code-review

name	code-review
description	Conduct thorough, actionable code reviews that catch real problems without drowning in noise
invocation	/review
aliases	["cr","review-pr"]

Code Review Skill

Conduct thorough, actionable code reviews that catch real problems without drowning in noise.

When to Use

• Reviewing changes on a feature branch before merge
• Auditing a PR for a project you maintain
• Self-review before opening a PR
• Evaluating contributions to unfamiliar codebases

Philosophy

Good code review serves three purposes:

1. Catch defects — bugs, security holes, performance traps
2. Enforce consistency — style, patterns, architectural boundaries
3. Transfer knowledge — to enable the reviewer to learn the change and the author to learn alternatives

A review that only finds style nits has failed. A review that only finds bugs but ignores maintainability has also failed. Balance matters.

Context Gathering

Never review blind. The diff alone lacks the information needed to judge whether code is correct, appropriate, or consistent.

Required Context

Source	Purpose
PR description / commit messages	Understand intent
AGENTS.md	Coding style rules
.rules/*	Additional project conventions
docs/*-design.md	Architectural intent
docs/roadmap.md	Current priorities
docs/documentation-style-guide.md	Doc conventions
Neighbouring files	Established patterns
.github/workflows/*	CI expectations

Detecting Project Type

# Determine primary language
if [ -f "Cargo.toml" ]; then
    echo "Rust"
elif [ -f "pyproject.toml" ]; then
    echo "Python"
elif [ -f "package.json" ]; then
    echo "TypeScript/JavaScript"
fi

Useful Git Incantations

# Commits on this branch
git log --oneline $(git merge-base HEAD main)..HEAD

# Files changed with line counts
git diff $(git merge-base HEAD main)..HEAD --stat

# Full diff
git diff $(git merge-base HEAD main)..HEAD

# Show a specific commit
git show <sha> --stat

# Find the merge base
git merge-base HEAD main

GitHub CLI Commands

# Check if a PR exists for the current branch
gh pr view --json number,title,state 2>/dev/null && echo "PR exists" || echo "No PR"

# Get PR description (body) and metadata
gh pr view --json title,body,baseRefName,headRefName,labels,milestone

# Get just the PR body (useful for piping)
gh pr view --json body --jq '.body'

# Get PR with comments (for context on ongoing discussion)
gh pr view --json body,comments --jq '{body, comments: [.comments[].body]}'

# Get linked issues from PR
gh pr view --json body --jq '.body' | grep -oE '#[0-9]+' | sort -u

# View a linked issue
gh issue view <number> --json title,body

# List PR checks and their status
gh pr checks

# Get PR diff directly (alternative to git diff)
gh pr diff

# Get PR files changed with stats
gh pr diff --stat

Putting It Together

# Full context dump for review
echo "=== PR Details ==="
gh pr view --json number,title,state,baseRefName,headRefName,labels

echo -e "\n=== PR Description ==="
gh pr view --json body --jq '.body'

echo -e "\n=== Linked Issues ==="
for issue in $(gh pr view --json body --jq '.body' | grep -oE '#[0-9]+' | tr -d '#' | sort -u); do
    echo "--- Issue #$issue ---"
    gh issue view "$issue" --json title,body --jq '"\(.title)\n\(.body)"' 2>/dev/null || echo "Could not fetch issue"
done

echo -e "\n=== CI Status ==="
gh pr checks

echo -e "\n=== Commits ==="
git log --oneline $(git merge-base HEAD main)..HEAD

echo -e "\n=== Files Changed ==="
gh pr diff --stat

Review Dimensions

1. Correctness

The code should do what it claims to do.

• Does the implementation match the PR description?
• Are edge cases handled?
• Are error conditions recoverable or at least reported clearly?
• Do loops terminate? Are bounds checked?
• Is concurrent code free of races? Are invariants protected?

2. Style Compliance

The code should follow project conventions.

• Naming conventions (casing, prefixes, verb forms)
• Formatting (should be automated, but check if not)
• Import ordering and grouping
• Comment style and placement
• Error message formatting

Check against: AGENTS.md, .rules/*, language-specific linters

3. Architectural Fit

The code should respect established boundaries.

• Does it introduce new dependencies? Are they justified?
• Does it bypass abstraction layers?
• Does it duplicate functionality that exists elsewhere?
• Does it create circular dependencies?
• Does it respect module boundaries?

Check against: docs/*-design.md, existing module structure

4. Implementation Quality

The code should be maintainable.

Code Smells to Flag:

Smell	Symptom
Repeated code	Copy-paste with minor variations
Complex conditionals	Nested if/else, boolean expressions with >3 terms
Bumpy road	Function alternates between high and low abstraction
High similarity	Two functions that differ only in one parameter
Magic literals	Unexplained numbers or strings
Long parameter lists	Functions taking >4 arguments
Feature envy	Method uses another object's data more than its own
Primitive obsession	Using strings/ints where a type would clarify intent

Positive Patterns to Encourage:

• Early returns to reduce nesting
• Guard clauses at function entry
• Descriptive intermediate variables
• Type aliases for complex generics
• Exhaustive pattern matching

5. Documentation

The code should explain itself where it cannot show itself.

