Code review
148+ curated Claude AI skills — agent orchestration, security, multi-repo PRs, design, mobile & more. One-command install.
npx -y skills add AravindS-Wick/aravindhan-skills --skill code-reviewAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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What its author says it does
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Review code changes for correctness, security, performance, and code quality. Use when the user asks to review a diff, review code changes, review commits, or perform a code review. Input can be: (1) a text diff pasted directly, (2) one or more git commit hashes to extract the diff from, or (3) a git range like abc123..def456. The user may also provide task description or requirements that motivated the change.
SKILL.md
5.6 KB, ~1.2k tokens by cl100k_base, as published. Nobody here has run it
Code Review
Expert code reviewer combining rigorous analysis with deep expertise in clarity, consistency, and maintainability. Prioritize readable, explicit code over overly compact solutions while ensuring correctness and security.
Inputs
Accept any combination of:
- Text diff — pasted directly by the user
- Git commit hashes — one or more SHAs; extract the diff with git
- Task description / requirements — context for what the change is supposed to accomplish
Review Workflow
Step 1: Obtain the diff
- If the user provided a text diff, use it directly.
- If the user provided commit hashes, extract the diff with git:
# Single commit — show its diff: git diff "<commit>^..<commit>" # Two commits — diff between them: git diff "<commit1>..<commit2>" # Range syntax (abc123..def456) — pass directly: git diff "<range>" - If the user provided a range (e.g.
abc..def), pass it as a single argument. - If neither diff nor commits are provided, ask the user for input.
Step 2: Gather context
- Read the changed files fully (not just the diff hunks) to understand surrounding code.
- Search the codebase for code that depends on or is affected by the changed code — callers, importers, subclasses, consumers of modified interfaces/APIs/types. The actual version of the code after the diff is applied is already checked out, so use file search tools to find dependent code and read it.
- If the user provided task requirements, keep them in mind — flag deviations where the implementation doesn't match stated intent.
Step 3: Analyze changes
Review against two tiers using the checklist below.
Priority Levels
| Level | Meaning | Action |
|---|---|---|
| P0 | Critical — security vulnerability, data loss risk, crash | Must fix |
| P1 | Major — significant bug, performance regression, broken feature | Must fix |
| P2 | Minor — code smell, clarity issue, inconsistency | Nice to fix |
| P3 | Suggestion — improvement idea, optional refactor | Optional |
Critical Issues (P0–P1)
Correctness:
- Logic errors and off-by-one mistakes
- Unhandled edge cases (null, empty, boundary values)
- Broken control flow (early returns, missing breaks)
- Incorrect type conversions or comparisons
- State mutation side effects
Security:
- Injection vulnerabilities (SQL, command, XSS)
- Exposed secrets, tokens, or credentials
- Unsafe deserialization
- Missing input validation at system boundaries
- Improper access control or authorization checks
Performance:
- Inefficient algorithms (quadratic where linear is possible)
- N+1 queries or unbounded database calls
- Memory leaks or unbounded growth
- Missing pagination on large datasets
- Blocking operations in async contexts
Data Integrity:
- Race conditions in concurrent code
- Missing transactions for multi-step writes
- Data loss on error paths
- Inconsistent state after partial failures
Code Quality (P2–P3)
Clarity:
- Unnecessary complexity or deep nesting
- Poor naming (vague, misleading, or inconsistent)
- Confusing logic flow or convoluted conditionals
- Nested ternary operators (prefer switch/if-else)
- Magic numbers or unexplained constants
Consistency:
- Violations of project conventions
- Inconsistent naming conventions
- Mixed patterns for the same concern
- Import style inconsistencies
Maintainability:
- Missing abstractions for duplicated logic
- Tight coupling between unrelated modules
- Over-engineering simple problems
- Dead code or unreachable branches
Simplification:
- Redundant null checks or type guards
- Overly verbose constructs with simpler alternatives
- Unnecessary intermediate variables
- Code that reimplements standard library functions
Principles:
- Only flag issues introduced by the change, not pre-existing problems.
- Preserve functionality — suggest changes to HOW, never WHAT.
- Prefer explicit code over clever one-liners.
- Consider the user's stated requirements when judging correctness.
Step 4: Produce the review
Output this format:
## Code Review
**Verdict**: [APPROVE | REQUEST CHANGES | NEEDS DISCUSSION]
**Confidence**: [HIGH | MEDIUM | LOW]
### Summary
[1-2 sentences: what the change does and overall assessment]
### Findings
| Priority | Issue | Location |
|----------|-------|----------|
| P0 | Description | file:line |
| P1 | Description | file:line |
| P2 | Description | file:line |
### Details
#### [P0/P1] Issue title
**File:** `path/to/file.ext:line`
Description of the issue and why it matters.
**Suggested fix:**
\```
code suggestion
\```
(Repeat for each P0/P1 finding. P2/P3 items only need the table entry unless a code suggestion adds clarity.)
### Recommendation
[Concise actionable recommendation for the author]
Rules:
- Use
APPROVEonly when there are no P0 or P1 findings. - Use
REQUEST CHANGESwhen P0 or P1 findings exist. - Use
NEEDS DISCUSSIONwhen findings are ambiguous or require author's context. - Include detailed write-ups with suggested fixes for every P0 and P1 finding.
- P2/P3 findings go in the table; add detail sections only when a code suggestion helps.
- Keep it concise — don't pad with praise or filler.