Official agent skill

Cloud Run Basics

by google in google/skills

Manages Cloud Run services, jobs, and worker pools. An agent skill from google/skills.

OfficialApache-2.0Auto-check passedProductivity & Automation

Install Cloud Run Basics

skills CLI
$ npx skills add google/skills --skill cloud-run-basics -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install google/skills cloud-run-basics --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/cloud/cloud-run-basics .claude/skills/cloud-run-basics && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
cloud-run-basics
GitHub stars
21k
Used in
1 other repo
Token cost
~4k tokens
SKILL.md length
1,788 words
Files
8 (incl. references)
Skills in repo
147
Repo updated
First seen
Licence
Apache-2.0

At a glance

Manages Cloud Run services, jobs, and worker pools. An agent skill from google/skills.

  • Works in 3 steps: Services: Responds to HTTP requests sent… → Jobs: Executes parallelizable tasks that… → Worker pools: Handles always-on…
  • You need to deploy applications responding to HTTP requests (services)
  • SKILL.md covers Prerequisites, Deploy a Cloud Run service, Create and execute a Cloud Run… and Deploy a worker pool, plus 1 more section
  • Calls gcloud

What it does

Cloud Run Basics is an agent skill from google/skills, published by the product's own GitHub organization. Manages Cloud Run services, jobs, and worker pools. Use when you need to deploy applications responding to HTTP requests (services), run event-triggered or scheduled tasks (jobs), or handle always-on pull-based background processing (worker pools).

Its SKILL.md is about 4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including reference files (for example `references/cli-usage.md`, `references/client-library-usage.md` and `references/core-concepts.md`).

It sits in Productivity & Automation, covering Scheduled and recurring tasks and Containers. It works with Cloud Run. The repository describes itself as: Agent Skills for Google products and technologies. The licence is Apache-2.0.

When your agent uses it

  • You need to deploy applications responding to HTTP requests (services)
  • Run event-triggered
  • Scheduled tasks (jobs)
  • Handle always-on pull-based background processing (worker pools)

Example prompts

  • “Use the cloud-run-basics skill to manage Cloud Run services, jobs, and worker pools. An agent skill from google/skills”
  • “/cloud-run-basics”

Requirements

  • Docker

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. Services: Responds to HTTP requests sent to a unique and stable
  2. Jobs: Executes parallelizable tasks that are executed manually, or on a
  3. Worker pools: Handles always-on background workloads such as pull-based

What it can do on your machine

Read from SKILL.md and the folder at commit 7d97937. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • gcloud

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • docs.cloud.google.com
    • hub.docker.com
    • docs.docker.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Cloud Run Basics loads about 4k tokens when it runs, and up to ~11k if it reads all its reference files. Until then it costs about 66 tokens; SKILL.md has 1,788 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~66
When it runs · the whole SKILL.md, loaded when a task matches
~4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~11k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from google/skills at commit 7d97937, republished under its Apache-2.0 licence (© google). 1,788 words, ~3,968 tokens.

Download SKILL.mdSave it as .claude/skills/cloud-run-basics/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
cloud-run-basics
description
Manages Cloud Run services, jobs, and worker pools. Use when you need to deploy applications responding to HTTP requests (services), run event-triggered or scheduled tasks (jobs), or handle always-on pull-based background processing (worker pools).
metadata.version
1.0.0
metadata.category
Serverless

Cloud Run Basics

Cloud Run is a fully managed application platform for running your code, function, or container on top of Google's highly scalable infrastructure. It abstracts away infrastructure management, providing three primary resource types:

  1. Services: Responds to HTTP requests sent to a unique and stable endpoint, using stateless instances that autoscale based on a variety of key metrics, also responds to events and functions.
  2. Jobs: Executes parallelizable tasks that are executed manually, or on a schedule, and run to completion.
  3. Worker pools: Handles always-on background workloads such as pull-based workloads, for example, Kafka consumers, Pub/Sub pull queues, or RabbitMQ consumers.

Prerequisites

  1. Enable the Cloud Run Admin API and Cloud Build APIs:

    bash
    gcloud services enable run.googleapis.com cloudbuild.googleapis.com --quiet
  2. If you are under a domain restriction organization policy restricting unauthenticated invocations for your project, you will need to access your deployed service as described under Testing private services.

