Guide for implementing ChatGPT Apps using OpenAI Apps SDK. Use when building MCP servers with interactive UI components that integrate with ChatGPT, including widget runtime, authentication, state management, and deployment to the ChatGPT platform.
Build prompts for Effect AI using messages, parts, and composition operators. Covers the complete Prompt API for constructing, merging, and manipulating conversations with language models.
Spawn an AI coding sub-agent to make code changes, fix bugs, or add features to GitHub repositories. Use when the user wants to modify code in Comfy-Org repositories, create pull requests, or implement new functionality.
Model Context Protocol (MCP) server development and tool management. Languages: Python, TypeScript. Capabilities: build MCP servers, integrate external APIs, discover/execute MCP tools, manage multi-server configs, design agent-centric tools. Actions: create, build, integrate, discover, execute, configure MCP servers/tools. Keywords: MCP, Model Context Protocol, MCP server, MCP tool, stdio transport, SSE transport, tool discovery, resource provider, prompt template, external API integration, Gemini CLI MCP, Claude MCP, agent tools, tool execution, server config. Use when: building MCP servers, integrating external APIs as MCP tools, discovering available MCP tools, executing MCP capabilities, configuring multi-server setups, designing tools for AI agents.
Create PydanticAI agents with type-safe dependencies, structured outputs, and proper configuration. Use when building AI agents, creating chat systems, or integrating LLMs with Pydantic validation.
LiteLLM-RS Routing Architecture. Covers 7 routing strategies, lock-free design with DashMap, health-aware selection, fallback chains, and load balancing.
Write specifications at the right depth for any project. Progressive disclosure from quick Linear issues to full AI feature specs. Embeds Linear Method philosophy (brevity, clarity, momentum) with context engineering for AI features. Use for any spec work - quick tasks, features, or AI products.
Expert system for designing and architecting AI agent workflows based on proven Meta methodologies. Use when users need to build AI agents, create agent workflows, solve problems using agentic systems, integrate multiple tools into agent architectures, or need guidance on agent design patterns. Helps translate business problems into structured agent solutions with clear scope, tool integration, and multi-layer architecture planning.
Configure LLM providers, use fallback models, handle streaming, and manage model settings in PydanticAI. Use when selecting models, implementing resilience, or optimizing API calls.
Define and implement AI tools using @effect/ai's Tool and Toolkit APIs. Use when building LLM integrations with type-safe tool definitions, parameter validation, and handler implementations. Covers user-defined tools, provider-defined tools, and toolkit composition.
Vercel AI SDK for building chat interfaces with streaming. Use when implementing useChat hook, handling tool calls, streaming responses, or building chat UI. Triggers on useChat, @ai-sdk/react, UIMessage, ChatStatus, streamText, toUIMessageStreamResponse, addToolOutput, onToolCall, sendMessage.
Configure and compose AI provider layers using @effect/ai packages. Covers Anthropic, OpenAI, OpenRouter, Google, and Amazon Bedrock providers with config management, model abstraction, and runtime overrides for language model integration.
Process external code review feedback with technical rigor. Use when receiving feedback from another LLM, human reviewer, or CI tool. Verifies claims before implementing, tracks disposition.
Use this skill to discover all available AgenticDev skills and their capabilities. Provides a bootstrap context for AI agents by listing all skills, their descriptions, and script paths from the .claude/skills/ directory.
LiteLLM-RS Provider Architecture Guide. Covers 66+ provider integration, trait object design, unified error handling, connection pooling, and LLMProvider trait implementation patterns.
OpenKBS AI agent development framework. Use when creating, modifying, or deploying AI agents with backend handlers (onRequest, onResponse, actions.js), frontend components (contentRender.js), or elastic services (functions, postgres, storage, pulse). Trigger keywords: openkbs, kb, agent, handler, contentRender, elastic, memory, scheduled task.
Guide for implementing MCP Apps (SEP-1865) - interactive UI extensions for MCP servers. Use when building MCP servers that need to return rich, interactive HTML-based UIs alongside tool results for conversational AI hosts like Claude Desktop or ChatGPT.
Perform 12-Factor Agents compliance analysis on any codebase. Use when evaluating agent architecture, reviewing LLM-powered systems, or auditing agentic applications against the 12-Factor methodology.
SwarmKit SDK development for TypeScript and Python. Use when building applications with SwarmKit to run AI agents (Claude, Codex, Gemini, Qwen) in secure sandboxes. Triggers: (1) Creating SwarmKit applications, (2) Configuring agents with skills, Composio, MCP servers, (3) Using Swarm abstractions (map, filter, reduce, best_of), (4) Building Pipelines, (5) Structured output with schemas, (6) Session management, streaming, observability. Covers both TypeScript (@swarmkit/sdk) and Python (swarmkit) SDKs.
Google Agent Development Kit (ADK) for Python. Capabilities: AI agent building, multi-agent systems, workflow agents (sequential/parallel/loop), tool integration (Google Search, Code Execution), Vertex AI deployment, agent evaluation, human-in-the-loop flows. Actions: build, create, deploy, evaluate, orchestrate AI agents. Keywords: Google ADK, Agent Development Kit, AI agent, multi-agent system, LlmAgent, SequentialAgent, ParallelAgent, LoopAgent, tool integration, Google Search, Code Execution, Vertex AI, Cloud Run, agent evaluation, human-in-the-loop, agent orchestration, workflow agent, hierarchical coordination. Use when: building AI agents, creating multi-agent systems, implementing workflow pipelines, integrating LLM agents with tools, deploying to Vertex AI, evaluating agent performance, implementing approval flows.
