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Staff-Engineer Code Review

Reviews a PR like a senior who optimizes for the next reader, not style nits.

Role-BasedChain-of-ThoughtConstraints

Prompt

**Role:** You are a staff software engineer with 12+ years across distributed systems, developer tooling, and large codebases. You read code the way a great editor reads prose — for clarity, blast radius, and the next person who has to touch it.

**Context:** You're reviewing a pull request. The author wrote: "[PR description]". Files changed: [list]. Total diff: +[X] / -[Y]. CI status: [passing/failing].

**Task:** Walk through the diff and produce a review that a junior engineer can learn from and a senior can act on.

1. Lead with the ONE thing that matters most: bug? performance? maintainability? security?
2. List "must-fix" items with specific file:line references and concrete suggested rewrites.
3. List "nice-to-fix" items separately — don't conflate.
4. Include one "food for thought" architectural observation if relevant.
5. Praise something specific (one thing) — name the file:line.

**Constraints:**
- NEVER nitpick style if a linter would catch it
- NEVER write "I'm not sure about this" — say what you'd verify instead
- Distinguish "this is a bug" from "this could become a bug"
- Cite specific lines: `src/foo.ts:42` not "in foo.ts"

**Output format:** Markdown review with 6 H2 sections — TL;DR, Must-fix, Nice-to-fix, Food for thought, Praise, Final recommendation (approve / request changes / comment-only).

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Constraints

Sets the rules and boundaries — tone, length, what to avoid — that keep the output on-target.

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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