GitHub stacked PRs let coding agents break giant features into reviewable layers

Coding agents are becoming a mainstream productivity tool, and Gartner projects they will drive a 50% productivity gain across every SDLC stage by 2028. However, agents trained on how code has historically been written default to shipping entire features in single large pull requests—a pattern that creates a genuine review bottleneck. A 1,000+ line diff generates reviewer fatigue, loss of context, reduced feedback quality, and ultimately code that lands under-reviewed or sits in limbo. This is not a new problem for human developers, but agents amplify it because they work at scale and speed; what once took a developer days now takes an agent minutes, flooding the review queue with massive, hard-to-parse diffs.

GitHub's stacked pull requests feature addresses this by offering a native decomposition structure: instead of one giant PR, a feature is split into a dependency chain of small, logically-ordered pull requests, each scoped to a single concern (data, API, wiring, UI). The body provides a concrete example: a product search feature for a shopping assistant that would normally land as a 1,721-line change is broken into four layers—catalog data, search API, chat integration, and grounded UI—each with its own review checkpoint and its own reviewer audience. This structure has immediate benefits for review quality (smaller diffs are easier to reason about), merge speed (no single PR blocks the stack), and conflict resolution (layers above an updated branch can be rebased and synced in one command). For agents, stacked PRs create an explicit scoping discipline; agents must learn the pattern (via `gh skill install`), but once they do, they enforce small, single-concern pull requests by default rather than treating the entire feature as one atomic unit of work.