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Railway Status

  • 291 installs
  • 30.1k repo stars
  • Updated August 4, 2026
  • davila7/claude-code-templates

railway-status is an agent skill that runs railway status --json to report linked Railway project, environment, service, and deployment health for developers triaging deploy failures.

About

railway-status is a davila7/claude-code-templates agent skill that checks live Railway deployment health for the current directory using railway status --json. It verifies the Railway CLI is installed, confirms railway login authentication, detects linked projects, and parses JSON for project name, workspace, environment, per-service deployment state, activeDeployments build progress, and configured domains. The skill is scoped as a pre-flight gate before deploys or variable changes and explicitly defers environment edits to the railway-environment skill. Reach for railway-status when an incident, release, or support ticket needs confirmation whether failures are platform, build, or runtime related. Skip it when you need to change Railway env vars, redeploy code, or configure services—use railway-deploy or railway-environment instead.

  • Quick Railway deploy health checks
  • Incident and release verification
  • Reduces context switching to dashboard
  • Pairs with metrics and projects skills
  • Support-friendly operational snapshot

Railway Status by the numbers

  • 291 all-time installs (skills.sh)
  • Ranked #339 of 1,435 DevOps & CI/CD skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/davila7/claude-code-templates --skill railway-status

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Listed on Skillselion
Installs291
repo stars30.1k
Last updatedAugust 4, 2026
Repositorydavila7/claude-code-templates

How do you check Railway deployment status from CLI?

Check live Railway deployment and service health during incidents, releases, or support tickets to confirm whether failures are platform, build, or runtime related.

Who is it for?

Developers triaging Railway incidents who need a quick JSON status snapshot before deploys or support responses.

Skip if: Developers who need to change Railway environment variables, push new builds, or reconfigure services.

When should I use this skill?

User asks railway status, what is deployed, whether a service is running, or wants a pre-deploy health check.

What you get

Parsed Railway project, environment, service list, active deployment states, and configured domain URLs.

  • Railway service status summary
  • Active deployment progress report
  • Linked domain URL list

By the numbers

  • Parses 5 status sections: project, environment, services, activeDeployments, domains

Files

SKILL.mdMarkdownGitHub ↗

Design to Code

High-fidelity UI restoration from Figma designs to production-ready React + TypeScript components. This SKILL uses a robust helper script to minimize manual errors and ensure pixel-perfect results.

Prerequisites

1. Figma API Token: Get from Figma → Settings → Personal Access Tokens 2. Node.js: Version 18+ 3. coderio: Installed in scripts/ folder (handled by Setup phase)

Workflow Overview

Phase 0: SETUP    → Create helper script and script environment
Phase 1: PROTOCOL → Generate design protocol (Structure & Props)
Phase 2: CODE     → Generate components and assets

---

Phase 0: Setup

Step 0.1: Initialize Helper Script

User Action: Run these commands to create the execution helper and isolate its dependencies.

mkdir -p scripts

# 1. Copy script files
# Note: Ensure you have the 'skills/design-to-code/scripts' directory available
cp skills/design-to-code/scripts/package.json scripts/package.json
cp skills/design-to-code/scripts/coderio-skill.mjs scripts/coderio-skill.mjs

# 2. Install coderio in scripts directory (adjust version if needed)
cd scripts && pnpm install && cd ..

Step 0.2: Scaffold Project (Optional)

If starting a new project:

1. Run: node scripts/coderio-skill.mjs scaffold-prompt "MyApp" 2. AI Task: Follow the instructions output by the command to create files.

---

Phase 1: Protocol Generation

Step 1.1: Fetch Data

# Replace with your URL and Token
node scripts/coderio-skill.mjs fetch-figma "https://figma.com/file/..." "figd_..."

Verify: process/thumbnail.png should exist.

Step 1.2: Generate Structure

1. Generate Prompt:

    node scripts/coderio-skill.mjs structure-prompt > scripts/structure-prompt.md

2. AI Task (Structure):

  • ATTACH: process/thumbnail.png (MANDATORY)
  • READ: scripts/structure-prompt.md
  • INSTRUCTION: "Generate the component structure JSON based on the prompt and the attached thumbnail. Focus on visual grouping. Use text content to name components accurately (e.g. 'SafeProducts', not 'FAQ')."
  • SAVE: Paste the JSON result into scripts/structure-output.json.

3. Process Result:

    node scripts/coderio-skill.mjs save-structure

Step 1.3: Extract Props (Iterative)

1. List Components:

    node scripts/coderio-skill.mjs list-components

2. For EACH component in the list:

a. Generate Prompt:

    node scripts/coderio-skill.mjs props-prompt "ComponentName" > scripts/current-props-prompt.md

b. AI Task (Props):

  • ATTACH: process/thumbnail.png (MANDATORY)
  • READ: scripts/current-props-prompt.md
  • INSTRUCTION: "Extract props and state data. Be pixel-perfect with text and image paths."
  • SAVE: Paste the JSON result into scripts/ComponentName-props.json.

c. Save & Validate:

    node scripts/coderio-skill.mjs save-props "ComponentName"
    # If this fails, re-do step 'b' with better attention to the thumbnail

---

Phase 2: Code Generation

Step 2.1: Plan Tasks

node scripts/coderio-skill.mjs list-gen-tasks

This outputs a list of tasks with indices (0, 1, 2...).

Step 2.2: Generate Components (Iterative)

For EACH task index (starting from 0):

1. Generate Prompt:

    node scripts/coderio-skill.mjs code-prompt 0 > scripts/code-prompt.md
    # Replace '0' with current task index

2. AI Task (Code):

  • ATTACH: process/thumbnail.png (MANDATORY)
  • READ: scripts/code-prompt.md
  • INSTRUCTION: "Generate the React component code. Match the thumbnail EXACTLY. Use STRICT text content from input data, do not hallucinate."
  • SAVE: Paste the code block into scripts/code-output.txt.

3. Save Code:

    node scripts/coderio-skill.mjs save-code 0
    # Replace '0' with current task index

Step 2.3: Final Integration

Inject the root component into App.tsx. Use the path found in the last task of Phase 2.1.

---

Troubleshooting

  • "Props validation failed": The AI generated empty props. Check if process/thumbnail.png was attached and visible to the AI. Retry the props generation step.
  • "Module not found": Ensure node scripts/coderio-skill.mjs save-code was run for the child component before the parent component. Phase 2 must be done in order (0, 1, 2...).
  • "Visuals don't match": Did you attach the thumbnail? The AI relies on it for spacing and layout nuances not present in the raw data.

Related skills

How it compares

Use railway-status for read-only health checks; pair with railway-deploy or railway-environment when the task mutates deployments or configuration.

FAQ

What command does railway-status run?

railway-status executes railway status --json in the linked project directory after confirming the Railway CLI is installed and railway login succeeded. It parses project, environment, services, activeDeployments, and domains from the JSON output.

When should railway-status not be used?

railway-status is read-only monitoring. Use the railway-environment skill for environment variable or service configuration changes, and railway-deploy for pushing new builds with railway up.

DevOps & CI/CDmonitoringdeploysupport

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