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DevayoshaUS
DevayoshaUSCOMMUNITY
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code-review-checklist

Reviews code changes against correctness, security, performance, and AI-specific quality checklists.

41|Updated Apr 14, 2026
Search Tags:#code quality#code review#best practices#checklist#anti-patterns#security#prompt injection
One-click install
npx skills add https://github.com/DevayoshaUS/Women-scholarship --skill code-review-checklist-devayoshaus
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: code-review-checklist
Source: https://github.com/DevayoshaUS/Women-scholarship/tree/main/hackathon-main%20%281%29/hackathon-main/.agent/skills/code-review-checklist
Command: npx skills add https://github.com/DevayoshaUS/Women-scholarship --skill code-review-checklist-devayoshaus

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Code reviews are often inconsistent, with reviewers missing security vulnerabilities, anti-patterns, or AI-generated code issues. This Skill provides a structured checklist that standardizes reviews across correctness, security, performance, testing, and documentation. ## Core Features & Use Cases - Comprehensive Review Checklist: Covers correctness, security (including prompt injection and output sanitization for AI code), performance, code quality, testing, and documentation. - AI & LLM Review Patterns: Flags hallucination-prone logic, unsafe prompt engineering, and unvalidated AI outputs in modern codebases. - Anti-Pattern Detection: Identifies magic numbers, deep nesting, long functions, and untyped code with concrete before/after examples. - Standardized Review Comments: Uses a severity-tagged comment convention (blocking, suggestion, nit, question) for clear feedback. - Use Case: Before merging a pull request containing AI-generated API handlers, run this checklist to catch missing input validation, hardcoded secrets, and unsanitized LLM outputs. ## Quick Start Review the changes in this pull request using the code review checklist and flag any security or correctness issues.

Frequently Asked Questions about code-review-checklist

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I review code for security vulnerabilities?▼

Check that inputs are validated and sanitized, look for SQL/NoSQL injection, XSS, and CSRF vulnerabilities, and confirm no hardcoded secrets exist. For AI features, also verify protection against prompt injection and sanitization of model outputs.

What should a code review checklist include?▼

A thorough checklist covers correctness, edge cases, error handling, security, performance issues like N+1 queries, code quality principles like DRY and SOLID, test coverage, and documentation of public APIs.

How do I review AI-generated code for hallucinations?▼

Verify the logic follows a verifiable path, check that empty states, timeouts, and partial failures are handled, and confirm the code makes safe assumptions about file systems and network calls rather than inventing behavior.

What are common code review anti-patterns to flag?▼

Flag magic numbers instead of named constants, deeply nested conditionals instead of early returns, functions over 100 lines, and use of any types instead of proper type definitions.

How should I format code review comments by severity?▼

Use a tagged convention: blocking issues marked as critical, improvement suggestions as medium priority, minor style nits as low priority, and open questions when intent is unclear. This keeps feedback actionable and prioritized.

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