railway-deployment▌
davila7/claude-code-templates · updated Apr 8, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
Manage existing Railway deployments: list, view logs, redeploy, or remove.
Railway Deployment Management
Manage existing Railway deployments: list, view logs, redeploy, or remove.
Important: "Remove deployment" (railway down) stops the current deployment but keeps the service. To delete a service entirely, use the railway-environment skill with isDeleted: true.
When to Use
- User says "remove deploy", "take down service", "stop deployment", "railway down"
- User wants to "redeploy", "restart the service", "restart deployment"
- User asks to "list deployments", "show deployment history", "deployment status"
- User asks to "see logs", "show logs", "check errors", "debug issues"
List Deployments
railway deployment list --limit 10 --json
Shows deployment IDs, statuses, and metadata. Use to find specific deployment IDs for logs or debugging.
Specify Service
railway deployment list --service backend --limit 10 --json
View Logs
Deploy Logs
railway logs --lines 100 --json
In non-interactive mode, streaming is auto-disabled and CLI fetches logs then exits.
Build Logs
railway logs --build --lines 100 --json
For debugging build failures or viewing build output.
Logs for Failed/In-Progress Deployments
By default railway logs shows the last successful deployment. Use --latest for current:
railway logs --latest --lines 100 --json
Filter Logs
# Errors only
railway logs --lines 50 --filter "@level:error" --json
# Text search
railway logs --lines 50 --filter "connection refused" --json
# Combined
railway logs --lines 50 --filter "@level:error AND timeout" --json
Time-Based Filtering
# Logs from last hour
railway logs --since 1h --lines 100 --json
# Logs between 30 and 10 minutes ago
railway logs --since 30m --until 10m --lines 100 --json
# Logs from specific timestamp
railway logs --since 2024-01-15T10:00:00Z --lines 100 --json
Formats: relative (30s, 5m, 2h, 1d, 1w) or ISO 8601 timestamps.
Logs from Specific Deployment
Deploy logs:
railway logs <deployment-id> --lines 100 --json
Build logs:
railway logs --build <deployment-id> --lines 100 --json
Get deployment ID from railway deployment list.
Note: The deployment ID is a positional argument, NOT --deployment <id>. The --deployment flag is a boolean that selects deploy logs (vs --build for build logs).
Redeploy
Redeploy the most recent deployment:
railway redeploy --service <name> -y
The -y flag skips confirmation. Useful when:
- Config changed via railway-environment skill
- Need to restart without new code
- Previous deploy succeeded but service misbehaving
Restart Container Only
Restart without rebuilding (picks up external resource changes):
railway restart --service <name> -y
Use when external resources (S3 files, config maps) changed but code didn't.
Remove Deployment
Takes down the current deployment. The service remains but has no running deployment.
# Remove deployment for linked service
railway down -y
# Remove deployment for specific service
railway down --service web -y
railway down --service api -y
This is what users mean when they say "remove deploy", "take down", or "stop the deployment".
Note: This does NOT delete the service. To delete a service entirely, use the railway-environment skill with isDeleted: true.
CLI Options
deployment list
| Flag | Description |
|---|---|
-s, --service <NAME> |
Service name or ID |
-e, --environment <NAME> |
Environment name or ID |
--limit <N> |
Max deployments (default 20, max 1000) |
--json |
JSON output |
logs
| Flag | Description |
|---|---|
-s, --service <NAME> |
Service name or ID |
-e, --environment <NAME> |
Environment name or ID |
-d, --deployment |
Show deploy logs (default, boolean flag) |
-b, --build |
Show build logs (boolean flag) |
-n, --lines <N> |
Number of lines (required) |
-f, --filter <QUERY> |
Filter using query syntax |
--since <TIME> |
Start time (relative or ISO 8601) |
--until <TIME> |
End time (relative or ISO 8601) |
--latest |
Most recent deployment (even if failed) |
--json |
JSON output |
[DEPLOYMENT_ID] |
Specific deployment (optional) |
redeploy
| Flag | Description |
|---|---|
-s, --service <NAME> |
Service name or ID |
-y, --yes |
Skip confirmation |
restart
| Flag | Description |
|---|---|
-s, --service <NAME> |
Service name or ID |
-y, --yes |
Skip confirmation |
down
| Flag | Description |
|---|---|
-s, --service <NAME> |
Service name or ID |
-e, --environment <NAME> |
Environment name or ID |
-y, --yes |
Skip confirmation |
Presenting Logs
When showing logs:
- Include timestamps
- Highlight errors and warnings
- For build failures: show error and suggest fixes
- For runtime crashes: show stack trace context
- Summarize patterns (e.g., "15 timeout errors in last 100 logs")
Composability
- Push new code: Use railway-deploy skill
- Check service status: Use railway-status skill
- Fix config issues: Use railway-environment skill
- Create new service: Use railway-new skill
Error Handling
No Service Linked
No service linked. Run `railway service` to select one.
No Deployments Found
No deployments found. Deploy first with `railway up`.
No Logs Found
Deployment may be too old (log retention limits) or service hasn't produced output.
How to use railway-deployment on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add railway-deployment
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches railway-deployment from GitHub repository davila7/claude-code-templates and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate railway-deployment. Access the skill through slash commands (e.g., /railway-deployment) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★49 reviews- ★★★★★Ganesh Mohane· Dec 24, 2024
I recommend railway-deployment for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Kofi Sharma· Dec 20, 2024
Keeps context tight: railway-deployment is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Dev Haddad· Dec 16, 2024
Useful defaults in railway-deployment — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Anaya Gupta· Dec 8, 2024
Registry listing for railway-deployment matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Lucas Nasser· Nov 27, 2024
railway-deployment fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Anaya Tandon· Nov 23, 2024
railway-deployment reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Neel Sethi· Nov 11, 2024
railway-deployment is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Neel Anderson· Nov 7, 2024
We added railway-deployment from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Yusuf Patel· Oct 26, 2024
Solid pick for teams standardizing on skills: railway-deployment is focused, and the summary matches what you get after install.
- ★★★★★Emma Sharma· Oct 18, 2024
railway-deployment is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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