What specific code issues does this skill look for?

It focuses on egregious structural issues such as functions over 100 lines, code duplication, dead code, weak type usage (like Any), and type-deficient patterns like hasattr/getattr.

How does this skill handle large Pull Requests?

It uses a tiered filtering approach to remove lock files, documentation, and tests, and can integrate with external large-context LLMs to analyze diffs that exceed standard context limits.

Why does it ignore naming conventions or whitespace?

To maximize impact, it leaves minor style issues to automated linters and formatters, focusing the AI's attention on architectural problems that require human-level reasoning to detect.

Does it automatically apply changes to my code?

No, it follows a human-in-the-loop philosophy. It presents findings and a list of suggested actions, then waits for explicit user approval before executing any fixes or creating TODOs.

Python Standards Code Review

Name: Python Standards Code Review
Author: gigaverse-app

bygigaverse-app

Collaboration Tools

Performs high-level architectural code reviews focused on structural integrity, type safety, and cross-file patterns while filtering out noise.

About

This skill optimizes the code review process by focusing on high-impact structural issues like God functions, code duplication, and weak typing while automatically filtering out noise such as lock files. It emphasizes cross-file pattern recognition and provides a structured workflow for handling large diffs through LLM integration. By prioritizing egregious architectural problems over trivial styling, it helps developers maintain high-quality codebases efficiently, concluding every review with clear, actionable suggestions and a human-in-the-loop approval step.

Key Features

Detection of structural issues like God functions and code duplication
0 GitHub stars
Identification of type deficiencies, weak annotations, and missing interfaces
Human-in-the-loop workflow for explicit approval of suggested fixes
Automated noise filtering for lock files, docs, and tests to reduce diff size
Large diff management using high-context LLM tooling for cross-file patterns

Use Cases

Identifying repeated patterns across multiple files that require abstraction
Reviewing complex Pull Requests to catch architectural drift and technical debt
Auditing type safety and interface consistency in large Python projects

About

Key Features

Detection of structural issues like God functions and code duplication
0 GitHub stars
Identification of type deficiencies, weak annotations, and missing interfaces
Human-in-the-loop workflow for explicit approval of suggested fixes
Automated noise filtering for lock files, docs, and tests to reduce diff size
Large diff management using high-context LLM tooling for cross-file patterns

Use Cases

Identifying repeated patterns across multiple files that require abstraction
Reviewing complex Pull Requests to catch architectural drift and technical debt
Auditing type safety and interface consistency in large Python projects