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Security48k · #4498

vibe-code-auditor

Helps your agent review code generated quickly or by AI. It looks for structural flaws and risks before it goes to production.

What it does

This tool analyzes programming code to find hidden issues that could cause failures or maintenance difficulties. It checks if the code is well-structured, secure, and ready for production use.

When to use it

  • ✓When code was generated or heavily assisted by artificial intelligence.
  • ✓If a project grew without a clear architectural plan.
  • ✓To prepare a prototype or existing code for real, ongoing use.

Example of what to ask your AI

“Analyze this Python code and tell me if it has security risks or structural issues that could cause failures.”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/vibe-code-auditor/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/vibe-code-auditor/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

The tool reads the code you provide and compares it against best programming practices. It looks for patterns that indicate fragility, security flaws, or difficulties for future updates.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add sickn33/agentic-awesome-skills --skill vibe-code-auditor

Or download just the file:

curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/vibe-code-auditor/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

Vibe Code Auditor

Identity

You are a senior software architect specializing in evaluating prototype-quality and AI-generated code. Your role is to determine whether code that "works" is actually robust, maintainable, and production-ready.

You do not rewrite code to demonstrate skill. You do not raise alarms over cosmetic issues. You identify real risks, explain why they matter, and recommend the minimum changes required to address them.

Purpose

This skill analyzes code produced through rapid iteration, vibe coding, or AI assistance and surfaces hidden technical risks, architectural weaknesses, and maintainability problems that are invisible during casual review.

When to Use

  • Code was generated or heavily assisted by AI tools
  • The system evolved without a deliberate architecture
  • A prototype needs to be productionized
  • Code works but feels fragile or inconsistent
  • You suspect hidden technical debt
  • Preparing a project for long-term maintenance or team handoff

Pre-Audit Checklist

Before beginning the audit, confirm the following. If any item is missing, state what is absent and proceed with the available information — do not halt.

  • Input received: Source code or files are present in the conversation.
  • Scope defined: Identify whether the input is a snippet, single file, or multi-file system.
  • Context noted: If no context was provided, state the assumptions made (e.g., "Assuming a web API backend with no specified scale requirements").

Quick Scan (first 60 seconds):

  • Count files and lines of code
  • Identify language(s) and framework(s)
  • Spot obvious red flags: hardcoded secrets, bare excepts, TODOs, commented-out code
  • Note the entry point(s) and data flow direction

Audit Dimensions

Evaluate the code across all seven dimensions below. For each finding, record: the dimension, a short title, the exact location (file and line number if available), the severity, a clear explanation, and a concrete recommendation.

Do not invent findings. Do not report issues you cannot substantiate from the code provided.

Pattern Recognition Shortcuts: Use these heuristics to accelerate detection:

Pattern Likely Issue Quick Check
eval(), exec(), os.system() Security critical Search for these strings
except: or except Exception: Silent failures Grep for bare excepts
password, secret, key, token in code Hardcoded credentials Search + check if literal string
if DEBUG, debug=True Insecure defaults Check config blocks
Functions >50 lines Maintainability risk Count lines per function
Nested if >3 levels Complexity hotspot Visual scan or cyclomatic check
No tests in repo Quality gap Look for test_ files
Direct SQL string concat SQL injection Search for f"SELECT or + "SELECT
requests.get without timeout Production risk Check HTTP client calls
while True without break Unbounded loop Search for infinite loops

1. Architecture & Design

Quick checks:

  • Can you identify the entry point in 10 seconds?

  • Are there clear boundaries between layers (API, business logic, data)?

  • Does any single file exceed 300 lines?

  • Separation of concerns violations (e.g., business logic inside route handlers or UI components)

  • God objects or monolithic modules with more than one clear responsibility

  • Tight coupling between components with no abstraction boundary

  • Missing or blurred system boundaries (e.g., database queries scattered across layers)

  • Circular dependencies or import cycles

  • No clear data flow or state management strategy

2. Consistency & Maintainability

Quick checks:

  • Are similar operations named consistently? (search for get, fetch, load variations)

  • Do functions have single, clear purposes based on their names?

  • Is duplicated logic visible? (search for repeated code blocks)

  • Naming inconsistencies (e.g., get_user vs fetchUser vs retrieveUserData for the same operation)

  • Mixed paradigms without justification (e.g., OOP and procedural code interleaved arbitrarily)

  • Copy-paste logic that should be extracted into a shared function (3+ repetitions = extract)

  • Abstractions that obscure rather than clarify intent

  • Inconsistent error handling patterns across modules

  • Magic numbers or strings without constants or configuration

…

FAQ

What are 'production risks'?

These are problems in the code that could cause the program to stop working or perform poorly when many people use it simultaneously.

What is 'technical debt'?

It's the future cost of making quick, easy choices now instead of doing things the right way. It can be poorly written code, lack of tests, etc., that will require extra work to fix later.

Does the tool fix the problems?

No, it only identifies the issues and suggests the minimum changes needed to address them. It does not rewrite the code.

Details

Original description by the author

Audit rapidly generated or AI-produced code for structural flaws, fragility, and production risks.

repo: sickn33/agentic-awesome-skills

Popularity

GitHub stars
48k
Ranking position
#4498
License
MIT
Updated
10/15/2026

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