:gorilla: Kong for Kubernetes: The official Ingress Controller for Kubernetes.
C-Rank Grade: A (Welcoming) - 64/100
External PR Merge Rate: 86%
Response Time: 1d
First Timer Success: 57%
Kong/kubernetes-ingress-controller has a recorded first-timer success rate of 57.1%. Repositories ranked A typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in Kong/kubernetes-ingress-controller respond to incoming external pull requests in approximately 26.6 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 64.4 places Kong/kubernetes-ingress-controller in the A tier.
The external contributor pull request merge rate for Kong/kubernetes-ingress-controller is 85.8%, based on public PR activity from non-core contributors.
Kong/kubernetes-ingress-controller does not currently have active "good first issue" tags indexed, but accepts external contributions through standard GitHub issue tracking.
Average Response Latency
Tracks hours until a maintainer leaves a review, comment, or PR response.
External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
Rate at which developers submitting their first repository PR succeed.
Show contributors that your repository actively reviews and merges external pull requests.
[](https://getmerged.abhishekco.de/Kong/kubernetes-ingress-controller?utm_source=github&utm_medium=badge)
Maintainer review activity heatmap & prime window prediction
Calculating local time...
Calculating local time...
Detected: UTC
Maintainers are most responsive around 17:00 UTC (17:00 UTC in your timezone). Recommended submission window is Monday - Thursday from 15:00 to 19:00 UTC (15:00 - 19:00 UTC local).
Limited recent maintainer review activity recorded for this repository. Prediction based on heuristic baseline.
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No cached good first issues currently tracked for Kong/kubernetes-ingress-controller.
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When evaluating whether to contribute to Kong/kubernetes-ingress-controller, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.
Currently, maintainers of Kong/kubernetes-ingress-controller acknowledge new external contributions in approximately 1 days+. Out of all submitted pull requests from non-core authors in the last 180-day window, 85.8% were successfully merged into the primary branch.
Kong/kubernetes-ingress-controller has a recorded first-timer success rate of 57.1%. Repositories ranked Welcoming typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~1 days+. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
The C-Rankâ„¢ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 64.4 places Kong/kubernetes-ingress-controller in the Welcoming tier.
The external pull request merge rate is 85.8%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Kong/kubernetes-ingress-controller does not have open beginner labels indexed currently, but external PRs for bugs and documentation improvements are evaluated via normal issue triage.
All metrics displayed for Kong/kubernetes-ingress-controller are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.