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Found 14 Skills
Use this skill when the user asks to add documentation, add docs, add references, or install documentation about Neon. Adds Neon best practices reference links to project AI documentation (CLAUDE.md, AGENTS.md, or Cursor rules). Does not install packages or modify code.
Audit an Anthropic Cookbook notebook based on a rubric. Use whenever a notebook review or audit is requested.
Translate "The Interactive Book of Prompting" chapters and UI strings to a new language
Remove telltale signs of AI-generated 'slop' writing from README files and documentation. Make your docs sound authentically human.
Use when working with the OpenAI API (Responses API) or OpenAI platform features (tools, streaming, Realtime API, auth, models, rate limits, MCP) and you need authoritative, up-to-date documentation (schemas, examples, limits, edge cases). Prefer the OpenAI Developer Documentation MCP server tools when available; otherwise guide the user to enable `openaiDeveloperDocs`.
Set up and maintain a persistent, LLM-managed knowledge base for a digital health project — turning clinical observations, papers, interviews, and planning docs into a compounding, interlinked wiki.
Manages the ai-context/ memory layer: initialize from scratch, update with session work, or maintain/cleanup. Trigger: /memory-init, /memory-update, /memory-maintain, initialize memory, update memory, maintain memory.
Generate comprehensive documentation with intelligent orchestration and parallel execution
[Hyper] Create and refactor AI-readable docs, instruction bases, runbooks, specs, and harness-ready rule packs for context, prompt, tool, eval, sourcing, safety, and validation workflows.
Analyzes markdown files for token efficiency. TRIGGERS: optimize markdown, reduce tokens, token count, token bloat, too many tokens, make concise, shrink file, file too large, optimize for AI, token efficiency, verbose markdown, reduce file size
Access AI-generated documentation and insights for GitHub repositories via DeepWiki. This skill should be used when exploring unfamiliar codebases, understanding repository architecture, finding implementation patterns, or asking questions about how a GitHub project works. Supports any public GitHub repository.
Write AI-scannable technical documentation.