metrics

railwayapp/railway-skills · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/railwayapp/railway-skills --skill metrics
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summary

Query CPU, memory, network, and disk usage metrics for Railway services.

  • Supports nine metric types including CPU usage, memory, network traffic (RX/TX), and disk usage across deployments and instances
  • Query metrics for a single service or all services in an environment using optional groupBy parameters (by deployment, instance, region, or service)
  • Requires environmentId from railway status --json ; serviceId is optional to retrieve metrics across all services
  • Time-based queries w
skill.md

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 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"]}')

scripts/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"]}')

scripts/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 status skill or railway status --json
  • Check service health: Use service skill for deployment status
  • View logs: Use deployment skill if metrics show issues
  • Scale service: Use 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 metrics

How to use 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 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/railwayapp/railway-skills --skill metrics

The skills CLI fetches metrics from GitHub repository railwayapp/railway-skills 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/metrics

Reload or restart Cursor to activate metrics. Access the skill through slash commands (e.g., /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

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

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

Ratings

4.672 reviews
  • Pratham Ware· Dec 28, 2024

    We added metrics from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Luis Sethi· Dec 16, 2024

    metrics reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Chinedu Taylor· Dec 16, 2024

    I recommend metrics for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Amina Iyer· Dec 16, 2024

    Solid pick for teams standardizing on skills: metrics is focused, and the summary matches what you get after install.

  • Aanya Flores· Dec 12, 2024

    Registry listing for metrics matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Kofi Menon· Dec 8, 2024

    metrics has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Yash Thakker· Nov 19, 2024

    metrics fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Amina Johnson· Nov 7, 2024

    metrics is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Chinedu Liu· Nov 7, 2024

    Useful defaults in metrics — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Luis Reddy· Nov 3, 2024

    Solid pick for teams standardizing on skills: metrics is focused, and the summary matches what you get after install.

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