What is railway-service-management?

This Claude skill provides specialized expertise for managing multi-environment Railway deployments, offering comprehensive support for infrastructure setup, service configuration, and full lifecycle management. It enables developers to automate Railway CLI operations, optimize build processes using Railpack or Docker, and implement robust CI/CD workflows for production and preview environments.

When should I use railway-service-management?

railway-service-management is useful in the following scenarios: • Automating multi-environment infrastructure setup, including production, staging, and ephemeral PR environments for seamless development cycles. • Streamlining deployment workflows and build configurations using Railpack, NIXPACKS, or custom Dockerfiles to ensure optimized service delivery. • Managing and troubleshooting Railway services through CLI-driven log monitoring, status tracking, and automated health check analysis. • Implementing secure secrets management and environment variable synchronization across different project stages and team members. • Optimizing cloud resource usage and costs through efficient service scaling, volume management, and resource configuration.

railway-service-management – AI Agent Skills

name	railway-service-management
description	Specialized knowledge for managing multi-environment Railway deployments. Use when: (1) Setting up Railway projects/environments/services, (2) Configuring deployments and builds (NIXPACKS/Railpack/Dockerfile), (3) Managing Railway CLI operations and logs, (4) Implementing PR environments or branch-based workflows, (5) Troubleshooting deployment failures or health checks, (6) Managing secrets/variables across environments, (7) Optimizing Railway costs and resources, (8) Configuring databases/volumes/networking, (9) Setting up CI/CD with GitHub Actions, (10) Cloud Agent sessions needing Railway CLI access with RAILWAY_API_TOKEN-based authentication.

Quick Start for Cloud Agents
Quick Start
Essential Workflows
Reference Documentation
Critical Guidelines

Quick Start for Cloud Agents

Railway CLI is automatically configured for Cloud Agents (GitHub Copilot Workspace).

Installs Railway CLI via npm
Authenticates using RAILWAY_API_TOKEN
Auto-detects and links to the correct environment (PR or production)
Links to project: yoto, service: yoto-smart-stream

You can immediately use Railway CLI commands:

railway status --json
railway logs --lines 50 --filter "@level:error" --json
railway var list --json
railway deployment list --json

For detailed instructions: See Cloud Agent Authentication

Quick Start

Monitoring Deployment Status Loop

 sleep 5 && while true; do STATUS=$(railway deployment list --json | jq -r '.[0].status'); echo "[$(date '+%H:%M:%S')] Deployment status: $STATUS"; if [ "$STATUS" = "SUCCESS" ]; then echo "✅ Deployment succeeded!"; break; elif [ "$STATUS" = "FAILURE" ]; then echo "❌ Deployment failed!"; exit 1; fi; sleep 5; done

Common CLI Commands

# Check deployment status
railway status --json

# View filtered logs (prefer filtering for efficiency)
railway logs --filter "@level:error"
railway logs --filter "\"uvicorn\" OR \"startup\""

# Link workspace to environment
railway link --project <project_id>
railway environment --environment develop

For complete CLI reference: See cli_scripts.md

Essential Workflows

1. Deployment Configuration

Railpack (preferred build system):

{
  "$schema": "https://schema.railpack.com",
  "provider": "python",
  "packages": {"python": "3.11"},
  "deploy": {"startCommand": "uvicorn app:main --host 0.0.0.0 --port $PORT"}
}

Key principle: Prefer auto-detection over custom steps. Only configure what needs to differ from defaults.

For detailed build configuration: See configuration_management.md

2. Multi-Environment Setup

Typical structure:

Production (main branch) - Customer-facing
Staging (develop branch) - Pre-production testing
PR Environments - Automatic ephemeral environments per PR

For complete multi-environment architecture: See multi_environment_architecture.md

3. Troubleshooting Deployments

Common issues:

Health check failures → Check railway logs --filter "@level:error"
Package not found → Verify build succeeded, check layering (see configuration_management.md)
Environment deleted → Re-link with railway link and railway environment

For deployment workflows and CI/CD: See deployment_workflows.md

Reference Documentation

Load these files as needed for detailed guidance:

cli_scripts.md - Complete Railway CLI reference, Cloud Agent authentication, automation scripts
configuration_management.md - Railpack, NIXPACKS, Dockerfile builds, railway.toml
deployment_workflows.md - GitHub Actions, CI/CD, PR workflows, rollback strategies
multi_environment_architecture.md - Environment setup, branch mapping, isolation patterns
secrets_management.md - Environment variables, GitHub Secrets integration
monitoring_logging.md - Log aggregation, metrics, alerting, debugging
database_services.md - PostgreSQL, MySQL, Redis setup and management
pr_environments.md - Railway's native PR environment feature
cost_optimization.md - Resource optimization, billing, usage monitoring
platform_fundamentals.md - Core Railway concepts, architecture, terminology

Critical Guidelines

Always filter logs when using Railway CLI for efficiency
Prefer auto-detection for build configuration (Railpack)
Verify environment link before CLI operations: railway status --json
Check health checks are properly configured for all services

railway-service-management

When & Why to Use This Skill

Use Cases

Contents