railway-metrics▌
davila7/claude-code-templates · updated Apr 8, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
Query resource usage metrics for Railway services.
Railway Service Metrics
Query resource usage metrics for Railway services.
When to Use
- User asks "how much memory is my service using?"
- User asks about CPU usage, network traffic, disk usage
- User wants to debug performance issues
- User asks "is my service healthy?" (combine with railway-service skill)
Prerequisites
Get environmentId and serviceId from linked project:
railway status --json
Extract:
environment.id→ environmentIdservice.id→ serviceId (optional - omit to get all services)
MetricMeasurement Values
| Measurement | Description |
|---|---|
| CPU_USAGE | CPU usage (cores) |
| CPU_LIMIT | CPU limit (cores) |
| MEMORY_USAGE_GB | Memory usage in GB |
| MEMORY_LIMIT_GB | Memory limit in GB |
| NETWORK_RX_GB | Network received in GB |
| NETWORK_TX_GB | Network transmitted in GB |
| DISK_USAGE_GB | Disk usage in GB |
| EPHEMERAL_DISK_USAGE_GB | Ephemeral disk usage in GB |
| BACKUP_USAGE_GB | Backup usage in GB |
MetricTag Values (for groupBy)
| Tag | Description |
|---|---|
| DEPLOYMENT_ID | Group by deployment |
| DEPLOYMENT_INSTANCE_ID | Group by instance |
| REGION | Group by region |
| SERVICE_ID | Group by service |
Query
query metrics(
$environmentId: String!
$serviceId: String
$startDate: DateTime!
$endDate: DateTime
$sampleRateSeconds: Int
$averagingWindowSeconds: Int
$groupBy: [MetricTag!]
$measurements: [MetricMeasurement!]!
) {
metrics(
environmentId: $environmentId
serviceId: $serviceId
startDate: $startDate
endDate: $endDate
sampleRateSeconds: $sampleRateSeconds
averagingWindowSeconds: $averagingWindowSeconds
groupBy: $groupBy
measurements: $measurements
) {
measurement
tags {
deploymentInstanceId
deploymentId
serviceId
region
}
values {
ts
value
}
}
}
Example: Last Hour CPU and Memory
Use heredoc to avoid shell escaping issues:
bash <<'SCRIPT'
START_DATE=$(date -u -v-1H +"%Y-%m-%dT%H:%M:%SZ" 2>/dev/null || date -u -d "1 hour ago" +"%Y-%m-%dT%H:%M:%SZ")
ENV_ID="your-environment-id"
SERVICE_ID="your-service-id"
VARS=$(jq -n \
--arg env "$ENV_ID" \
--arg svc "$SERVICE_ID" \
--arg start "$START_DATE" \
'{environmentId: $env, serviceId: $svc, startDate: $start, measurements: ["CPU_USAGE", "MEMORY_USAGE_GB"]}')
${CLAUDE_PLUGIN_ROOT}/skills/lib/railway-api.sh \
'query metrics($environmentId: String!, $serviceId: String, $startDate: DateTime!, $measurements: [MetricMeasurement!]!) {
metrics(environmentId: $environmentId, serviceId: $serviceId, startDate: $startDate, measurements: $measurements) {
measurement
tags { deploymentId region serviceId }
values { ts value }
}
}' \
"$VARS"
SCRIPT
Example: All Services in Environment
Omit serviceId and use groupBy to get metrics for all services:
bash <<'SCRIPT'
START_DATE=$(date -u -v-1H +"%Y-%m-%dT%H:%M:%SZ" 2>/dev/null || date -u -d "1 hour ago" +"%Y-%m-%dT%H:%M:%SZ")
ENV_ID="your-environment-id"
VARS=$(jq -n \
--arg env "$ENV_ID" \
--arg start "$START_DATE" \
'{environmentId: $env, startDate: $start, measurements: ["CPU_USAGE", "MEMORY_USAGE_GB"], groupBy: ["SERVICE_ID"]}')
${CLAUDE_PLUGIN_ROOT}/skills/lib/railway-api.sh \
'query metrics($environmentId: String!, $startDate: DateTime!, $measurements: [MetricMeasurement!]!, $groupBy: [MetricTag!]) {
metrics(environmentId: $environmentId, startDate: $startDate, measurements: $measurements, groupBy: $groupBy) {
measurement
tags { serviceId region }
values { ts value }
}
}' \
"$VARS"
SCRIPT
Time Parameters
| Parameter | Description |
|---|---|
| startDate | Required. ISO 8601 format (e.g., 2024-01-01T00:00:00Z) |
| endDate | Optional. Defaults to now |
| sampleRateSeconds | Sample interval (e.g., 60 for 1-minute samples) |
| averagingWindowSeconds | Averaging window for smoothing |
Tip: For last hour, calculate startDate as now - 1 hour in ISO format.
Output Interpretation
{
"data": {
"metrics": [
{
"measurement": "CPU_USAGE",
"tags": { "deploymentId": "...", "serviceId": "...", "region": "us-west1" },
"values": [
{ "ts": "2024-01-01T00:00:00Z", "value": 0.25 },
{ "ts": "2024-01-01T00:01:00Z", "value": 0.30 }
]
}
]
}
}
ts- timestamp in ISO formatvalue- metric value (cores for CPU, GB for memory/disk/network)
Composability
- Get IDs: Use railway-status skill or
railway status --json - Check service health: Use railway-service skill for deployment status
- View logs: Use railway-deployment skill if metrics show issues
- Scale service: Use railway-environment skill to adjust resources
Error Handling
Empty/Null Metrics
Services without active deployments return empty metrics arrays. When processing with jq, handle nulls:
# Safe iteration - skip nulls
jq -r '.data.metrics[]? | select(.values != null and (.values | length) > 0) | ...'
# Check if metrics exist before processing
jq -e '.data.metrics | length > 0' response.json && echo "has metrics"
No Metrics Data
Service may be new or have no traffic. Check:
- Service has active deployment (stopped services have no metrics)
- Time range includes deployment period
Invalid Service/Environment ID
Verify IDs with railway status --json.
Permission Denied
User needs access to the project to query metrics.
How to use railway-metrics 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-metrics
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches railway-metrics 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-metrics. Access the skill through slash commands (e.g., /railway-metrics) 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
4.8★★★★★69 reviews- ★★★★★Ganesh Mohane· Dec 24, 2024
Keeps context tight: railway-metrics is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Min Abebe· Dec 24, 2024
Keeps context tight: railway-metrics is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Evelyn Choi· Dec 16, 2024
I recommend railway-metrics for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Min Srinivasan· Dec 16, 2024
railway-metrics fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Kaira Thomas· Dec 8, 2024
We added railway-metrics from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Kaira Li· Nov 27, 2024
Solid pick for teams standardizing on skills: railway-metrics is focused, and the summary matches what you get after install.
- ★★★★★Rahul Santra· Nov 15, 2024
Registry listing for railway-metrics matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Jin Bansal· Nov 15, 2024
Registry listing for railway-metrics matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Zaid Gill· Nov 7, 2024
railway-metrics has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Ishan Gill· Nov 3, 2024
Useful defaults in railway-metrics — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
showing 1-10 of 69