From ai-devkit
Documents a code entry point with structured analysis, dependency mapping, and knowledge docs. Triggered by requests to document, understand, or map code.
How this skill is triggered — by the user, by Claude, or both
Slash command
/ai-devkit:document-codeThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Build structured understanding of code entry points with an analysis-first workflow.
Build structured understanding of code entry points with an analysis-first workflow.
npx ai-devkit@latest memory search --query "<entry point name or purpose>"calculateTotalPrice → calculate-total-price).docs/ai/implementation/knowledge-{name}.md using the Output Template — this is the source of truth.docs/ai/implementation/knowledge-{name}.html per the HTML Artifact spec. Regenerate from the markdown on subsequent runs; never hand-edit.Generated only when the user opts in at step 6. A self-contained HTML file optimized for scanning, not reference reading. Complements the markdown — does not replace it.
Constraints:
https://cdn.jsdelivr.net/npm/mermaid/dist/mermaid.min.js).Section mapping (from the Output Template):
| Rationalization | Why It's Wrong | Do Instead |
|---|---|---|
| "I already understand this code" | Understanding ≠ documented understanding | Write it down, then verify |
| "The code is self-documenting" | Future readers lack your current context | Capture the why, not just the what |
| "Dependencies are obvious" | Implicit dependencies cause surprises | Map them explicitly to depth 3 |
npx claudepluginhub codeaholicguy/ai-devkitGenerates documentation explaining codebase architecture, key components, data flow, and development guidelines. Useful for understanding unfamiliar code, creating onboarding docs, or documenting system architecture.
Generates API docs, architecture diagrams, user guides, and technical references from code using AI-powered analysis and best practices. Useful for documentation pipelines and repo standardization.
Generates and updates README.md and API reference docs by building a knowledge graph of your codebase with graphify, then writing accurate docs from it.