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Markdown Token Optimizer

microsoftbymicrosoft
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109
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Content Management

Optimizes Markdown files for AI efficiency by identifying and reducing token-heavy patterns and redundant content.

The Markdown Token Optimizer is a specialized utility designed to streamline documentation for AI-driven workflows. By scanning Markdown files for 'token bloat'—such as excessive emojis, verbose phrasing, and redundant blocks—it helps developers stay within the context window limits of Large Language Models like Claude. The skill provides a comprehensive audit, including precise token counts and actionable suggestions with estimated savings, ensuring your documentation remains clear while becoming significantly more cost-effective and efficient for AI consumption.

Key Features

01Accurate token count calculation for Markdown files
02109 GitHub stars
03Token savings estimates for each recommended optimization
04Compliance checking for skill-specific token limits
05Detailed suggestion tables with location and fix descriptions
06Pattern-based scanning for verbosity and anti-patterns

Use Cases

01Condensing large technical documentation for better AI context window management
02Shrinking SKILL.md files to meet strict token limit requirements
03Reducing API costs by optimizing verbose content before processing
name description
markdown-token-optimizer
Analyzes markdown files for token efficiency. TRIGGERS: optimize markdown, reduce tokens, token count, token bloat, too many tokens, make concise, shrink file, file too large, optimize for AI, token efficiency, verbose markdown, reduce file size

Markdown Token Optimizer

This skill analyzes markdown files and suggests optimizations to reduce token consumption while maintaining clarity.

When to Use

  • Optimize markdown files for token efficiency
  • Reduce SKILL.md file size or check for bloat
  • Make documentation more concise for AI consumption

Workflow

  1. Count - Calculate tokens (~4 chars = 1 token), report totals
  2. Scan - Find patterns: emojis, verbosity, duplication, large blocks
  3. Suggest - Table with location, issue, fix, savings estimate
  4. Summary - Current/potential/savings with top recommendations

See ANTI-PATTERNS.md for detection patterns and OPTIMIZATION-PATTERNS.md for techniques.

Rules

  • Suggest only (no auto-modification)
  • Preserve clarity in all optimizations
  • SKILL.md target: <500 tokens, references: <1000 tokens

References