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Pre-review sweep: your own PR through a security-and-edge-case lens
The PR is 'done'. Run the pre-review sweep so human reviewers spend their attention on design — not on nits and the missed null check.
The prompt — copy and run it
You are a staff engineer reviewing a pull request. I will paste the diff and its context. Produce: A) REVIEW TABLE — columns: file/line reference, severity (blocker / major / nit), category (correctness, security, performance, readability, tests), the issue in one sentence, and a suggested fix as a concrete code change. B) EDGE-CASE LIST — inputs and states the diff does not handle: empty, null, concurrent access, oversized input, malformed input, permission-denied. C) VERDICT — approve or request-changes, plus the 2 highest-risk lines in the diff and why. Inputs: [PASTE DIFF] · [WHAT THE CHANGE DOES + WHY] · [LANGUAGE/FRAMEWORK + TEAM CONVENTIONS] Rules: Do not invent code that is not in the diff — reference only pasted lines. Any claim about behavior you cannot see (callers, config, upstream state) must be marked "verify in repo". Never echo secrets or keys, and do not include proprietary code beyond what I pasted.
How to use this prompt
- Copy the full prompt above with the Copy button.
- Fill in your inputs. Replace each bracketed placeholder with your specifics:
[PASTE DIFF][WHAT THE CHANGE DOES + WHY][LANGUAGE/FRAMEWORK + TEAM CONVENTIONS] - Paste into ChatGPT, Claude, or Gemini and run. Read the reality guardrail below before you act on the output.
Why this prompt works
LLM review works best as a filter before human review — the machine catches the mechanical 80% so humans argue about design. Severity plus category forces triage instead of a wall of nits, and the 'verify in repo' rule prevents confident-but-wrong claims about code the model cannot see.
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When should I use this prompt?
The PR is 'done'. Run the pre-review sweep so human reviewers spend their attention on design — not on nits and the missed null check.
Why does this prompt work?
LLM review works best as a filter before human review — the machine catches the mechanical 80% so humans argue about design. Severity plus category forces triage instead of a wall of nits, and the 'verify in repo' rule prevents confident-but-wrong claims about code the model cannot see.
What mistake does this prompt help you avoid?
Confident-but-wrong review claims about code the model cannot see — the verify-in-repo rule, plus severity/category triage instead of a wall of nits.
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