Total 50,510 skills, AI & Machine Learning has 8479 skills
Showing 12 of 8479 skills
Using the Pi terminal agent — workspace setup, sessions, /commands, compaction, settings.json/AGENTS.md, skill discovery, providers/models, plus theme/keybinding/prompt customization (SYSTEM.md, APPEND_SYSTEM.md, settings.json, keybindings.json). Use for any "how do I configure/run Pi" question.
Use this skill whenever the user is working with the Pydantic AI framework — including building AI agents, defining structured outputs with Pydantic models, wiring up tools/function calling, configuring model providers (OpenAI, Anthropic, Gemini, etc.), managing dependencies via agent context, handling streaming responses, or debugging agent runs. Trigger this skill even for adjacent tasks like "how do I make my agent return JSON", "set up a multi-step agent", "add a tool to my agent", or "validate LLM output with Pydantic" — any time Pydantic AI is mentioned or implied as the target framework.
Build LLM-powered chat apps with the right SDK — Anthropic SDK / Claude API (prompt caching, thinking, tool use, batch, files, citations, memory, model migrations) AND Vercel AI SDK (useChat, streamText, tool calls, UIMessage, ChatStatus, addToolOutput). Use when implementing chat interfaces, tuning Claude features, migrating between Claude model versions, or wiring up streaming with @ai-sdk/react.
Generate or update CLAUDE.md from blueprint artifacts. Use when adding team instructions, converting inline content to @imports, or setting up CLAUDE.local.md.
MCP server for AI image & video generation with 9 models (GPT Image 2, Nanobanana 2, Flux 2, Midjourney V8.1, Veo 3.1, local ComfyUI), 1,446 curated prompts, and parallel batch orchestration
Execute Python code in isolated rootless containers with MCP server proxying for token-efficient agent workflows
Expert in using ktx, the executable context layer for data and analytics agents that enables accurate querying through MCP with skills, memory and a semantic layer
Integrate Anki spaced repetition flashcards with AI assistants through Model Context Protocol for study sessions, deck management, and card creation
Local MCP memory server for AI coding assistants with verbatim recall, semantic search, and automatic session capture
Use OpenAI Codex CLI through MCP to get AI-powered code analysis, generation, review, and web search directly in your editor
Context layer for data and analytics AI agents with semantic layer, skills, and memory via MCP
This skill should be used when the user asks to "콘텐츠 정리", "아티클 요약", "PDF 학습", "영상 정리", "트윗 정리", "digest", "summarize", "정리해줘", or provides a YouTube URL, X/Twitter URL (x.com, twitter.com), webpage URL, or PDF file for analysis. Supports YouTube (transcript), X/Twitter (via fetch-tweet skill), webpage (full content via browser), and PDF (text + image per page). Generates Quiz-First learning with 9 questions across 3 difficulty levels.