google-agents-cli-scaffold

name	google-agents-cli-scaffold
description	This skill should be used when the user wants to "create an agent project", "start a new ADK project", "build me a new agent", "add CI/CD to my project", "add deployment", "enhance my project", or "upgrade my project". Part of the Google ADK (Agent Development Kit) skills suite. Covers `agents-cli scaffold create`, `scaffold enhance`, and `scaffold upgrade` commands, template options, deployment targets, and the prototype-first workflow. Do NOT use for writing agent code (use google-agents-cli-adk-code) or deployment operations (use google-agents-cli-deploy).
metadata	{"author":"Google","license":"Apache-2.0","version":"1.1.0","requires":{"bins":["agents-cli"],"install":"uv tool install google-agents-cli"}}

ADK Project Scaffolding Guide

Requires: agents-cli (uv tool install google-agents-cli) — install uv first if needed.

Use the agents-cli CLI to create new ADK agent projects or enhance existing ones with deployment, CI/CD, and infrastructure scaffolding.

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Prerequisite: Clarify Requirements (MANDATORY for new projects)

Before scaffolding a new project, load /google-agents-cli-workflow and complete Phase 0 — clarify the user's requirements before running any scaffold create command. Ask what the agent should do, what tools/APIs it needs, and whether they want a prototype or full deployment.

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Step 1: Choose Architecture

Mapping user choices to CLI flags:

Choice	CLI flag
RAG (vector or document search)	Not a scaffold flag — clone-and-study rag-vector-search / rag-agent-search (see /google-agents-cli-workflow Phase 1)
A2A protocol	built into every ADK agent — scaffold normally (--agent adk)
Prototype (no deployment)	--prototype
Deployment target	--deployment-target <agent_runtime|cloud_run|gke>
CI/CD runner	--cicd-runner <github_actions|google_cloud_build>
Session storage	--session-type <in_memory|cloud_sql|agent_platform_sessions>

Product name mapping

Older names → CLI values (vertexai SDK package name unchanged):

• Agent Engine / Vertex AI Agent Engine → --deployment-target agent_runtime
• Agent Engine sessions / Agent Platform Sessions → --session-type agent_platform_sessions
• Vertex AI Search / Vertex AI Vector Search / RAG → clone-and-study recipe, not a flag (see /google-agents-cli-workflow Phase 1)

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Step 2: Create or Enhance the Project

Create a New Project

agents-cli scaffold create <project-name> \
  --agent <template> \
  --deployment-target <target> \
  --region <region> \
  --prototype

Constraints:

• Project name must be 26 characters or less, lowercase letters, numbers, and hyphens only.
• Do NOT mkdir the project directory before running create — the CLI creates it automatically. If you mkdir first, create will fail or behave unexpectedly.
• Auto-detect the guidance filename based on the IDE you are running in and pass --agent-guidance-filename accordingly (GEMINI.md for Antigravity CLI, CLAUDE.md for Claude Code, AGENTS.md for OpenAI Codex/other).
• When enhancing an existing project, check where the agent code lives. If it's not in app/, pass --agent-directory <dir> (e.g. --agent-directory agent). Getting this wrong causes enhance to miss or misplace files.

Reference Files

File	Contents
references/flags.md	Full flag reference for create and enhance commands

Enhance an Existing Project

agents-cli scaffold enhance . --deployment-target <target>
agents-cli scaffold enhance . --cicd-runner <runner>

Run this from inside the project directory (or pass the path instead of .).

Upgrade a Project

Upgrade an existing project to a newer agents-cli version, intelligently applying updates while preserving your customizations:

agents-cli scaffold upgrade                # Upgrade current directory
agents-cli scaffold upgrade <project-path> # Upgrade specific project
agents-cli scaffold upgrade --dry-run      # Preview changes without applying
agents-cli scaffold upgrade --auto-approve  # Auto-apply non-conflicting changes

Execution Modes

The CLI defaults to strict programmatic mode — all required params must be supplied as CLI flags or a UsageError is raised. No approval flags needed. Pass all required params explicitly.

Common Workflows

Always ask the user before running these commands. Present the options (CI/CD runner, deployment target, etc.) and confirm before executing.

# Add deployment to an existing prototype (strict programmatic)
agents-cli scaffold enhance . --deployment-target agent_runtime

# Add CI/CD pipeline (ask: GitHub Actions or Cloud Build?)
agents-cli scaffold enhance . --cicd-runner github_actions

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Template Options

Template	Deployment	Description
adk	Agent Runtime, Cloud Run, GKE	Standard ADK agent (default); A2A protocol built in

RAG is a clone-and-study recipe, not a template. Build it by studying rag-vector-search or rag-agent-search and adapting the sample into your project — see /google-agents-cli-workflow Phase 1.

