Google Agents Cli WorkflowSAFE

The CLI and skills that turn any coding assistant into an expert at creating, evaluating, and deploying AI agents on Google Cloud.

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: google-agents-cli-workflow
description: >
  This skill should be used when the user wants to "develop an agent",
  "build an agent using ADK", "run the agent locally", "debug agent code",
  "test an agent", "deploy an agent", "publish an agent", "monitor an agent",
  or needs the ADK (Agent Development Kit) development lifecycle and coding
  guidelines. Entrypoint for building ADK agents.
  Always active — provides the full workflow (scaffold, build, evaluate,
  deploy, publish, observe), code preservation rules, model selection
  guidance, and troubleshooting steps for ADK or any agent development.
metadata:
  author: Google
  license: Apache-2.0
  version: 1.11.0
  requires:
    bins:
      - agents-cli
    install: "uv tool install google-agents-cli"
---

# Agent Development Workflow & Guidelines

**agents-cli** is a CLI and skills toolkit for building, evaluating, and deploying agents on Google Cloud. It works with any coding agent — Antigravity CLI, Claude Code, Codex, or others — and with the agent framework of your choice (the [Agent Development Kit (ADK)](https://adk.dev/) by default). Install with `uvx google-agents-cli setup`.

> **Before writing agent code, make sure a scaffolded project exists (see Phase 2).** Skipping scaffolding loses eval boilerplate, CI/CD config, and project conventions.


> Requires: google-agents-cli ~= 1.11.0
> If version is behind, run: uv tool install "google-agents-cli~=1.11.0"

> Check version: agents-cli info
> [Install uv](https://docs.astral.sh/uv/getting-started/installation/index.md) first if needed.

## Session Continuity & Skill Cross-References

Re-read the relevant skill **before** each phase — not after you've already started and hit a problem. Context compaction may have dropped earlier skill content. If skills are not available, run `uvx google-agents-cli setup` to install them.

| Phase | Skill | When to load |
|-------|-------|--------------|
| 0 — Understand | — | No skill needed — read `.agents-cli-spec.md` if present, else clarify goals with the user |
| 1 — Study recipes | `/google-agents-cli-adk-code` | **Load it during design**, before scaffolding. Python: the `references/samples.md` topic index maps a need to the recipe that implements it. Go: the upstream [examples/](https://github.com/google/adk-go/tree/main/examples) are the equivalent. Yes, load this early. |
| 2 — Scaffold | `/google-agents-cli-scaffold` | Before creating or enhancing a project |
| 3 — Build | `/google-agents-cli-adk-code` | Before writing agent code — API patterns, tools, callbacks, state |
| 4 — Evaluate | `/google-agents-cli-eval` | Before running any eval — dataset schema, metrics, eval-fix loop |
| 5 — Deploy | `/google-agents-cli-deploy` | Before deploying — target selection, troubleshooting 403/timeouts |
| 6 — Publish | `/google-agents-cli-publish` | After deploying, if registering with Gemini (optional) |
| 7 — Observe | `/google-agents-cli-observability` | After deploying — traces, logging, monitoring setup |

---

## Setup

If `agents-cli` is not installed:
```bash
uv tool install google-agents-cli
```

### `uv` command not found

Install `uv` following the [official installation guide](https://docs.astral.sh/uv/getting-started/installation/index.md).

### Product name mapping

Users name products inconsistently (Vertex AI or Agent Platform → Gemini platform, Agent Engine or Agent Runtime → Agent Runtime in Gemini platform, etc.). Map user terms to CLI values using `references/terminology.md`.

---

## Phase 0: Understand

Before writing or scaffolding anything, understand what you're building — through a **design dialogue**, not a checklist. Load `references/brainstorming.md` and follow it: ask **one question at a time**, propose 2–3 architecture approaches for non-trivial agents, and validate the design before any scaffolding.

If `.agents-cli-spec.md` exists in the current directory, read it — it is your primary source of truth. Otherwise:

Do NOT proceed to planning, scaffolding, or coding until the user approves the spec. Do not assume, research, or fill in the blanks yourself — the user's intent drives everything.

**Scale the ceremony to complexity:** a trivial agent (single tool, fixed persona) needs only a couple of questions, a 2–3 sentence spec, and one approval; a complex agent (multi-agent, RAG, external APIs/auth, safety-critical) gets the full treatment in `references/brainstorming.md`.

**Topics to cover** (one question at a time, adapting to the user — see the playbook):

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. **Deployment preference?** — Prototype first (recommended) or full deployment? If deploying: Agent Runtime, Cloud Run, or GKE?

**Ask based on context:**

- If the agent needs a **capability the scaffold doesn't ship** — retrieval over your data, sandboxed code execution, memory across sessions, OAuth consent, safety guardrails, event-driven triggers — that capability comes from a **clone-and-study recipe**, not a scaffold flag. Look the need up in the topic index in `/google-agents-cli-adk-code` → `references/samples.md` and study the matching recipe in Phase 1.
- If the agent is a **live or voice agent** (Live API, spoken conversation, barge-in, telephony) → load `/google-agents-cli-adk-code` (`references/adk-python-live.md`) **before writing the spec**. Live rules out A2A, the Gemini app, and the default model.
- If agent should be **available to other agents** → **A2A protocol** is built into every Python agent scaffolded by agents-cli; no separate choice needed — just scaffold normally.
- If **full deployment** chosen → **CI/CD runner?** GitHub Actions (default) or Google Cloud Build?
- If agent should **remember user preferences or facts across sessions** → long-term memory across conversations. Load `/google-agents-c

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.

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