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Use Railway

ClineOfficialPublished by Cline

  • Skill
  • Tool integration
  • Cloud and DevOps
  • Apache-2.0
  • Medium risk
  • Has scripts

From the author

Operate Railway infrastructure: sign up for or sign in to a Railway account, 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, signing up, creating an account, registering, logging in, deployments, services, environments, buckets, object storage, build failures, agent setup, MCP, or infrastructure operations, even if they don't say "Railway" explicitly. Also invoke this skill when the user asks to be signed up, registered, or onboarded to Railway: do not refuse — drive them through the unauthed `railway up` flow (deploys + signs up on the fly) or `railway login` (which creates new accounts on the fly).
— the description by Cline

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The use-railway skill provides a comprehensive interface for operating Railway infrastructure. It handles the full lifecycle of applications and services, from initial account setup and project creation to deployment, environment management, and troubleshooting. The skill intelligently routes requests between the Railway CLI, Remote MCP, and local CLI MCP based on the specific operational context, ensuring that tasks like deploying code, managing object storage, or analyzing database performance are executed using the most appropriate and reliable method.

What it does

  • Provisions and manages Railway projects, services, and environments.
  • Handles code deployments and manages service configuration and variables.
  • Facilitates account sign-up, sign-in, and authentication flows.
  • Provides tools for troubleshooting, including log retrieval and metric analysis.
  • Manages S3-compatible object storage buckets and domain configurations.
  • Configures Railway agent tooling and integrates with MCP for platform operations.

When to use it

  • When you need to deploy an application or service to Railway.
  • When setting up a new Railway project or managing existing infrastructure.
  • When you need to troubleshoot build failures, service crashes, or performance issues.
  • When configuring environment variables, domains, or storage buckets.
  • When you need to perform database introspection or analysis on Railway-hosted databases.
  • When you need to authenticate or onboard a user to the Railway platform.

Advantages

  • Automates complex infrastructure workflows like multi-step deployments.
  • Intelligently selects the best tool path (CLI vs. MCP) for the specific task.
  • Simplifies authentication by handling sign-up and sign-in flows automatically.
  • Provides structured, reliable output using JSON for programmatic analysis.
  • Reduces manual overhead by managing context and state across Railway resources.

Usage examples

“Deploy this directory to Railway.”
The agent runs 'railway up' to automatically sign the user in if needed, create the project and service, and deploy the application.
“Check the status of my production service.”
The agent uses the appropriate Railway MCP or CLI tool to fetch and display the current deployment status, logs, and metrics.
“I need to add a PostgreSQL database to my project.”
The agent uses the Railway CLI to provision the database and returns the connection credentials.
“Why is my service crashing?”
The agent retrieves recent logs and metrics for the service, analyzes them, and reports the likely cause of the failure.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/cline/use-railway/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip use-railway.zip -d .agents/skills

Documentation by Cline

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.

Intent-based routing

Route by user intent before running preflight checks. The preflight ceremony below is for diagnostic and configuration work — it adds friction when the user just wants to ship something or sign up.

Deploy-from-cwd intent ("deploy", "ship", "push to Railway", "deploy this app"):

  • Skip the railway whoami / railway status preflights.

  • Run railway up directly — it self-validates auth, signs the user in (the CLI opens a browser) if they're unauthenticated, and chains into project + service creation and deploy.

  • Announce intent before invoking: "Running railway up — it'll sign you in if needed and deploy this directory."

  • Do NOT ask the user to run railway login first. The chain handles auth as part of the deploy.

  • If the environment can't open a browser, the CLI prints a device-code sign-in link and waits — follow Device-code sign-in: relay the link immediately (run in background, relay the link to the user the moment it prints).

