jeremylongshore / adk-agent-builder

Skill Content

---
name: adk-agent-builder
description: |
  Build production-ready AI agents using Google's Agent Development Kit with AI assistant integration, React patterns, multi-agent orchestration, and comprehensive tool libraries. Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
allowed-tools: Read, Write, Edit, Grep, Bash(cmd:*)
version: 1.0.0
author: Jeremy Longshore <jeremy@intentsolutions.io>
license: MIT
---
# 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.
- Auth/permission failures: identify the missing role/API and propose least-privilege fixes.
- Tool failures/rate limits: add retries/backoff guidance and a regression test to prevent recurrence.

## Examples

**Example: Scaffold a single ReAct agent**
- Request: “Create an ADK agent that summarizes PRs and proposes test updates.”
- Result: agent entrypoint + tool registry + a smoke test command for local verification.

**Example: Multi-agent orchestrator**
- Request: “Build a supervisor + deployer + verifier team and deploy to Agent Engine.”
- Result: orchestrator skeleton, per-agent responsibilities, and `adk deploy ...` + post-deploy health checks.

## Resources

- Full detailed guide (kept for reference): `{baseDir}/references/SKILL.full.md`
- Repo standards (source of truth):
  - `000-docs/6767-a-SPEC-DR-STND-claude-code-plugins-standard.md`
  - `000-docs/6767-b-SPEC-DR-STND-claude-skills-standard.md`
- ADK / Agent Engine docs: https://cloud.google.com/vertex-ai/docs/agent-engine