Total 50,523 skills, AI & Machine Learning has 8481 skills
Showing 12 of 8481 skills
Design optimal agent team compositions with sizing heuristics, preset configurations, and agent type selection. Use this skill when deciding team size, selecting agent types, or configuring team presets for multi-agent workflows.
Multi-agent workflow examples to work together on the OpenServ Platform. Covers agent discovery, multi-agent workspaces, task dependencies, and workflow orchestration using the Platform Client. Read reference.md for the full API reference. Read openserv-agent-sdk and openserv-client for building and running agents.
Generate extractive summaries from long text documents. Control summary length, extract key sentences, and process multiple documents.
AI Hot Topic Collection Tool. Collect AI-related hot content from Twitter/X, Product Hunt, Reddit, Hacker News, blogs and other platforms. Triggered when users say "Start today's topic selection", "Collect hot topics", "See what news is today", "Today's AI hot topics". Focus areas: Vibe Coding, Claude Skill, AI Knowledge Management, AI Model Updates, AI New Products, Overseas Hot Topics.
Expert-level manufacturing systems, Industry 4.0, production optimization, quality control, and smart factory solutions
Comprehensive guide for building AI agents that interact with Solana blockchain using SendAI's Solana Agent Kit. Covers 60+ actions, LangChain/Vercel AI integration, MCP server setup, and autonomous agent patterns.
Project setup wizard for AI agents. Use when user requests setup or when .agents/CONTEXT.md is missing or incomplete and setup recovery is needed. Generates .agents/CONTEXT.md with stack, structure, coding rules, and skill mapping.
Task decomposition, goal-oriented planning, and adaptive execution strategies for AI agents. Use when facing complex multi-step tasks that require structured approach.
Multi-agent communication, task delegation, and coordination patterns. Use when working with multiple agents or complex collaborative workflows.
McKinsey Consultant-style Problem Solving System. Starting from business problems, it generates McKinsey-style research reports and PPTs through hypothesis-driven structured analysis methods. It integrates Problem Solving methodology, MECE principles, Issue Tree decomposition, Hypotheses formulation, Dummy Page design, intelligent data collection, and professional PPT generation capabilities.
Configure LangChain4J vector stores for RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, Neo4j), implementing embedding storage/retrieval, setting up hybrid search, or optimizing vector database performance for production AI applications.
Use when integrating MCPCat analytics into a TypeScript MCP server, adding mcpcat to an existing TypeScript MCP project, setting up MCP server usage tracking, or when the user mentions mcpcat, MCPCat, or MCP analytics in a TypeScript context