# Sunholo Data AI Protocol Platform Adk Scaffold

> ADK Project Scaffolding Guide

- Skill: `tomevault-io/sunholo-data-ai-protocol-platform-adk-scaffold` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/sunholo-data-ai-protocol-platform-adk-scaffold`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/sunholo-data-ai-protocol-platform-adk-scaffold/raw
- Safety review: WARNING (external: skill-scanner WARNING, skillspector CAUTION)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/sunholo-data-ai-protocol-platform-adk-scaffold

---


# ADK Project Scaffolding Guide

Use the `agent-starter-pack` CLI (via `uvx`) to create new ADK agent projects or enhance existing ones with deployment, CI/CD, and infrastructure scaffolding.

---

## Step 1: Gather Requirements

Ask these questions in two rounds. Start with the use case, then move to architecture.

Start with the use case, then ask follow-ups based on answers.

**Always ask:**

1. **What problem will the agent solve?** — Core purpose and capabilities
2. **External APIs or data sources needed?** — Tools, integrations, auth requirements
3. **Safety constraints?** — What the agent must NOT do, guardrails
4. **Preferred language?** — Python, Go, TypeScript, or Java
5. **Deployment preference?** — Prototype first (recommended) or full deployment? If deploying: Agent Engine or Cloud Run?

**Ask based on context:**

- If **RAG or data ingestion** mentioned → **Datastore?** Vertex AI Search or Vertex AI Vector Search? Use `--agent agentic_rag --datastore <choice>`.
- If agent should be **available to other agents** → **A2A protocol?** Use `--agent adk_a2a` to expose the agent as an A2A-compatible service.
- If **full deployment** chosen → **CI/CD runner?** GitHub Actions (default) or Google Cloud Build?
- If **Cloud Run** chosen → **Session storage?** In-memory (default), Cloud SQL (persistent), or Agent Engine (managed).
- If **deployment with CI/CD** chosen → **Git repository?** Does one already exist, or should one be created? If creating, public or private?


---

## Step 2: Write DESIGN_SPEC.md

Compose a **detailed** spec with these sections. Present the full spec for user approval before scaffolding.

```markdown
# DESIGN_SPEC.md

## Overview
2-3 paragraphs describing the agent's purpose and how it works.

## Example Use Cases
3-5 concrete examples with expected inputs and outputs.

## Tools Required
Each tool with its purpose, API details, and authentication needs.

## Constraints & Safety Rules
Specific rules — not just generic statements.

## Success Criteria
Measurable outcomes for evaluation.

## Edge Cases to Handle
At least 3-5 scenarios the agent must handle gracefully.
```

The spec should be thorough enough for another developer to implement the agent without additional context.

---

## Step 3: Create or Enhance the Project

### Create a New Project

```bash
uvx agent-starter-pack create <project-name> \
  --agent <template> \
  --deployment-target <target> \
  --region <region> \
  --prototype \
  -y
```

**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.
- 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.

#### Create Flags

| Flag | Short | Default | Description |
|------|-------|---------|-------------|
| `--agent` | `-a` | `adk` | Agent template (see template table below) |
| `--deployment-target` | `-d` | `agent_engine` | Deployment target (`agent_engine`, `cloud_run`, `none`) |
| `--region` | | `us-central1` | GCP region |
| `--prototype` | `-p` | off | Skip CI/CD and Terraform (recommended for first pass) |
| `--cicd-runner` | | `skip` | `github_actions` or `google_cloud_build` |
| `--datastore` | `-ds` | — | Datastore for data ingestion (`vertex_ai_search`, `vertex_ai_vector_search`) |
| `--session-type` | | `in_memory` | Session storage (`in_memory`, `cloud_sql`, `agent_engine`) |
| `--auto-approve` | `-y` | off | Skip confirmation prompts |
| `--skip-checks` | `-s` | off | Skip GCP/Vertex AI verification checks |
| `--agent-directory` | `-dir` | `app` | Agent code directory name |
| `--google-api-key` | `-k` | — | Use Google AI Studio instead of Vertex AI |
| `--agent-guidance-filename` | | `GEMINI.md` | Guidance file name (`CLAUDE.md`, `AGENTS.md`) |
| `--debug` | | off | Enable debug logging for troubleshooting |