Required roles

You need the following roles to deploy your Cloud Run resource:

  • Cloud Run Admin (roles/run.admin) on the project
  • Cloud Run Source Developer (roles/run.sourceDeveloper) on the project
  • Service Account User (roles/iam.serviceAccountUser) on the service identity
  • Logs Viewer (roles/logging.viewer) on the project

Cloud Build automatically uses the Compute Engine default service account as the default Cloud Build service account to build your source code and Cloud Run resource, unless you override this behavior.

For Cloud Build to build your sources, grant the Cloud Build service account the Cloud Run Builder (roles/run.builder) role on your project:

bash
gcloud projects add-iam-policy-binding PROJECT_ID \
    --member=serviceAccount:SERVICE_ACCOUNT_EMAIL_ADDRESS \
    --role=roles/run.builder \
    --quiet

Replace PROJECT_ID with your Google Cloud project ID and SERVICE_ACCOUNT_EMAIL_ADDRESS with the email address of the Cloud Build service account.

Deploy a Cloud Run service

You can deploy your service to Cloud Run by using a container image or deploy directly from source code using a single Google Cloud CLI command.

CRITICAL RULE: Any deployed code MUST listen on 0.0.0.0 (not 127.0.0.1) and use the injected $PORT environment variable (defaults to 8080), or it will crash on boot.

Deploy a container image to Cloud Run

Cloud Run imports your container image during deployment. Cloud Run keeps this copy of the container image as long as it is used by a serving revision. Container images are not pulled from their container repository when a new Cloud Run instance is started.

Supported container images

You can directly use container images stored in the Artifact Registry, or Docker Hub. Google recommends the use of Artifact Registry since Docker Hub images are cached for up to one hour.

You can use container images from other public or private registries (like JFrog Artifactory, Nexus, or GitHub Container Registry), by setting up an Artifact Registry remote repository.

You should only consider Docker Hub for deploying popular container images such as Docker Official Images or Docker Sponsored OSS images. For higher availability, Google recommends deploying these Docker Hub images using an Artifact Registry remote repository.

To deploy a container image, run the following command:

bash
    gcloud run deploy SERVICE_NAME \
        --image IMAGE_URL \
        --region us-central1 \
        --allow-unauthenticated \
        --quiet

Replace the following:

  • SERVICE_NAME: the name of the service you want to deploy to. Service names must be 49 characters or less and must be unique per region and project. If the service does not exist yet, this command creates the service during the deployment. You can omit this parameter entirely, but you will be prompted for the service name if you omit it.
  • IMAGE_URL: a reference to the container image, for example, us-docker.pkg.dev/cloudrun/container/hello:latest. If you use Artifact Registry, the repository REPO_NAME must already be created. The URL follows the format of LOCATION-docker.pkg.dev/PROJECT_ID/REPO_NAME/PATH:TAG. Note that if you don't supply the --image flag, the deploy command will attempt to deploy from source code.
Deploy from source code

There are two different ways to deploy your service from source:

  • Deploy from source with build (default): This option uses Google Cloud's buildpacks and Cloud Build to automatically build container images from your source code without having to install Docker on your machine or set up buildpacks or Cloud Build. By default, Cloud Run uses the default machine type provided by Cloud Build.

    • To deploy from source with automatic base image updates enabled, run the following command:

      bash
      gcloud run deploy SERVICE_NAME --source . \
      --base-image BASE_IMAGE \
      --automatic-updates \
      --quiet

      Cloud Run only supports automatic base images that use Google Cloud's buildpacks base images.

      • To deploy from source using a Dockerfile, run the following command:
      bash
       gcloud run deploy SERVICE_NAME --source . --quiet
      When you provide a Dockerfile, Cloud Build runs it in the cloud, and
      deploys the service.
  • Deploy from source without build (Preview): This option deploys artifacts directly to Cloud Run, bypassing the Cloud Build step. This allows for rapid deployment times. To deploy from source without build, run the following command:

    bash
    gcloud beta run deploy SERVICE_NAME \
     --source APPLICATION_PATH \
     --no-build \
     --base-image=BASE_IMAGE \
     --command=COMMAND \
     --args=ARG \
     --quiet

    Replace the following:

    • SERVICE_NAME: the name of your Cloud Run service.
    • APPLICATION_PATH: the location of your application on the local file system.
    • BASE_IMAGE: the runtime base image you want to use for your application. For example, us-central1-docker.pkg.dev/serverless-runtimes/google-24-full/runtimes/nodejs24. You can also deploy a pre-compiled binary without configuring additional language-specific runtime components using the OS only base image, such as osonly24.
    • COMMAND: the command that the container starts up with.
    • ARG: an argument you send to the container command. If you use multiple arguments, specify each on its own line.

