code-review-expert

name	code-review-expert
description	Conducts multi-axis code review. Use before merging any change. Use when reviewing code written by yourself, another agent, or a human. Use when you need to assess code quality across multiple dimensions before it enters the main branch.

Code Review and Quality

Overview

Multi-dimensional code review with quality gates. Every change gets reviewed before merge — no exceptions. Review covers five axes: correctness, readability, architecture, security, and performance.

The approval standard: Approve a change when it definitely improves overall code health, even if it isn't perfect. Perfect code doesn't exist — the goal is continuous improvement. Don't block a change because it isn't exactly how you would have written it. If it improves the codebase and follows the project's conventions, approve it.

When to Use

• Before merging any PR or change
• After completing a feature implementation
• When another agent or model produced code you need to evaluate
• When refactoring existing code
• After any bug fix (review both the fix and the regression test)

The Five-Axis Review

Every review evaluates code across these dimensions:

1. Correctness

Does the code do what it claims to do?

• Does it match the spec or task requirements?
• Are edge cases handled (null, empty, boundary values)?
• Are error paths handled (not just the happy path)?
• Does it pass all tests? Are the tests actually testing the right things?
• Are there off-by-one errors, race conditions, or state inconsistencies?

2. Readability & Simplicity

Can another engineer (or agent) understand this code without the author explaining it?

• Are names descriptive and consistent with project conventions? (No temp, data, result without context)
• Is the control flow straightforward (avoid nested ternaries, deep callbacks)?
• Is the code organized logically (related code grouped, clear module boundaries)?
• Are there any "clever" tricks that should be simplified?
• Could this be done in fewer lines? (1000 lines where 100 suffice is a failure)
• Are abstractions earning their complexity? (Don't generalize until the third use case)
• Would comments help clarify non-obvious intent? (But don't comment obvious code.)
• Are there dead code artifacts: no-op variables (_unused), backwards-compat shims, or // removed comments?

3. Architecture

Does the change fit the system's design?

• Does it follow existing patterns or introduce a new one? If new, is it justified?
• Does it maintain clean module boundaries?
• Is there code duplication that should be shared?
• Are dependencies flowing in the right direction (no circular dependencies)?
• Is the abstraction level appropriate (not over-engineered, not too coupled)?

4. Security

For detailed security guidance, see security-and-hardening. Does the change introduce vulnerabilities?

• Is user input validated and sanitized?
• Are secrets kept out of code, logs, and version control?
• Is authentication/authorization checked where needed?
• Are SQL queries parameterized (no string concatenation)?
• Are outputs encoded to prevent XSS?
• Are dependencies from trusted sources with no known vulnerabilities?
• Is data from external sources (APIs, logs, user content, config files) treated as untrusted?
• Are external data flows validated at system boundaries before use in logic or rendering?

5. Performance

For detailed profiling and optimization, see performance-optimization. Does the change introduce performance problems?

• Any N+1 query patterns?
• Any unbounded loops or unconstrained data fetching?
• Any synchronous operations that should be async?
• Any unnecessary re-renders in UI components?
• Any missing pagination on list endpoints?
• Any large objects created in hot paths?

Fowler Smell Baseline (Always Applied)

A fixed set of 12 "Bad Smells in Code" (Fowler, Refactoring, ch.3) that serves as a baseline even when the repo documents no coding standards. Two binding rules:

• The repo overrides. A documented repo standard always wins; where it endorses something the baseline would flag, suppress the smell.
• Always a judgement call. Each smell is a labelled heuristic, never a hard violation. Skip anything tooling already enforces.

#	Smell	What it is	How to fix
1	Mysterious Name	A function, variable, or type whose name doesn't reveal what it does	Rename it; if no honest name comes, the design is murky
2	Duplicated Code	The same logic shape appears in more than one hunk or file in the change	Extract the shared shape, call from both
3	Feature Envy	A method that reaches into another object's data more than its own	Move the method onto the data it envies
4	Data Clumps	The same few fields or params keep travelling together	Bundle them into one type, pass that
5	Primitive Obsession	A primitive or string standing in for a domain concept that deserves its own type	Give the concept its own small type
6	Repeated Switches	The same switch/if-cascade on the same type recurs across the change	Replace with polymorphism, or one map both sites share
7	Shotgun Surgery	One logical change forces scattered edits across many files in the diff	Gather what changes together into one module
8	Divergent Change	One file or module is edited for several unrelated reasons	Split so each module changes for one reason
9	Speculative Generality	Abstraction, parameters, or hooks added for needs the spec doesn't have	Delete it; inline back until a real need shows
10	Message Chains	Long a.b().c().d() navigation the caller shouldn't depend on	Hide the walk behind one method on the first object
11	Middle Man	A class or function that mostly just delegates onward	Cut it, call the real target directly
12	Refused Bequest	A subclass or implementer that ignores or overrides most of what it inherits	Drop the inheritance, use composition

Change Sizing

Small changes are safer. Target:

~100 lines  → Good. Reviewable in one sitting.
~300 lines  → Acceptable for single logical change.
~1000 lines → Too large. Split it.

Splitting strategies: Stack (sequential), by file group, horizontal (shared code first), vertical (full-stack slices). Large deletions and automated refactoring are acceptable exceptions.

Separate refactoring from feature work. Small cleanups (renaming) can be included; structural refactors must be separate commits.

Change Descriptions

Every commit needs a standalone description. First line: short, imperative ("Delete FizzBuzz RPC" not "Deleting..."). Body: what and why — decisions, reasoning, links. Avoid: "Fix bug," "Add patch," "Moving code from A to B."