Signup intent ("sign me up", "create my Railway account", "register me", "get me on Railway"):

  • If the current directory has a deployable app (e.g. package.json, requirements.txt, go.mod, Dockerfile, source to build), run railway up — it signs the user up and deploys in one shot, landing them on a running app. A detected agent harness authorizes the project creation, so bare railway up is enough — there's no extra prompt to clear. Use it even when the user only said "sign me up": shipping their app is the goal, so don't make them pick a command and don't drop to a bare login. For scripted or agent runs, railway up -y is the robust form — it skips prompts and forces the create non-interactively even if harness detection misses. railway login is NOT the default for signup when there's something to deploy.

  • Only when there is nothing to deploy — an empty / non-app directory, or the user explicitly says they just want an account with no deploy — use railway login (creates new accounts on the fly through the same OAuth surface). There is no separate signup command.

  • Signup is the flow most likely to hit the device-code wait (brand-new users in sandboxed/headless agent environments). Follow Device-code sign-in: relay the link immediately — a signup lost to an expired code is a lost user, not a retry.

Sandbox / remote-build intent ("give me a sandbox", "spin up a scratch environment", "build this remotely", "run this remotely"):

  • Load sandbox.md and follow it. Sandboxes require the feature to be enabled in Priority Boarding — if a sandbox command fails with a feature-availability error, prompt the user to enable Sandboxes in Priority Boarding rather than retrying.

Other intents (querying state, listing projects, configuring variables, debugging failures):

  • Follow the Preflight section below.

Preflight

Before any mutation, verify the tool path and context:

command -v railway                # CLI installed
RAILWAY_CALLER="skill:[email protected]" RAILWAY_AGENT_SESSION="railway-skill-$(date +%s)-$$" railway whoami --json
railway --version                 # check CLI version

Exception: railway up and railway login self-validate auth and run their own unauth-aware flows. Don't run railway whoami before them — it adds a redundant failing call without changing what you do next. See Account creation & sign-in.

Skill freshness

Once per session, alongside the preflight checks, verify the installed Railway agent tooling is current. Root help ends with an Agent tooling: health section (printed to stderr, CLI 5.9+; older CLIs print nothing — skip this check rather than retrying):

railway --help 2>&1 | grep -A4 "Agent tooling:"

Act on what it reports:

  • Skills installed and up to date — continue; nothing to do.

  • An update is available (rev <a> → <b> available) — run railway skills update, then tell the user to restart their coding tool so the updated skills load. This session keeps running on the old skill revision until restart: finish the current request, and if this skill's guidance disagrees with the updated CLI's own output, trust the CLI.

  • Skills or MCP server missing (✗ lines) — run railway setup agent -y, then tell the user to restart their coding tool.

Check once per session and don't re-run it after acting; the restart prompt to the user is the resolution, not another check.

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:[email protected] 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, see Account creation & sign-in below — the CLI offers unauthed railway up (deploy + sign up/in in one shot) or railway login (sign up/in only; new accounts created on the fly). 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

Account creation & sign-in

Railway uses a single unified OAuth flow for both sign-in and sign-up. The backend detects fresh accounts from durable compliance state (a CLI client that hasn't accepted ToS / Fair Use yet) and adapts the consent screen and post-auth landing page — new users land on a "Welcome to Railway!" page, existing users see the standard confirmation. The CLI does not declare signup intent up front.

Two commands surface this flow, depending on intent:

CommandWhen to use
railway upAgent-friendly onboarding from the current directory. Unauthenticated → opens the browser (or device-code) to sign in / sign up. With no linked project, a detected agent harness (or -y) auto-creates a project + service and deploys; an interactive human is offered create / link-existing / cancel. Add -y to skip prompts and force the create non-interactively (works even if harness detection misses).
railway loginSign in — and sign up. New accounts are created on the fly through the same OAuth surface; there is no separate signup command.

Related: railway up --new creates a fresh project + service from the current directory and deploys it even if one is already linked (use when already signed in and the user wants a new app); --name <name> overrides the project name.