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Deployment Options

Target	Description
agent_runtime	Managed by Google (Vertex AI Agent Runtime). Container-based — Agent Engine builds the project Dockerfile. Sessions handled automatically.
cloud_run	Container-based deployment. More control; you build and deploy the Dockerfile.
gke	Container-based on GKE Autopilot. Full Kubernetes control.
none	No deployment scaffolding. Code only (still includes a Dockerfile).

"Prototype First" Pattern (Recommended)

Start with --prototype to skip CI/CD and Terraform. Focus on getting the agent working first, then add deployment later with scaffold enhance:

# Step 1: Create a prototype
agents-cli scaffold create my-agent --agent adk --prototype

# Step 2: Iterate on the agent code...

# Step 3: Add deployment when ready
agents-cli scaffold enhance . --deployment-target agent_runtime

Agent Runtime and session_type

When using agent_runtime as the deployment target, Agent Runtime manages sessions internally. If your code sets a session_type, clear it — Agent Runtime overrides it.

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Step 3: Load Dev Workflow

After scaffolding, immediately load /google-agents-cli-workflow — it contains the development workflow, coding guidelines, and operational rules you must follow when implementing the agent.

Key files to customize: app/agent.py (instruction, tools, model), app/tools.py (custom tool functions), .env (project ID, location, API keys). Files to preserve: agents-cli-manifest.yaml (CLI reads this), deployment configs under deployment/, Makefile, app/__init__.py (the App(name=...) must match the directory name — default app), and the generated runtime/A2A infra (app/fast_api_app.py, app/app_utils/a2a.py, app/app_utils/services.py, Dockerfile) — these wire up serving, sessions, and the built-in A2A surface; don't hand-edit them.

RAG projects — clone-and-study, not a template: RAG isn't a scaffold option. Build it by studying rag-vector-search or rag-agent-search (see /google-agents-cli-workflow Phase 1) and adapting the sample's app/, infra/terraform/, and ingestion into your project. Provisioning and ingestion run from the sample's own Makefile (make setup-infra, make data-ingestion).

Verifying your agent works: Use agents-cli run "test prompt" for quick smoke tests, then agents-cli eval generate and agents-cli eval grade for systematic validation. Do NOT write pytest tests that assert on LLM response content — that belongs in eval.

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Scaffold as Reference

When you need specific files (Terraform, CI/CD workflows, Dockerfile) but don't want to scaffold the current project directly, create a temporary reference project in /tmp/:

agents-cli scaffold create /tmp/ref-project \
  --agent adk \
  --deployment-target cloud_run

Inspect the generated files, adapt what you need, and copy into the actual project. Delete the reference project when done.

This is useful for:

• Non-standard project structures that enhance can't handle
• Cherry-picking specific infrastructure files
• Understanding what the CLI generates before committing to it

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Critical Rules

• NEVER skip requirements clarification — load /google-agents-cli-workflow Phase 0 and clarify the user's intent before running scaffold create
• NEVER change the model in existing code unless explicitly asked
• NEVER mkdir before create — the CLI creates the directory; pre-creating it causes enhance mode instead of create mode
• NEVER create a Git repo or push to remote without asking — confirm repo name, public vs private, and whether the user wants it created at all
• Always ask before choosing CI/CD runner — present GitHub Actions and Cloud Build as options, don't default silently
• Agent Runtime clears session_type — if deploying to agent_runtime, remove any session_type setting from your code
• Start with --prototype for quick iteration — add deployment later with enhance
• Project names must be ≤26 characters, lowercase, letters/numbers/hyphens only
• NEVER write A2A code from scratch — A2A is built into every Python ADK agent (adk); the A2A Python API surface (import paths, AgentCard schema, to_a2a() signature) is non-trivial and changes across versions. Scaffold normally; never hand-write the A2A surface.

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Examples

Using scaffold as reference: User says: "I need a Dockerfile for my non-standard project" Actions:

1. Create temp project: agents-cli scaffold create /tmp/ref --agent adk --deployment-target cloud_run
2. Copy relevant files (Dockerfile, etc.) from /tmp/ref
3. Delete temp project Result: Infrastructure files adapted to the actual project

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A2A project: User says: "Build me a Python agent that exposes A2A and deploys to Cloud Run" Actions:

1. Follow the standard flow (understand requirements, choose architecture, scaffold)
2. agents-cli scaffold create my-a2a-agent --agent adk --deployment-target cloud_run --prototype Result: Valid A2A imports and Dockerfile — no manual A2A code written.

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Troubleshooting

agents-cli command not found

See /google-agents-cli-workflow → Setup section.

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Related Skills

• /google-agents-cli-workflow — Development workflow, coding guidelines, and the build-evaluate-deploy lifecycle
• /google-agents-cli-adk-code — ADK Python API quick reference for writing agent code
• /google-agents-cli-deploy — Deployment targets, CI/CD pipelines, and production workflows
• /google-agents-cli-eval — Evaluation methodology, dataset schema, and the eval-fix loop