code-reviewer
SkillFiles & storageReviews source code for bugs, readability, maintainability, performance, and best-practice violations. Use for general-purpose code review of a diff, file, or function, not tied to a specific language concern.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the code-reviewer skill
What this skill tells your AI
The instructions your AI receives, as published by codebygarv/ai-skills in skills/development/code-reviewer/SKILL.md and read by ahel’s review.
Purpose
Provide a thorough, general-purpose code review covering correctness, readability, maintainability, and performance — the kind of review a careful senior engineer would leave on a diff.
When to Use
- Reviewing a diff, PR, file, or function before merge.
- The user asks for "a code review" without narrowing to a specific concern (security, types, performance) — those narrower concerns have their own skills (Security Auditor, TypeScript Guardian, Performance Auditor) and this skill should defer to them for deep dives in those areas while still flagging obvious issues.
What to Analyze
- Correctness — logic errors, off-by-ones, incorrect assumptions about inputs, unhandled null/undefined/empty cases.
- Readability — naming, function length, nesting depth, whether intent is obvious without extra explanation.
- Maintainability — duplication, tight coupling, magic numbers/strings, missing abstraction where a pattern repeats 3+ times.
- Performance — obviously wasteful operations (N+1 queries, unnecessary re-computation, quadratic loops over data that can grow).
- Consistency — does it match the surrounding codebase's existing patterns and conventions.
- Error handling — are failure paths handled, or silently swallowed/ignored.
Output Format
- Group findings by severity: Blocking (bugs, correctness issues) → Should Fix (maintainability, missing error handling) → Nit (style, naming).
- Each finding: file/line reference, what's wrong, and a concrete suggested fix — not just "this could be better."
- End with what's done well, briefly — but only if genuinely true, not as a formality.
Avoid
- Rewriting the whole file as "feedback" instead of pointing at specific issues.
- Nitpicking style that a linter/formatter already enforces.
- Flagging a pattern as wrong when it's actually the codebase's established convention — check for consistency first.
Signals
- GitHub stars
- 25
- Forks
- 1
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
code-reviewer-codebygarv- Source
- github.com/codebygarv/ai-skills