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Railway Templates

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

railway-templates is an agent skill that searches and deploys Railway marketplace templates for developers who add preconfigured services like Ghost, Strapi, or n8n to cloud projects.

About

railway-templates is a version 1.0.0 MIT agent skill from davila7/claude-code-templates that searches and deploys services from Railway's template marketplace using railway-cli and a GraphQL helper script. Allowed tools are Bash scoped to railway:* commands. The skill documents common template codes such as ghost, strapi, n8n, minio, and uptime-kuma, while directing database-only adds to the companion railway-database skill. Workflows fetch verified templates, read serializedConfig JSON, and deploy into a target Railway project and environment. Developers invoke railway-templates when they need CMS, automation, storage, or monitoring services without browsing the Railway UI. Prerequisites include railway status for project and environment IDs plus workspace context from the Railway API.

  • Railway starter template selection
  • Pre-wired service scaffolding
  • Dockerfile and build config
  • Faster cloud project bootstrap
  • Multi-service template patterns

Railway Templates by the numbers

  • 285 all-time installs (skills.sh)
  • Ranked #343 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-templates

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Listed on Skillselion
Installs285
repo stars30.1k
Last updatedAugust 4, 2026
Repositorydavila7/claude-code-templates

How do you deploy Railway template services?

Bootstrap new projects from Railway starter templates with prewired services, Dockerfiles, and deploy configs to accelerate cloud-ready scaffolding.

Who is it for?

Engineers using Railway who want agent-driven template search and one-shot deployment of CMS, automation, or monitoring services.

Skip if: Non-Railway hosting platforms or database-only provisioning better handled by the railway-database skill.

When should I use this skill?

A developer asks to add Ghost, Strapi, n8n, find Railway templates, or deploy a preconfigured Railway marketplace service.

What you get

Deployed Railway service from marketplace template, template search results, and serializedConfig deployment payload.

  • Deployed Railway service
  • Template search results
  • serializedConfig deployment

By the numbers

  • Skill version 1.0.0 with MIT license and railway-cli dependency
  • GraphQL template search defaults to first 20 verified templates

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-templates for marketplace app services; switch to railway-database when the task is provisioning Postgres, Redis, or other data stores.

FAQ

Which Railway templates does railway-templates document?

railway-templates documents common codes including ghost, strapi, n8n, minio, and uptime-kuma across CMS, automation, storage, and monitoring categories. Additional templates are discovered via the Railway GraphQL search query.

Should railway-templates deploy Postgres or Redis?

railway-templates defers database templates such as Postgres, Redis, MySQL, and MongoDB to the railway-database skill. railway-templates focuses on non-database marketplace services and tooling deployments.

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