What problem does it solve? Database performance regressions often go unnoticed until they cause outages or degraded user experience. This Skill provides a structured monitoring strategy for PostgreSQL databases, detecting slow queries, connection pool saturation, low cache hit rates, and unused indexes before they become production incidents. ## Core Features & Use Cases - Slow Query Detection: Queries pg_stat_statements to surface queries exceeding configurable latency thresholds with call counts and execution time breakdowns. - Resource & Connection Monitoring: Tracks connection pool utilization, CPU, memory, disk usage, and cache hit rates with warning and critical alert thresholds. - Index Usage Analysis: Identifies unused indexes and tables with high sequential scans that may need indexing. - Automated Scheduling: Uses node-cron to run threshold checks every 5 minutes, hourly reports, and weekly index analysis, plus Grafana dashboard queries. - Use Case: A backend team notices rising API latency. They apply this Skill to generate a performance report, discover three queries averaging over 500ms, find a missing index on a high-traffic table, and set up automated alerts to catch future regressions. ## Quick Start Ask the AI to analyze your PostgreSQL database for slow queries and connection pool issues using the db performance watchlist monitoring strategy.