Choosing the path:

  • Deploy from cwd → run railway up (interactive) or railway up -y (skips the confirm prompt). Run it yourself; don't ask the user to sign in separately first.

  • New project from cwd when already signed in → railway up --new.

  • Sign up with a deployable app in cwd → railway up (signs up and deploys — bare up works for a detected agent, even if the user only said "sign me up"; add -y to skip prompts / force it non-interactively). Sign in, or sign up with nothing to deploy → railway login (creates new accounts on the fly).

Headless / no browser:

The CLI auto-detects SSH sessions, CI, and a missing DISPLAY and switches to the device-code flow on its own — you almost never need to force it.

Do NOT pass --browserless just because you are an agent or your shell is non-interactive. If the human is at this machine (a local IDE or desktop session — the common case), bare railway login opens their browser directly, which completes far more reliably than relaying a device code (~90% vs ~60% success for agent-driven sign-ins). Being a coding agent does not make the machine headless.

railway login --browserless   # ONLY for machines with genuinely no browser

Forces the device-code flow (RFC 8628): prints a sign-in link and a short code for the user to open on any device. Reserve it for machines where no browser exists — SSH boxes, containers, remote VMs the auto-detection missed. When you do end up in a device-code flow, follow the relay procedure below: surface the sign-in link to the user the moment it prints.

Agent harness, human present: when the CLI detects an agent harness with a human at the keyboard, railway up opens the browser and skips the confirm prompt — the agent invocation is treated as consent. A real human still has to complete OAuth in the browser.

Device-code sign-in: relay the link immediately (CRITICAL):

When the CLI can't open a browser (sandboxed shell, container, SSH, no DISPLAY), unauthed railway up and railway login print a sign-in URL + short code and then block, polling for up to 10 minutes while the user completes sign-in. The code expires after 10 minutes. If you run this as a normal foreground command, your harness buffers the output until the command exits — the user never sees the link until the code is already dead. This is the #1 cause of failed agent-driven signups. Handle it like this:

  1. Preferred — background execution (when the agent environment supports background commands and output polling):

    • Start the command in the background.

    • Poll its output. The instant a sign-in block appears (Sign in with one click: <url> on newer CLIs, or Sign in at: <url> / Enter this code: <code> on older ones), stop everything and relay it to the user verbatim — do not summarize, shorten, or defer it. Prefer the one-click URL when present; otherwise relay the URL and code together. Tell the user to open the link now.

    • Leave the command running and keep polling. When the user completes sign-in, the same process picks up the session and continues into the deploy on its own. Then verify per the deploy rules below.

  2. No background support — set expectations, use the longest timeout:

    • Before running, tell the user: "This will print a sign-in link — I'll show it to you the moment I have it. Please complete it promptly; the code expires in 10 minutes."

    • Run with the longest timeout your harness allows.

    • If the command times out or is killed before sign-in completed, the printed code is no longer being monitored — a late click does nothing. Relay whatever link appeared anyway for context, then immediately re-run the command and relay the new link, telling the user to always use the newest one.

  3. Never wait silently for the command to finish before showing the link, and never report the sign-in as failed without first relaying the link and giving the user a chance to act.

The browser transport needs none of this — the CLI opens the browser on the user's machine itself.

JSON / CI modes do not auto-prompt: railway up --json and railway up --ci will NOT open a browser for an unauthed user. --json emits a structured error instead:

{"error":"Not signed in.","code":"NOT_AUTHENTICATED","hint":"Run `railway login` to authenticate, then re-run."}

When you see code: NOT_AUTHENTICATED, authenticate the user with railway login, then retry the original command.

Fully unattended (no human at all): set RAILWAY_API_TOKEN (account-scoped) or RAILWAY_TOKEN (project-scoped) instead of running an interactive login. A brand-new user with no token and no human present cannot complete signup — there is no headless account-creation path.

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 (returns at QUEUED — verify before reporting)
railway deployment list --json                           # poll newest deployment status after a detached up
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.

