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Found 42 Skills
Eino ADK agent construction, middleware, and runner. Use when a user needs to build an AI Agent, configure ChatModelAgent with ReAct pattern, use middleware (filesystem, tool search, tool reduction, summarization, plan-task, skill), set up the Runner for event-driven execution, implement human-in-the-loop with interrupt/resume, or wrap agents as tools. Covers ChatModelAgent and DeepAgents.
LLM-assisted human-in-the-loop review. Make sense of a change, focus attention where it matters, test. Use when the user says "checkpoint", "human review", or "walk me through this change".
Build AI copilots, chatbots, and agentic UIs in React and Next.js using CopilotKit. Use this skill when the user wants to add an AI assistant, copilot, chat interface, AI-powered textarea, or agentic UI to their app. Covers setup, hooks (useCopilotAction, useCopilotReadable, useCoAgent, useAgent), chat components (CopilotPopup, CopilotSidebar, CopilotChat), generative UI, human-in-the-loop, CoAgents with LangGraph, AG-UI protocol, MCP Apps, and Python SDK integration. Triggers on CopilotKit, copilotkit, useCopilotAction, useCopilotReadable, useCoAgent, useAgent, CopilotRuntime, CopilotChat, CopilotSidebar, CopilotPopup, CopilotTextarea, AG-UI, agentic frontend, in-app AI copilot, AI assistant React, chatbot React, useFrontendTool, useRenderToolCall, useDefaultTool, useCoAgentStateRender, useLangGraphInterrupt, useCopilotChat, useCopilotAdditionalInstructions, useCopilotChatSuggestions, useHumanInTheLoop, CopilotTask, copilot runtime, LangGraphAgent, BasicAgent, BuiltInAgent, CopilotKitRemoteEndpoint, A2UI, MCP Apps, AI textarea, AI form completion, add AI to React app.
Apply DriveMind, the calm reliability layer for AI agents. Use when a task needs steady follow-through, clearer progress, stronger persistence without recklessness, explicit safety boundaries, human-in-the-loop collaboration, post-task review, reusable memory, or when the user says things like 'keep pushing', 'don’t stop too early', 'be steady', 'if risk is unclear ask me', 'review this after', or 'write down the lesson'.
Production-grade Next.js chatbot builder. Covers tool calling with human-in-the-loop (HITL) approval, PostgreSQL session persistence, GDPR consent gating, SQL-first search, per-tool UI rendering, message feedback, and follow-up suggestions. Use when building chat apps, conversational AI interfaces, customer support bots, or any chatbot needing database-backed sessions, tool approval workflows, consent gating, or custom tool output components. Reference implementation: fair-helpdesk project.
Use when building custom agent backends, implementing the AG-UI protocol, debugging streaming issues, or understanding how agents communicate with frontends. Covers event types, SSE transport, AbstractAgent/HttpAgent patterns, state synchronization, tool calls, and human-in-the-loop flows.
Comprehensive guide to the AgentMail Python and TypeScript SDKs. Use when building AI agents that need their own email inboxes, sending or receiving emails programmatically, managing threads and conversations, handling attachments, creating drafts for human-in-the-loop approval, setting up real-time notifications via webhooks or WebSockets, configuring custom domains, managing allow/block lists, using pods for multi-tenant isolation, or integrating email into any AI agent workflow. Covers the full AgentMail API with code examples, best practices, and production patterns.
Run a single Terminal-Bench problem through Paperclip in a bounded, human-in-the-loop improvement cycle until the smoke passes, the board rejects the next fix, the iteration budget is exhausted, or a real blocker is named. Each iteration runs a bounded smoke against an isolated Paperclip App worktree, captures artifacts, diagnoses the exact stop point with `/diagnose-why-work-stopped`, requests board confirmation before any product fix, then reruns against the same worktree. Use whenever an issue asks to "run Terminal-Bench in a loop", "drive Terminal-Bench until it passes", "loop fix-git through Paperclip", or otherwise points at a Terminal-Bench task and asks for bounded iteration with diagnosis.
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 resilient, long-running, multi-step applications with AWS Lambda durable functions with automatic state persistence, retry logic, and orchestration for long-running executions. Covers the critical replay model, step operations, wait/callback patterns, error handling with saga pattern, testing with LocalDurableTestRunner. Triggers on phrases like: lambda durable functions, workflow orchestration, state machines, retry/checkpoint patterns, long-running stateful Lambda functions, saga pattern, human-in-the-loop callbacks, and reliable serverless applications.
A 10-step methodology for building software with AI collaboration - from north star through automated Ralph loop execution with zero human-in-the-loop code writing
LangGraph framework for building stateful, multi-agent AI applications with cyclical workflows, human-in-the-loop patterns, and persistent checkpointing.