### Enhance an Existing Project

```bash
uvx agent-starter-pack enhance . \
  --deployment-target <target> \
  -y
```

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

#### Enhance Flags

All create flags are supported, plus:

| Flag | Short | Default | Description |
|------|-------|---------|-------------|
| `--name` | `-n` | directory name | Project name for templating |
| `--base-template` | `-bt` | — | Override base template (e.g. `agentic_rag` to add RAG) |
| `--dry-run` | | off | Preview changes without applying |
| `--force` | | off | Force overwrite all files (skip smart-merge) |

### Common Workflows

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

```bash
# Add deployment to an existing prototype
uvx agent-starter-pack enhance . --deployment-target agent_engine -y

# Add CI/CD pipeline (ask: GitHub Actions or Cloud Build?)
uvx agent-starter-pack enhance . --cicd-runner github_actions -y

# Add RAG with data ingestion
uvx agent-starter-pack enhance . --base-template agentic_rag --datastore vertex_ai_search -y

# Preview what would change (dry run)
uvx agent-starter-pack enhance . --deployment-target cloud_run --dry-run -y
```

---

## Template Options

| Template | Language | Deployment | Description |
|----------|----------|------------|-------------|
| `adk` | Python | Agent Engine, Cloud Run | Standard ADK agent (default) |
| `adk_a2a` | Python | Agent Engine, Cloud Run | Agent-to-agent coordination (A2A protocol) |
| `agentic_rag` | Python | Agent Engine, Cloud Run | RAG with data ingestion pipeline |
| `adk_live` | Python | Cloud Run | Real-time multimodal agent (Live API) |
| `adk_go` | Go | Cloud Run only | Go ADK agent |
| `adk_ts` | TypeScript | Cloud Run only | TypeScript ADK agent |
| `adk_java` | Java | Cloud Run only | Java ADK agent (Spring Boot) |

**Note:** Go, TypeScript, and Java templates only support Cloud Run deployment. Do not use `--deployment-target agent_engine` with these templates.

---

## Deployment Options

| Target | Description |
|--------|-------------|
| `agent_engine` | Managed by Google (Vertex AI Agent Engine). Sessions handled automatically. Python only. |
| `cloud_run` | Container-based deployment. More control, requires Dockerfile. All languages. |
| `none` | No deployment scaffolding. Code only. |

### "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 `enhance`:

```bash
# Step 1: Create a prototype
uvx agent-starter-pack create my-agent --agent adk --prototype -y

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

# Step 3: Add deployment when ready
uvx agent-starter-pack enhance . --deployment-target agent_engine -y
```

### Agent Engine and session_type

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

---

## Step 4: Save DESIGN_SPEC.md

After scaffolding, save the approved spec from Step 2 to the project root as `DESIGN_SPEC.md`.

---

## 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/`:

```bash
uvx agent-starter-pack create /tmp/ref-project \
  --agent adk \
  --deployment-target cloud_run \
  --cicd-runner github_actions \
  -y
```

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 ASP generates before committing to it

---

## Critical Rules

- **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 Engine clears session_type** — if deploying to `agent_engine`, remove any `session_type` setting from your code
- **Agent Engine is Python only** — Go, TypeScript, and Java templates require `cloud_run`
- **Start with `--prototype`** for quick iteration — add deployment later with `enhance`
- **Project names** must be ≤26 characters, lowercase, letters/numbers/hyphens only

---

# Examples

Using scaffold as reference:
User says: "I need a Dockerfile for my non-standard project"
Actions:
1. Create temp project: `uvx agent-starter-pack create /tmp/ref --agent adk --deployment-target cloud_run -y`
2. Copy relevant files (Dockerfile, etc.) from /tmp/ref
3. Delete temp project
Result: Infrastructure files adapted to the actual project

---

## Troubleshooting

### `uvx` command not found

Install `uv`: `curl -LsSf https://astral.sh/uv/install.sh | sh`

If `uv` is not an option, use pip instead:

```bash
# macOS/Linux
python -m venv .venv && source .venv/bin/activate
# Windows
python -m venv .venv && .venv\Scripts\activate

pip install agent-starter-pack
agent-starter-pack create <project-name> ...
```

---
> Source: [sunholo-data/ai-protocol-platform](https://github.com/sunholo-data/ai-protocol-platform) — distributed by [TomeVault](https://tomevault.io).
<!-- tomevault:4.0:skill_md:2026-05-22 -->