zegl/kube-score

Kubernetes object analysis with recommendations for improved reliability and security. kube-score actively prevents downtime and bugs in your Kubernetes...

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Summary Information

Updated 2 hours ago
Added to GitGenius on September 22nd, 2026
Created on September 16th, 2018
Open Issues & Pull Requests: 54 (+0)
GitHub issues: Enabled
Number of forks: 203
Total Stargazers: 3,104 (+0)
Total Subscribers: 25 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 20.1 hours
Mean response time: 46.9 days
90th percentile: 234.3 days
Tracked items: 14

Charts & Analytics

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Issue Activity (beta)

Open issues: 11
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 746 days
Stale 30+ days: 10
Stale 90+ days: 10

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • waiting for info (2)
  • help wanted (1)

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Detailed Description

kube-score is a static code analyzer for Kubernetes manifests and Helm charts that scans object definitions and produces recommendations for improved reliability and security.

The tool addresses the problem of catching configuration issues before deployment by performing static analysis on Kubernetes YAML files. It examines aspects like container resource limits, network policies, pod disruption budgets, pod anti-affinity rules, health probes, security contexts, and API stability. The analysis produces a prioritized list of recommendations, with findings categorized by severity level. The tool integrates into CI/CD pipelines and can fail builds when critical issues are detected or optionally when warnings are present.

Teams should adopt this tool if they want to enforce Kubernetes best practices across their manifests before deployment. It suits projects using Helm charts, Kustomize, static YAML files, or existing clusters. The tool works well when analyzing all applications deployed to the same namespace together for comprehensive results. Individual checks can be ignored or enabled per-object using Kubernetes annotations, and globally via command-line flags, allowing teams to tailor the analysis to their specific requirements and risk tolerance.

The project maintains active development with regular commits addressing bug fixes and feature additions. The codebase shows consistent refinement of existing checks and expansion of the analysis capabilities. Community engagement is present through issue tracking and pull request activity. The tool has broad distribution availability including package managers and container registries, indicating sustained maintenance across multiple deployment channels.