code-review-and-quality skill

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?
• Is a new conditional bolted onto an unrelated flow? That's a design smell, not a nit — push the logic into its own helper, state, or policy instead of tangling an existing path.
• Do repeated conditionals on the same shape appear? They signal a missing model or dispatcher. A "temporary" branch is usually permanent debt.

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)?
• Does this refactor reduce complexity or just relocate it? Count the concepts a reader must hold to follow the change. If a "cleaner" version leaves that count unchanged, it isn't cleaner — prefer the restructuring that makes whole branches, modes, or layers disappear over one that re-centralizes the same logic. Prefer deleting an abstraction to polishing it.
• Is feature-specific logic leaking into a shared or general-purpose module? Keep logic in its owning layer, reuse the existing canonical helper instead of a near-duplicate, and don't normalize architectural drift.
• Are type boundaries explicit? Question gratuitous any/unknown/optional/casts and silent fallbacks that paper over an unclear invariant — making the boundary explicit often makes the surrounding control flow simpler.

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?

Structural Remedies

When you flag a structural problem, propose the move — not just the problem. A review that only says "this is complex" leaves the author guessing. Reach for a named restructuring:

• Replace a chain of conditionals with a typed model or an explicit dispatcher.
• Collapse duplicate branches into a single clearer flow.
• Separate orchestration from business logic so each reads on its own.
• Move feature-specific logic out of a shared module into the package that owns the concept.
• Reuse the canonical helper instead of a bespoke near-duplicate.
• Make a type boundary explicit so downstream branching disappears.
• Delete a pass-through wrapper that adds indirection without clarifying the API.
• Extract a helper, or split a large file into focused modules.

Prefer the remedy that removes moving pieces over one that spreads the same complexity around.

Change Sizing

Small, focused changes are easier to review, faster to merge, and safer to deploy. Target these sizes:

~100 lines changed   → Good. Reviewable in one sitting.
~300 lines changed   → Acceptable if it's a single logical change.
~1000 lines changed  → Too large. Split it.

Watch file size, not just diff size. A small diff can still push a file past a healthy boundary — around 1000 total lines in a single file (distinct from the ~1000 changed-lines threshold above) is a common inspection signal, not a hard cap. When a change materially grows an already-large file, ask whether to extract helpers, subcomponents, or modules first, before piling more on. Decompose, then add.

What counts as "one change": A single self-contained modification that addresses one thing, includes related tests, and keeps the system functional after submission. One part of a feature — not the whole feature.

Splitting strategies when a change is too large:

When large changes are acceptable: Complete file deletions and automated refactoring where the reviewer only needs to verify intent, not every line.

Separate refactoring from feature work. A change that refactors existing code and adds new behavior is two changes — submit them separately. Small cleanups (variable renaming) can be included at reviewer discretion.

Change Descriptions

Every change needs a description that stands alone in version control history.

First line: Short, imperative, standalone. "Delete the FizzBuzz RPC" not "Deleting the FizzBuzz RPC." Must be informative enough that someone searching history can understand the change without reading the diff.

Body: What is changing and why. Include context, decisions, and reasoning not visible in the code itself. Link to bug numbers, benchmark results, or design docs where relevant. Acknowledge approach shortcomings when they exist.

Anti-patterns: "Fix bug," "Fix build," "Add patch," "Moving code from A to B," "Phase 1," "Add convenience functions."

Review Process

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:

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

Lead with what matters. Order findings by leverage: correctness and security first, then structural regressions and missed simplifications, then everything else. Don't bury a real issue under cosmetic nits — a few high-conviction comments beat a long list. If you have one structural problem and ten nits, the structural problem is the review.

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 Pattern

Use different models for different review perspectives:

Model A writes the code
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Model B reviews for correctness and architecture
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Model A addresses the feedback
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Human makes the final call