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AI-powered code editor prompts for Composer, Chat, and inline editing
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Read the guideAI code review earns its place as the pass before the human pass. Run it on your own diff and it reliably catches the category of thing reviewers get tired of writing: the unhandled error path, the off-by-one, the value that can be null on the third call, the case the tests don't cover, the resource that isn't closed on the failure branch. That clears the noise so your teammate spends their attention on design, naming, and whether this was the right change at all — which is where human review is actually valuable. The limits are structural, not temporary. The model does not know your system's invariants, what's already handled upstream, or why the odd-looking line is odd on purpose, so it produces confident false positives — and reviewing its output costs time too. It is not a security audit; it misses whole classes of vulnerability that depend on context it can't see. And treat the tool boundary seriously: pasting proprietary code into a service your employer hasn't approved is a real problem, so use whatever's on your company's approved list. The prompts below all push it toward severity-ranked, evidence-backed findings rather than a wall of style opinions.
AI-first code editor that writes, edits, and debugs with you. Cursor excels at AI-assisted coding, Multi-file editing, Codebase chat making it particularly effective for the work covered on this page.
💡 Pro Tip
Use @codebase in Cursor chat to search your whole project before asking questions, it finds relevant context across files and gives answers grounded in your actual code rather than guessing.
Specific, copy-paste-ready prompts for real code review work, each with a note on what Cursor hands back. Swap in your own details and send.
Review this diff before I open the PR. Rank findings by severity: BUG (will misbehave at runtime), RISK (works but will bite us), NIT (style/preference). For each, give the exact line, what goes wrong, and the minimal fix. Skip anything a formatter or linter would catch. If you're not confident a finding is real, say so instead of asserting it. Diff: [paste].
What to expect back
A ranked findings list where the top items are worth acting on — and confidence flagged, so you don't chase phantom bugs.
Here's a function and how it's called. Try to break it: list the inputs and sequences of calls that produce wrong output, an exception, or corrupted state. For each, give me the concrete input, not a category — actual values I could paste into a test. Include empty, boundary, concurrent, and failure-of-the-thing-it-depends-on cases. Code: [paste]. Callers: [paste].
What to expect back
Runnable failing cases rather than abstract warnings — the ones worth keeping become regression tests.
Here's a change and its existing tests. Tell me what behaviour in the change is not covered by any test, which existing tests would still pass if I broke the new logic, and the three tests that would give the most coverage for the least code. Don't write the tests yet — just the gap analysis. Change: [paste]. Tests: [paste].
What to expect back
A prioritised test-gap list, including the assertions that only look like they're protecting you.
Context: this code must hold these invariants — [list them, e.g. 'the cache is never written outside the lock', 'IDs are opaque and never parsed']. Review the change below only against those invariants and the correctness of the logic. Where something looks wrong but might be intentional given code you can't see, phrase it as a question for me rather than a finding. Change: [paste].
What to expect back
Review focused on what actually matters in your system, with uncertainty raised as questions instead of false accusations.
I'm reviewing someone else's change in an area I don't know well. First explain what this code does and what the change alters, in plain language, and state what you're assuming about the parts you can't see. Then list the questions I should ask the author. Change: [paste]. Surrounding file: [paste].
What to expect back
A quick orientation plus a question list, so you can review substantively instead of rubber-stamping unfamiliar code.
Build a custom, copy-ready Cursor prompt for code review in seconds. Fill in your specifics below, the prompt updates live and is scaffolded the way Cursor responds best.
You are an expert in code review. I am working with Cursor to produce code review on the topic: "[your code review topic]". Follow these instructions in order: 1. Goal, [what you want this to achieve]. 2. Audience, write for Software Engineer. 3. Tone, professional. 4. Length, around 1,000 words. 5. Format, structure it as a step-by-step guide. 6. Must include, [key points, data, examples, or keywords to cover]. 7. Open with a specific, value-first introduction, no filler or "in today's world" openers. Work in clear, ordered steps. Produce the draft, then do one self-review pass to tighten clarity, remove repetition, and check that every requirement above is met.
Tuned for Cursor (200K tokens context). Tip: Use @codebase in Cursor chat to search your whole project before asking questions, it finds relevant context across files and gives answers grounded in your actual code rather than guessing.
Its composer agent mode for multi-file ai edits with full codebase awareness makes it especially suited to code review.
Copy any prompt, replace the bracketed placeholders with your specifics, and paste into Cursor.
I need help with Code Review using Cursor. AI reviews code for bugs, security vulnerabilities, performance issues, and best practice adherence. Start by asking me 3-5 clarifying questions to understand my specific context, then provide a comprehensive framework tailored to my situation.
Using Cursor's Composer agent mode for multi-file AI edits with full codebase awareness, help me execute Code Review for [MY COMPANY/PROJECT]. Create a step-by-step action plan with specific deliverables, timelines, and success metrics. Tailor your approach for a Software Engineer.
Use @codebase in Cursor chat to search your whole project before asking questions, it finds relevant context across files and gives answers grounded in your actual code rather than guessing. Act as a Code Review expert. Help me create a professional template that I can reuse for ongoing work. Include: key sections with guidance, common variations, and examples of best-in-class outputs.
