Code Review

Code Review Best Practices

This skill activates when reviewing code for quality, correctness, security, and maintainability.

Anti-fabrication

This skill follows core:anti-fabrication, but has little to verify against an external source: the checklist and etiquette content is house convention (Google's review guide, cited in sources.md, for the checklist's shape), and "Language-Specific Considerations" is broad, stable idiom rather than versioned API claims. Don't assert a language-specific rule this skill doesn't cover without checking that language's own style skill first.

Code Review Checklist

1. Correctness and Functionality

Does the code do what it's supposed to do?

• Logic is correct and handles all cases
• Edge cases are considered
• Error handling is appropriate
• No obvious bugs or logical errors
• Assertions and validations are present
• Return values are correct

Questions to ask:

• What happens if this receives null/nil?
• What if the list is empty?
• What if the number is negative/zero?
• Are there off-by-one errors?
• Are comparisons correct (>, >=, <, <=)?

2. Security

Is the code secure?

• No SQL injection vulnerabilities
• No XSS (Cross-Site Scripting) vulnerabilities
• No CSRF vulnerabilities (CSRF protection in place)
• User input is validated and sanitized
• Sensitive data is not logged
• Authentication and authorization are properly implemented
• No hardcoded secrets or credentials
• File uploads are validated (type, size, content)
• External URLs are validated
• Rate limiting is in place for APIs

Worked BAD/GOOD examples (SQL injection, XSS, hardcoded secrets, mass assignment): references/checklist-examples.md.

3. Performance

Is the code efficient?

• No N+1 query problems
• Appropriate data structures chosen
• Algorithms are efficient
• Database indexes are used
• Caching is implemented where appropriate
• Large datasets are paginated or streamed
• Unnecessary computations are avoided
• Resources are cleaned up properly

Worked BAD/GOOD examples (N+1 queries, loading full datasets, list vs set lookups): references/checklist-examples.md.

4. Code Quality and Maintainability

Is the code readable and maintainable?

• Clear, descriptive variable and function names
• Functions are small and focused (single responsibility)
• No code duplication (DRY principle)
• Comments explain "why", not "what" — watch for restated, over-explained, missing-purpose, missing-inputs, or contradicting comments. See comment-reviewer for a dedicated pass.
• Code follows project conventions and style guide
• Magic numbers are replaced with named constants
• Complexity is minimized
• Code is self-documenting

Worked BAD/GOOD examples (unclear names, multi-responsibility functions, magic numbers): references/checklist-examples.md.

5. Error Handling

Are errors handled properly?

• Errors don't crash the system unexpectedly
• Error messages are helpful
• Errors are logged appropriately
• Happy path and error paths are both tested
• No swallowed errors (empty catch blocks)
• Proper error types are used

Worked BAD/GOOD examples (swallowed errors, generic error messages, bang functions that crash): references/checklist-examples.md.

6. Testing

Is the code properly tested?

• New functionality has tests
• Edge cases are tested
• Error conditions are tested
• Tests are clear and focused
• Tests are deterministic (no flaky tests)
• Test names describe what they test
• Mocks are used appropriately
• Test coverage is adequate

Worked BAD/GOOD examples (unclear test names, over-broad tests, non-deterministic sleeps): references/checklist-examples.md.

7. Documentation

Is the code documented?

• Public APIs have documentation
• Complex logic has explanatory comments
• README is updated if needed
• Changelog is updated for user-facing changes
• API documentation is accurate
• Examples are provided

8. Dependencies

Are dependencies handled properly?

• New dependencies are justified
• Dependencies are up-to-date and maintained
• Licenses are compatible with project
• Security vulnerabilities are checked
• Dependency versions are pinned or bounded

9. Restraint and Scope

Does every new symbol earn its place in the diff?

For each NEW symbol introduced by the diff (function, class, config, dependency), check it against the restraint ladder before approving:

• Does this need to exist at all, or is it speculative scaffolding? (YAGNI)
• Is it a duplicate of an existing helper, stdlib function, or already-installed dependency? (Reuse-First)
• Could it be expressed in a smaller form — fewer lines, no new abstraction?

Flag speculative abstractions (interfaces with one implementation, config for values that never change, factories for one product) and unrequested features as findings, not nits. See /core:restraint.

Review Process

Before Reviewing

1. Understand the context

   • Read the PR description
   • Understand the problem being solved
   • Check related issues

2. Build and test locally

   • Pull the branch
   • Run tests
   • Test the functionality manually

During Review

1. Start with the big picture

   • Is the approach sound?
   • Does it fit the architecture?
   • Is there a better way?

2. Review for correctness

   • Does it work as intended?
   • Are edge cases handled?
   • Is error handling appropriate?

3. Check security and performance

   • Are there security vulnerabilities?
   • Will it perform well at scale?

4. Review code quality

   • Is it readable and maintainable?
   • Does it follow conventions?
   • Is it well-tested?

Providing Feedback

Be constructive and specific:

# BAD: Vague criticism
"This function is bad."

# GOOD: Specific, actionable feedback
"This function has three responsibilities: validation, database update, and email sending. Consider splitting it into separate functions for better testability and maintainability:

```elixir
def update_user(user, attrs) do
  with {:ok, changeset} <- validate_user_update(user, attrs),
       {:ok, user} <- save_user(changeset),
       :ok <- send_update_notification(user) do
    {:ok, user}
  end
end

BAD: Demanding

"You must change this."

GOOD: Collaborative

"What do you think about extracting this into a separate function? It would make the code easier to test."

BAD: Nitpicking without context

"Use single quotes instead of double quotes."

GOOD: Explain reasoning

"Our style guide prefers single quotes for consistency (see CONTRIBUTING.md section 3.2)."

**Use labels to categorize feedback:**

- **[blocking]**: Must be fixed before merging
- **[suggestion]**: Optional improvement
- **[question]**: Asking for clarification
- **[nit]**: Very minor, cosmetic issue
- **[security]**: Security concern
- **[performance]**: Performance concern

**Example** (SQL-injection-shaped BAD/GOOD code for a `[blocking]` label: [references/checklist-examples.md](references/checklist-examples.md)):

```markdown
[blocking] This creates a SQL injection vulnerability. Use parameterized queries.

[suggestion] Consider extracting this logic into a separate function for reusability.

[question] Why are we using a map here instead of a struct?

[nit] Extra blank line here.

After Review

1. Respond to author's questions
2. Re-review after changes
3. Approve when satisfied
4. Celebrate good code

Key Principles

• Correctness first: Code must work correctly
• Security matters: Always consider security implications
• Be specific: Provide actionable, concrete feedback
• Be respectful: Kind, constructive communication
• Focus on important issues: Don't bike-shed
• Explain reasoning: Help author learn, don't just dictate

• Approve good code: Don't let perfect be enemy of good

• Collaborate: You're on the same team
• Check restraint: Every new symbol earns its place on the ladder — see /core:restraint

References

• references/no-todos-scan.md — Scan PR diffs for new TODO/FIXME/XXX/HACK/KLUDGE/DEFERRED markers; treat as BLOCKER, not nit
• references/language-specifics.md — read this when reviewing Elixir, JavaScript/TypeScript, Python, or Rust code for the per-language checklist items on top of the general checklist above
• references/smells-and-antipatterns.md — read this when naming a code smell (complexity, duplication, naming, design) or checking a diff against premature optimization, premature abstraction, or error-swallowing anti-patterns with worked BAD/GOOD examples
• references/review-etiquette.md — read this for how to conduct yourself during a review (DO/DON'T etiquette) or the self-review checklist an author runs before submitting code