Agent skill · software engineering · jeremylongshore
adk-agent-builder
Scaffold production-ready AI agents on Google's Agent Development Kit (ADK): ReAct-style single agents, multi-agent orchestration (Sequential/Parallel/Loop), tool wiring, evaluation, and optional Vertex AI Agent Engine deployment. Use when building, scaffolding, or deploying ADK agents on Google Cloud, or when wiring ADK tools and orchestration patterns. Trigger with "build an ADK agent", "scaffold an agent on ADK", or "deploy to Agent Engine".
Why this skill is useful
Adds executable scaffolding scripts and deployment commands for building and deploying agents using Google's ADK that are not available in public documentation.
What it needs
Requires google-adk installed locally. Requires google-cloud account access. About 2k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
ADK Agent Builder Build production-ready agents with Google’s Agent Development Kit (ADK): scaffolding, tool wiring, orchestration patterns, testing, and optional deployment to Vertex AI Agent Engine. Overview Creates a minimal, production-oriented ADK scaffold (agent entrypoint, tool registry, config, and tests). Supports single-agent ReAct-style workflows and multi-agent orchestration (Sequential/Parallel/Loop). Produces a validation checklist suitable for CI (lint/tests/smoke prompts) and optional Agent Engine deployment verification. Prerequisites Python runtime compatible with your project (often Python 3.10+) google-adk installed and importable If deploying: access to a Google Cloud project with Vertex AI enabled and permissions to deploy Agent Engine runtimes Secrets available via environment variables or a secret manager (never hardcoded) Instructions 1. Confirm scope: local-only agent scaffold vs Vertex AI Agent Engine deployment. 2. Choose an architecture: Single agent (ReAct) for adaptive tool-driven tasks Multi-agent system (specialists + orchestrator) for complex, multi-step workflows 3. Define the tool surface (built-in ADK tools + any custom tools you need) and required credentials. 4. Scaffold the project: src/agents/, src/tools/, tests/, and a dependency file (pyproject.toml or requirements.txt) 5. Implement the minimum viable agent and a smoke test prompt; add regression tests for tool failures. 6. If deploying, produce an adk deploy ... command and a post-deploy validation checklist (AgentCard/task endpoints, permissions, logs). Output A repo-ready ADK scaffold (files and directories) plus starter agent code Tool stubs and wiring points (where to add new tools safely) A test + validation plan (unit tests and a minimal smoke prompt) Optional: deployment commands and verification steps for Agent Engine Error Handling Dependency/runtime issues: provide pinned install commands and validate imports. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/adk-agent-builder