How does this skill help reduce token usage?

It externalizes heavy Python implementations (over 20 lines) to separate files and replaces them with concise tool references, significantly lowering the context Claude must process.

Does optimization remove functionality from the skill?

No, the methodology preserves all functionality by moving logic to external modules and CLI tools while keeping core concepts and usage instructions within the main SKILL.md.

What is the recommended target length for a Claude skill?

The target for an optimized skill is typically 150-200 lines, ensuring a clear separation of concerns and efficient token budgeting.

When should I use the Skill Size Optimizer?

Use it when your SKILL.md file exceeds 300 lines, contains more than 10 similar code blocks, or triggers 'Large skill file' warnings during skills-eval validation.

Skill Size Optimizer

Name: Skill Size Optimizer
Author: Ven0m0

byVen0m0

Developer Tools

Reduces Claude Code skill file size and token usage through systematic refactoring, externalization, and modularization patterns.

About

The Skill Size Optimizer provides a comprehensive methodology for managing complex Claude Code skills that have become bloated or inefficient. When SKILL.md files exceed 300 lines or contain excessive inline Python implementations, this skill guides developers through a multi-phase workflow to externalize heavy logic into CLI tools, consolidate repetitive patterns, and implement progressive loading. By reducing token overhead and improving file structure, it ensures that high-functionality skills remain maintainable, performant, and compliant with system validation standards.

Key Features

Automated analysis of skill file complexity and line-count bottlenecks
Systematic externalization of heavy Python logic into dedicated CLI tools
Progressive loading patterns for non-essential documentation and examples
Consolidation of redundant functions through parameterization and structured data
0 GitHub stars
Detailed 4-phase optimization workflow from analysis to validation

Use Cases

Transitioning monolithic skill implementations into maintainable, modular tool-based architectures
Reducing token consumption in complex workflows to improve Claude's responsiveness
Refactoring oversized SKILL.md files that trigger 'Large skill file' warnings

About

Key Features

Automated analysis of skill file complexity and line-count bottlenecks
Systematic externalization of heavy Python logic into dedicated CLI tools
Progressive loading patterns for non-essential documentation and examples
Consolidation of redundant functions through parameterization and structured data
0 GitHub stars
Detailed 4-phase optimization workflow from analysis to validation

Use Cases

Transitioning monolithic skill implementations into maintainable, modular tool-based architectures
Reducing token consumption in complex workflows to improve Claude's responsiveness
Refactoring oversized SKILL.md files that trigger 'Large skill file' warnings