Extension that shows information about the estimated token usage and more of AI in editors/CLI's
C-Rank Grade: S (Elite) - 75/100
External PR Merge Rate: 84%
Response Time: <1h
First Timer Success: 100%
rajbos/ai-engineering-fluency has a recorded first-timer success rate of 100.0%. Repositories ranked S typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in rajbos/ai-engineering-fluency respond to incoming external pull requests in approximately 0.5 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 75.1 places rajbos/ai-engineering-fluency in the S tier.
The external contributor pull request merge rate for rajbos/ai-engineering-fluency is 83.6%, based on public PR activity from non-core contributors.
rajbos/ai-engineering-fluency currently has 1 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Extension that shows information about the estimated token usage and more of AI in editors/CLI's
Strict review standards in rajbos/ai-engineering-fluency. Ensure PR scope matches issue requirements closely before requesting review.
Discussions in rajbos/ai-engineering-fluency focus heavily on practical implementation feedback, code formatting standards, and issue reproduction details.
Unlinked PRs without issue context and changes that fail automated test suites face the highest review friction.
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/rajbos/ai-engineering-fluency?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 07:00 UTC (07:00 UTC in your timezone). Recommended submission window is Monday - Wednesday from 07:00 to 11:00 UTC (07:00 - 11:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~4 hours (96% approval). Large PRs (>500 lines) take ~2 hours (38% approval). Keep your diff compact for rapid review.
Is your feature request related to a problem? Please describe. It would be great if we can find and ingest the data used in from other editors and show the same info Claude Code in #443 Claude Cowork in #572 Cursor Eclipse in #1511 Windsurf from inside Windsurf in #625 Mistral Vibe in #640 Crush (fork of OpenCode with new direction) in #451 Support for the Continue extension with Ollama in #447 Support for Visual Studio sessions in #477 Pi.ai in #1317 JetBrains session support in #719, JetBrains extension as host in #733 Goose CLI agent (Block/AAIF) in #1490 Cline VS Code extension in #1491 Aider AI coding CLI in #1492 OpenAI Codex CLI in #1493 ZooCode VS Code extension in #1494 Zed IDE AI threads in #1495 Kiro in #1559 Kilo Code in #2000
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When evaluating whether to contribute to rajbos/ai-engineering-fluency, 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 rajbos/ai-engineering-fluency acknowledge new external contributions in approximately <1 hour. Out of all submitted pull requests from non-core authors in the last 180-day window, 83.6% were successfully merged into the primary branch.
rajbos/ai-engineering-fluency has a recorded first-timer success rate of 100.0%. Repositories ranked Elite typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~<1 hour. 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 75.1 places rajbos/ai-engineering-fluency in the Elite tier.
The external pull request merge rate is 83.6%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, rajbos/ai-engineering-fluency currently has 1 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.
All metrics displayed for rajbos/ai-engineering-fluency 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.