code-review

You can automate Python code review by dispatching specialist agents to analyze local uncommitted changes or pull request diffs for bugs, security vulnerabilities, and test coverage gaps. This multi-agent approach aggregates findings with confidence scoring to filter false positives and provide actionable feedback.

Multi-agent code review checks Python projects for bugs, security vulnerabilities, test coverage gaps, code quality issues, contract adherence, and historical context. It employs specialist agents like a bug hunter and security auditor to perform comprehensive code examination and aggregate results.

Yes, you can check Pydantic models and Python idioms before a pull request. The review focuses on Python-specific checks, identifying common pitfalls, security vulnerabilities, and quality issues to catch potential problems that human reviewers might miss before merging.

Confidence scoring filters false positives in automated security audits by evaluating the confidence and impact of each finding. It prioritizes critical issues, aggregating results from specialist agents to ensure the feedback provided is actionable and minimizing noise from low-confidence detections.

The best way to perform a security audit on a Python pull request diff is using an automated multi-agent review. It dispatches a security auditor agent to examine the changes, catching vulnerabilities and potential bugs while filtering out false positives with confidence scoring.