    For examples on deploying from source without build, see Examples of deploying from source without build.

Create and execute a Cloud Run job

To create a new job, run the following command:

bash
gcloud run jobs create JOB_NAME --image IMAGE_URL OPTIONS --quiet

Alternatively, use the deploy command:

bash
gcloud run jobs deploy JOB_NAME --image IMAGE_URL OPTIONS --quiet

Replace the following:

  • JOB_NAME: the name of the job you want to create. If you omit this parameter, you will be prompted for the job name when you run the command.

  • IMAGE_URL: a reference to the container image—for example, us-docker.pkg.dev/cloudrun/container/job:latest.

  • Optionally, replace OPTIONS with any of the following flags:

    • --tasks: Accepts integers greater or equal to 1. Defaults to 1; maximum is 10,000. Each task is provided the environment variables CLOUD_RUN_TASK_INDEX with a value between 0 and the number of tasks minus 1, along with CLOUD_RUN_TASK_COUNT, which is the number of tasks.
    • --max-retries: The number of times a failed task is retried. Once any task fails beyond this limit, the entire job is marked as failed. For example, if set to 1, a failed task will be retried once, for a total of two attempts. The default is 3. Accepts integers from 0 to 10.
    • --task-timeout: Accepts a duration like "2s". Defaults to 10 minutes; maximum is 168 hours (7 days). For tasks using GPUs, the maximum available timeout is 1 hour.
    • --parallelism: The maximum number of tasks that can execute in parallel. By default, tasks will be started as quickly as possible in parallel.
    • --execute-now: If set, immediately after the job is created, a job execution is started. Equivalent to calling gcloud run jobs create followed by gcloud run jobs execute.

    In addition to these preceding options, you also specify more configuration such as environment variables or memory limits.

For a full list of available options when creating a job, refer to the gcloud run jobs create command line documentation.

Wait for the job creation to finish. You'll see a success message upon a successful completion.

To execute an existing job, run the following command:

bash
gcloud run jobs execute JOB_NAME --quiet

If you want the command to wait until the execution completes, run the following command:

bash
gcloud run jobs execute JOB_NAME --wait --region=REGION --quiet

Replace the following:

  • JOB_NAME: the name of the job.
  • REGION: the region in which the resource can be found. For example, europe-west1. Alternatively, set the run/region property.
Show full SKILL.md (551 more words)Show less

Deploy a worker pool

You can deploy a Cloud Run worker pool using container images or deploy directly from the source.

Deploy a container image

You can specify a container image with a tag (for example, us-docker.pkg.dev/my-project/container/my-image:latest) or with an exact digest (for example, us-docker.pkg.dev/my-project/container/my-image@sha256:41f34ab970ee...).

Supported container images

You can directly use container images stored in the Artifact Registry, or Docker Hub. Google recommends the use of Artifact Registry since Docker Hub images are cached for up to one hour.

You can use container images from other public or private registries (like JFrog Artifactory, Nexus, or GitHub Container Registry), by setting up an Artifact Registry remote repository.

You should only consider Docker Hub for deploying popular container images such as Docker Official Images or Docker Sponsored OSS images. For higher availability, Google recommends deploying these Docker Hub images using an Artifact Registry remote repository.

To deploy a container image, run the following command:

bash
gcloud run worker-pools deploy WORKER_POOL_NAME --image IMAGE_URL --quiet

Replace the following:

  • WORKER_POOL_NAME: the name of the worker pool you want to deploy to. If the worker pool does not exist yet, this command creates the worker pool during the deployment. You can omit this parameter entirely, but you will be prompted for the worker pool name if you omit it.

  • IMAGE_URL: a reference to the container image that contains the worker pool, such as us-docker.pkg.dev/cloudrun/container/worker-pool:latest. Note that if you don't supply the --image flag, the deploy command attempts to deploy from source code.

Wait for the deployment to finish. Upon successful completion, Cloud Run displays a success message along with the revision information about the deployed worker pool.

Deploy a worker pool from source

You can deploy a new worker pool or worker pool revision to Cloud Run directly from source code using a single gcloud CLI command, gcloud run worker-pools deploy with the --source flag.

The deploy command defaults to source deployment if you don't supply the --image or --source flags.

