A Git extension for tracking the AI-generated code in your repos
C-Rank Grade: A (Welcoming) - 62/100
External PR Merge Rate: 47%
Response Time: -
First Timer Success: 27%
git-ai-project/git-ai has a recorded first-timer success rate of 26.9%. Repositories ranked A typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in git-ai-project/git-ai respond to incoming external pull requests in approximately 0.0 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 62.4 places git-ai-project/git-ai in the A tier.
The external contributor pull request merge rate for git-ai-project/git-ai is 46.5%, based on public PR activity from non-core contributors.
git-ai-project/git-ai currently has 12 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Balanced & professional review environment in git-ai-project/git-ai. Maintains strict focus on technical quality and test standards.
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/git-ai-project/git-ai?utm_source=github&utm_medium=badge)
Historical health & responsiveness evolution
External PR acceptance
First PR success rate
50% receive response in
90% receive response in
Maintainer review activity heatmap & prime window prediction
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Detected: UTC
Maintainers are most responsive around 14:00 UTC (14:00 UTC in your timezone). Recommended submission window is Monday - Wednesday from 14:00 to 18:00 UTC (14:00 - 18:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~16 hours (58% approval). Large PRs (>500 lines) take ~115 hours (21% approval). Keep your diff compact for rapid review.
Add a Junie CLI agent preset so that git-ai can track AI-authored code from JetBrains Junie sessions. Must await Junie hooks support for file edits and bash
Hey, thanks for working on git-ai. I tried the VS Code extension — committed AI attribution shows up nicely, but newly accepted AI edits don't have any attribution in the editor until they're committed. In an IDE, the pre-commit moment is exactly when I most want to see the original prompt that generated this code. Suggested behavior: Committed lines → stable attribution from refs/notes/ai (already works) Uncommitted lines with checkpoint data → temporary attribution, e.g. Claude Code · uncommitted New lines without checkpoint data → unknown/untracked, not guessed Important: I'm not asking git-ai to detect AI-written code heuristically. This would only surface attribution that git-ai already has in .git/ai/working_logs/ from checkpoints, including the associated prompt. Maybe this could be exposed through a stable CLI surface, for example by letting git ai blame optionally include working-tree attribution when given the current file contents. That would let the VS Code exten
Using GitHub Copilot in JetBrains (PyCharm) git ai logs the model for the changes made by GitHub Copilot always as unknown: "agent_id": { "tool": "github-copilot-jetbrains", "id": "1777449178xxx", "model": "unknown" }, Looking into the git ai source code the model is simply not set, in DocumentChangeListener.kt line 190, when calling AgentV1Input.AiAgent(). Resulting in the default value of unknown (CheckpointModels.kt line 79). It would be really nice if git ai could also keep track of the models in the JetBrains IDE's.
Hi, From the description I could not find out if Codeberg, QWen or Kimi are supported and how to set up for that. Any pointers (if it is possible)? Warm Regards Rob Oudendijk
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When evaluating whether to contribute to git-ai-project/git-ai, 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 git-ai-project/git-ai acknowledge new external contributions in approximately -. Out of all submitted pull requests from non-core authors in the last 180-day window, 46.5% were successfully merged into the primary branch.
git-ai-project/git-ai has a recorded first-timer success rate of 26.9%. Repositories ranked Welcoming typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~-. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
The C-Rank™ 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 62.4 places git-ai-project/git-ai in the Welcoming tier.
The external pull request merge rate is 46.5%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, git-ai-project/git-ai currently has 12 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.
All metrics displayed for git-ai-project/git-ai 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.