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code-review-and-quality

Conduct staff-engineer-level code reviews across correctness, security, performance, readability, and architecture

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Quellinformationen

Repository
vignesh2027/AI-AGENT-SKILLS
Letzte Quellaktivität
13. Mai 2026 um 19:03
Erkannte Sprache von SKILL.md
Englisch
Sterne
2
Forks
0

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SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
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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