Behind the scenes, this command uses Google Cloud's buildpacks and Cloud Build to automatically build container images from your source code without having to install Docker on your machine or set up buildpacks or Cloud Build. By default, Cloud Run uses the default machine type provided by Cloud Build.

To deploy a worker pool from source, run the following command:

bash
gcloud run worker-pools deploy WORKER_POOL_NAME --source . --quiet

Replace WORKER_POOL_NAME with the name you want for your worker pool.

What to do if a deployment fails:
  1. IAM/Permission Error: Read iam-security.md.
  2. Crash on Boot / Healthcheck failed: Fetch the logs immediately using gcloud logging read "resource.labels.service_name=SERVICE_NAME" --limit=20 to find the exact runtime error.
  3. Native Dependency Error (Node/Python): If using --no-build, switch to --source . (Buildpacks) to compile native extensions properly for Linux.

Reference Directory

  • Core Concepts: Services vs. Jobs vs. Worker pools, resource model, and auto-scaling behavior for services.

  • CLI Usage: Essential gcloud run commands for deployment and management.

  • Client Libraries: Using Google Cloud client libraries to interact with Cloud Run.

  • MCP Usage: Using the Cloud Run remote MCP server.

  • Infrastructure as Code: Terraform examples for services, jobs, worker pools, and IAM bindings.

  • IAM & Security: Roles, service identities, and ingress/egress controls.

  • Networking Best Practices & Cost Optimization: Cost optimization strategies, Direct VPC egress, IP address and port exhaustion strategies, performance throughput tuning, and MTU settings.

If you need product information not found in these references, use the Developer Knowledge MCP server search_documents tool.

© google, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (references) in skills/cloud/cloud-run-basics of google/skills.

  • SKILL.md
  • references/cli-usage.md
  • references/client-library-usage.md
  • references/core-concepts.md
  • references/iac-usage.md
  • references/iam-security.md
  • references/mcp-usage.md
  • references/networking.md

Open the folder on GitHubat commit 7d97937

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in google/skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Cloud Run Basics compared with similar skills
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Works with

Questions about Cloud Run Basics

What does Cloud Run Basics do?

Manages Cloud Run services, jobs, and worker pools. An agent skill from google/skills. Cloud Run Basics is an agent skill from google/skills, published by the product's own GitHub organization. Manages Cloud Run services, jobs, and worker pools.

When should I use Cloud Run Basics?

Cloud Run Basics fits situations like: you need to deploy applications responding to HTTP requests (services); run event-triggered; scheduled tasks (jobs); handle always-on pull-based background processing (worker pools).

How do I install Cloud Run Basics in Claude Code?

Run `npx skills add google/skills --skill cloud-run-basics -a claude-code`. Or copy the skill folder (skills/cloud/cloud-run-basics in google/skills) into .claude/skills/cloud-run-basics in your project. Claude Code loads it when a task matches its description.

How do I install Cloud Run Basics in Codex?

Run `npx skills add google/skills --skill cloud-run-basics -a codex`. Or copy the skill folder (skills/cloud/cloud-run-basics in google/skills) into .agents/skills/cloud-run-basics in your project. Codex loads it when a task matches its description.

Can I use Cloud Run Basics in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add google/skills --skill cloud-run-basics -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cloud-run-basics, .gemini/skills/cloud-run-basics, .github/skills/cloud-run-basics and .opencode/skills/cloud-run-basics in your project.

What does Cloud Run Basics need to run?

Going by SKILL.md and its folder, Cloud Run Basics needs the command-line tools its instructions call (gcloud). Our summary lists: Docker.

Does Cloud Run Basics access the network?

SKILL.md names 3 domains. As links in the text: docs.cloud.google.com, hub.docker.com and docs.docker.com. This is read from the text; nothing was executed.

Is Cloud Run Basics safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Cloud Run Basics use?

Cloud Run Basics is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Cloud Run Basics use?

About 4k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 7k tokens, read only when the agent opens those files.

What are the alternatives to Cloud Run Basics?

Skills that share tags, products or a category with Cloud Run Basics: Copaw Ops (chujianyun/skills, 740 stars), GitHub Actions Creator (FNOSP/FlyNarwhal, 495 stars), Cron Ops (czl9707/build-your-own-openclaw, 1.9k stars) and Devops (nicepkg/auto-company, 192 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Cloud Run Basics?

google (a GitHub organization, an official publisher) maintains it in google/skills, which has 21,032 GitHub stars. The repository holds 147 skills in this directory. The repository was last updated on October 8, 2026.

Source: google/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.