Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Shared orchestration patterns for all workflow orchestrators. NOT an executable skill - provides reference documentation for phase execution, state management, interactive mode, and initialization. All orchestrators reference these patterns.
Configure and optimize .geminiignore files for AI context window efficiency and token cost reduction (FinOps).
Build Model Context Protocol (MCP) servers and tools from scratch. Full-stack MCP development with TypeScript/Python, testing, deployment, and registry publishing.
Build tools that agents can use effectively, including architectural reduction patterns. Use when creating new tools for agent systems, debugging tool-related failures or misuse, or optimizing existing tool sets for better agent performance.
Scaffolds a complete agent TUI in TypeScript using @openrouter/agent — like create-react-app for terminal agents. Generates a customizable terminal interface with three input styles, four tool display modes, ASCII banners, streaming output, session persistence, and configurable tools. Use when building an agent, creating a TUI, scaffolding an agent project, or building a coding assistant.
Launch one sub-agent per decomposition plan after direct screening has identified the key stuck points for each plan. Use when all current plans have been screened by direct proving, none fully solves the problem, and parallel recursive work is needed.
Use this skill to evaluate an ADK agent with Google's Agent Quality Flywheel, run an evalset, choose the right metric, and read the results. Covers the ADK metric menu and which metrics run locally versus which call an AI judge, how the LLM judge scores meaning rather than words, how a rubric judge writes a plain-English reason for each verdict graded against trusted evidence, and how to close the eval fix loop. SDKs and tools used, agents-cli, adk eval, google-adk[eval], the Gemini API for the agent and the judge.
Use this skill when running Google's AlphaEvolve, the Gemini-powered evolutionary coding agent, to discover or optimize an algorithm on Google Cloud. Covers wrapping a function in EVOLVE-BLOCK markers, writing a scoring function, driving the controller loop against a Gemini Enterprise engine, reading back the best evolved program, and the license, cost, and non-monotonic-progress gotchas. Uses the alpha_evolve client library, Discovery Engine, gemini-3.5-flash and gemini-3.1-pro-preview.
Use this skill when evaluating agent memory options, when running or reviewing the always-on memory agent sample (Google generative-ai repo, by Shubham Saboo), or when deciding between a self-hosted memory loop and the managed Vertex AI Memory Bank. Covers how the sample works and its rough edges, and a verified Memory Bank quickstart. SDKs used, Google ADK, google-genai, vertexai agent engines, Gemini 3.1 Flash Lite.
Run the Antigravity CLI (Gemini) as a collaborating AI inside Claude Code, with intelligent model routing across the software development lifecycle. Claude is the conductor/orchestrator — requirements, architecture, the hard 20%, verification, and review — and routes deterministic, high-volume work (scaffolding, boilerplate, test generation, first-pass review, migrations, web/Vertex AI Search) to Antigravity (Gemini), the cheaper, faster model. Use when the user wants to "use Antigravity / agy", "vibe code / agentic engineering", "accelerate the SDLC", "delegate to Gemini", "scaffold / generate tests / migrate", "first-pass code review", "search web or internal/company data", "deep research / multi-source research report", "second-model cross-check", or "lower token cost on a big job". Claude always verifies Antigravity's output and re-checks itself if unsatisfied.