code-review-assistant

name	code-review-assistant
description	Analyzes code changes for security vulnerabilities, performance issues, and maintainability concerns. Provides structured feedback with SOLID principle checks and anti-pattern detection. Triggers on: "review this code", "code review", "check my PR", "review my changes".
license	MIT
metadata	{"author":"Community","version":"1.0.0","category":"code-quality"}

Code Review Assistant

Instructions

Step 1: Gather Context

Before reviewing, determine:

1. What language and framework is the code written in?
2. Is this a PR diff, a single file, or a full module?
3. What is the review scope? (Security | Performance | Maintainability | All)

If reviewing a PR, ask for the diff or file list. If a single file, ask for the surrounding context (what calls it, what it calls).

Step 2: Security Analysis

Check for these categories in order of severity:

Critical:

• SQL injection (string concatenation in queries)
• Command injection (unsanitized shell commands)
• Path traversal (user input in file paths without validation)
• Hardcoded secrets (API keys, passwords, tokens)

High:

• Missing authentication/authorization checks
• Insecure deserialization
• SSRF vulnerabilities (user-controlled URLs)
• Missing input validation on public endpoints

Medium:

• Missing rate limiting on public APIs
• Verbose error messages leaking internals
• Missing CSRF protection
• Insecure random number generation for security contexts

Step 3: Performance Review

Look for:

1. N+1 queries - database calls inside loops
2. Unbounded collections - loading all records without pagination
3. Missing indexes - queries filtering on non-indexed columns
4. Unnecessary allocations - creating objects in hot paths
5. Blocking I/O - synchronous calls in async contexts
6. Missing caching - repeated expensive computations with same inputs

Step 4: SOLID Principles Check

Evaluate each principle:

Principle	Red Flag
Single Responsibility	Class has multiple reasons to change
Open/Closed	Modifying existing code instead of extending
Liskov Substitution	Subclass breaks parent contract
Interface Segregation	Forcing implementation of unused methods
Dependency Inversion	High-level module depends on concrete class

Only flag violations that cause real maintainability problems, not theoretical ones.

Step 5: Anti-Pattern Detection

Check for:

• God objects - classes with more than 10 public methods or 300+ lines
• Feature envy - methods that use another class's data more than their own
• Primitive obsession - using strings/ints where a value object improves clarity
• Long parameter lists - functions with more than 4 parameters
• Deep nesting - more than 3 levels of indentation in conditionals

Step 6: Produce Structured Feedback

Format findings as:

## Code Review Summary

**Risk Level:** Critical | High | Medium | Low

### Findings

#### [Category] Finding Title
- **Location:** file:line
- **Severity:** Critical | High | Medium | Low
- **Issue:** What is wrong
- **Impact:** What could happen
- **Fix:** Concrete suggestion with code

### Positive Observations
- List things done well (always include at least one)

### Recommendations
1. Prioritized action items

STOP after producing the summary. Ask if the user wants deeper analysis on any specific finding.

Example

User says: "Review this Python function"

def get_user(user_id):
    query = f"SELECT * FROM users WHERE id = {user_id}"
    result = db.execute(query)
    return result

Response:

• Critical: SQL Injection at line 2 - string interpolation in SQL query
• Medium: Overfetching - SELECT * returns all columns
• Fix: Use parameterized queries: db.execute("SELECT id, name, email FROM users WHERE id = ?", [user_id])

Guidelines

• Always start with security findings, then performance, then style
• Be specific with line numbers and concrete fix suggestions
• Do not nitpick formatting or style unless it impacts readability significantly
• Acknowledge good patterns alongside problems
• If the code is already good, say so briefly and suggest one area for further improvement
• Limit findings to the top 5 most impactful issues unless asked for exhaustive review
• Never suggest changes that alter business logic without flagging it as a behavioral change