# How to Use Google Agent Skills for AI Workflows on Google Cloud

> Learn how to use Google Agent Skills to build AI workflows on Google Cloud. These reusable Markdown guides simplify Terraform and deployment script generation for AI agents.

- Repository: [Google/skills](https://github.com/google/skills)
- Tags: how-to-guide
- Published: 2026-09-04

---

**Google Agent Skills are Markdown-based reusable guides that the Agents CLI consumes to generate Terraform, deployment scripts, and validation plans for building AI agents on Google Cloud.**

The `google/skills` repository provides a structured approach to deploying AI agents through declarative skill definitions. By using Google Agent Skills for AI workflows on Google Cloud, developers can transform high-level requirements into concrete Infrastructure-as-Code without manual configuration drift. The repository ships with a **Skill Engine** (the Agents CLI) that parses these Markdown files and orchestrates the entire build-deploy lifecycle.

## Architecture of the Google Agent Skills Framework

The framework consists of four core components that work together to translate declarative intent into executable cloud resources.

**Skill Catalog** – A collection of Markdown files under the `skills/` directory that encode use-cases such as building agents, configuring monitoring, and deploying GKE workloads. These files serve as the authoritative, version-controlled knowledge base.

**Agents CLI (ADK)** – The command-line tool `agents-cli` that parses a skill's [`SKILL.md`](https://github.com/google/skills/blob/main/SKILL.md) file, presents interactive checklists, and generates required artifacts. According to the `google/skills` source code, this component turns declarative skill definitions into actionable implementation plans.

**Reference Assets** – Directories such as `references/` and `assets/` that hold product-mapping tables and template files. These supply concrete Cloud product recommendations and Markdown templates used during generation.

**Generated Artifacts** – The output includes [`solution-architecture.md`](https://github.com/google/skills/blob/main/solution-architecture.md), [`implementation-instructions.md`](https://github.com/google/skills/blob/main/implementation-instructions.md), and [`validation-plan.md`](https://github.com/google/skills/blob/main/validation-plan.md), which persist recommendations from each workflow phase.

## Installing the Agents CLI and Skill Catalog

To begin using Google Agent Skills, install the skill package locally and verify available capabilities.

```bash

# Install the skill package (adds all skill definitions locally)

npx skills add google/skills

```

```bash

# List available skills and identify the agent deployment skill

skills list | grep "build and deploy AI agents"

# → google-cloud-solution-build-deploy-agents

```

## Executing the Four-Phase Workflow

The core skill defined in [`skills/cloud/google-cloud-solution-build-deploy-agents/SKILL.md`](https://github.com/google/skills/blob/main/skills/cloud/google-cloud-solution-build-deploy-agents/SKILL.md) implements a rigid four-phase workflow (lines 25-34): **Requirements**, **Design**, **Implementation**, and **Validation**. The CLI orchestrates each phase sequentially.

```bash

# Start the skill's interactive workflow

agents-cli run google-cloud-solution-build-deploy-agents

```

During execution, the CLI:

- **Collects** missing information from the user or uses existing context from the prompt
- **Maps** logical components to concrete Google Cloud services using [`references/product-mappings.md`](https://github.com/google/skills/blob/main/references/product-mappings.md)
- **Generates** architecture diagrams in Mermaid, Terraform snippets, and deployment scripts
- **Validates** deployments with dry-run commands and automated test suites

Because skill files are plain Markdown, they remain version-controllable and auditable without binary dependencies.

## Mapping Components to Google Cloud Services

The [`skills/cloud/google-cloud-solution-build-deploy-agents/references/product-mappings.md`](https://github.com/google/skills/blob/main/skills/cloud/google-cloud-solution-build-deploy-agents/references/product-mappings.md) file contains the mapping logic that translates logical agent components into specific Google Cloud products. During the Design phase, the Agents CLI references this file to recommend services such as Cloud Run for compute, Vertex AI for model serving, or Firestore for state management.

## Generating Infrastructure as Code and Deployment Artifacts

Phase 3 (Implementation) generates concrete artifacts based on templates stored in the `assets/` directory. The CLI utilizes [`assets/implementation-template.md`](https://github.com/google/skills/blob/main/assets/implementation-template.md) to render deployment instructions.

