What problem does it solve?
It helps you rapidly pinpoint the root cause of Kubernetes workload, networking, and storage failures by turning vague symptoms into a structured, evidence-driven troubleshooting path.
Core Features & Use Cases
- Deterministic debugging workflow: Guides you through preflight, problem-layer identification, diagnostics collection, reference navigation, targeted fixes, and verification.
- Focused diagnostic scripts for common failure modes: Cluster health snapshots, pod-level diagnostics, and network/DNS investigations with clear output and guardrails.
- Safety-first handling for disruptive actions: Requires snapshot/rollback planning before destructive or high-impact commands.
Use case: When a deployment starts producing CrashLoopBackOff pods and DNS/service connectivity complaints, run the right pod diagnostics and follow the symptom-mapped reference sections to identify whether the cause is application crashes, image pull/auth issues, resource pressure, DNS/CoreDNS health, network policy blocking, or storage/PVC failures.
Quick Start
Run the pod diagnostics for your failing pod by executing: python3 ./scripts/pod_diagnostics.py <pod-name> -n <namespace>.