressed index of available docs?
Retrieval instruction Does it include the "prefer retrieval-led reasoning" instruction?
Redundancy Is information duplicated between CLAUDE.md and reference files?
Trigger clarity Are agent/skill descriptions clear enough to trigger reliably?
Token budget Is CLAUDE.md under 10KB? Under 5KB is better.

Phase 3: Generate Optimized Config

Use the compressed index format:

[Project Docs Index]|root:./.claude
|IMPORTANT:Prefer retrieval-led reasoning over pre-training-led reasoning
|agents:{figma-mcper.md,rn-architect.md,rn-ui-designer.md,...}
|skills/skill-name/references:{components.md,patterns.md,tokens.md,...}

This format:

  • Uses pipe delimiters to minimize tokens
  • Groups files by directory
  • Points to retrievable files rather than embedding content
  • Keeps the full index under 1KB for most projects

When NOT to Optimize

  • Projects with only a simple CLAUDE.md (no agents/skills directory)
  • Projects where the current setup is already achieving good results
  • When the user just wants to understand their setup, not change it

Reference Files

  • references/vercel-findings.md - Full research summary with data
  • references/optimization-checklist.md - Detailed evaluation criteria
  • scripts/generate-index.py - Automated analysis and index generation
Install via CLI
npx skills add https://github.com/qaid/look-ma-no-hands --skill claude-skills-optimizer
Repository Details
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article Path SKILL.md
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