**Generating Terraform from Phase 3:**

```bash

# The generated implementation-instructions.md contains ready-to-use Terraform

cat <<'EOF' > main.tf
module "agent" {
  source   = "github.com/google/agents-cli//modules/agent"
  project  = var.project_id
  region   = var.region
  model    = "gemini-1.5-flash"
  env_vars = { "API_KEY" = var.api_key }
}
EOF
terraform init
terraform apply

```

The solution architecture document is rendered using [`assets/solution-template.md`](https://github.com/google/skills/blob/main/assets/solution-template.md), while the validation plan uses [`assets/validation-template.md`](https://github.com/google/skills/blob/main/assets/validation-template.md) to structure Phase 4 activities.

## Validating AI Agent Deployments

Phase 4 (Validation) executes conformance checks using the plan generated from [`assets/validation-template.md`](https://github.com/google/skills/blob/main/assets/validation-template.md). The Agents CLI provides specific commands for dry-run verification and quality evaluation.

```bash

# Dry-run the deployment without applying changes

agents-cli deploy --dry-run

# Test the running service endpoint

agents-cli run --url https://my-agent-run.run.app

# Evaluate agent quality against test suites

agents-cli eval run --suite basic

```

## Summary

- **Google Agent Skills** are Markdown-based specifications stored in the `google/skills` repository that define reusable AI workflows.
- The **Agents CLI (`agents-cli`)** parses [`SKILL.md`](https://github.com/google/skills/blob/main/SKILL.md) files and orchestrates four distinct phases: Requirements, Design, Implementation, and Validation.
- **Reference assets** in [`references/product-mappings.md`](https://github.com/google/skills/blob/main/references/product-mappings.md) map logical components to concrete Google Cloud services.
- **Generated artifacts** include [`solution-architecture.md`](https://github.com/google/skills/blob/main/solution-architecture.md), [`implementation-instructions.md`](https://github.com/google/skills/blob/main/implementation-instructions.md), and [`validation-plan.md`](https://github.com/google/skills/blob/main/validation-plan.md), created using templates from the `assets/` directory.
- The workflow produces **Terraform configurations** and deployment scripts that can be validated using `agents-cli deploy --dry-run` and evaluation suites.

## Frequently Asked Questions

### What is the Agents CLI (ADK) in Google Agent Skills?

The Agents CLI (`agents-cli`) is the command-line interface that implements the Skill Engine. It reads Markdown skill definitions from the `google/skills` repository, particularly [`SKILL.md`](https://github.com/google/skills/blob/main/SKILL.md) files, and guides users through interactive workflows. The CLI handles the translation from declarative skill descriptions into executable commands, Terraform code, and validation scripts.

### How does the SKILL.md file structure work?

The [`SKILL.md`](https://github.com/google/skills/blob/main/SKILL.md) file, such as [`skills/cloud/google-cloud-solution-build-deploy-agents/SKILL.md`](https://github.com/google/skills/blob/main/skills/cloud/google-cloud-solution-build-deploy-agents/SKILL.md), follows a structured Markdown format that defines the four workflow phases (Requirements, Design, Implementation, Validation). Lines 25-34 explicitly enumerate these phases. The file contains prompts, checklists, and references to mapping tables that the CLI uses to generate context-specific guidance.

### Can I customize the generated Terraform configurations?

Yes. The Agents CLI generates Terraform code based on [`assets/implementation-template.md`](https://github.com/google/skills/blob/main/assets/implementation-template.md) and [`references/product-mappings.md`](https://github.com/google/skills/blob/main/references/product-mappings.md), but outputs standard `.tf` files that you can modify before execution. After running `agents-cli run`, review the generated [`implementation-instructions.md`](https://github.com/google/skills/blob/main/implementation-instructions.md) and extract or edit the Terraform blocks before running `terraform apply`.

### What validation methods are available for AI agent deployments?

The validation phase uses [`assets/validation-template.md`](https://github.com/google/skills/blob/main/assets/validation-template.md) to structure conformance checks. Available methods include `agents-cli deploy --dry-run` for infrastructure validation, `agents-cli run --url` for endpoint testing, and `agents-cli eval run --suite basic` for automated quality evaluation against predefined test suites.