Query resource usage metrics for Railway services.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionrailway-metricsExecute the skills CLI command in your project's root directory to begin installation:
Fetches railway-metrics from davila7/claude-code-templates and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate railway-metrics. Access via /railway-metrics in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
0
total installs
0
this week
24.2K
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
24.2K
stars
Query resource usage metrics for Railway services.
Get environmentId and serviceId from linked project:
railway status --json
Extract:
environment.id → environmentIdservice.id → serviceId (optional - omit to get all services)| 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 |
| Tag | Description |
|---|---|
| DEPLOYMENT_ID | Group by deployment |
| DEPLOYMENT_INSTANCE_ID | Group by instance |
| REGION | Group by region |
| SERVICE_ID | Group by service |
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: $measurements
) {
measurement
tags {
deploymentInstanceId
deploymentId
serviceId
region
}
values {
ts
value
}
}
}
Use heredoc to avoid shell escaping issues:
bash <<'SCRIPT'
START_DATE=$(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
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
| 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.
{
"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)railway status --jsonServices 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"
Service may be new or have no traffic. Check:
Verify IDs with railway status --json.
User needs access to the project to query metrics.
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
davila7/claude-code-templates
davila7/claude-code-templates
davila7/claude-code-templates
davila7/claude-code-templates
davila7/claude-code-templates
davila7/claude-code-templates
Keeps context tight: railway-metrics is the kind of skill you can hand to a new teammate without a long onboarding doc.
Keeps context tight: railway-metrics is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend railway-metrics for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
railway-metrics fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added railway-metrics from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: railway-metrics is focused, and the summary matches what you get after install.
Registry listing for railway-metrics matched our evaluation — installs cleanly and behaves as described in the markdown.
Registry listing for railway-metrics matched our evaluation — installs cleanly and behaves as described in the markdown.
railway-metrics has been reliable in day-to-day use. Documentation quality is above average for community skills.
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