Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add hamzaPixl/pixl-ai --skill docker-cloudrungit clone --depth 1 https://github.com/hamzaPixl/pixl-aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/hamzapixl/pixl-ai/docker-cloudrun)<a href="https://agentmods.dev/skills/hamzapixl/pixl-ai/docker-cloudrun"><img src="https://agentmods.dev/badge/skills/hamzapixl/pixl-ai/docker-cloudrun/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hamzapixl/pixl-ai/docker-cloudrun"><img src="https://agentmods.dev/badge/skills/hamzapixl/pixl-ai/docker-cloudrun.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00043 | $0.00665 |
| Opus 5.5 | $0.00017 | $0.00266 |
| Sonnet 5.5 | $0.00009 | $0.00133 |
| Haiku 4.5 | $0.00004 | $0.00067 |
Grade A, and why
docker-cloudrun scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Containerizes an application with Docker and creates a complete Cloud Run deployment pipeline including multi-stage Dockerfile, docker-compose for local development, and GitHub Actions CI/CD.
Why multi-stage builds: Separating build and runtime stages reduces image size by 60-80% (no compiler, dev deps) and shrinks the attack surface. Running as a non-root user prevents container escape exploits from gaining host-level access.
Required References
Before starting, read these files:
references/devops/docker-best-practices.md— Dockerfile patterns, layer caching, securityreferences/devops/ci-cd-patterns.md— CI/CD pipeline patterns and deployment strategies
Step 1: Discovery
- Detect application language, framework, and build system
- Identify environment variables and secrets needed
- Check for existing Docker configuration
- Determine Cloud Run requirements (CPU, memory, scaling)
Step 2: Dockerfile
- Create multi-stage Dockerfile (deps → build → production)
- Optimize for layer caching
- Pin base image versions
- Run as non-root user
- Add
.dockerignore
Step 3: Docker Compose
- Create
docker-compose.yamlfor local development - Add service dependencies (database, Redis, etc.)
- Configure health checks
- Mount source for hot reload in development
Step 4: Cloud Run Deployment
- Create GitHub Actions workflow for Cloud Run
- Configure GCP authentication
- Set up environment variables and secrets
- Configure auto-scaling, min/max instances
- Add staging and production environments
Gotchas
- Cloud Run requires the container to listen on the
PORTenvironment variable (not hardcoded ports) — the service will fail health checks and never become healthy if the port is hardcoded - Multi-stage builds must copy only production dependencies — copying
devDependenciesor test files into the runtime stage bloats the image and leaks build tooling - Cloud Run cold starts are proportional to image size — keep the container image under 500MB and avoid heavy initialization logic (eager DB connections, large file reads at startup)
.dockerignoremust exclude.git,node_modules,.env, and test directories — a missing or incomplete.dockerignorecan bloat images 10x and leak secrets into layers- GitHub Actions needs Workload Identity Federation for keyless auth to GCP — service account JSON keys are a security risk and should not be stored as repository secrets
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 66 lines · 43 tokens per session scan A 847ad573eb0a
docker-cloudrun is a skill published in the GitHub repository hamzaPixl/pixl-ai (2 stars, last pushed 6mo ago), licensed MIT. It adds 43 tokens to every session and 665 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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