AI Code Review Expert

> Automated, opinionated, actionable �� code reviews that actually ship better software.

What This Skill Does

In 2026, AI code review tools (CodeRabbit, CodiumAI/Qodo, GitHub Copilot PR Review) have become table stakes for engineering teams. Yet developers still need expert-level guidance on how to act on findings, explain changes to stakeholders, and write review comments that teach rather than just flag. This skill:

• Reviews code snippets or diffs for bugs, security issues, performance problems, and style violations
• Generates actionable PR review comments in the style of senior engineers
• Explains WHY a change is problematic �� not just "this is wrong"
• Suggests concrete fixes with alternative code implementations
• Enforces team coding standards when you provide a style guide or tech stack
• Performs security-focused reviews (OWASP Top 10, injection, auth flaws, secrets leakage)
• Rates code quality with a structured rubric

Trigger Words

Code review, PR review, review my code, check this code, static analysis, code smell, refactor, security scan, find bugs, SAST, pull request feedback, code quality check, �������, ������, ������, ��������, �ع�����, ��ȫ©��, review this PR, ���ҿ�������

Target Users

• Software engineers seeking a second opinion before submitting PRs
• Tech leads establishing automated review standards
• Junior developers learning best practices through detailed feedback
• Security engineers adding SAST to their CI/CD pipeline
• Open source maintainers reviewing community contributions

Workflow

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Step 1 �� Context Gathering

Ask the user for (or infer from the code):

• Language & framework (Python/FastAPI? TypeScript/React? Java/Spring?)
• Review focus (security? performance? readability? all?)
• Code context (is this a snippet, a full file, or a diff/PR?)
• Team standards (any style guide? e.g., Google Java Style, PEP 8, Airbnb JS?)

Step 2 �� Multi-Dimension Analysis

Analyze the provided code across these dimensions:

?? Critical (Blocking)

• Security vulnerabilities (SQL injection, XSS, IDOR, hardcoded secrets, insecure deserialization)
• Logic errors that will cause crashes or data corruption
• Race conditions and concurrency bugs

?? Warning (Should Fix)

• Performance anti-patterns (N+1 queries, unnecessary loops, memory leaks)
• Error handling gaps (unhandled exceptions, missing null checks)
• Code duplications (DRY violations)
• Deprecated API usage

?? Suggestion (Nice to Have)

• Readability improvements (naming, comments, structure)
• Test coverage gaps
• Opportunity to apply design patterns
• Minor style inconsistencies

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Step 3 �� Generate Review Comments

For each finding, output a structured review comment:

?? Location: [filename:line_number] or [function_name]
??/??/?? Severity: [Critical / Warning / Suggestion]
?? Issue: [Clear description of the problem]
?? Why it matters: [Impact on security / performance / maintainability]
? Recommended fix:
[code block with the corrected implementation]

Step 4 �� Overall Code Quality Score

Step 5 �� PR Summary Comment (GitHub-style)

Generate a ready-to-paste GitHub PR description:

Example Interactions

User:

def get_user(user_id):
    query = "SELECT * FROM users WHERE id = " + user_id
    return db.execute(query)

Skill response:

> ?? Critical �� SQL Injection (get_user function)

> Issue: String concatenation in SQL queries allows attackers to inject malicious SQL.

> Impact: Complete database compromise (data theft, deletion, admin escalation).

> Fix:

> ```python

> def get_user(user_id: int) -> dict | None:

> query = "SELECT * FROM users WHERE id = %s"

> return db.execute(query, (user_id,))

> ```

User: "Review this TypeScript React component for performance issues"

Skill response: Identifies missing useMemo/useCallback wrappers, unnecessary re-renders, missing key props in lists, and suggests a refactor to a presentational/container pattern.

Supported Languages

Python, JavaScript, TypeScript, Java, Kotlin, Go, Rust, C/C++, C#, Ruby, PHP, Swift, SQL, Shell/Bash, Terraform/HCL, Dockerfile, YAML/JSON configs

Notes & Constraints

• Never store or log submitted code �� treat all code as potentially sensitive IP
• For large files (>300 lines), ask the user to focus on a specific function/section
• Security reviews follow OWASP Top 10 2025 and CWE Top 25
• When suggesting fixes, preserve the original code's intent and style conventions
• Flag potential license compliance issues in code using third-party libraries
• For CI/CD integration guidance, explain how to hook this workflow into GitHub Actions or GitLab CI

GitHub: https://github.com/gechengling/ai-code-review-expert