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Production Code Audit

Listed
sickn33·vlatest·security·MIT

Identifies potential bugs, security vulnerabilities, and style inconsistencies in production code using advanced analysis techniques.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add sickn33/agentic-awesome-skills --skill production-code-audit -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

sickn33

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • Declares what it does: failed

    No SKILL.md was found in the skill's directory.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned sickn33/antigravity-awesome-skills at 5a4cdd9df8.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
5a4cdd9df8
Manifest
skills/production-code-audit/SKILL.md
Scanned
0 files
Copies in repo
3 — all of them scanned
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The production-code-audit skill enables AI agents to analyze existing codebases for potential issues, such as security vulnerabilities, performance bottlenecks, and adherence to coding standards. It provides actionable insights that help developers improve the quality and reliability of their software in a production environment.

When to use it

  • Before deploying new features to identify hidden bugs or inefficiencies.
  • After a code review to ensure compliance with internal or industry best practices.
  • To assess the impact of third-party libraries on performance and security.
  • As part of a continuous integration/continuous deployment (CI/CD) pipeline for automated quality checks.

Key capabilities

  • Static code analysis for common vulnerabilities (e.g., SQL injection, XSS).
  • Detection of inefficient algorithms or memory leaks.
  • Code style and formatting consistency checks.
  • Integration with popular linters and security tools like ESLint, SonarQube, and OWASP ZAP.

Example prompts

  • "Audit this Python code for potential security issues."
  • "Analyze the JavaScript files in this repository for performance optimizations."
  • "Check if this C++ library complies with our coding standards."

Tips & gotchas

  • Ensure that the AI agent has access to the necessary tools and dependencies for accurate analysis.
  • Be mindful of false positives—always validate findings manually or with additional testing.