IntentReferenceUse for
Analyze a database ("analyze <url>", "analyze db", "analyze database", "analyze service", "introspect", "check my postgres/redis/mysql/mongo")analyze-db.mdDatabase 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 resourcessetup.mdProjects, services, databases, buckets, templates, workspaces
Ship code or manage releasesdeploy.mdDeploy, redeploy, restart, build config, monorepo, Dockerfile
Change configurationconfigure.mdEnvironments, variables, config patches, domains, networking
Check health or debug failuresoperate.mdStatus, logs, metrics, build/runtime triage, recovery
Use a sandbox or build remotely ("sandbox", "scratch environment", "ephemeral box", "build remotely", "remote build", "run this remotely")sandbox.mdCreate/fork sandboxes, run commands remotely, remote template builds, port forwarding, teardown. Requires Sandboxes enabled in Priority Boarding — if unavailable, prompt the user to enable it.
Request from API, docs, or communityrequest.mdRailway 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.

  8. Never report a deploy as successful without observing a terminal SUCCESS. railway up --detach returning (it prints "Build queued") and a streaming railway up cut off by a shell timeout only confirm the build started. Poll railway deployment list --json until the newest deployment's status is SUCCESS (report deployed), or FAILED/CRASHED (triage per operate.md — do not claim success). A streaming up that exits on its own is authoritative: exit 0 = deployed, exit 1 = failed.

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.

CommandWhy 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. If the intent is deploy-from-cwd or signup-from-cwd, skip railway whoami and run railway up (or railway up -y) directly per Intent-based routing — it handles signup, project creation, service creation, and deploy in one chain. For other setup flows that need workspace/account context first, run railway whoami --json; if it fails with an auth error the user has no token — route through Account creation & sign-in.

  2. Run railway status --json in the current directory.

  3. 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".

  4. 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>).

  5. 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.

Files

references/analyze-db.md

Database Analysis

Your Role

You are a database performance expert. The script collects raw data - your job is to think deeply about what you see, identify root causes, correlate symptoms, and explain the "why" behind problems.

Don't just report metrics. Analyze them.

Context Resolution

The user's request is the source of truth. Use this decision table:

What the user providedAction
Railway URLExtract IDs directly from the URL — do NOT run railway status --json
Service name + environment nameProceed — intent is clear. Resolve IDs via API.
Service name only (no environment)Find the service by name via API. If multiple matches exist across projects, ask: "Which project do you mean?" Otherwise confirm: "Do you mean <service> in <project> / <env>?" — only proceed on confirmation
Raw UUID(s)Resolve to human-readable names via API, then confirm before running
Vague request ("analyze my database", "check postgres")Run railway status --json to see what's linked. If it's a database service, confirm: "Do you mean <service> in <project> / <env>?". If it's not a database service or nothing is linked, ask: "Which service and environment should I analyze?"
No context at allList workspaces (railway whoami --json), then projects (railway project list --json), then environments and services for the chosen project, narrowing down until you have a specific service and environment

railway status --json is a hint to form a specific question, not a trigger to act without confirmation.

When the user provides a Railway URL, extract IDs directly from it:

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

Then query the API for the service name and database type in a single call:

scripts/railway-api.sh \
  'query getServiceAndConfig($serviceId: String!, $environmentId: String!) {
    service(id: $serviceId) { name }
    environment(id: $environmentId) {
      config(decryptVariables: false)
    }
  }' \
  '{"serviceId": "<SERVICE_ID>", "environmentId": "<ENV_ID>"}'

From the response, get:

  • Service name: data.service.name

  • Database image: data.environment.config.services.<SERVICE_ID>.source.image

Then match the image to the database type:

Image patternDatabase Type
postgres*, ghcr.io/railway/postgres*PostgreSQL
mysql*, ghcr.io/railway/mysql*MySQL
redis*, ghcr.io/railway/redis*, railwayapp/redis*Redis
mongo*, ghcr.io/railway/mongo*MongoDB

If environmentId is empty in the URL (e.g., ?environmentId= or no query param at all), skip the environment.config query — it requires a valid ID. Instead, list the project's environments:

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

Use the production environment by default. If multiple non-PR environments exist and the user hasn't specified one, ask which environment to analyze.