I'm working on Code Review and facing [SPECIFIC CHALLENGE]. Using Cursor, analyze my situation, identify the root cause, and suggest evidence-based solutions. Provide a prioritized action plan with expected outcomes for each step.
Help me use Cursor to significantly improve my Code Review process. First, audit my current approach (I'll describe it), identify inefficiencies, then recommend an AI-enhanced workflow. Include specific prompts I can use at each stage.
Using Cursor, create a comprehensive Code Review checklist for a Software Engineer. Include: preparation steps, execution checklist, quality review criteria, and common pitfalls to avoid. Make it actionable and specific.
I need to present Code Review results to leadership. Using Cursor, help me structure the narrative with: key findings, business implications, recommendations with supporting rationale, and a clear ask. Format for an executive audience.
Use @codebase in Cursor chat to search your whole project before asking questions, it finds relevant context across files and gives answers grounded in your actual code rather than guessing. Help me use Cursor to benchmark my Code Review performance. Research industry best practices, identify what excellent looks like, and create a gap analysis framework I can use to prioritize improvements.
As someone who regularly does Code Review, I want to build a reusable system with Cursor. Create a prompt library with: situation-specific prompts, templates, and variations for different contexts. Include usage guidelines.
Using Cursor, help me develop expertise in Code Review faster. Create a 30-day learning plan with: key concepts to master, practical exercises, resources to study, and milestones to track my progress. Focus on practical application.
I need to train my team on Code Review. Using Cursor, create training materials including: a structured curriculum, practical exercises, assessment questions, and reference guides. Make it accessible for Tech Lead at different experience levels.
Help me use Cursor to measure the ROI of Code Review in my organization. Design a measurement framework with: key metrics to track, data collection methods, analysis approach, and reporting template for stakeholders.
Using Cursor's Composer agent mode for multi-file AI edits with full codebase awareness, automate or streamline the repetitive aspects of Code Review. Identify which parts can be AI-assisted, create reusable prompt templates, and design a workflow that maximizes efficiency.
I'm dealing with a challenging Code Review situation: [DESCRIBE SITUATION]. Using Cursor, analyze the problem from multiple angles, identify options I haven't considered, and recommend the best path forward with implementation steps.
Start with context
Before using any prompt, give Cursor relevant background: your role, organization type, audience, and any constraints. The more context, the better the output.
Use the prompts as starting points
Copy the prompts above and customize the bracketed sections. You can also chain multiple prompts together for complex tasks.
Iterate and refine
Cursor works best with back-and-forth conversation. If the first output isn't quite right, ask it to adjust tone, add specifics, or reformat the content.
Build a personal prompt library
Save prompts that work well for you. Over time, you'll build a custom toolkit that dramatically accelerates your work on recurring tasks.
Better code quality
Faster reviews
Consistent standards
No, and teams that try it notice quickly. AI review misses anything that depends on context outside the diff — your architecture, what's guaranteed upstream, the incident last quarter that explains the defensive check — and it generates confident findings that are simply wrong about your codebase. It also can't do the parts of review that matter most: whether this is the right design, whether it fits the team's direction, and mentoring the author. Use it as the pre-pass that clears mechanical issues so the human review is about the interesting questions.
No. It will flag obvious patterns — string-concatenated SQL, a hard-coded secret, a missing input check — and that's worth having. But real vulnerabilities usually live in the interaction between components: an authorization check that happens in the wrong layer, a trust boundary crossed two files away, a race that only matters under load. A model reading a diff cannot see any of that. Keep your SAST/DAST tooling, dependency scanning, and human security review; treat AI as one more cheap filter, not the gate.
The diff plus enough surrounding context to make it interpretable — the full function, its callers, the type definitions it touches, and any invariant that isn't visible in the code. A bare diff produces generic advice and false positives because the model has to guess what's handled elsewhere; a whole repository dumped in dilutes attention and buries the change you care about. Say explicitly what the change is meant to do, and tell it to ask rather than assume when something looks wrong but might be deliberate.
AI reviews code for bugs, security vulnerabilities, performance issues, and best practice adherence. Specifically, Cursor's Composer agent mode for multi-file AI edits with full codebase awareness makes it excellent for Code Review, helping you better code quality and faster reviews.
Code Review is commonly used by Software Engineer, Tech Lead, Engineering Manager, Developer. All of these professionals can leverage Cursor to streamline their workflow and produce higher-quality outputs more efficiently.
The best prompts for Code Review with Cursor are highly specific and context-rich. Use @codebase in Cursor chat to search your whole project before asking questions, it finds relevant context across files and gives answers grounded in your actual code rather than guessing. Include your specific context, constraints, desired output format, and audience. This page includes 14 proven prompts you can copy and customize immediately.
Cursor by Anysphere handles Code Review through its Composer agent mode for multi-file AI edits with full codebase awareness and 200K tokens context window. This allows it to process complex information, maintain consistency throughout long documents, and generate nuanced professional outputs.
Users typically experience: Better code quality, Faster reviews, Consistent standards. While individual results vary, Cursor consistently helps professionals complete Code Review faster while maintaining or improving quality.
Cursor is a powerful tool for Code Review but works best as an augmentation tool rather than a replacement for human judgment. Always review AI-generated content for accuracy, ensure outputs align with your organization's standards, and verify any data or claims made in the generated content.
Cursor offers a free tier, get started immediately with no commitment.