Turn one giant AI-generated pull request to a reviewable stack

GitHub has introduced native support for stacked pull requests—a way to decompose large features into smaller, reviewable layers. According to the GitHub Blog, as coding agents grow more productive, they tend to generate enormous single pull requests that pile data models, APIs, and UI all into one thousand-line diff. Reviewers struggle to hold the context, feedback quality drops, and the entire process stalls before the code even lands. Stacked pull requests flip that dynamic. Instead of one mega-PR, you break the feature into logical layers: data foundation, API endpoint, application wiring, UI and citations. Each gets its own focused pull request, small enough to review carefully. GitHub's native tooling—the gh-stack CLI extension and new agent skills—lets both humans and AI agents structure work this way by default. For teams deploying coding agents at scale, this tool addresses a real tension: agents can ship fast, but reviewability can't keep pace.

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