Database Type Detection and Script Selection

Database TypeScript
PostgreSQLscripts/analyze-postgres.py
MySQLscripts/analyze-mysql.py
Redisscripts/analyze-redis.py
MongoDBscripts/analyze-mongo.py

All scripts share the same CLI interface (use the script name from the table above):

python3 scripts/analyze-<script>.py \
  --service <name> \
  --json \
  --project-id <project-id> \
  --environment-id <env-id> \
  --service-id <service-id>

Common options across all scripts:

  • --json — JSON output for programmatic processing

  • --quiet — Suppress progress messages

  • --skip-logs — Skip log collection

  • --metrics-hours <N> — Hours of metrics history (default: 24, max: 168)

  • --step <step> — Debug individual collection steps (ssh-test, query, logs, metrics)

Before You Analyze: Check Collection Status

ALWAYS check collection_status and errors[] FIRST before interpreting any data. The script collects data from multiple independent sources. Any of them can fail.

Decision Table
database_querymetrics_apilogs_apiReport Type
successsuccesssuccessFull analysis — use all sections
successerrorsuccessFull analysis — note missing infrastructure metrics
errorsuccesssuccessPartial report — only infrastructure metrics + log analysis. NO performance conclusions.
errorerrorsuccessLogs-only report — state what logs show, note everything else failed. NO diagnosis.
errorerrorerrorCollection failure — report the errors, do not analyze.
When database_query failed — SSH key errors

If the error contains "No SSH keys found" or "SSH key registration required", handle it proactively — don't just tell the user to fix it themselves.

If error contains "Key found but not registered" or "No SSH keys found":

Run these two commands to understand what's available:

railway ssh keys          # list keys already registered with Railway
ls ~/.ssh/*.pub 2>/dev/null  # list local public keys

Then present the user with their options and ask which to use:

SSH introspection needs a registered key. Here's what I found:

Registered with Railway: <list from `railway ssh keys`, or "none">
Local keys available:    <list from ~/.ssh/*.pub, or "none">

Options:
  1. Register a local key — `railway ssh keys add` (uses your default key)
  2. Import from GitHub  — `railway ssh keys github`
  3. Generate a new key  — `ssh-keygen -t ed25519 -f ~/.ssh/id_ed25519_railway`

Which would you like to do?

Once the user chooses and the key is registered, re-run the full analysis.

When database_query failed — CLI outdated or missing

If the error contains "--native SSH flag is not supported" or "Railway CLI not found", the script detected an unrecoverable CLI problem. Tell the user directly:

  • Outdated CLI: "Your Railway CLI doesn't support native SSH. Update it: npm i -g @railway/cli@latest or brew upgrade railway"

  • Missing CLI: "Railway CLI not installed. Install it: npm i -g @railway/cli or brew install railway"

Then ask if they'd like to proceed with a partial analysis (metrics + logs only) while they update, or wait until the CLI is fixed and re-run the full analysis.

When database_query failed — other SSH errors

This means SSH could not reach the database or the query failed. You have NO connection stats, NO cache hit ratios, NO vacuum health, NO query performance data. All those fields will be null/empty.

