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ai-md

combined

AI.MD — Convert any human-written CLAUDE.md into AI-native structured format. Your CLAUDE.md is read by AI every single turn, not by you — so write it in AI's language. Battle-tested: 5 rounds, 4 models (GPT-5.3, Gemini 2.5 Pro, Grok-4, Claude Opus 4.6). Structured-label format raised Codex compliance from 6/8 → 8/8 on identical content. Same rules, fewer tokens, higher precision. This skill contains the complete methodology: how to take natural language instructions and convert them into a format that LLMs actually follow better. Run with: "distill my CLAUDE.md" or "rewrite my MD for AI" or "蒸餾" or "AI.MD" Convert human-written CLAUDE.md into AI-native structured-label format. Battle-tested across 4 models. Same rules, fewer tokens, higher compliance.

When to use

• Use when your CLAUDE.md is long but AI still ignores your rules
• Use when token usage is too high from verbose system instructions
• Use when you want to optimize any LLM system prompt for compliance
• Use when migrating rules between AI tools (Claude, Codex, Gemini, Grok)

When not to use

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Skill page

Conflict

The first copy (sickn33) is a high-level wrapper that delegates the actual procedure to an external file (`references/detailed-guide.md`) and provides only usage triggers and limitations. The second copy (LeoYeAI) contains the complete, self-contained methodology, including the specific mechanisms (Attention Splitting, Zero-Inference Labels, Semantic Anchoring) and the step-by-step conversion process (Understand, Decompose, etc.). Because the first copy lacks the actual steps and the second copy contains the full procedure, crowning the second as 'authentic' would ignore the first's specific framing and delegation structure, while crowning the first would hide the actual method. They overlap in purpose but differ significantly in content depth and structure.