Total 30,167 skills, AI & Machine Learning has 4870 skills
Showing 10 of 4870 skills
Search Twitter/X in real-time using Grok API. Use when searching X posts, tracking trends, monitoring accounts, or analyzing social discussions.
Design MCP resources to expose content for LLM consumption. Use when creating static or dynamic resources in xmcp.
Guide for designing effective MCP servers with agent-friendly tools. Use when creating a new MCP server, designing MCP tools, or improving existing MCP server architecture.
PocketFlow framework for building LLM applications with graph-based abstractions, design patterns, and agentic coding workflows
Run Gemini CLI for AI-powered tasks, code understanding, file operations, and automation. Free tier with Google OAuth (included in Gemini Advanced). Use for fast generation, bulk content, debugging, and research. Preferred for load balancing sub-agent work (35% weight).
Spawn a Team Leader agent that manages multiple sub-agents working toward a common goal. Team Leader reads requirements, decomposes work, assigns personalities and tasks, manages communication between team members, tracks progress, and reports results following ogt-docs task workflow. Integrates fully with docs-first system via task signals and status tracking.
Amazon Bedrock AgentCore Policy for defining agent boundaries using natural language and Cedar. Deterministic policy enforcement at the Gateway level. Use when setting agent guardrails, access control, tool permissions, or compliance rules.
Amazon Bedrock AgentCore Memory for persistent agent knowledge across sessions. Episodic memory for learning from interactions, short-term for session context. Use when building agents that remember user preferences, learn from conversations, or maintain context across sessions.
CRITICAL - Guide for using Claudish CLI ONLY through sub-agents to run Claude Code with any AI model (OpenRouter, Gemini, OpenAI, local models). NEVER run Claudish directly in main context unless user explicitly requests it. Use when user mentions external AI models, Claudish, OpenRouter, Gemini, OpenAI, Ollama, or alternative models. Includes mandatory sub-agent delegation patterns, agent selection guide, file-based instructions, and strict rules to prevent context window pollution.
Guides the agent through building LLM-powered applications with LangChain and stateful agent workflows with LangGraph. Triggered when the user asks to "create an AI agent", "build a LangChain chain", "create a LangGraph workflow", "implement tool calling", "build RAG pipeline", "create a multi-agent system", "define agent state", "add human-in-the-loop", "implement streaming", or mentions LangChain, LangGraph, chains, agents, tools, retrieval augmented generation, state graphs, or LLM orchestration.