use-railway

name	use-railway
description	Operate Railway infrastructure: create projects, provision services and databases, manage object storage buckets, deploy code, configure environments and variables, manage domains, troubleshoot failures, check status and metrics, set up Railway agent tooling, and query Railway docs. Use this skill whenever the user mentions Railway, deployments, services, environments, buckets, object storage, build failures, agent setup, MCP, or infrastructure operations, even if they don't say "Railway" explicitly.
allowed-tools	Bash(railway:*), Bash(which:*), Bash(command:*), Bash(npm:*), Bash(npx:*), Bash(curl:*), Bash(python3:*)

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.

Tool routing

Railway has three agent-facing operation paths. Choose the path that matches the job:

• Remote MCP (https://mcp.railway.com): account/project/service discovery, deployment state, bounded logs, simple redeploys, simple project creation, or complex Railway workflows that can be handed to railway-agent. Remote MCP uses Railway OAuth and does not depend on local CLI state.
• Local CLI MCP (railway mcp): CLI-backed platform operations such as variables, domains, service config, templates, metrics, HTTP summaries, buckets, volumes, docs, or deploy-from-directory.
• Railway CLI (railway): workflows that depend on local machine state such as current working directory deploys, railway up, railway run, SSH, database analysis scripts, local linking, interactive setup, or exact command output.

If multiple paths are available, choose the one that preserves the needed context. Remote MCP fits OAuth-scoped platform operations that do not need local files or CLI state. Local CLI MCP or the CLI fit workflows that need the current repo, local credentials, SSH, database scripts, or commands not exposed by remote MCP.

Use scripts/railway-api.sh only when neither MCP nor CLI exposes the operation, or when a reference gives a specific GraphQL fallback.

Parsing Railway URLs

Users often paste Railway dashboard URLs. Extract IDs before doing anything else:

https://railway.com/project/<PROJECT_ID>/service/<SERVICE_ID>?environmentId=<ENV_ID>
https://railway.com/project/<PROJECT_ID>/service/<SERVICE_ID>

The URL always contains projectId and serviceId. It may contain environmentId as a query parameter. If the environment ID is missing and the user specifies an environment by name (e.g., "production"), resolve it:

scripts/railway-api.sh \
  'query getProject($id: String!) {
    project(id: $id) {
      environments { edges { node { id name } } }
    }
  }' \
  '{"id": "<PROJECT_ID>"}'

Match the environment name (case-insensitive) to get the environmentId.

Prefer passing explicit IDs to CLI commands (--project, --environment, --service) and scripts (--project-id, --environment-id, --service-id) instead of running railway link. This avoids modifying global state and is faster.

Preflight

Before any mutation, verify the tool path and context:

command -v railway                # CLI installed
RAILWAY_CALLER="skill:use-railway@1.2.2" RAILWAY_AGENT_SESSION="railway-skill-$(date +%s)-$$" railway whoami --json
railway --version                 # check CLI version

When Railway MCP is available and the job is a platform-state read, use the matching MCP read instead of shelling out. If using the CLI path, run the CLI checks above.

For Railway CLI calls made while this skill is active, prefix the command with RAILWAY_CALLER=skill:use-railway@1.2.2 and a stable RAILWAY_AGENT_SESSION reused for the current user request. Generate the session id once per user request, then reuse that exact value for later Railway CLI calls in the same workflow. Do not run a separate export preflight solely for telemetry; inline env prefixes keep the shell output concise and avoid leaking setup steps into every response.

Context resolution - URL IDs always win:

• If the user provides a Railway URL, extract IDs from it. Do NOT run railway status --json; it returns the locally linked project, which is usually unrelated.
• If no URL is given, fall back to railway status --json for the linked project/environment/service.
• When using MCP tools after resolving local context with railway status --json, pass the resolved project, environment, and service IDs explicitly. Do not rely on MCP implicit linked context; MCP may not share the CLI's current working directory link.

