Use cases · audit
Audit Code
Auditing code is the process of systematically reviewing source code for bugs, security vulnerabilities, performance issues, and adherence to coding standards. AI agents excel at code audits because they can analyze large codebases quickly, identify patterns that humans might miss, and provide consistent, actionable feedback. By leveraging agent skills, you can automate repetitive checks, enforce best practices, and get detailed reports without manual effort. Below are 4 skills we evaluated for this task.
4 skills for this task
ethskills
Use when a request involves Ethereum, the EVM, or blockchain systems.
ai-safe2-secure-build-copilot
Apply the AI SAFE2 v3.0 framework (161 controls across 5 pillars plus CP.1-CP.10 Cross-Pillar Governance) to design, build, audit, and govern AI agents, agentic workflows, RAG systems, MCP servers,…
security-best-practices
Perform language and framework specific security best-practice reviews and suggest improvements.
author-contributions
Identify all files a specific author contributed to on a branch vs its upstream, tracing code through renames.
Common questions
- Can an AI agent audit my entire codebase for security vulnerabilities?
- Yes, many agent skills are designed to scan code for common security issues like SQL injection, hardcoded credentials, and insecure dependencies. They can flag risky patterns and suggest fixes, though a human review is still recommended for critical systems.
- How do I set up an agent to audit code on every commit?
- Most skills can be integrated into CI/CD pipelines. You can configure the agent to run on each pull request, analyzing the changed files and posting comments with findings. This ensures continuous code quality without manual effort.
- Will the agent check for coding style and formatting issues?
- Absolutely. Many code audit skills include linting and style checks against popular standards like PEP8 for Python or ESLint for JavaScript. They can enforce consistency across your team's codebase automatically.