Scryable pulls each engineer's commit behaviour, working rhythms, churn ratios, and AI adoption delta across all connected repos into a single view. The patterns that only become visible when you see all of someone's work together.
Commit counts, additions, deletions, and churn for each contributor, aggregated across all the repos they touch. Useful for people whose work spans multiple codebases and isn't visible in any single repo view.
An hour-of-week heatmap showing each developer's commit activity by day and hour. Useful for understanding collaboration patterns, time zone considerations, and whether work is concentrated in unsustainable windows.
7-day, 30-day, 90-day and 1-year rolling averages that smooth out the natural variation in commit activity and show genuine directional change. A quiet week looks very different from a sustained decline. Full history available on paid plans.
Velocity, churn, and code quality metrics benchmarked against each contributor's own pre-AI baseline. You can see which engineers have adapted well to AI tooling, where output patterns have changed, and in what direction.
Most engineering teams are spread across multiple repos. An individual contributor might be active in three or four of them, and their full picture is only visible when you look across all of it at once. Scryable aggregates each contributor's commit behaviour, working rhythms, churn ratios, and AI adoption delta across every repo they touch.
You can see when each engineer is most active, how their output has trended over time, where their commit quality is strongest, and how their habits and practices have shifted since AI tools entered the workflow. None of that is available in any single repo view, and assembling it manually from multiple sources isn't practical.
This is not about rating code quality or producing a ranking. It's about giving managers the context to have better conversations, without building that context by hand.
The hour-of-week heatmap shows when each contributor is most active across the week. Combined with rolling averages and the AI adoption delta, it gives you a complete picture of how each engineer works without needing to ask them.
All of it aggregated across every repo they touch, updated automatically from git history.
Connect your repos and see contributor patterns across all of them and understand how they're using AI.
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