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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: 1.7.0 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) 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 ~= 1.7.0 If version is behind, run: uv tool install "google-agents-cli~=1.7.0"

Check version: agents-cli info Install uv 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/ 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 Enterprise (optional) | | 7 — Observe | /google-agents-cli-observability | After deploying — traces, logging, monitoring setup |

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Setup

If agents-cli is not installed:

uv tool install google-agents-cli

uv command not found

Install uv following the official installation guide.

Product name mapping

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

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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, Gemini Enterprise, 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-cli-adk-code — it has both the recipe (in references/samples.md) and the ADK memory API details.
• If Cloud Run or GKE chosen → Session storage? In-memory (default), Cloud SQL (persistent), or Agent Platform Sessions (managed).
• If deployment with CI/CD chosen → Git repository? Does one already exist, or should one be created? If creating, public or private?

Once the design is agreed, write the spec to .agents-cli-spec.md using the template in references/spec-template.md, self-review it, then get the user's approval. See /google-agents-cli-scaffold for how these choices map to CLI flags.

Once you have a clear understanding, proceed to Phase 1.

Phase 1: Study Reference Recipes

Trigger. If the request involves any of: searching your own documents · running shell or Python code on a user's behalf · a sandboxed or isolated per-user environment · loading skills the agent picks up at runtime · work that spans days, resumes, or runs unattended · remembering across conversations · approving a risky action before it executes · blocking harmful content or moderating what agents say · acting with a user's own API keys or credentials · OAuth consent to reach a user's own data · delegating to sub-agents with isolated context · speaking A2A to other agents · reacting to events or a schedule · researching a topic online and reporting it with citations · generating images or video — then a recipe already implements it. Look it up before you commit to an implementation.

This list covers the same capabilities as the topic index in /google-agents-cli-adk-code → references/samples.md. If you extend one, extend the other.

Load /google-agents-cli-adk-code now.Load it even though nothing is scaffolded and you are not writing code yet; "wrong phase for the code skill" is the rationalisation that makes agents skip this step, and that skill's Prerequisites for writing code does not apply to you.

Look up each capability the design calls for. ADK Python: the topic index in references/samples.md maps needs (retrieval, sandboxed execution, memory, approval gates, guardrails, per-user credentials, scheduling) to the recipe that teaches them and shows how to clone one. ADK Go: there is no references catalog — read references/adk-go.md for the API and the upstream examples/.

Multiple recipes can match — clone and study all that are relevant, starting with each one's AGENTS.md.

If no recipe matches, proceed to Phase 2. But first — are you sure? Re-read the user's request and re-check the topic index for your language. Skipping a matching recipe means rebuilding patterns that already exist, usually worse.

IMPORTANT — Exit criteria: After studying a recipe, ask yourself: can I apply anything from it to help me deliver the design? Note what you'll reuse before moving on. Do NOT proceed until you've answered this.

This catalog is useful at any phase — revisit it when you hit deployment, publishing, or infrastructure questions. A recipe's Terraform or registration pattern may be exactly what you need later.

Phase 2: Scaffold (if needed)

First check whether a project already exists: run agents-cli info from the project root. If one was already created or enhanced by agents-cli, skip this phase.

Otherwise, scaffold before writing any code:

• No project yet → agents-cli scaffold create <name>
• Existing code to import → agents-cli scaffold enhance . (adds the agents-cli structure)

Use /google-agents-cli-scaffold for the full workflow — it covers architecture choices (deployment target, agent type, session storage) and project creation or enhancement.

Phase 3: Build and Implement

Implement the agent logic:

1. Write/modify code in the agent directory (check GEMINI.md / CLAUDE.md for directory name)
2. Quick smoke test: Use agents-cli run "your prompt" to verify the agent works after changes — this is the fastest way to check behavior without leaving the terminal
3. Iterate on the implementation based on user feedback

If the user asks for interactive testing, suggest agents-cli playground — it opens a web-based playground for manual conversation with the agent.

For ADK API patterns and code examples, use /google-agents-cli-adk-code.

Smoke-test only here — do not write behavioral unit tests. LLM output is non-deterministic; behavioral checks belong in eval (Phase 4), not in pytest or go test. Use agents-cli run "prompt" for quick checks.

Provision recipe infrastructure (if you adapted one)

A recipe that needs backing infrastructure (a datastore, an index, a sandbox, a queue) ships its own provisioning: follow its Makefile (e.g. make setup-infra, make data-ingestion) and its AGENTS.md / README.md, adapting the recipe's infra/terraform/ and .env into your project. agents-cli has no command for this.

Phase 4: Evaluate

This is the most important phase. Evaluation validates agent behavior end-to-end.

MANDATORY: Activate /google-agents-cli-eval before running evaluation. It contains the dataset schema, config format, and critical gotchas. Do NOT skip this.

Do NOT skip this phase. After building the agent, you MUST proceed to evaluation.

Unit tests vs agents-cli eval — know the difference:

• Unit tests (uv run pytest for Python, go test ./... for Go) — Tests code correctness: imports work, functions return expected types, API contracts hold. Does NOT test whether the agent behaves well.
• agents-cli eval — Tests agent behavior: response quality, tool usage, persona consistency, safety compliance. This is what validates your agent actually works.
• agents-cli run "prompt" — Quick one-off smoke test during development. If testing multiple prompts use the --start-server option to persist the local server, which reduces overhead for repeated calls and allows resuming local sessio