Skill graphs enable progressive disclosure for complex domains

A single SKILL.md file is a really good book on one topic. A skill graph is a library where books reference each other. When domains get complex — therapy (CBT + attachment + regulation), trading (risk + psychology + sizing) — a monolithic file becomes unnavigable. Skill graphs solve this with progressive disclosure: the agent navigates progressively from metadata to wikilinks to specific sections to full content, loading only what’s relevant at each step.

The primitives are the same ones powering this knowledge graph: Files are the universal interface between humans and agents for the substrate, wikilinks in prose for meaningful connections, YAML frontmatter for scannable metadata, and MOCs for clustering. This is Structure plus reasoning beats flat similarity for complex domains applied to agent knowledge — the agent navigates by reasoning rather than searching by similarity. It extends Markdown skill files may replace expensive fine-tuning from single files to interconnected graphs, and connects to The context window is the fundamental constraint — everything else follows because progressive disclosure is fundamentally about loading only what’s relevant into a limited context window. At the market level, Domain-specific skill libraries are the real agent moat, not core infrastructure validates this architecture — when Excel AI agents were reverse-engineered, the real competitive advantage wasn’t core infrastructure but accumulated domain skill libraries that took years to build.