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Gcloud

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  • Skill
  • Tool integration
  • Cloud and DevOps
  • Apache-2.0
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Provides safety-critical validation, guardrails, and data reduction for gcloud CLI operations across Google Cloud Platform (GCP) services and infrastructure. Use when planning, generating, constructing, proposing, describing, or executing any gcloud CLI commands - including when answering questions about gcloud syntax, or formatting flags. Don't use when writing Google Cloud client library code or raw REST/gRPC API requests.
— the description by Google

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The gcloud skill provides a structured framework for AI agents to interact with the Google Cloud SDK. It enforces strict safety protocols, including mandatory leaf-level syntax validation via help commands, non-interactive execution modes, and explicit data reduction strategies to manage context window usage. The skill includes a denylist for high-risk operations and requires explicit user authorization for destructive actions, ensuring that cloud infrastructure management remains predictable, secure, and deterministic.

What it does

  • Validates gcloud command syntax using built-in help commands before execution.
  • Enforces data reduction via filtering, projection, and limiting on all list operations.
  • Applies safety guardrails to prevent autonomous execution of destructive or high-risk commands.
  • Standardizes non-interactive execution using the quiet flag to prevent process hangs.
  • Ensures deterministic behavior by requiring explicit project and location scoping.
  • Provides standardized usage attribution for tracking agent-driven cloud interactions.

When to use it

  • When planning or executing gcloud CLI commands for Google Cloud infrastructure.
  • When you need to query or list cloud resources while maintaining a small context footprint.
  • When you require a safe, non-interactive way to manage GCP resources via an agent.
  • When you need to verify flag syntax or parameter requirements for specific gcloud subcommands.

Advantages

  • Prevents command hallucinations by mandating leaf-level help lookups.
  • Reduces context window bloat through mandatory data filtering and projection.
  • Protects against accidental resource destruction or unauthorized IAM changes.
  • Ensures reliable automation by forcing non-interactive flags and explicit scoping.
  • Provides a clear, repeatable 4-step planning template for complex tasks.

Usage examples

“List all running compute instances in my project.”
The agent will first run gcloud compute instances list --filter=status:RUNNING --limit=1 --format=json to verify the schema, then execute the filtered list command with appropriate project scoping.
“How do I create a new Cloud Storage bucket?”
The agent will run gcloud help storage buckets create to validate the syntax, then propose a command including the project flag and a dry-run step before requesting authorization.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/google/gcloud/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip gcloud.zip -d .agents/skills

Documentation by Google

gcloud CLI Skill for AI Agents

[!CAUTION]

MANDATORY PRE-CONDITION: EXPLICIT LEAF-LEVEL SYNTAX VALIDATION

All pre-existing knowledge of gcloud commands, flags, flag values, and positional argument syntax is stale and prone to hallucination.

NEVER propose command parameters, output flag options, execute commands, OR outline step-by-step plans for any gcloud task before validating leaf-level syntax via gcloud help <command> (or including leaf-level help lookup as a mandatory step in the plan).

Mandatory Action Rules:

  1. Direct Execution & Code Generation: ALWAYS invoke gcloud help <leaf_command> (e.g. gcloud help compute instances create or gcloud help sql instances create) before proposing or executing the final command syntax.

  2. Planning & Strategy Queries: When asked for a plan, strategy, or next steps to achieve a user goal (e.g., "What is your plan to accomplish X..."), the response MUST explicitly include running gcloud help <leaf_command> as Step 1 of the plan before proposing flags or executing commands.

  3. Non-Transitive Validation: Parent command group help (e.g. gcloud help compute) is not sufficient for leaf-level syntax validation. Validation must occur at the specific leaf subcommand level.

  4. FORBIDDEN Web Search Fallback: NEVER use search_web, web search, or external documentation search tools for gcloud CLI syntax. gcloud help <leaf_command> is the EXCLUSIVE authorized authority for command syntax.

  5. User Flag & Project Preservation: When proposing intermediate command steps, ALWAYS preserve all user-specified flags (including --project=<project_id>) in the proposed response text.

  6. Mandatory Plan Template: When generating a plan, the response MUST copy this exact 4-step structure:

    • Step 1: Syntax Validation via gcloud help <leaf_command>

    • Step 2: Parameter Verification (confirming required and optional flags, and explicitly checking if the --dry-run or --validate-only flag is supported)

    • Step 3: Dry-Run Command Proposal (If --dry-run or --validate-only is supported, there MUST be a --dry-run or --validate-only invocation before the next step.)

    • Step 4: Command Proposal & Authorization (If the command is on the "Prohibited Operations" denylist, state that autonomous execution is forbidden, and the user MUST be explicitly asked for authorization to proceed. If the command is NOT on the denylist, propose or proceed with execution, while following ALL "Execution Constraints" below.)

