What is the 500-line rule for Claude Code skills?

The 500-line rule is a best practice recommending that main SKILL.md files stay under 500 lines. This ensures the primary instructions stay within the dynamic context limit while moving deeper details to reference files.

Can I use Skill Optimizer on existing skills?

Yes, it includes a dedicated migration workflow designed to analyze existing skills, identify optimization opportunities, and split content into a modular architecture.

How does this skill help save tokens?

It implements a tiered loading strategy where comprehensive documentation (Level 3) only enters the context window when Claude specifically accesses the file, preventing constant token drain.

What is progressive disclosure in the context of AI skills?

Progressive disclosure is a pattern where the main skill file provides a high-level overview and links to specific reference files for deep details, allowing the AI to load only what is relevant to the current task.

Skill Optimizer

Name: Skill Optimizer
Author: JNLei

byJNLei

•

開発者ツール

Optimizes Claude Code skills for token efficiency using progressive disclosure and multi-level content architecture.

概要

Skill Optimizer is a meta-utility designed to help developers minimize token consumption and enhance the performance of their Claude Code skills. It provides a standardized framework for implementing a three-level loading architecture, ensuring that large documentation and code libraries only consume context when specifically needed. By applying the 500-line rule and progressive disclosure patterns, the skill helps restructure monolithic skills into modular, high-performance tools that are easier for Claude to discover and execute accurately.

主な機能

6 GitHub stars
Three-level loading system (Metadata, Instructions, Resources)
YAML frontmatter optimization for better skill discovery
Automated migration workflows for monolithic skill files
Implementation of the 500-line rule for dynamic context management
Progressive disclosure patterns for linking reference files

ユースケース

Restructuring extensive API documentation into on-demand reference files
Auditing and improving skill performance through keyword-rich frontmatter
Reducing token costs and context overhead for complex skill libraries

概要

主な機能

6 GitHub stars
Three-level loading system (Metadata, Instructions, Resources)
YAML frontmatter optimization for better skill discovery
Automated migration workflows for monolithic skill files
Implementation of the 500-line rule for dynamic context management
Progressive disclosure patterns for linking reference files

ユースケース

Restructuring extensive API documentation into on-demand reference files
Auditing and improving skill performance through keyword-rich frontmatter
Reducing token costs and context overhead for complex skill libraries