·12 min read

How to Use AI Assistants for Code Review

Set up AI-assisted code review workflows using review checklists, custom commands, and shared skills. Catch bugs faster without slowing down your team.

tipsteams

Code review is one of the most important parts of the development process, but it's also one of the most draining. Context switching into someone else's code, holding a mental model of the change, checking for edge cases, verifying patterns: it takes real energy. And when a team is shipping fast, reviews become a bottleneck.

AI coding assistants can help. Not by replacing human reviewers, but by handling the mechanical parts of review so that humans can focus on design, intent, and the things only a person with full project context can evaluate.

This guide covers practical workflows for using AI assistants like Claude Code, Cursor, and Windsurf as part of your code review process.

What AI review is good at (and what it isn't)

Before setting up any workflows, it helps to understand where AI review adds value and where it falls short.

AI review works well for:

  • Catching common bugs: null checks, off-by-one errors, missing error handling
  • Verifying consistency with existing patterns in the codebase
  • Checking for security issues like SQL injection, XSS, and exposed secrets
  • Spotting missing test coverage for new code paths
  • Validating naming conventions, import structure, and file organization
  • Flagging overly complex functions that could be simplified

AI review does not replace humans for:

  • Evaluating whether the approach is the right one for the business problem
  • Assessing performance implications that require understanding of production load
  • Reviewing system design and architecture decisions
  • Catching subtle logic errors that require domain expertise
  • Deciding whether a feature should exist at all

The goal is to use AI for the first category so that human reviewers can spend their time on the second. A reviewer who doesn't need to point out missing null checks has more bandwidth to think about whether the API design is right.

Building review checklists as AI rules

Every team has a mental checklist that reviewers run through. The problem is that it usually lives in people's heads. One reviewer checks for error handling religiously. Another cares most about test coverage. A third focuses on naming. The review quality depends on who reviews the PR.

The fix: write the checklist down as an AI rule, then let the AI run it consistently on every change.

Example review checklist rule

## Code review checklist

When reviewing code changes, check for:

### Error handling
- All async operations have proper try/catch or .catch() handling
- Error messages are descriptive and include context (what failed, why)
- API routes return appropriate HTTP status codes, not just 500 for everything
- Database operations handle constraint violations gracefully

### Security
- No secrets, API keys, or credentials in the code
- User input is validated before use
- SQL queries use parameterized queries, never string concatenation
- Auth checks are present on all protected routes

### Testing
- New functions have corresponding test cases
- Edge cases are covered (empty arrays, null values, boundary conditions)
- Mocks are realistic and don't hide bugs

### Patterns
- New code follows existing patterns in the codebase
- No new dependencies added without justification
- Components are in the correct directory per our project structure
- Types are explicit, no `any` usage

Put this in your project's AI rules (Cursor's .cursor/rules/, Claude Code's CLAUDE.md or .claude/skills/, Windsurf's .windsurf/rules/) and the AI uses it as its review framework every time. For more on structuring rules across tools, see our guide on AI coding rules best practices.

The checklist becomes the team's shared standard. When a new engineer joins, they review with the same rigor as a senior engineer because the checklist encodes that senior engineer's knowledge.

Setting up custom review commands

Most AI coding tools support custom commands or slash commands. These let you trigger specific workflows with a single command instead of typing a long prompt each time.

Claude Code review command

Create a file at .claude/commands/review.md:

Review the current git diff for the following:

1. **Bugs and logic errors**: Check for null pointer issues, off-by-one errors,
   race conditions, and incorrect assumptions about data shapes.

2. **Security**: Look for SQL injection, XSS vulnerabilities, exposed secrets,
   and missing authentication checks.

3. **Consistency**: Verify the changes follow the patterns established elsewhere
   in the codebase. Flag anything that introduces a new pattern without reason.

4. **Error handling**: All new code paths should handle failure gracefully.
   Check that errors are logged with enough context to debug in production.

5. **Test coverage**: Identify any new code paths that lack tests.

Format the output as a list grouped by severity: critical, warning, suggestion.
Only include findings where you have reasonable confidence there's an actual issue.
Do not flag stylistic preferences.

Now running /review in Claude Code triggers a structured review of your staged changes. You can run this before committing, before pushing, or as part of your PR prep workflow.

For more Claude Code workflow tips, see our 15 Claude Code tips for faster development.

Cursor review command

In Cursor, you can achieve a similar workflow with custom rules. Create a rule file at .cursor/rules/review.mdc with the same checklist. When you ask Cursor to review code in chat, it uses that rule as context.

You can also create specialized review commands for different parts of the codebase:

.claude/commands/
  review.md            # General review
  review-api.md        # API-specific checks (auth, rate limiting, response format)
  review-db.md         # Database-specific checks (migrations, indexes, query performance)
  review-security.md   # Security-focused review

This gives reviewers (human or AI) the right lens for different types of changes.

PR review workflows

The real power of AI-assisted review shows up when you integrate it into your pull request workflow. Here are three patterns that work well.

Pattern 1: Pre-push self-review

Before pushing a branch, run your AI review command on the full diff:

# In Claude Code
git diff main...HEAD  # Review this diff with /review

This catches obvious issues before your teammates even see the PR. It's the AI equivalent of reading your own code before submitting for review. The difference is that the AI doesn't skim past things the way you do when reviewing your own work.

The pre-push self-review is especially valuable for large PRs. When a change spans 15 files, it's easy to miss a null check in file 12 that breaks an assumption from file 3. The AI reads every file with equal attention.