This document provides essential guidelines and best practices for AI agents interacting with the Google Cloud SDK (gcloud CLI). Following these rules is critical to avoid hallucinated commands, flags, flag values, and positional argument syntax, prevent destructive actions, and minimize context window usage.

Usage Attribution

MUST READ: Before executing any gcloud, REST API, or MCP command, you MUST read and follow the instructions in Usage Attribution. If a more specific product or domain skill (e.g., cloud-run-basics, bigquery-basics, gke-basics) is also loaded or active for the current task, ALWAYS use that specific skill's skill:<name> attribution tag instead of skill:gcloud. For commands executed directly in the terminal when no product-specific skill applies (never in generated scripts, Terraform, or user code), prefix gcloud with CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:gcloud)" and append agent-skills/1.0 (skill:gcloud) to User-Agent headers.

Execution Modes

AI agents can interact with Google Cloud resources in two primary ways:

  • Direct CLI Execution: Executing gcloud commands directly in a local or automated shell environment. See CLI Usage for installation, authentication flows, and configuration management.

  • Model Context Protocol (MCP): Invoking structured tools via the Cloud CLI remote MCP server (run_gcloud_command). See MCP Usage for tool schemas, parameter rules, and server configuration.

Core Principles

1. Explicit Command Validation (Mandatory)
  • Action: ALWAYS call gcloud help <command> for the exact command that is intended to be run (e.g., gcloud help compute instances create).

  • Verify: Ensure the command, flags, flag values, and positional argument syntax are valid for that specific leaf command before attempting execution or presenting plans. Validation is not transitive from parent groups.

2. Data Reduction Strategies (Mandatory)

Minimize the volume of data returned by gcloud to save context window space and reduce latency. DO NOT execute any list command without including at least one data reduction flag (--limit, --filter, or --format).

  • Projection: Use --format="json(key1, key2, ...)" to select only the specific fields needed for the task. To understand the advanced projection and formatting syntax, refer to gcloud topic projections and gcloud topic formats.

  • Limiting: Use --limit=N to cap the number of resources returned.

  • Filtering: Use --filter to narrow down results server-side. Prioritize : for pattern matching and never quote the right side of the colon. Treat the entire filter flag as a singular string without quoting or escaping characters. To study the filter expression syntax, refer to gcloud topic filters.

  • Schema Discovery: Unconstrained resource lists can quickly exhaust the context window with redundant data. To prevent this, discover a resource's schema before executing queries. If unsure of the JSON key path for projecting fields (--format) or filtering (--filter), run the targeted resource's list command (if supported) with a single-item limit:

    gcloud <GROUP> <RESOURCE> list --limit=1 --format=json

    Examine this single instance's JSON structure to safely identify the correct schema keys before requesting full or filtered datasets.

3. Execution Constraints
  • Single Commands: Execute a single gcloud command at a time. No command chaining or sequencing.

  • No Shell Operators: Do not use command substitution ($(...)), pipes (|), or redirection (>, >>, <). This is to increase command safety and ensure commands are more easily understandable and reviewable by users.

  • Non-Interactive Execution (--quiet / -q): Pass the --quiet (or -q) global flag on all execution commands (e.g., gcloud pubsub topics delete temp-topic --quiet --project=test-project). AI agents run in headless, non-interactive environments without a TTY or stdin input handler. Without --quiet, commands that prompt for user confirmation (such as deleting resources, approving defaults, or selecting unspecified regions) will pause execution indefinitely waiting for input, causing background task timeouts. Including --quiet forces non-interactive mode, causing gcloud to automatically accept safe default choices or fail immediately with an explicit error if required parameters are missing.

  • No Blind Lists: NEVER execute a list command without --limit, --filter, or --format.

4. Project and Location Scoping (Critical)

To ensure commands are deterministic, non-interactive, and target the correct environment, they must explicitly provide project and location scoping.

  • Explicit Project Target: Do not rely on active configuration defaults. Always append --project=<PROJECT_ID> to all resource-manipulating and querying commands (unless running pure local config commands). This avoids accidental execution against the wrong project.

  • Prevent Location Prompts: Many Google Cloud resources are regional or zonal. If the location flag is omitted (e.g., --region, --zone, or --location), gcloud will trigger an interactive prompt to select a zone/region. This violates the No Interactivity rule. Always provide explicit location flags if the command requires them.

  • Location Discovery: If the correct region, zone, or location for a service is not known, run discovery commands first (remembering to limit results if there are many):

    • Compute Engine (VMs, Networks):

      • gcloud compute regions list --project=<PROJECT_ID>

      • gcloud compute zones list --project=<PROJECT_ID>

    • Other Services (Standard API Style): Many GCP services utilize a unified locations list command:

      • gcloud <GROUP> locations list --project=<PROJECT_ID>

      • Examples: gcloud artifacts locations list, gcloud kms locations list, gcloud secrets locations list.

