Understanding the contributor view
Clicking any row in the Authors table opens the author deep-dive: a detailed view of a single contributor's patterns, compared against their own pre-AI baseline. This is the most granular view in Scryable.
The metrics grid
The top of the author view shows ten metrics in a grid, each displayed with the current value and a comparison against the contributor's pre-AI baseline. The comparison shows both the baseline figure and the percentage change.
The metrics are the same ones described in Metrics explained, applied to a single contributor rather than the full team. The most useful comparison is usually churn: has this contributor's churn ratio risen materially since AI adoption, and if so, by how much relative to the team average?
Rolling averages chart
The rolling averages chart shows a contributor's commit frequency over time, smoothed across three windows: 7 days, 14 days, and 30 days. The dashed line shows their pre-AI daily average.
The three rolling windows serve different purposes. The 7-day line is the most reactive: it shows recent momentum clearly, but spikes and quiet periods show up as sharp movements. The 30-day line is the most stable: it smooths out weekly variation to show the overall trend. The 14-day line sits between them.
What to look for
A contributor whose 30-day rolling average is consistently above the pre-AI baseline line is shipping more on a sustained basis. Short spikes that don't translate into a rising 30-day average suggest burst activity without sustained change. A declining 30-day average despite higher burst scores may indicate that large AI-assisted pushes are being partially reverted or replaced shortly after.
Hour of week heatmap
The heatmap shows when a contributor commits across the week. Days of the week run vertically; hours of the day run horizontally. Darker cells indicate more commits at that day-hour combination. Two heatmaps are shown side by side: the current period (in purple) and the pre-AI baseline period (in green).
What to look for
Most contributors show a clear cluster of activity during working hours on weekdays. What's interesting is how this pattern changes post-baseline. A spread of activity into late evenings or weekends can indicate that AI tools are encouraging longer or more irregular working patterns. This is worth noting alongside the Weekend % metric.
A heatmap that has become more concentrated (darker cells in a narrower window) suggests that a contributor is achieving more in less time. A heatmap that has spread out suggests the opposite: more activity spread across more hours.
Compare the two heatmaps by looking at the density of the pattern overall, not just individual cells. Small differences are noise. A shift in the overall shape of the pattern is the signal.
Returning to the overview
Use the Back to overview button in the top-right of the author view to return to the team overview. The date range and filter selections you had active are preserved when you navigate between views.