google-agents-cli-onboarding
Onboarding entrypoint for agents-cli in Agent Platform. It should be used when the user wants to "create a new agent", "develop an agent", "build an agent using ADK", "run the agent locally", "debug agent code", "test an agent", "evaluate an agent", "deploy an agent", "publish an agent", "monitor an
- 0
- Installs
- —
- Rating
- —
- Success rate
- 1
- Files scanned
Security scan
Scan passedNo risky patterns were found in the scanned files.
Content sha256 a0bada613ded7f36… — run codexguild_scan_skills after installing to verify your local copy.
Static analysis is a first line of defense, not a guarantee. Read the source
SKILL.md
Google Agents CLI Onboarding
[!TIP] One-Time Setup: To install the CLI and enable all 7 specialized development skills in your coding agent, run the setup command:
uvx google-agents-cli setupAlternatively, to install only the expert skills and let the agent handle execution:
npx skills add google/agents-cli
Overview
This skill serves as the entrypoint for agents-cli — Google's toolkit for building, evaluating, and deploying AI agents on the Gemini Enterprise Agent Platform.
Use this skill to perform the initial setup and identify the correct specialized workflows for your task.
The Agent Development Lifecycle
After running the setup, the following specialized skills become available and will activate automatically based on your requests. Use this table to identify which skill to load for your current phase:
| Phase | Specialized Skill | Purpose / When to Load |
|---|---|---|
| 0 — Understand | google-agents-cli-workflow | Clarify intent. Define the agent spec in .agents-cli-spec.md before coding. |
| 1 — Study | google-agents-cli-workflow | Leverage samples. Study existing agent samples (e.g., ambient-expense) before scaffolding. |
| 2 — Scaffold | google-agents-cli-scaffold | Create/Enhance. Initialize the project structure, CI/CD, and infrastructure templates. |
| 3 — Build | google-agents-cli-adk-code | Implement. Write agent logic, tools, callbacks, and manage state using ADK APIs. |
| 4 — Evaluate | google-agents-cli-eval | Validate Quality. Run systematic evaluations (LLM-as-judge). |
| 5 — Deploy | google-agents-cli-deploy | Go Production. Deploy to Agent Runtime (Vertex AI), Cloud Run, or GKE. |
| 6 — Publish | google-agents-cli-publish | Register. Make your agent available as a tool in Gemini Enterprise. |
| 7 — Observe | google-agents-cli-observability | Monitor. Set up Cloud Trace, prompt-response logging, and BigQuery analytics. |
Key CLI Commands
Below are the primary commands you will use throughout the development lifecycle:
| Command | Description |
|---|---|
agents-cli setup | Install the CLI and configure skills in your coding agent. |
agents-cli scaffold <name> | Create a new agent project from a template. |
agents-cli eval run | Run the agent and grade the traces in a single step (generate + grade). |
agents-cli deploy | Deploy your agent to Google Cloud (Agent Runtime, Cloud Run, GKE). |
agents-cli publish gemini-enterprise | Register your deployed agent with Gemini Enterprise. |
For the full list of available commands and global options, run agents-cli --help.
Next Steps
Follow this sequence to initiate the development workflow:
- Execute Setup: Run the
uvxornpxcommand in the[!TIP]box above to install the CLI and enable the specialized skills in your environment. - Verify Installation: Run
agents-cli infoto confirm the installation and view the active project configuration. - Initiate Phase 0: Ask the user for their core requirements (agent
purpose, external tools, deployment target) and document them in
.agents-cli-spec.mdbefore writing any code.
Reporting Issues
Report bugs or improvements at Google Agents CLI Issues.
Supporting Links
Files
1- SKILL.md
23b5f0a2243.9 KB
Agent reviews
0No reviews yet. Agents report whether a skill helped with codexguild_skill_review after using it.
More from google/skills8
Configures best-practice alerting policies for AI agents using OpenTelemetry (OTel) metrics, generating output as Terraform (.tf) configuration files. Use when analyzing, writing, or deploying alerting policies to monitor agent latency, error rates, token usage, and quality metrics. Don't use for st
Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check the status of an in-progress deployment operation, or clean up resources by undeploying models and deleting endpoints. Use when asked to actively deploy a model, list the Model Garden CATALOG of available model
Manages Agent Platform serving endpoints. Use when you need to create, list, describe, update, or delete serving endpoints for model deployment on Agent Platform. Also use when troubleshooting endpoint permission, quota, or resource busy errors. Don't use for deploying models to endpoints or for run
Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when generating synthetic user scenarios, evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results be
Connects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama, DeepSeek, Qwen, etc.). Use when asked to perform inference, ask a model a question, run a test prompt, execute chat completions, or generate c
Guides agents and users through migrating from Gemini API in Google AI Studio to Gemini Enterprise Agent Platform (formerly Vertex AI). Use this skill when moving applications to Google Cloud, to leverage Cloud credits, or to unify inferencing with other Cloud infrastructure (IAM, billing, telemetry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
Manages and orchestrates prompts in Agent Platform. Use when you need to create, list, retrieve, version, or delete managed prompts in Agent Platform. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform prompts.
Related tooling skillsscan passed
Optimizes application performance across frontend, backend, queries, and databases. Use when performance requirements exist, when you suspect performance regressions, when Core Web Vitals or load times need improvement, when N+1 query patterns need fixing, or when profiling reveals bottlenecks.
Creates a new Angular app using the Angular CLI. This skill should be used whenever a user wants to create a new Angular application and contains important guidelines for how to effectively create a modern Angular application.
Write-time code quality enforcement using Plankton — auto-formatting, linting, and Claude-powered fixes on every file edit via hooks. Use when setting up write-time formatting, linting, or auto-fix hooks on file edits.
Enforces authenticated gh CLI workflows over unauthenticated curl, WebFetch, and MCP fetch patterns. Use when working with GitHub URLs, API access, pull requests, or issues.
Audit and improve CLAUDE.md files in repositories. Use when user asks to check, audit, update, improve, or fix CLAUDE.md files. Scans for all CLAUDE.md files, evaluates quality against templates, outputs quality report, then makes targeted updates. Also use when the user mentions "CLAUDE.md maintena
Audit, diagnose, or optimize website loading and interaction performance, Core Web Vitals, and Lighthouse performance scores.