Loading...
Loading...
Found 1,290 Skills
Recursive Language Model context management for processing documents exceeding context window limits. Enables Claude to match Gemini's 2M token context capability through chunking, sub-LLM delegation, and synthesis.
One-click initialization of a multi-agent repository from the Antigravity template. Use this skill when users want to scaffold a new project quickly (`quick` mode) or with runtime defaults (`full` mode) including LLM provider profile, MCP toggle, swarm preference context, sandbox type, and optional git init.
Integrate Databuddy analytics into applications using the SDK or REST API. Use when implementing analytics tracking, feature flags, custom events, Web Vitals, error tracking, LLM observability, or querying analytics data programmatically.
Add or refresh a fixed 20-line file-header comment that summarizes a source file and indexes key classes/functions with line-number addresses. Use when annotating large codebases for fast navigation, onboarding, refactors, or when you want LLMs/humans to locate relevant symbols quickly without reading entire files.
AI and ML expert including PyTorch, LangChain, LLM integration, and scientific computing
Data engineering, machine learning, AI, and MLOps. From data pipelines to production ML systems and LLM applications.
Convert documents (PDF, Word, Excel, PowerPoint, images, HTML) to Markdown using microsoft/markitdown. Use for document analysis, content extraction, preprocessing for LLMs, or batch document conversion. Supports images with OCR/LLM descriptions, audio transcription, and ZIP archives.
创建高质量 MCP（模型上下文协议）服务器的指南，使 LLM 能够通过精心设计的工具与外部服务交互。在构建 MCP 服务器以集成外部 API 或服务时使用，无论是 Python (FastMCP) 还是 Node/TypeScript (MCP SDK)。
Rewrite AI-sounding text into natural, human writing by removing common LLM patterns while preserving meaning and tone.
Build AI-powered chat applications with TanStack AI and React. Use when working with @tanstack/ai, @tanstack/ai-react, @tanstack/ai-client, or any TanStack AI packages. Covers useChat hook, streaming, tools (server/client/hybrid), tool approval, structured outputs, multimodal content, adapters (OpenAI, Anthropic, Gemini, Ollama, Grok), agentic cycles, devtools, and type safety patterns. Triggers on AI chat UI, function calling, LLM integration, or streaming response tasks using TanStack AI.
Use this skill when crafting, iterating, or optimizing prompts for LLMs including zero-shot, few-shot, chain-of-thought, role prompting, structured output, and prompt chaining. Not for fine-tuning or training models. Not for evaluating model quality across benchmarks.
Prompt design patterns for LLMs including few-shot, chain-of-thought, structured output, and injection defense. Use when crafting prompts, optimizing LLM outputs, or building prompt-based features.