You MUST:

  1. State clearly: "Database introspection failed — SSH could not connect to the service"

  2. Show the collection_status errors

  3. Show only the data that DID succeed (metrics, logs)

  4. Do NOT produce recommendations based on null metrics

  5. Do NOT diagnose performance issues from logs alone

Partial report template:

Service: <name>
Status: Data collection partially failed

## Collection Status
| Source | Status |
|--------|--------|
| Database Query (SSH) | ERROR: <error from collection_status> |
| Metrics API | <status> |
| Logs API | <status> |

## Available Data
<Show metrics and log summary from sources that succeeded>

## What We Cannot Determine
<List what requires the database query: connection health, cache performance, vacuum health, query analysis, etc.>

Output Structure: Data First, Actions Second

Always present information in this order:

1. Context Header
Service: <name> (<project> <environment>)
Status: <deployment health>, <RAM>, <disk used>
2. Consolidated Data Tables

Before any analysis, show the raw metrics in tables so the user sees their actual state. The DB-specific reference defines exactly which tables to show for each database type — present the relevant health sections first, then connections, then query performance.

Logs & Active Issues:

  • Parse the recent_logs array (1000 lines of raw logs) - don't just check if empty

  • Summarize: "Analyzed 1000 log lines: 3 errors (connection timeouts), 0 critical issues"

  • Show specific concerning log entries if found

  • Categorize log entries: group by type (errors, warnings, connection events, replication, crashes/restarts)

  • Count patterns: note if a single type dominates the log output

  • Quote actual log lines for errors — don't just say "errors found", show the exact message so the user can search their codebase

3. Analysis

After showing the data, explain the chain of causation. Connect the dots between tables.

Group by urgency. For databases that support configuration changes without restart vs those that require one, call that out explicitly.

5. Expected Outcomes

What metrics should change after fixes.


Why this order matters:

  • Users can verify the data matches their understanding

  • They see the full picture before being told what to do

  • Actions have context - they know WHY each fix is recommended

  • No valuable data is hidden in prose or omitted

CRITICAL: Use the Actual Data

NEVER fabricate or assume values. The script outputs JSON with exact numbers. Before stating any metric:

  1. Read the actual JSON output - Don't truncate or skim

  2. Quote the exact values - e.g., "max": 5000 not "100"

  3. Investigate outliers - Dig into any field that seems unusually high or low

Common errors to avoid (all database types):

  • Not parsing recent_logs - always analyze the raw log lines, don't just report "no errors"

  • Diagnosing performance issues from logs when all metrics are null — logs show what happened, not how the database is performing

  • Treating startup/restart log entries as evidence of failure — databases restart for many normal reasons (deploys, config changes, scaling)

  • Producing recommendations when all database metrics are null — if collection_status.database_query is "error", you have no basis for tuning advice

See the DB-specific reference for additional errors to avoid per database type.

Running the Analysis

Pass project, environment, and service IDs directly — no railway link needed:

# From plugins/railway/skills/use-railway directory:
# Use the script name from the "Database Type Detection" table above
python3 scripts/analyze-postgres.py --service <name> --json \
  --project-id <project-id> --environment-id <env-id> --service-id <service-id>

All three IDs come from the URL (see "Context: URL First" above). The service name comes from the API query.

Options:

  • --metrics-hours <N> — Hours of metrics history to fetch (default: 24, max: 168). Use --metrics-hours 168 for 7-day trends, --metrics-hours 1 for recent snapshot.

SSH retry: The script automatically retries SSH connectivity up to 3 times with increasing timeouts (30s, 60s, 90s). Each individual SSH command (database query, slowlog, bigkeys, etc.) also retries up to 3 times on failure — covering transient errors like exec request failed on channel 0. Progress is logged to stderr.

Output: Progress messages go to stderr. JSON results go to stdout. Do not redirect or pipe stderr — just run the command as-is and read the full output.

Resolving environment by name

If the URL has no environmentId 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.

