AI Documentation Standards
Write AI-readable documentation following concise-over-comprehensive principle, hierarchical CLAUDE.md/AGENTS.md inheritance (100-200 line rule), structured formats (tables over prose), parallel validation, and session knowledge capture. Use when writing documentation, updating docs, or optimizing existing docs.
make-distilled
Transform raw captured content into distilled knowledge by extracting summary, key points, principles, patterns, entities, and quotes, storing the result in the distilled/ directory.
soul
Core identity and continuity system. Use to grow wisdom, record failures, observe identity, and access accumulated knowledge.
retrospective
Generate comprehensive learning documents by combining information from tickets, memories, GitHub PRs, and proposals. Use after completing significant work to capture lessons learned.
using-forgetful-memory
Guidance for using Forgetful semantic memory effectively. Applies Zettelkasten atomic memory principles. Use when deciding whether to query or create memories, structuring memory content, or understanding memory importance scoring.
learning-capture
Recognize and capture reusable patterns, workflows, and domain knowledge from work sessions into new skills. Use when completing tasks that involve novel approaches repeated 2+ times, synthesizing complex domain knowledge across conversations, discovering effective reasoning patterns, or developing workflow optimizations. Optimizes for high context window ROI by identifying patterns that will save 500+ tokens per reuse across 10+ future uses.
notion-knowledge-capture
Transforms conversations and discussions into structured documentation pages in Notion. Captures insights, decisions, and knowledge from chat context, formats appropriately, and saves to wikis or databases with proper organization and linking for easy discovery.