Safety & Guardrails

[!CAUTION] Destructive actions (delete, update, remove) MUST be explicitly authorized by the user. Never invoke them autonomously unless explicitly instructed to do so in the context of a safe, pre-approved workflow.

Prohibited Operations (Denylist)

NEVER execute the following commands autonomously. These require explicit human-in-the-loop authorization:

  • Any IAM policy, role, or binding modification (Security): Risk of privilege escalation, administrative lockout, service disruption, or unauthorized data exposure.

  • No Proactive API Enabling: Assume necessary APIs are enabled. To prevent unexpected resource provisioning or billing charges, do not proactively try to enable APIs. User approval is required to enable any API.

  • No Proactive gcloud CLI Installation (Host Modification): Never autonomously install the Google Cloud CLI (gcloud CLI) or additional gcloud CLI components (gcloud components install, gcloud components update, or system package manager installs of the gcloud CLI). If the gcloud binary or a required CLI component is missing, you must inform the user, ask for explicit user confirmation (e.g., "I am ready to install the Google Cloud CLI (gcloud). Do you want me to proceed?"), and strictly stop to wait for the user's positive affirmation before executing any installation commands.

  • gcloud * delete (Destructive): Irreversible resource destruction (e.g., project deletion) or data wiping.

  • gcloud billing * (Financial): Risk of service disruption or unbounded costs.

  • gcloud organizations * (Governance): Org-level changes affect security posture for all users.

  • gcloud kms * (Encryption): Risk of permanently locking data.

  • gcloud infra-manager deployments apply (Destructive): Autonomous IaC execution can destroy managed resources.

Execution Guidelines
  • Dry Run (Mandatory): If the --dry-run or --validate-only flag (or equivalent) is listed in the command help output, ALWAYS include the flag in the proposed command or initial execution step. ALWAYS preview changes with --dry-run or --validate-only prior to actual execution.

  • Long Running Operations: For commands that support it, the --async flag is highly recommended for long-running operations to avoid blocking the agentic flow. Note that not every command has an --async flag. For commands that return an operation ID (whether via --async or by default), operation status must be polled for completion, if needed for the next step.

  • Non-Interactive Flag (--quiet): Include --quiet (or -q) on all proposed or executed commands to guarantee non-interactive execution without waiting for TTY confirmation prompts.

Structured Workflows

Discovery Workflow

When asked to perform a task on a service that is unfamiliar:

  1. Invoke Help: Call gcloud help <COMMAND> on the target leaf command prior to execution.

  2. Traverse Command Tree: Run help on command groups (e.g., gcloud help compute or gcloud help) to discover available subgroups and commands if the exact command is unknown.

  3. Discover Schema: Run gcloud <GROUP> <RESOURCE> list --limit=1 --format=json to inspect JSON keys before constructing filters or projections. DO NOT execute unconstrained list commands without scoping flags (e.g., --limit=1) to prevent context window exhaustion.

  4. Enforce Data Reduction: Include data reduction flags (--limit, --filter, --format) on all command executions.

Quick Reference / Cheat Sheet

TaskCommand Template
Discover Schemagcloud <GROUP> <RESOURCE> list --limit=1 --format=json
Filtered Listgcloud <GROUP> <RESOURCE> list --filter="status:RUNNING"
Specific Columnsgcloud <GROUP> <RESOURCE> list --format="json(name, id)"
Learn Filtersgcloud topic filters
Learn Formatsgcloud topic formats
Learn Projectionsgcloud topic projections
Asynchronous Opgcloud <COMMAND> --async
Check Operationgcloud operations describe <OPERATION_ID>
Common commandsgcloud cheat-sheet
List Regions (GCE)gcloud compute regions list --project=<PROJECT_ID>
List Zones (GCE)gcloud compute zones list --project=<PROJECT_ID>
List Locationsgcloud <GROUP> locations list --project=<PROJECT_ID>

Refer to the gcloud CLI Scripting Guide for guidance on using the gcloud CLI in automation.

Reference Directory

  • CLI Usage: Platform installation, authentication methods (interactive, headless, ADC, service account keys, impersonation), and local configuration management.

  • MCP Usage: Using the Cloud CLI remote MCP server (run_gcloud_command), project parameter scoping, input files, and execution guidelines.

  • Usage Attribution: Standardized usage attribution tags and multi-skill precedence rules for gcloud CLI, REST API, and MCP requests.

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an MCP server

Security

No issues found by our static scan· No issues found

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Attribution and licence

Published by
Google
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Google (Official)
Source
google/skills · skills/cloud/gcloud · commit 2194d3b
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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