railway-metrics

davila7/claude-code-templates · updated Apr 8, 2026

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$npx skills add https://github.com/davila7/claude-code-templates --skill railway-metrics
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summary

Query resource usage metrics for Railway services.

skill.md

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 → environmentId
  • service.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 format
  • value - 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

How to use railway-metrics on Cursor

AI-first code editor with Composer

1

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
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/davila7/claude-code-templates --skill railway-metrics

The skills CLI fetches railway-metrics from GitHub repository davila7/claude-code-templates and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/railway-metrics

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

GET_STARTED →

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. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 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

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.869 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.

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