Pull requests in AI SDLC Insights
AI SDLC Insights now has a Pull requests tab. See every pull request across your repositories with the AI sessions behind it, what they cost, and how long they took to merge, then drag across a heatmap to find the outliers.
AI SDLC Insights shows what your coding agents cost and how assisted pull requests move through review, but only in aggregate. When a number looked off, you could not see which pull requests were behind it, or what the agents did on any one of them.
The new Pull requests tab, next to Sessions, lists every pull request across your repositories. Each row shows the repository, author, type, state, reviewers, and how long it has been open, and the list follows your time range like every other tab.
What's new
AI sessions per pull request: Click a pull request to open a side panel with the AI sessions that worked on it, the models they used, their tokens, and what they cost. One click takes you to those sessions in the Sessions tab.
A heatmap you can select from: The chart above the list plots pull requests by AI cost or by time to merge. Drag a box over the cells that stand out, such as the most expensive pull requests on one day or the merges that took more than a week, and the list shows only those. Keep the selection as filters and it survives a reload or a shared link.
Status filter: List the pull requests that are open, awaiting review, draft, merged, or closed.
From a chart to what's behind it: Click a chart such as investment distribution, cycle time, or AI penetration and choose Explore. The Pull requests tab opens on the pull requests that chart counted, with your filters kept. The coding charts work the same way: click a model, skill, MCP server, or team and Explore opens the Sessions tab on the sessions it counted.
Totals at a glance: Switch the chart to Stats for total pull requests, AI-assisted pull requests, and total AI cost across the whole list, or hide it to give the list the full page.