Add semantic markers and AI-visible annotations to Markdown documents.
npx skills add https://github.com/gaoqiongxie/skills-ai --skill hads-gaoqiongxie
Please help me install this Agent Skill. Skill: hads Source: https://github.com/gaoqiongxie/skills-ai/tree/main/hads Command: npx skills add https://github.com/gaoqiongxie/skills-ai --skill hads-gaoqiongxie
Traditional Markdown documents are optimized for human readability but often poorly parsed by AI models, leading to missed context, misinterpreted key decisions, and incorrect extraction of structured data from technical documentation.
Use the hads skill to convert your existing product requirement document into HADS format with semantic headings and AI annotation blocks.
High-intent search queries and answers about installing and using this skill.
Make Markdown technical documentation AI-friendly by applying lightweight semantic markers like [CONTEXT] and [DECISION], AI-visible annotation blocks, and structured data tags for accurate dual parsing.
AI misinterprets API specifications because traditional Markdown lacks semantic structure. Adding AI-visible annotation blocks and structured data type labels clarifies critical warnings and decision rationales for accurate AI parsing.
The best way to format architecture decision records for AI parsing is using lightweight ADR templates with semantic heading markers to instantly identify content types without reading full paragraphs.
Yes, you can tag table columns with explicit data types in Markdown. Using structured data annotations allows AI systems to accurately extract and parse tabular data without confusion.
Adding semantic markup to product requirement documents does not disrupt human reading flow. The markers, annotation blocks, and data labels are lightweight and maintain natural readability while enabling AI context extraction.
Convert existing technical documentation into an AI-friendly format by integrating semantic heading markers, adding AI-visible annotation blocks for context, and tagging structured data for dual parsing.
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