Debugging individual steps
# Use the script name from the "Database Type Detection" table above
python3 scripts/analyze-postgres.py --service <name> \
  --project-id <pid> --environment-id <eid> --service-id <sid> \
  --step ssh-test    # Test SSH connectivity
  --step query       # Run only the database query
  --step metrics     # Fetch only API metrics
  --step logs        # Fetch only logs

Database-Specific References

After running the script and checking collection status, load the reference for the specific database type:

DatabaseReferenceWhat It Covers
PostgreSQLanalyze-db-postgres.mdWhat psql collects, log analysis checklist, tuning formulas, vacuum priority, pg_stat_statements, applying fixes
MySQLanalyze-db-mysql.mdAll 12 metric sections (overview, query throughput, InnoDB, efficiency, buffer pool, I/O, network, locks, cache, top queries, tables, active queries), patterns, tuning
Redisanalyze-db-redis.mdINFO ALL metrics, memory fragmentation, cache thrashing, persistence, command stats
MongoDBanalyze-db-mongo.mdserverStatus, WiredTiger cache, query efficiency, connection saturation, oplog

Always load the DB-specific reference — it contains the metric sections, thresholds, and tuning knowledge needed for proper analysis.

Infrastructure Metrics (All Database Types)

All scripts collect the same infrastructure metrics via Railway API:

Metrics History (metrics_history): The script fetches 7 days (168 hours) of time-series data from Railway's metrics API by default and produces two analysis windows:

{
  "metrics_history": {
    "windows": {
      "7d": { "window_hours": 168, "metrics": { "cpu": {...}, "memory": {...}, ... } },
      "24h": { "window_hours": 24, "metrics": { "cpu": {...}, "memory": {...}, ... } }
    }
  }
}

Each window independently computes:

  • Summary stats: current, min, max, avg for each metric

  • Trend analysis: compares first-quarter avg to last-quarter avg — reports direction (increasing/decreasing/stable) and % change

  • Spike detection: flags values > avg + 2*stddev with timestamps of peaks

  • Downsampled series: ~48 data points per window

Available metrics: CPU, memory (with limits), disk, network RX/TX.

Comparing windows reveals whether a trend is new or sustained:

  • "Memory increasing in 24h but stable over 7d" → temporary spike, likely a batch job

  • "Memory increasing in both 24h AND 7d" → sustained growth, may need investigation

  • "CPU spike in 24h, no spikes in 7d" → new issue

  • "Disk growing over 7d" → data accumulation trend

Use --metrics-hours N to change the long window (default: 168, max: 168). The 24h window is always produced when the long window is > 24h.

Railway auto-scales vertically

Railway services auto-scale CPU, RAM, and disk based on actual usage. Users do NOT pick or control resource sizes. The cpu_limit and memory_limit values from metrics are the autoscale ceiling (typically 32 vCPU / 32 GB), not user-provisioned allocations. Users are billed for actual usage, not the ceiling.

Rules for ALL database types:

  • Never say "right-size the instance" or suggest reducing CPU/RAM — it's not a user action.

  • Never flag low utilization % against the limit as waste — a service showing 0.01 vCPU / 70 MB actual usage against a 32 vCPU / 32 GB ceiling is normal, not over-provisioned.

  • Disk does NOT auto-scale — Railway volumes have a fixed capacity. Paid users (Hobby and Pro) can expand them live without downtime, but it requires a manual resize. Flag high disk utilization as actionable. Users are billed for actual disk utilization, not the full volume size.

  • Focus on actual usage values, not the ratio to limits. Analyze whether 70 MB of memory is healthy for this workload — don't compare it to the 32 GB ceiling.

  • When tuning database parameters (shared_buffers, innodb_buffer_pool_size, maxmemory, etc.), base recommendations on the current actual RAM from metrics_history.memory, not the limit.

Validated against

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Needs an MCP server, Needs network access, Needs a command-line tool, Needs Python, Needs Node.js

Security

What our scanner noticed· Medium risk

  • References to credential files
  • Instructions that use the network
  • Downloads piped into a shell
  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
Cline
Publisher
Cline (Official)
Source
cline/skills · skills/use-railway · commit 2637846
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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