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I read the summary which for me focuses on intent: What's the current state, why it's not good, what is being changed and how, and then look at performance numbers.

Then I read the code but only after it got scrutinized by pr review skills on multiple models and those feedback got marked completed by the author.

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Yeah, same here, but I do not pay that much attention to the PR description since it's all written by the same AI, it just narrates its own code. I prefer to skim it then jump to the code. The problem thesis is already in the ticket, so we could just reference it in the PR without duplication. The current state and how it's being changed are already the code diff itself under the changes tab, right? Reading it straight makes me question the code better since the AI is usually overconfident in its writing, so I don't focus on it unless I face a hard blocker or constraint that should be mentioned there.

I also do this with my swarm of review agents, asking them to review the code without context, that way, they produce more high-quality feedback that is backed by self-sourcing the context from the codebase instead of relying on the AI-provided PR description that might justify a code change that others might disagree with otherwise. it works well especially with code changes that could miss other parts of the codebase during refactoring or implementing features that touch multiple domains.

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