Pattern 2: Reviewer preparation

When you're assigned to review a PR, use the AI to get a quick summary before diving in:

Read the changes in this PR. Give me:
1. A one-paragraph summary of what this PR does
2. The riskiest changes (most likely to cause issues in production)
3. Any patterns that differ from the rest of the codebase
4. Questions I should ask the author

This saves the first 10 minutes of context-building that every review requires. You start the review already knowing where to focus your attention.

Pattern 3: Standards enforcement with shared skills

For teams, the most scalable approach is to encode your review standards as a shared skill on localskills.sh and install it across all your repositories:

localskills install your-org/review-standards --target cursor claude windsurf

This way every developer on the team, using any tool, gets the same review checklist. When the team updates the standard (say, adding a new security check after an incident), one publish updates every repository on the next pull.

# After updating the review skill
localskills publish
# Everyone else runs:
localskills pull

No more "we added that review check six months ago but half the team never got the memo." For more on this approach, see how to standardize AI coding across your team.

Writing effective review rules

Not all review rules are created equal. A vague rule produces vague results. Here's what separates rules that work from rules that waste time.

Bad: vague and subjective

- Make sure the code is clean
- Check for performance issues
- Verify the code is well-tested

These rules are too broad. "Clean" means different things to different people. "Performance issues" without specifics just generates noise. The AI will flag everything or nothing.

Good: specific and actionable

- Functions over 40 lines should be flagged for possible decomposition
- Database queries inside loops should be flagged as potential N+1 issues
- Any new API endpoint must have at least one test for the success case and
  one for the primary error case
- React components that accept more than 5 props should use a typed props object

These rules have clear thresholds. The AI can check for them mechanically. When it flags something, the reviewer can make a judgment call about whether the flag is valid for this specific case.

Include examples of what to flag and what to skip

The best review rules include examples:

## Error handling review

Flag this (missing error handling):
\`\`\`typescript
const data = await fetch('/api/users');
const users = await data.json();
// No check for response.ok, no try/catch
\`\`\`

Don't flag this (properly handled):
\`\`\`typescript
const response = await fetch('/api/users');
if (!response.ok) {
  throw new ApiError('Failed to fetch users', { status: response.status });
}
const users = await response.json();
\`\`\`

Examples reduce false positives. The AI understands the threshold you're targeting, not just the concept.

Domain-specific review rules

Generic review rules get you 70% of the way there. The remaining 30% comes from rules specific to your project and domain.

API review rules

## API review checklist
- All endpoints check authentication via the `requireAuth` middleware
- Response shapes match the TypeScript types in `src/types/api.ts`
- Error responses use the `ApiError` class, not raw Response objects
- Rate limiting is applied to all public endpoints
- Request body validation uses Zod schemas from `src/schemas/`

Frontend review rules

## Frontend review checklist
- New components are in `src/components/` with PascalCase filenames
- State that is shared across components uses a Zustand store, not prop drilling
- Data fetching uses React Query hooks, not useEffect + useState
- All user-facing strings are wrapped in the translation function
- Loading and error states are handled for all async operations

Database review rules

## Database review checklist
- Migrations are backwards-compatible (no column drops without a multi-step plan)
- New queries have appropriate indexes (check the query plan)
- All queries are scoped by tenant ID in multi-tenant contexts
- Transactions are used for multi-table writes
- No raw SQL strings: use the query builder

Build these up over time. Every time a code review catches something that could have been caught by a rule, add it. After a few months, your review rules encode the collective experience of your entire team.

For a deeper dive on enforcing standards this way, read our guide on enforcing coding standards with AI assistants.

Measuring the impact

How do you know if AI-assisted review is actually helping? Track a few signals:

Review cycle time. Measure how long it takes from PR submission to merge. If AI self-review catches issues before the PR is submitted, human review cycles should get shorter.

Review comment categories. Categorize review comments as "mechanical" (formatting, naming, missing error handling) vs. "design" (wrong approach, missing abstraction, performance concern). Over time, the ratio should shift toward design comments as AI handles the mechanical ones.

Bugs caught in review vs. production. If AI review is working, you should catch more issues during review and fewer in production. Track regression rates before and after adopting AI review.

Reviewer satisfaction. This is qualitative, but it matters. Ask your team: "Do reviews feel more productive? Are you spending less time on tedious checks?" If reviewers feel like they're doing more meaningful work, adoption will sustain itself.

Putting it all together

Here's a practical setup to adopt AI-assisted code review starting today:

  1. Write a review checklist. Take your team's top 10 review comments from the last month and turn them into a rule file.

  2. Create a review command. Set up /review (or equivalent) in your AI tool of choice so anyone can run a structured review in seconds.

  3. Add pre-push self-review to your workflow. Before every PR, run the AI review on your diff. Fix the easy stuff before your teammates see it.

  4. Publish your review standards as a shared skill. Use localskills.sh to distribute review rules across your team and repositories.

  5. Iterate on the rules. Every time a human reviewer catches something the AI missed, add a rule for it. The checklist gets better over time.

The teams that get the most out of AI review treat their review rules as living documents. They update them after incidents, after architecture changes, and after every sprint retro where "code quality" comes up. The AI doesn't get bored, forget, or have an off day. It runs the same checklist every time, freeing up human reviewers to focus on the decisions that actually require human judgment.

Ready to share your review standards across your team? Create a free account on localskills.sh and publish your first review skill today.

npm install -g @localskills/cli
localskills login
localskills publish