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name: markdown-token-optimizer description: | Analyze Markdown and SKILL.md files for token efficiency while preserving clarity and required resources. Use when reducing verbose skill instructions, trimming duplicated Markdown, reviewing token bloat, or making AI-facing documentation more concise without breaking Jimu skill behavior. author: Microsoft license: MIT category: workflow tags: Microsoft, Token优化, Markdown, SKILL.md, 文档优化 source: https://github.com/microsoft/GitHub-Copilot-for-Azure/tree/main/.github/skills/markdown-token-optimizer

Jimu Platform Adaptation

This skill was imported from a Microsoft upstream agent ecosystem. When running in Jimu, do not treat upstream host names, repository paths, or tool names as literal requirements. Map them to Jimu capabilities instead:

• Upstream skill-directory paths and Copilot test harness directories mean the relevant Jimu skill/resource directory unless the user explicitly asks to work in the upstream repository.
• Upstream CLI, MCP, test, token-counting, and git commands are optional implementation aids. Use Jimu file, shell, web/browser, todo, skill, and sub-agent tools where available, and only run external commands after verifying they exist in the target project.
• Upstream metadata fields such as host-specific tool allowlists or agentskills.io-only formatting rules may not map one-to-one to Jimu. Keep Jimu frontmatter valid for the resource registry and preserve useful intent rather than copying host-specific syntax blindly.
• Token budgets are heuristics. Optimize for lower context cost without deleting instructions, references, scripts, or examples that are required for the skill to work in Jimu.

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: keep concise enough for activation; references should stay focused and split when large. Do not remove required resources just to hit a token target.

References

• OPTIMIZATION-PATTERNS.md - Optimization techniques
• ANTI-PATTERNS.md - Token-wasting patterns