ruleset-optimization

What problem does it solve?

This Skill addresses the challenge of managing large, inefficient Claude rulesets by providing a systematic approach to minimize context usage, improve signal-to-noise ratio, and enhance overall AI reasoning effectiveness. It makes your AI faster, more focused, and easier to manage.

Core Features & Use Cases

• Context Efficiency Philosophy: Guides on extracting procedural "how-to" content into skills and keeping CLAUDE.md minimal, leveraging progressive disclosure to load only what's needed.
• Extraction Decision Tree: Provides a clear framework to decide what content to keep in the main ruleset versus what to extract into separate skills based on token count and usage frequency.
• Token Savings Calculation: Quantifies the impact of optimization, showing baseline and session-specific token reductions, demonstrating tangible performance improvements.
• Use Case: Your CLAUDE.md file has grown too large, making the AI slow or less focused. Use this skill to analyze your ruleset, identify sections that can be extracted into new, auto-activating skills (e.g., a "Python Workflow" skill), and calculate the token savings, resulting in a leaner, more efficient AI.

Quick Start

Analyze my current CLAUDE.md ruleset for optimization opportunities. Suggest content to extract into new skills and calculate potential token savings.