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vignesh2027/ai-agent-skills/code-review-and-quality
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PublishedSeptember 28, 2026 at 07:05 AM
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version: "1.0.0" name: code-review-and-quality description: Conduct staff-engineer-level code reviews across correctness, security, performance, readability, and architecture difficulty: staff domains: [general]


Overview

Code review is the most important quality gate in software development. A good code review catches bugs, prevents security vulnerabilities, improves design, and transfers knowledge — before code reaches production. This skill runs a structured review across five dimensions.

When to Use

  • Before any PR is merged
  • When reviewing AI-generated code
  • As part of the /review workflow

Process

Dimension 1: Correctness

  • Does the code do what the spec says?
  • Are all edge cases handled? (empty inputs, null, zero, max values, concurrent access)
  • Are error conditions handled? (network failure, invalid input, resource exhaustion)
  • Are there off-by-one errors?
  • Are there race conditions in concurrent code?
  • Does the code handle partial failures?

Dimension 2: Security

  • Is all user input validated before use?
  • Are all database queries parameterized?
  • Is no sensitive data logged?
  • Are all auth/authz checks correct?
  • Are there injection vulnerabilities? (SQL, shell, LDAP, XML)
  • Are there new attack surfaces (new endpoints, new data types)?
  • See security-and-hardening for full checklist.

Dimension 3: Performance

  • Are there N+1 query patterns?
  • Are there missing database indexes?
  • Is expensive computation done in a hot path?
  • Are large data sets loaded into memory when streaming would work?
  • Are there blocking synchronous calls that should be async?
  • Does the change affect the critical path? Profile it.

Dimension 4: Readability and Maintainability

  • Does the code express its intent clearly?
  • Are names accurate? (Function names that lie are worse than no names)
  • Is complexity necessary, or is there a simpler solution?
  • Are there long functions that should be extracted?
  • Is there duplicated logic that should be shared?
  • Are there magic numbers or strings that should be constants?
  • Will a new engineer understand this in 6 months?

Dimension 5: Architecture

  • Does this change fit the existing design?
  • Does this create new coupling that will be hard to remove?
  • Does this introduce a new pattern when an existing one could be used?
  • Does this create a new dependency that adds risk?
  • Does this make the system harder or easier to test?

Review process

  1. Read the PR description. If it's missing, ask for it.
  2. Understand the spec/ticket the PR is implementing.
  3. Review the tests first — they tell you what the author thinks the code should do.
  4. Review the implementation against each dimension above.
  5. Run the code locally for non-trivial PRs.
  6. Leave specific, actionable comments. Not "this is wrong" — "this will fail when X because Y; consider Z."

Anti-Rationalizations

"The tests pass — it's good to go" Tests passing proves the code passes the tests. It does not prove the tests are correct, complete, or that the code is secure.

"It's a small change — it doesn't need a full review" Small changes have caused large incidents. The review checklist is short enough to run on any change.

Verification Requirements

  • [ ] All five dimensions reviewed
  • [ ] Comments are specific and actionable (not vague)
  • [ ] Security dimension completed for any change touching auth, data, or external APIs
  • [ ] Tests reviewed before implementation
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