• Public APIs must have doc comments
• Complex algorithms need explanatory comments
• "Why" comments for non-obvious decisions
• No stale comments contradicting the code
• README updates for user-facing changes

Check against: docs/documentation-style-guide.md

6. Testing

The code should prove it works.

• Are new code paths covered by tests?
• Do tests verify behaviour, not implementation?
• Are edge cases tested?
• Are error paths tested?
• Do test names describe the scenario?

7. Security

The code should not introduce vulnerabilities.

• Input validation at trust boundaries
• No SQL/command/log/XSS injection vectors
• No hardcoded secrets
• Appropriate use of cryptographic primitives
• Safe deserialization
• No TOCTOU race conditions

See: guides/security-issues.md for detailed patterns and examples.

8. Performance

The code should not introduce regressions.

• No O(n²) where O(n) suffices
• No repeated computation in loops
• No blocking calls on hot paths
• Appropriate data structure choices
• Memory allocation patterns (especially in Rust)
• Resources released promptly (connections, handles, memory)
• No bad neighbour patterns (unbounded memory, CPU monopolization)

See: guides/performance-concerns.md for detailed patterns and examples.

Review Output Format

Structure findings by severity:

## Summary

<One paragraph overall assessment. Lead with the most important point.>

## Critical Issues

Issues that block merge. Security flaws, correctness bugs, data loss risks.

### [CRITICAL] <Short title>

**File:** `path/to/file.rs` (lines 42-56)

<Explanation of the problem>

```rust
// Problematic code

Suggested fix:

// Better approach

---

Suggestions

Improvements that strengthen the code but are not blocking.

[SUGGESTION]

File: path/to/file.rs (line 78)

---

Observations

Questions, minor notes, patterns worth discussing.

• Observation one
• Observation two

---

Checklist

• Passes CI
• Adheres to style guide
• Consistent with design documents
• Adequate documentation
• Test coverage appropriate
• No security concerns
• No performance regressions

## Best Practices

### Calibrate Severity

Not everything is critical. Reserve that label for:

- Security vulnerabilities
- Data corruption or loss
- Crashes or hangs
- Silent incorrect behaviour

Style violations and minor inefficiencies are suggestions, not blockers.

### Be Specific

Bad: "This function is too complex."

Good: "This function has a cyclomatic complexity of 15. The nested conditionals on lines 34-52 could be extracted into a `validate_input()` helper."

### Suggest, Don't Demand

Bad: "Change this to use `filter_map`."

Good: "Consider using `filter_map` here—it combines the filter and map into a single pass and makes the None-handling explicit."

### Acknowledge Good Work

If something is particularly well done, say so. Positive reinforcement shapes future contributions.

### Ask Questions

If you don't understand why something was done a certain way, ask. The author may have context you lack. Or they may realize their approach needs better documentation.

### Consider the Author

A junior contributor needs different feedback than a senior maintainer. Adjust your tone and the level of explanation accordingly.

### Timebox

Diminishing returns set in. If you've spent an hour on a 200-line PR, you're likely past the point of useful findings. Note your time limit and move on.

## Common Pitfalls

### Reviewing Without Context

Reading the diff without understanding the feature leads to superficial or incorrect feedback.

### Bikeshedding

Spending disproportionate time on trivial style matters while missing structural problems.

### Rubber Stamping

Approving without genuine review erodes the value of the process.

### Being Adversarial

Review is collaborative, not competitive. The goal is better code, not scoring points.

### Scope Creep

Requesting changes unrelated to the PR's purpose. File separate issues for pre-existing problems.

### Blocking on Preferences

Your preferred approach isn't necessarily better. If the code works, follows conventions, and is maintainable, accept it even if you'd have written it differently.

## Supplementary Guides

For detailed patterns and examples, see:

- `guides/security-issues.md` — Injection attacks (SQL, shell, log, XSS, prompt), TOCTOU race conditions, secret exposure, authentication/authorisation flaws, cryptographic issues, deserialization, path traversal
- `guides/performance-concerns.md` — Algorithmic complexity (accidental quadratic), resource leaks, bad neighbour problems, database performance, network efficiency, memory management, concurrency issues
- `checklists/language-specific.md` — Rust, Python, TypeScript checklists
- `examples/code-smells.md` — Before/after examples of common smells

## Language-Specific Considerations

### Rust

- Ownership and borrowing: unnecessary clones, lifetime elision opportunities
- Error handling: appropriate use of `?`, `Result` vs `panic!`
- Unsafe code: is it necessary? Is the safety invariant documented?
- Clippy lints: are they addressed or explicitly allowed with justification?

### Python

- Type hints: present and accurate?
- Exception handling: bare `except:` is almost always wrong
- Resource management: `with` statements for files, connections
- Import hygiene: no wildcard imports, logical grouping

### TypeScript

- Type safety: avoiding `any`, proper null handling
- Async patterns: proper error handling in promises
- Module boundaries: avoiding circular imports
- Runtime checks: zod or similar for external data

## Prompt Template

See `templates/review-prompt.md` for a ready-to-use prompt incorporating these practices.