GCP Cloud Run Documentation Generator

Generate comprehensive, best-practice documentation for Google Cloud Run deployments including services, jobs, and worker pools.

What This Skill Does

This skill helps you create detailed documentation for Google Cloud Run resources by analyzing your codebase, configuration files, and deployment patterns. It generates documentation covering:

Instructions

When the user requests Cloud Run documentation generation:

1. **Discover Cloud Run Resources**

- Search for Cloud Run configuration files: `cloudbuild.yaml`, `service.yaml`, `.gcloudignore`, Dockerfiles

- Look for deployment scripts: `gcloud run deploy`, Terraform/Pulumi configs, GitHub Actions workflows

- Identify the resource type: service (HTTPS endpoint), job (batch tasks), or worker pool (background work)

- Check for `app.yaml`, `Procfile`, or language-specific entry points

2. **Analyze Service Architecture**

- Determine the programming language and framework (Go, Node.js, Python, Java, .NET, Ruby, etc.)

- Identify if using source-based deployment or container images

- Check for multi-region deployments, custom domains, CDN configurations

- Review autoscaling settings: CPU/memory limits, max concurrent requests, min/max instances

- Document GPU configurations if present

3. **Review Configuration Patterns**

- **Capacity**: Memory limits, CPU allocation, GPU settings

- **Environment**: Container port/entrypoint, environment variables, secrets management

- **Networking**: VPC egress (Direct VPC/connectors), static IPs, ingress restrictions

- **Identity**: Service accounts, IAM roles, authentication methods

- **Storage**: Volume mounts (Cloud Storage, NFS, in-memory, CIFS/SMB)

- **Health checks**: Startup/liveness/readiness probes

4. **Document Integration Points**

- Identify connections to other GCP services: Pub/Sub, Cloud Storage, Firestore, BigQuery, etc.

- Check for event triggers via Eventarc

- Look for scheduled execution (Cloud Scheduler)

- Document Workflows or Cloud Functions integrations

- Note any gRPC, WebSocket, or HTTP/2 usage

5. **Security and Access Control**

- Document IAM policies and service identity

- Check for Cloud Armor, VPC Service Controls, Binary Authorization

- Review authentication patterns: public access, custom audiences, end-user auth, service-to-service

- Note encryption configurations (CMEK)

- Document any Cloud Identity-Aware Proxy (IAP) usage

6. **Generate Documentation Structure**

Create a markdown document with these sections:

```markdown

[Service/Job/Worker Pool Name] - Cloud Run Documentation

Overview

Architecture

Configuration

Container Settings

Networking

Storage and Volumes

Security

Integration with GCP Services

Deployment

Prerequisites

Deployment Commands

```bash

Include actual gcloud commands, Terraform, or CI/CD configs

```

Environment-Specific Configuration

Monitoring and Observability

Troubleshooting

Best Practices

Maintenance

References

```

7. **Add Language-Specific Optimizations**

- For Python: Include FastAPI/Flask/Streamlit optimizations

- For Node.js: Include Next.js/Nuxt.js/Angular SSR patterns

- For Java: Include Spring Boot and Kotlin best practices

- For Go: Include concurrency and memory management tips

8. **Include AI/ML Considerations (if applicable)**

- GPU configuration and optimization

- Model serving patterns (Ollama, vLLM, Hugging Face)

- Inference optimization strategies

- MCP server deployment patterns

Constraints

Example Usage

**User:** "Generate Cloud Run documentation for this service"

**AI Response:**

1. Searches for Cloud Run configs, Dockerfiles, deployment scripts

2. Identifies it's a Python FastAPI service with Direct VPC egress

3. Analyzes scaling settings, environment variables, secrets

4. Documents Pub/Sub triggers and Cloud Storage integrations

5. Creates comprehensive markdown documentation with all sections

6. Highlights that the service uses GPUs for ML inference and includes optimization tips

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**Generated with the GCP Cloud Run Documentation Generator skill**