post-deployment-monitor

Automate Kubernetes post-deployment health checks by correlating code diffs with observability metrics to generate a structured health report. This process retrieves deployment images, analyzes commit diffs, and validates Grafana metrics against deployment time boundaries.

Correlate code changes with Grafana metrics by building and validating queries against deployment time boundaries. The workflow analyzes complete commit diffs across all commits, loads repository skills to map changes to flows, and validates metrics to detect regressions.

You can monitor deployments across multiple Kubernetes clusters and namespaces. The health monitoring workflow applies to Kubernetes deployments across your infrastructure, enabling full lifecycle support from diff loading to metric validation for multiple flows.

The best way to detect performance regressions is by correlating code changes with production observability metrics. This approach retrieves deployment images, analyzes complete commit diffs, and validates metrics against deployment-time boundaries to produce a deterministic health assessment.

You need repository skills to map code changes to affected flows and endpoints. The workflow loads and applies these skills to identify impacted areas, then orchestrates a multi-phase process that analyzes code diffs and produces a structured health assessment.