Process raw-extracted JSON files from PDF book extractions into clean, well-structured markdown. Use this skill whenever a user uploads a raw-extracted.json (or similar JSON with metadata and chunks arrays) from a PDF text extraction pipeline, or when they mention cleaning up, formatting, structuring, or organizing extracted book text. Also trigger when the user mentions OCR output, pypdf extraction, book chapters, or converting messy PDF extractions into readable markdown. This skill handles books with varying extraction quality โ from clean text to garbled OCR โ and adapts its approach accordingly.
This skill should be used when the user invokes /code-documentation, or asks to "write/generate/audit/update documentation", "write a README", "document this API or codebase", "write a tutorial / how-to / runbook", "write user help docs", "generate docstrings", "write an ADR or RFC", "create a changelog", "produce llms.txt or AGENTS.md", or "turn these docs into a doc site". Produces first-class documentation for any audience (end users, API consumers, contributors, operators, evaluators, AI agents) across all four Diรกtaxis modes, for the full lifecycle โ authoring, generating from a codebase, auditing, and maintaining. Emits publish-ready Markdown or structured content for Docusaurus, MkDocs, Sphinx, Starlight, Mintlify, or Volume.
React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture.
Use when the user wants to brainstorm blog topics, generate or score article ideas, review/approve content ideas, track article status, or manage editorial planning โ including mentions of "content calendar", "editorial calendar", "blog ideas", "article pipeline", "content backlog", or "publishing schedule".
Checklist for auditing architecture contracts in a develop-by-contract environment. Covers the failure modes most commonly found during contract review: status drift, AI-invented decisions, cross-contract fact drift, stale feature references, missing state specs, and business policy gaps.
Use when analyzing, auditing, reviewing, or explaining a codebase, system, or project state โ whenever written claims could mix observed fact with interpretation.
Use when writing or reviewing implementation plans, roadmaps, tickets, or task lists that claim to be execution-ready โ especially before handing work to another agent or session.
Use when creating or updating in-app help content โ tooltips, empty states, contextual panels, help articles โ or auditing help coverage, identifiers, and accuracy.