metrics-collector

You can collect application metrics in serverless environments by persisting them directly to PostgreSQL or Supabase. This Skill standardizes counters, gauges, and histograms, storing time-series data in your database to avoid external scraping dependencies.

The best way to instrument a NodeJS app for PostgreSQL-backed observability is codifying standardized metric types and naming conventions. This approach persists time-series metrics directly in your database, enabling trend analysis and bottleneck identification.

Yes, this metrics instrumentation approach supports counters, gauges, and histograms. It standardizes these metric types with defined naming conventions and enables time-series aggregation for querying application performance over time.

Yes, you can export metrics to OpenTelemetry from Supabase. This Skill includes optional OpenTelemetry export functionality, allowing you to integrate your database-backed time-series metrics with broader serverless observability platforms.

You track API request latency and agent run duration by using histograms and counters stored in PostgreSQL. This Skill provides standardized instrumentation to automatically record these metrics, making it easy to monitor performance and identify bottlenecks.

You track API request latency and agent run duration by using histograms and counters stored in PostgreSQL. This Skill provides standardized instrumentation to automatically record these metrics, making it easy to monitor performance and identify bottlenecks.