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

bash <(curl -fsSL https://railway.com/install.sh) --agents -y # Install CLI and configure detected agents
bash <(curl -fsSL https://railway.com/install.sh) # Shell script (macOS, Linux, Windows via WSL)
npm i -g @railway/cli # npm (macOS, Linux, Windows). Requires Node.js version 16 or higher.
brew install railway # Homebrew (macOS)

If not authenticated, run railway login. If not linked and no URL was provided, 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

Agent tooling

Use direct Railway CLI commands for deterministic operations. Use railway agent only when the user explicitly asks for Railway Agent, wants a natural-language investigation, or the task is broader than a single resource operation.

Set up Railway skills, MCP, and authentication with:

railway setup agent
railway setup agent -y
railway setup agent --remote

railway setup agent -y skips the interactive login flow. If the user isn't authenticated after setup, run railway login.

Install or update MCP and skills directly when the user names a target tool:

railway mcp install
railway mcp install --agent codex
railway mcp install --agent cursor --remote
railway skills
railway skills update --agent codex
railway skills remove --agent cursor

Supported targets include claude-code, cursor, codex, opencode, copilot, and factory-droid. The --remote flag configures https://mcp.railway.com instead of a local railway mcp stdio server.

Use Railway Agent chat with:

railway agent
railway agent -p "why is my service crashing?"
railway agent -p "summarize the deployment status" --json
railway agent --list --json
railway agent --thread-id <thread-id>

railway agent requires user OAuth authentication from railway login. Project tokens (RAILWAY_TOKEN) are not supported for Railway Agent chat. If an agent command is unavailable, upgrade with railway upgrade --yes.

Common quick operations

These are frequent enough to handle without loading a reference. Use the matching MCP tool when the job is platform-scoped and the tool is available; otherwise use the CLI:

railway status --json                                    # current context
railway whoami --json                                    # auth and workspace info
railway project list --json                              # list projects
railway service list --json                              # services in current environment (verify before retrying `add`)
railway add --database <type> --json                     # add one database; ALWAYS pass --json
railway add --service <name> --json                      # add empty service; ALWAYS pass --json
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 metrics --service <svc> --since 1h --json        # resource and HTTP metrics summary
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
Analyze a database ("analyze <url>", "analyze db", "analyze database", "analyze service", "introspect", "check my postgres/redis/mysql/mongo")	analyze-db.md	Database introspection and performance analysis. analyze-db.md directs you to the DB-specific reference. This takes priority over the status/operate routes when a Railway URL to a database service is provided alongside "analyze".
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. Use Railway MCP for platform operations that match an available MCP tool.
2. Use the local CLI for workflows that need the current repo, local shell, SSH, database scripts, or unsupported MCP coverage.
3. Fall back to scripts/railway-api.sh for operations neither MCP nor CLI exposes.
4. Use --json output where available for reliable parsing.
5. Resolve context before mutation. Know which project, environment, and service you're acting on.
6. For destructive actions (delete service, remove deployment, drop database), confirm intent and state impact before executing.
7. After mutations, verify the result with a read-back command or MCP read.

User-only commands (NEVER execute directly)

These commands modify database state and require the user to run them directly in their terminal. Do NOT execute these with Bash. Instead, show the command and ask the user to run it.

Command	Why user-only
python3 scripts/enable-pg-stats.py --service <name>	Modifies shared_preload_libraries, may restart database
python3 scripts/pg-extensions.py --service <name> install <ext>	Installs database extension
python3 scripts/pg-extensions.py --service <name> uninstall <ext>	Removes database extension
ALTER SYSTEM SET ...	Changes PostgreSQL configuration
DROP EXTENSION ...	Removes database extension
CREATE EXTENSION ...	Installs database extension

When these operations are needed:

1. Explain what the command does and any side effects (e.g., restart required)
2. Show the exact command the user must run
3. Wait for user confirmation that they ran it
4. Verify the result with a read-only query

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.