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Analyze and compress Markdown to reduce tokens and improve AI efficiency.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "markdown-token-optimizer" skill from askskill: 1. Download https://raw.githubusercontent.com/microsoft/GitHub-Copilot-for-Azure/main/.github/skills/markdown-token-optimizer/SKILL.md 2. Save it as ~/.claude/skills/markdown-token-optimizer/SKILL.md 3. Reload skills and tell me it's ready
Analyze this Markdown technical document for the most token-expensive sections. Keep key information and heading structure, remove redundant wording, repeated examples, and unnecessary whitespace, then output an optimized version and explain what was reduced.
A more concise Markdown document plus notes on what redundant content was compressed.
I have a Markdown prompt library for AI that is too long. Optimize it for token efficiency by merging duplicate rules, shortening explanations, and preserving key constraints. Output a shorter version with the same meaning.
A compressed Markdown prompt library that preserves core rules while reducing token redundancy.
Review this Markdown file and identify why it is too large. Find token-bloat patterns such as repeated paragraphs, overly long lists, wordy phrasing, or unnecessary formatting, and provide itemized optimization suggestions.
A checklist of token-bloat issues with actionable Markdown optimization recommendations.
This skill analyzes markdown files and suggests optimizations to reduce token consumption while maintaining clarity.
See ANTI-PATTERNS.md for detection patterns and OPTIMIZATION-PATTERNS.md for techniques.
Write, review, and standardize Agent Skills to match agentskills.io requirements.
Create, validate, and run eval.yaml suites for agent skill evaluation.
Create a GitHub issue from local integration test failures.
Investigate failing GitHub integration tests and suggest likely fixes
Iteratively audit and fix skill frontmatter compliance, scoring, and token issues.
Investigate why a specific skill’s tests failed and surface error details.
Optimize Claude Code token usage to cut costs and improve development efficiency.
Lets users choose response depth and token usage before answering.
Convert raw HTML into clean Markdown for efficient LLM processing.
Convert Markdown into styled Word documents with customizable formatting and layout.
Analyze and refine raw prompts into ready-to-use high-quality prompts.
Convert many document formats to Markdown with local storage and retrieval.