Review Process

Step 0: Parallel Sub-Agent Review (Recommended for non-trivial changes)

For changes >50 lines, split review into two parallel sub-agents to keep contexts clean:

Sub-agent A — Standards: Review code style, naming, architecture, and smells. Include the Fowler Smell Baseline above. Sub-agent B — Spec: Review correctness against the originating spec/issue/task. Quote the spec line for each finding.

Spawn both via task tool simultaneously. Aggregate findings without merging or re-ranking — the two axes are deliberately separate.

Step 1: Understand the Context

Before looking at code, understand the intent:

- What is this change trying to accomplish?
- What spec or task does it implement?
- What is the expected behavior change?

Step 2: Review the Tests First

Tests reveal intent and coverage:

- Do tests exist for the change?
- Do they test behavior (not implementation details)?
- Are edge cases covered?
- Do tests have descriptive names?
- Would the tests catch a regression if the code changed?

Step 3: Review the Implementation

Walk through the code with the five axes in mind:

For each file changed:
1. Correctness: Does this code do what the test says it should?
2. Readability: Can I understand this without help?
3. Architecture: Does this fit the system?
4. Security: Any vulnerabilities?
5. Performance: Any bottlenecks?

Step 4: Categorize Findings

Label every comment with its severity so the author knows what's required vs optional:

Prefix	Meaning	Author Action
(no prefix)	Required change	Must address before merge
Critical:	Blocks merge	Security vulnerability, data loss, broken functionality
Nit:	Minor, optional	Author may ignore — formatting, style preferences
Optional: / Consider:	Suggestion	Worth considering but not required
FYI	Informational only	No action needed — context for future reference

This prevents authors from treating all feedback as mandatory and wasting time on optional suggestions.

Step 5: Verify the Verification

Check the author's verification story:

- What tests were run?
- Did the build pass?
- Was the change tested manually?
- Are there screenshots for UI changes?
- Is there a before/after comparison?

Multi-Model Review

Different models have different blind spots. Use Model A to implement, Model B to review for correctness/architecture, then Model A addresses feedback. Final call is human.

Dead Code Hygiene

After refactoring, check for unreachable/unused code. List it and ask before deleting: "Should I remove these now-unused elements: [list]?" Don't leave dead code — it confuses future readers and agents.

Honesty in Review

When reviewing code — whether by you, another agent, or a human:

• Don't rubber-stamp. "LGTM" without evidence helps no one.
• Don't soften real issues. If it's a production bug, say so.
• Quantify: "N+1 query adds ~50ms/item" beats "could be slow."
• Push back on flawed approaches. Sycophancy fails reviews. Propose alternatives directly.
• Accept override gracefully. Author has full context; defer when they disagree. Comment on code, not people.

Dependency Discipline

Part of code review is dependency review:

Before adding any dependency:

1. Does the existing stack solve this? (Often it does.)
2. How large is the dependency? (Check bundle impact.)
3. Is it actively maintained? (Check last commit, open issues.)
4. Does it have known vulnerabilities? (npm audit)
5. What's the license? (Must be compatible with the project.)

Rule: Prefer standard library and existing utilities over new dependencies. Every dependency is a liability.

The Review Checklist

## Review: [PR/Change title]

### Context
- [ ] I understand what this change does and why

### Correctness
- [ ] Change matches spec/task requirements
- [ ] Edge cases handled
- [ ] Error paths handled
- [ ] Tests cover the change adequately

### Readability
- [ ] Names are clear and consistent
- [ ] Logic is straightforward
- [ ] No unnecessary complexity

### Architecture
- [ ] Follows existing patterns
- [ ] No unnecessary coupling or dependencies
- [ ] Appropriate abstraction level

### Security
- [ ] No secrets in code
- [ ] Input validated at boundaries
- [ ] No injection vulnerabilities
- [ ] Auth checks in place
- [ ] External data sources treated as untrusted

### Performance
- [ ] No N+1 patterns
- [ ] No unbounded operations
- [ ] Pagination on list endpoints

### Verification
- [ ] Tests pass
- [ ] Build succeeds
- [ ] Manual verification done (if applicable)

### Verdict
- [ ] **Approve** — Ready to merge
- [ ] **Request changes** — Issues must be addressed

See Also

• For detailed security review guidance, see references/security-checklist.md
• For performance review checks, see references/performance-checklist.md

Common Rationalizations

Rationalization	Reality
"It works, that's good enough"	Working code that's unreadable, insecure, or architecturally wrong creates debt that compounds.
"I wrote it, so I know it's correct"	Authors are blind to their own assumptions. Every change benefits from another set of eyes.
"We'll clean it up later"	Later never comes. The review is the quality gate — use it. Require cleanup before merge, not after.
"AI-generated code is probably fine"	AI code needs more scrutiny, not less. It's confident and plausible, even when wrong.
"The tests pass, so it's good"	Tests are necessary but not sufficient. They don't catch architecture problems, security issues, or readability concerns.

Red Flags

• PRs merged without any review
• Review that only checks if tests pass (ignoring other axes)
• "LGTM" without evidence of actual review
• Security-sensitive changes without security-focused review
• Large PRs that are "too big to review properly" (split them)
• No regression tests with bug fix PRs
• Review comments without severity labels — makes it unclear what's required vs optional
• Accepting "I'll fix it later" — it never happens

Verification

After review is complete:

• All Critical issues are resolved
• All Important issues are resolved or explicitly deferred with justification
• Tests pass
• Build succeeds
• The verification story is documented (what changed, how it was verified)