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Found 540 Skills
This skill should be used when working with reinforcement learning tasks including high-performance RL training, custom environment development, vectorized parallel simulation, multi-agent systems, or integration with existing RL environments (Gymnasium, PettingZoo, Atari, Procgen, etc.). Use this skill for implementing PPO training, creating PufferEnv environments, optimizing RL performance, or developing policies with CNNs/LSTMs.
Use this skill for ANY multi-pane or multi-agent terminal orchestration in cmux. Required when the user wants to: run things in parallel in separate terminal panes, split the terminal, spawn a sub-agent (Claude Code, Codex) in another pane, fan out tasks across splits, send keystrokes or text to another pane (including ctrl-c), read terminal output from another pane, update sidebar status or progress bar, open a URL in cmux's built-in browser pane, or display markdown preview alongside the terminal. The cmux CLI is the ONLY way to do these things — Bash cannot split panes or spawn agents. Trigger phrases: 'in parallel', 'split pane', 'spawn agent', 'fan out', 'new pane', 'browser pane', 'sidebar', 'send to pane', 'read from pane', 'show the plan', 'ctrl-c to', '分屏', '并行', '开个 pane'. NOT for: single command execution, basic bash operations, or questions about tmux.
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.
Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
Vibe Kanban orchestration platform for AI coding agents: workspaces, sessions, task management, code review, git worktrees, multi-agent support. Keywords: Vibe Kanban, AI agents, Claude Code, Codex, Gemini, kanban board, git worktree, code review, MCP server, workspaces, sessions.
Evaluate options for a specific design decision node and recommend one with explicit trade-offs. Use when the design already exposes a concrete choice such as architecture style, state management approach, auth model, storage pattern, sync strategy, multi-agent coordination model, language or runtime, UI framework, data-layer library, or tooling selection. Trigger when the user needs structured comparison and recommendation for a bounded design decision. Do not use for broad design discovery, full-system decomposition, or final readiness review.
Generates production-ready FastGPT workflow JSON from natural language requirements. Uses AI-powered semantic template matching from built-in workflows (document translation, sales training, resume screening, financial news). Performs three-layer validation (format, connections, logic completeness). Supports incremental modifications to add/remove/modify nodes. Activates when user asks to "create FastGPT workflow", "generate workflow JSON", "design FastGPT application", or mentions workflow automation, multi-agent systems, or FastGPT templates.
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.
Build AI agent interfaces with Polpo UI — composable React chat components, CLI tools, and starter templates. Use when the user wants to create a chat app, add chat components, install @polpo-ai/chat, scaffold a Polpo project, configure theming/dark mode, use ChatInput, ChatMessage, ChatSessionList, or any Polpo UI component. Triggers on "polpo ui", "chat UI", "chat component", "@polpo-ai/chat", "@polpo-ai/ui", "create-polpo-app", "chat input", "session list", "agent selector", "chat interface", "polpo chat", "chat widget", "multi-agent".
Design and configure AI agents for Polpo — models, tools, identity, memory, vault, and system prompts. Use when the user wants to create an agent, configure agent capabilities, set up agent memory, manage agent credentials (vault), choose models, assign tools, or architect multi-agent systems. Triggers on "polpo agent", "configure agent", "agent design", "agent tools", "agent memory", "agent vault", "system prompt", "agent identity".
Expert MCP (Model Context Protocol) orchestration with n8n workflow automation. Master bidirectional MCP integration, expose n8n workflows as AI agent tools, consume MCP servers in workflows, build agentic systems, orchestrate multi-agent workflows, and create production-ready AI-powered automation pipelines with Claude Code integration.
Teneo CLI — query 400+ AI agents on the Teneo Protocol network from the terminal. Discover agents, manage rooms, handle x402 USDC micropayments, and auto-generate encrypted wallets. Use when the user needs real-time data (social media profiles, hotel search, crypto prices, gas fees, Amazon products, news) or multi-agent workflows.