Overview

Markdown Token Optimizer analyzes markdown files to identify token-wasting patterns and suggests concise edits. It helps reduce token usage while preserving clarity, benefiting AI ingestion and faster processing.

How This Skill Works

It counts tokens with a rough heuristic (roughly 4 chars per token). It scans for patterns like emojis, verbosity, duplication, and large blocks. It then outputs a Suggest table with location, issue, fix, and savings estimates, plus a Summary of potential gains.

When to Use It

• Optimize markdown files for token efficiency
• Reduce SKILL.md file size or check for bloat
• Make documentation more concise for AI consumption
• Prepare docs for AI ingestion or integration
• Audit for token-wasting patterns and opportunities

Quick Start

1. Step 1: Run Count to estimate total tokens
2. Step 2: Run Scan to detect verbosity, duplication, and blocks
3. Step 3: Review Suggest table and Summary, then apply chosen edits

Best Practices

• Only suggest changes, never auto-modify the source
• Preserve meaning and readability while trimming
• Target SKILL.md length to under 500 tokens and refs under 1000
• Prioritize high-impact savings in long blocks and repeated phrases
• Review emoji and formatting usage for token impact

Example Use Cases

• Trim a long README by removing redundant phrases
• Consolidate repeated boilerplate sentences
• Replace verbose bullet points with concise equivalents
• Reduce emoji usage if not essential to clarity
• Highlight top savings with a compact summary

Frequently Asked Questions