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Task Packs

Marketing Foundation

Compact two-skill starter: clarify positioning and choose a lead magnet. Use Marketing Launch for the broader eight-skill go-to-market workflow.

Marketing Launch

Build an evidence-led marketing plan from ICP and competition through positioning, campaigns, growth and measurement.

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Diagnose architecture and context, plan agent-team responsibilities, then organize project context and session handoffs. Memory and cost-runtime reviews remain outside this pack.

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Review the journey from landing page and lead capture through registration, first value and transparent upgrades.

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Plan a campaign, draft its channel content and review the work against actual brand guidance.

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Prioritize an editorial roadmap and plan how to launch and distribute it across suitable channels.

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Understand customer needs, compare competitors and plan a community around real member value.

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Choose a relevant lead magnet, then draft a permission-based nurture journey with entry, suppression and exit rules.

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Define the API contract, then plan how to observe its latency, failures and retries. Guidance and checklist; no production changes.

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Profile a dataset, choose and interpret statistical methods, then validate calculations and conclusions before sharing.

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Define the target account, prioritize buying signals, plan a human LinkedIn engagement routine and prepare evidence-led responses to buyer concerns.

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31 · Engineering · Cloud Operations

Google Cloud Run Basics

Manages Cloud Run services, jobs, and worker pools. Use when you need to deploy applications

engineeringcloud-operationsgoogle-cloudcloud-run-basics

Google Cloud Run Basics · Original SKILL.md

---
name: cloud-run-basics
metadata:
version: "1.0.0"
category: Serverless
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).
---

# 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
```

1. If you are under a domain restriction organization policy [restricting](https://docs.cloud.google.com/organization-policy/restrict-domains.md.txt)
unauthenticated invocations for your project, you will need to access your
deployed service as described under [Testing private
services](https://docs.cloud.google.com/run/docs/triggering/https-request.md.txt).

### 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](https://docs.cloud.google.com/artifact-registry/docs/overview.md.txt), or
[Docker Hub](https://hub.docker.com/). Google recommends the use of Artifact
Registry since Docker Hub images are
[cached](https://docs.cloud.google.com/artifact-registry/docs/pull-cached-dockerhub-images.md.txt)
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](https://docs.cloud.google.com/artifact-registry/docs/repositories/remote-repo.md.txt).

You should only consider [Docker Hub](https://hub.docker.com/) for deploying
popular container images such as [Docker Official
Images](https://docs.docker.com/docker-hub/official_images/) or [Docker
Sponsored OSS images](https://docs.docker.com/docker-hub/dsos-program/). For
higher availability, Google recommends deploying these Docker Hub images using
an [Artifact Registry remote
repository](https://docs.cloud.google.com/artifact-registry/docs/repositories/remote-repo.md.txt).

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](https://docs.cloud.google.com/docs/buildpacks/base-images.md.txt).

* 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](https://docs.cloud.google.com/run/docs/configuring/services/runtime-base-images.md.txt)
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](https://docs.cloud.google.com/run/docs/deploying-source-code.md.txt).

## 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`](https://docs.cloud.google.com/sdk/gcloud/reference/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.

## 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

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](https://docs.cloud.google.com/docs/buildpacks/overview.md.txt) 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](references/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](references/core-concepts.md): Services vs. Jobs vs.
Worker pools, resource model, and auto-scaling behavior for services.

- [CLI Usage](references/cli-usage.md): Essential `gcloud run` commands for
deployment and management.

- [Client Libraries](references/client-library-usage.md): Using Google
Cloud client libraries to interact with Cloud Run.

- [MCP Usage](references/mcp-usage.md): Using the Cloud Run remote MCP
server.

- [Infrastructure as Code](references/iac-usage.md): Terraform examples for
services, jobs, worker pools, and IAM bindings.

- [IAM & Security](references/iam-security.md): Roles, service identities,
and ingress/egress controls.

- [Networking Best Practices & Cost Optimization](references/networking.md): 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.*

When to use

Manages Cloud Run services, jobs, and worker pools. Use when you need to deploy applications

What you get

Complete original Google guidance with source attribution and declared execution limits.

How it works

Use supplied evidence to plan, review or explain the relevant workflow within authorized scope.

Requirements

Authorized project context, resource scope, current configuration evidence and a named decision owner.

Delivery and review notes

The original is official Apache-2.0 guidance. It may show cloud commands, deployment examples or credential placeholders; those are not run, configured, installed, or authorized by loading this catalog entry. Verify current provider documentation and resource-owner approval before external changes.

Starting prompt

Use Google Cloud Run Basics for [TASK]. Start from authorized project context and supplied evidence. Identify unknowns and propose changes for review; do not run commands, connect accounts, deploy, transfer data, or incur charges from this text alone.

Not for

Automatic cloud execution, credential handling, account access, deployments, destructive changes, or claims that examples are configured and verified.

What M11 added

M11 added German routing, integrity tracking and a strict no-execution boundary. Original attribution: Google (google/skills repository). The original is official Apache-2.0 guidance. It may show cloud commands, deployment examples or credential placeholders; those are not run, configured, installed, or authorized by loading this catalog entry. Verify current provider documentation and resource-owner approval before external changes.

Original authorship remains with google/skills · Original source ↗

Original license & copyright

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