Agent skill · mindrally
google-adk
Best practices for building AI agents with Google's Agent Development Kit (ADK) in Python, covering agent design, tools, sessions, memory, artifacts, evaluation, and deployment. Use when building LLM agents or multi-agent systems with ADK, defining ADK tools, wiring up sessions/state/memory, working with ADK artifacts, writing agent evals, or deploying ADK agents to Vertex AI Agent Engine or Cloud Run.
What it needs
About 5k tokens when loaded.
What this skill does
Google Agent Development Kit (ADK) This skill covers building production-grade AI agents with Google's Agent Development Kit (ADK) for Python, including agent composition, tool design, session/state/memory management, artifacts, evaluation, and deployment. Workflow for Building an ADK Agent 1. Define the agent — Create an LlmAgent (or Agent) with a clear name, model, instruction, and description. Keep the instruction focused on one job. 2. Author tools — Write plain Python functions with type hints and docstrings, or wrap existing APIs with FunctionTool. Validate all inputs before side effects. 3. Compose multi-agent systems — For complex workflows, split responsibility across sub-agents and use SequentialAgent, ParallelAgent, or LoopAgent for deterministic orchestration, or delegate via subagents for LLM-driven routing. 4. Wire up session and state — Choose a SessionService (in-memory for dev, DatabaseSessionService or Vertex AI-managed for production) and use session.state for conversation-scoped data. 5. Add memory (optional) — Configure a MemoryService for cross-session recall when the agent needs to remember facts between separate conversations. 6. Handle artifacts (optional) — Configure an ArtifactService when the agent generates or receives files, images, or other binary outputs. 7. Run locally — Use adk web, adk run, or the Runner API to exercise the agent against a Session. 8. Evaluate — Write .evalset.json test cases and run adk eval to check tool-call trajectories and response quality against regressions. 9. Deploy — Package the agent for Vertex AI Agent Engine, Cloud Run, or GKE, and separate dev/staging/prod configuration. Agent Design Keep each agent focused on a single clear goal, persona, and tool set — avoid one agent that tries to do everything. Use LlmAgent for flexible, reasoning-driven behavior and workflow agents (SequentialAgent, ParallelAgent, LoopAgent) for deterministic orchestration that doesn't need an LLM to decide the next step. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills mindrally/google-adk