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

Summary

Coding agents often consolidate full-stack features into monolithic pull requests spanning thousands of lines, resulting in delayed reviews and lower feedback quality. To resolve this bottleneck, engineers can decompose comprehensive features into ordered, single-concern layers using GitHub stacked pull requests and the gh-stack CLI extension. Distinct specialized agents handle individual branches—ranging from foundational data access modules up to client interfaces—while CI checks continuously evaluate each layer against the stack base. When reviewers request upstream modifications, running gh stack sync executes a cascading rebase across all dependent branches, preserving commit signatures and keeping the entire pull request chain mergeable.

Context

AI coding agents frequently generate massive, multi-layer pull requests with over a thousand lines of diff, which causes review bottlenecks, lower feedback quality, and merge delays.

Approach / What changed

Decomposing feature implementations into logically ordered, single-concern layers using GitHub stacked pull requests, the gh-stack CLI extension, and specialized coding agents assigned to each layer.

Takeaways

• Reviewers navigate stacked pull requests top-down to establish end-goal context, but perform the actual code review bottom-up starting from the foundational layer.
• Web-based stack rebasing on GitHub resets the committer and strips commit signatures, making local rebases with the gh stack CLI safer for repositories with signature verification rules.
• The gh stack sync command automates stack maintenance by fetching origin updates, cascading rebases through upper branches, and synchronizing remote pull request states.

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