gcp-cloud-run

The full skill.

Original instructions from the publisher’s SKILL.md

# GCP Cloud Run

Specialized skill for building production-ready serverless applications on GCP.
Covers Cloud Run services (containerized), Cloud Run Functions (event-driven),
cold start optimization, and event-driven architecture with Pub/Sub.

## Detailed Guide

Read [the detailed guide](references/detailed-guide.md) before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.

## Calculate memory including /tmp usage

```yaml
# cloudbuild.yaml
steps:
  - name: 'gcr.io/cloud-builders/gcloud'
    args:
      - 'run'
      - 'deploy'
      - 'my-service'
      - '--memory=1Gi'  # Include /tmp overhead
      - '--image=gcr.io/$PROJECT_ID/my-service'
```

## Monitor memory usage

```python
import psutil
import logging

def log_memory():
    memory = psutil.virtual_memory()
    logging.info(f"Memory: {memory.percent}% used, "
                f"{memory.available / 1024 / 1024:.0f}MB available")
```

### Concurrency=1 Causes Scaling Bottlenecks

Severity: HIGH

Situation: Setting concurrency to 1 for request isolation

Symptoms:
Auto-scaling creates many container instances.
High latency during traffic spikes.
Increased cold starts.
Higher costs from more instances.

Why this breaks:
Setting concurrency to 1 means each container handles only one
request at a time. During traffic spikes:

- 100 concurrent requests = 100 container instances
- Each instance has cold start overhead
- More instances = higher costs
- Scaling takes time, requests queue up

This should only be used when:
- Processing is truly single-threaded
- Memory-heavy per-request processing
- Using thread-unsafe libraries

Recommended fix:

## When to Use
Use this skill when the request clearly matches the capabilities and patterns described above.

## Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.