Agent Skills: Use Railway

Use Railway

Railway resource model

Railway organizes infrastructure in a hierarchy:

• Workspace is the billing and team scope. A user belongs to one or more workspaces.
• Project is a collection of services under one workspace. It maps to one deployable unit of work.
• Environment is an isolated configuration plane inside a project (for example, production, staging). Each environment has its own variables, config, and deployment history.
• Service is a single deployable unit inside a project. It can be an app from a repo, a Docker image, or a managed database.
• Bucket is an S3-compatible object storage resource inside a project. Buckets are created at the project level and deployed to environments. Each bucket has credentials (endpoint, access key, secret key) for S3-compatible access.
• Deployment is a point-in-time release of a service in an environment. It has build logs, runtime logs, and a status lifecycle.

Most CLI commands operate on the linked project/environment/service context. Use railway status --json to see the context, and --project, --environment, --service flags to override.

Preflight

Before any mutation, verify context:

command -v railway                # CLI installed
railway whoami --json             # authenticated
railway --version                 # check CLI version
railway status --json             # linked project/environment/service

If the CLI is missing, guide the user to install it.

bash <(curl -fsSL cli.new) # Shell script (macOS, Linux, Windows via WSL)
brew install railway # Homebrew (macOS)
npm i -g @railway/cli # npm (macOS, Linux, Windows). Requires Node.js version 16 or higher.

If not authenticated, run railway login. If not linked, run railway link --project <id-or-name>.

If a command is not recognized (for example, railway environment edit), the CLI may be outdated. Upgrade with:

railway upgrade

Common quick operations

These are frequent enough to handle without loading a reference:

railway status --json                                    # current context
railway whoami --json                                    # auth and workspace info
railway project list --json                              # list projects
railway service status --all --json                      # all services in current context
railway variable list --service <svc> --json             # list variables
railway variable set KEY=value --service <svc>           # set a variable
railway logs --service <svc> --lines 200 --json          # recent logs
railway up --detach -m "<summary>"                       # deploy current directory
railway bucket list --json                               # list buckets in current environment
railway bucket info --bucket <name> --json               # bucket storage and object count
railway bucket credentials --bucket <name> --json        # S3-compatible credentials

Routing

For anything beyond quick operations, load the reference that matches the user's intent. Load only what you need, one reference is usually enough, two at most.

| Intent | Reference | Use for | |---|---|---| | Create or connect resources | setup.md | Projects, services, databases, buckets, templates, workspaces | | Ship code or manage releases | deploy.md | Deploy, redeploy, restart, build config, monorepo, Dockerfile | | Change configuration | configure.md | Environments, variables, config patches, domains, networking | | Check health or debug failures | operate.md | Status, logs, metrics, build/runtime triage, recovery | | Request from API, docs, or community | request.md | Railway GraphQL API queries/mutations, metrics queries, Central Station, official docs |

If the request spans two areas (for example, "deploy and then check if it's healthy"), load both references and compose one response.

Execution rules

1. Prefer Railway CLI. Fall back to scripts/railway-api.sh for operations the CLI doesn't expose.
2. Use --json output where available for reliable parsing.
3. Resolve context before mutation. Know which project, environment, and service you're acting on.
4. For destructive actions (delete service, remove deployment, drop database), confirm intent and state impact before executing.
5. After mutations, verify the result with a read-back command.

Composition patterns

Multi-step workflows follow natural chains:

• Add object storage: setup (create bucket), setup (get credentials), configure (set S3 variables on app service)
• First deploy: setup (create project + service), configure (set variables and source), deploy, operate (verify healthy)
• Fix a failure: operate (triage logs), configure (fix config/variables), deploy (redeploy), operate (verify recovery)
• Add a domain: configure (add domain + set port), operate (verify DNS and service health)
• Docs to action: request (fetch docs answer), route to the relevant operational reference

When composing, return one unified response covering all steps. Don't ask the user to invoke each step separately.

Setup decision flow

When the user wants to create or deploy something, determine the right action from current context:

1. Run railway status --json in the current directory.
2. If linked: add a service to the existing project (railway add --service <name>). Do not create a new project unless the user explicitly says "new project" or "separate project".
3. If not linked: check the parent directory (cd .. && railway status --json).
   • Parent linked: this is likely a monorepo sub-app. Add a service and set rootDirectory to the sub-app path.
   • Parent not linked: run railway list --json and look for a project matching the directory name.
     • Match found: link to it (railway link --project <name>).
     • No match: create a new project (railway init --name <name>).
4. When multiple workspaces exist, match by name from railway whoami --json.

Naming heuristic: app names like "flappy-bird" or "my-api" are service names, not project names. Use the directory or repo name for the project.

Response format

For all operational responses, return:

1. What was done (action and scope).
2. The result (IDs, status, key output).
3. What to do next (or confirmation that the task is complete).

Keep output concise. Include command evidence only when it helps the user understand what happened.