railway-tools

Use when deploying to Railway, editing railway.toml, railway.json, Procfile, configuring Railway services, databases, workers, environment variables, or troubleshooting Railway deployments.

9 stars

Best use case

railway-tools is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Use when deploying to Railway, editing railway.toml, railway.json, Procfile, configuring Railway services, databases, workers, environment variables, or troubleshooting Railway deployments.

Teams using railway-tools should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/railway-tools/SKILL.md --create-dirs "https://raw.githubusercontent.com/cofin/flow/main/plugins/flow/skills/railway-tools/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/railway-tools/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How railway-tools Compares

Feature / Agentrailway-toolsStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Use when deploying to Railway, editing railway.toml, railway.json, Procfile, configuring Railway services, databases, workers, environment variables, or troubleshooting Railway deployments.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Railway Deployment (Flow Tools)

<workflow>

## 🚀 Official Railway Skills (Highly Recommended)

For full project management, deployment automation, and service orchestration, we highly recommend installing the official Railway agent skills:

- **use-railway**: Master skill for setting up projects, services, and handling deployments.

**Installation:**

```bash
npx skills add railwayapp/railway-skills
```

## Supplemental Patterns

The patterns below provide additional context for Flow-specific multi-service architectures and serverless constraints.

### Serverless / App Sleeping

Railway's serverless feature puts services to sleep after 10 minutes of no **outbound** traffic.

**When to disable serverless (`sleepApplication: false`):**

- Background workers (Celery, SAQ, RQ, Sidekiq)
- Queue processors
- Cron services
- Any service without an HTTP endpoint

### Multi-Service Architecture (Web + Worker)

For applications with background task processing, use distinct configuration files (e.g., `railway.app.json` and `railway.worker.json`) to manage different runtime requirements.

</workflow>

<guardrails>
## Guardrails

- **Disable Serverless for Workers:** Always set `sleepApplication: false` for background worker services.
- **Use Variable References:** Prefer `${{Service.VARIABLE}}` syntax (e.g., `${{Postgres.DATABASE_URL}}`) instead of hardcoding values.
- **Dynamic Port Binding:** Never hardcode the port. Always reference `${{PORT}}` for application port injection.
</guardrails>

<validation>
## Validation

- **Verify `sleepApplication: false` for Workers:** Ensure any background worker has `sleepApplication` explicitly set to `false`.
- **Check PORT Dynamic Reference:** Confirm that start commands and environment variables reference `${{PORT}}`.
</validation>

<example>
## Multi-Service Worker Example

**Configuration (`railway.worker.json`):**

```json
{
  "service": {
    "name": "worker",
    "sleepApplication": false
  },
  "deploy": {
    "startCommand": "python -m saq my_app.tasks.worker --workers 4"
  }
}
```

</example>

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