Build production-ready AI agents using Google's Agent Development Kit with AI assistant integration, React patterns, multi-agent orchestration, and comprehensive tool libraries.
SkillScore86Maintained
Automatic
◆AI Engineer pack
Last verified 8/18/2026
What it changes
Passed 19 of 22 test cases with the skill, and 12 of 22 without it.
Without skill55%
With skill86%
Checks it passed that the model alone missed
The dependencies include `google-adk`.
The directory structure includes `src/agents/`.
The runtime requirement specifies Python 3.10 or higher.
and 5 more in the full report
7 fixed12 passed either way3 still failing0 broke
Pass-rate lift
+32 pp
Tokens
+9%
Agent turns
0%
Response time
-24%
Model gemini-3.6-flashMeasured 8/18/2026v119 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 7
Prompt
Which file path contains the full reference documentation for ADK agent building standards?
What was checked, without and with the skill
Checked
Without skill
With skill
The path references `{baseDir}/references/SKILL.full.md`.
Missed
Passed
case-20 · gemini-3.6-flash · 8/18/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md59 linessingle file
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
Confirm scope: local-only agent scaffold vs Vertex AI Agent Engine deployment.
Choose an architecture:
Single agent (ReAct) for adaptive tool-driven tasks
Multi-agent system (specialists + orchestrator) for complex, multi-step workflows
Define the tool surface (built-in ADK tools + any custom tools you need) and required credentials.
Scaffold the project:
src/agents/, src/tools/, tests/, and a dependency file (pyproject.toml or requirements.txt)
Implement the minimum viable agent and a smoke test prompt; add regression tests for tool failures.
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.
Auth/permission failures: identify the missing role/API and propose least-privilege fixes.