Total 56,302 skills, AI & Machine Learning has 9374 skills
Showing 12 of 9374 skills
Install and configure ToolUniverse with MCP integration for any AI coding client (Cursor, Claude Desktop, Windsurf, VS Code, Codex, Gemini CLI, Trae, Cline, Antigravity, OpenCode, etc.). Covers uv/uvx setup, MCP configuration, API key walkthrough, skill installation, and upgrading. Use when setting up ToolUniverse, configuring MCP servers, troubleshooting installation issues, upgrading versions, or when user mentions installing ToolUniverse or setting up scientific tools.
This skill should be used when users request comprehensive, in-depth research on a topic that requires detailed analysis similar to an academic journal or whitepaper. The skill conducts multi-phase research using web search and content analysis, employing high parallelism with multiple subagents, and produces a detailed markdown report with citations.
Build LiveKit Agent backends in Python. Use this skill when creating voice AI agents, voice assistants, or any realtime AI application using LiveKit's Python Agents SDK (livekit-agents). Covers AgentSession, Agent class, function tools, STT/LLM/TTS models, turn detection, and multi-agent workflows.
Agent skill for trading-predictor - invoke with $agent-trading-predictor
Meta-skill for designing orchestrator+phases structured workflow skills. Creates SKILL.md coordinator with progressive phase loading, TodoWrite patterns, and data flow. Triggers on "design workflow skill", "create workflow skill", "workflow skill designer".
Create or update Langfuse prompt with development label. Use when creating new prompts, updating existing prompts, or improving prompt content.
调用扣子(Coze)智能体 API 进行对话、工作流执行等操作。当用户需要集成 Coze 智能体、调用 Coze API、或开发 Coze 相关应用时使用。支持流式和非流式对话、工作流调用等功能。
Vision framework API, VNDetectHumanHandPoseRequest, VNDetectHumanBodyPoseRequest, person segmentation, face detection, VNImageRequestHandler, recognized points, joint landmarks, VNRecognizeTextRequest, VNDetectBarcodesRequest, DataScannerViewController, VNDocumentCameraViewController, RecognizeDocumentsRequest
Nano Banana Pro (nano-banana-pro) image generation skill. Use this skill when the user asks to "generate an image", "generate images", "create an image", "make an image", uses "nano banana", or requests multiple images like "generate 5 images". Generates images using Google's Gemini 2.5 Flash for any purpose - frontend designs, web projects, illustrations, graphics, hero images, icons, backgrounds, or standalone artwork. Invoke this skill for ANY image generation request.
Use this skill when building MCP (Model Context Protocol) servers with TypeScript on Cloudflare Workers. This skill provides production-tested patterns for implementing tools, resources, and prompts using the official @modelcontextprotocol/sdk. It prevents 10+ common errors including export syntax issues, schema validation failures, memory leaks from unclosed transports, CORS misconfigurations, and authentication vulnerabilities. This skill should be used when developers need stateless MCP servers for API integrations, external tool exposure, or serverless edge deployments. For stateful agents with WebSockets and persistent storage, consider the Cloudflare Agents SDK instead. Supports multiple authentication methods (API keys, OAuth, Zero Trust), Cloudflare service integrations (D1, KV, R2, Vectorize), and comprehensive testing strategies. Production tested with token savings of ~70% vs manual implementation. Keywords: mcp, model context protocol, typescript mcp, cloudflare workers mcp, mcp server, mcp tools, mcp resources, mcp sdk, @modelcontextprotocol/sdk, hono mcp, streamablehttpservertransport, mcp authentication, mcp cloudflare, edge mcp server, serverless mcp, typescript mcp server, mcp api, llm tools, ai tools, cloudflare d1 mcp, cloudflare kv mcp, mcp testing, mcp deployment, wrangler mcp, export syntax error, schema validation error, memory leak mcp, cors mcp, rate limiting mcp
End-to-end data science and ML engineering workflows: problem framing, data/EDA, feature engineering (feature stores), modelling, evaluation/reporting, plus SQL transformations with SQLMesh. Use for dataset exploration, feature design, model selection, metrics and slice analysis, model cards/eval reports, experiment reproducibility, and production handoff (monitoring and retraining).
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