google-adk
Develop agentic software and multi-agent systems using Google ADK in Python
Find the perfect capability for your agent.
Develop agentic software and multi-agent systems using Google ADK in Python
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Expert guidance for using the DSPy framework to design, optimize, debug, and refactor LLM programs. This skill should be used when asked to use DSPy, when a task involves DSPy components, when changing code that impacts a DSPy implementation, or when analyzing a codebase for DSPy opportunities.
Generate working Python/rclpy code examples for Physical AI & Humanoid Robotics textbook with tier-specific variants (Simulation/Jetson/Robot).
Authoritative guidelines for writing production-grade ROS 2 (Humble) Python 3.11+ code (leveraging 3.12+ features where compatible) for Physical AI applications.
Scaffolds a new Python agent for autonomous AI tasks in 'src/agents/'. Agents are autonomous components that handle complex operations (resume analysis, job matching, KSC generation). Use when asked to create a new AI agent or automation component.
Build AI agents with Google ADK Python (Agent Development Kit). Use for multi-agent systems, workflow agents (sequential/parallel/loop), Vertex AI deployment, tool integration, human-in-the-loop.
Master backend development with Node.js, Python, Java, Go, Rust, API design, databases, and microservices. Use when building APIs, designing systems, or learning backend frameworks.
Expert guidance for researching, documenting, and integrating Model Context Protocol (MCP) servers and tools. Covers MCP architecture, server/client implementation patterns, tool discovery, integration workflows, security best practices, and multi-language SDK usage (Python, TypeScript, C#, Java, Rust). Enables seamless integration of MCP tools into Claude Code and AI applications.
Complete FastAPI API development framework for Python. Provides comprehensive assistance for building APIs with routing, authentication (JWT, OAuth2, Better Auth), Pydantic models, database integration, and deployment using uv package manager. Use when users ask to build FastAPI applications, implement authentication, create API endpoints, or develop backend services in Python.
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
Execute complex multi-tool MCP workflows directly using TypeScript or Python. As the MCP executor agent, you have MCP servers configured and write code that composes multiple tool calls. Use for 3+ MCP tool calls, complex data processing, parallel operations, or retry logic. This skill is colony-aware - you execute code directly via Bash without subagents.
Deterministic syntax for Frappe Whitelisted Methods (Python API endpoints) for v14/v15/v16. Use when Claude needs to generate code for API functions, REST endpoints, @frappe.whitelist() decorator, frappe.call() or frm.call() invocations, permission checks in APIs, error handling patterns, or when questions concern API structure, response formats, or client-server communication. Triggers: whitelisted, API endpoint, frappe.call, frm.call, REST API, @frappe.whitelist, allow_guest, API method.
Integrate and embed OpenAI ChatKit UI into TypeScript/JavaScript frontends (Next.js, React, or vanilla) using either hosted workflows or a custom backend (e.g. Python with the Agents SDK). Use this Skill whenever the user wants to add a ChatKit chat UI to a website or app, configure api.url, auth, domain keys, uploadStrategy, or debug blank/buggy ChatKit widgets.
Query and analyze W&B experiment data and Weave LLM traces using Python scripts. Use when working with Weights & Biases data, including (1) querying ML experiment runs, metrics, and hyperparameters, (2) analyzing LLM traces and evaluations, (3) creating W&B reports, (4) listing projects and entities.
Beginner workflow for CrewAI (Python + YAML + CLI). Use when the user wants to build crews with YAML agents and tasks.