code-review

Analyze Python code changes with multi-agent review and confidence scoring.

1|Updated Jul 20, 2022

Search Tags:#code quality #testing #code review #python #security audit #bug detection

Ready to run

npx skills add https://github.com/fruch/coodie --skill code-review-fruch

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: code-review
Source: https://github.com/fruch/coodie/tree/main/.github/skills/code-review
Command: npx skills add https://github.com/fruch/coodie --skill code-review-fruch

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the process of code review, identifying potential bugs, security vulnerabilities, and quality issues in Python projects, ensuring higher code quality and faster development cycles.

Core Features & Use Cases

Multi-Agent Analysis: Employs specialist agents (bug hunter, security auditor, etc.) for comprehensive code examination.
Confidence Scoring: Filters findings based on confidence and impact, prioritizing critical issues.
Python-Specific Checks: Focuses on Python idioms, Pydantic models, and common pitfalls.
Use Case: Before merging a pull request, use this Skill to perform an automated audit of the changes, catching potential issues that human reviewers might miss.

Quick Start

Run a code review on my local uncommitted changes.

Frequently Asked Questions about code-review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate Python code review for uncommitted changes?▼

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.

What does multi-agent code review check for in Python projects?▼

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.

Can I use this to check Pydantic models and Python idioms before a pull request?▼

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.

How does confidence scoring filter false positives in automated security audits?▼

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.

What is the best way to perform a security audit on a Python pull request diff?▼

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.