Agent skill · data analytics · jeremylongshore
monitoring-apis
Build real-time API monitoring dashboards with metrics, alerts, and
Why this skill is useful
Adds executable scripts for Prometheus metrics collection, health check endpoints, and Grafana dashboard configurations that extend monitoring capabilities.
What it needs
Requires cloudwatch, datadog, grafana, newrelic, prometheus installed locally. Requires pagerduty, slack account access. About 3k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
Monitoring APIs Overview Build real-time API monitoring with metrics collection (request rate, latency percentiles, error rates), health check endpoints, and alerting rules. Instrument API middleware to emit Prometheus metrics or StatsD counters, configure Grafana dashboards with SLO tracking, and implement synthetic monitoring probes for uptime verification. Prerequisites Prometheus + Grafana stack, or Datadog/New Relic/CloudWatch for metrics and dashboards Metrics client library: prom-client (Node.js), prometheusclient (Python), or Micrometer (Java) Alerting channel configured: PagerDuty, Slack webhook, or email for alert routing Structured logging library: Winston, Pino (Node.js), structlog (Python), or Logback (Java) Synthetic monitoring tool: Checkly, Uptime Robot, or custom cron-based health probes Instructions 1. Examine existing middleware and logging setup using Grep and Read to identify current observability coverage and gaps. 2. Implement metrics middleware that records per-request data: httprequestdurationseconds histogram (with method, path, status labels), httprequeststotal counter, and httprequestsinflight gauge. 3. Create a /health endpoint returning structured health status including dependency checks (database connectivity, cache availability, external service reachability) with response time for each. 4. Add a /ready endpoint separate from health that returns 503 during startup initialization and graceful shutdown, for load balancer integration. 5. Configure histogram buckets aligned with SLO targets: [0.01, 0.05, 0.1, 0.25, 0.5, 1, 2.5, 5, 10] seconds for comprehensive latency distribution. 6. Build Grafana dashboard panels: request rate (QPS), p50/p95/p99 latency, error rate percentage, active connections, and per-endpoint breakdown. 7. Define alerting rules: error rate > 5% for 5 minutes (critical), p99 latency > 2s for 10 minutes (warning), health check failure for 3 consecutive probes (critical). 8. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/monitoring-apis--e82b12