Can I use this for automated reporting?

Yes, by using the --json flag, the tool outputs structured data that is perfect for integration into automated reports or for Claude to parse and act upon.

Does it require any external dependencies?

Python 3 is required. For the most accurate results, having tools like ripgrep (rg), fd, and the respective language runtimes (Node/Go) installed is highly recommended.

What specifically does the 'size' check analyze?

The size check monitors for exceptionally large files, high function counts, and git churn to identify code hotspots that are likely difficult to maintain.

Which programming languages does this skill support?

The skill features specialized AST-based support for Python, Go, and JavaScript/TypeScript projects, but can perform general directory-level health checks on most codebases.

How does the skill handle different severity levels?

Findings are categorized into 'critical' (needs immediate attention), 'warning' (should address soon), and 'info' (nice to fix) categories with suggested actions for each.

Code Health Auditor

Name: Code Health Auditor
Author: lepinkainen

bylepinkainen

安全与测试

Analyzes codebases to identify technical debt, test coverage gaps, and refactoring opportunities across multiple languages.

关于

The Code Health Auditor skill provides a comprehensive suite of tools for auditing the quality and maintainability of a software project. By utilizing AST-based parsing and language-specific checks for Python, Go, and JavaScript/TypeScript, it automatically detects architectural issues such as overly large files, duplicate logic, dead code, and undocumented exports. It is particularly useful for developers performing code reviews, planning refactoring sprints, or onboarding onto legacy codebases where understanding technical debt and code churn is critical for progress.

主要功能

Multi-language support for Python, Go, and JS/TS with auto-detection.
Structured JSON output optimized for LLM consumption and programmatic workflows.
0 GitHub stars
Automated identification of dead code, legacy markers, and stale exports.
Test coverage gap analysis to find missing test files and quality issues.
AST-based function analysis for high-precision code scanning and signature detection.

使用场景

Auditing public API surfaces for missing documentation and visibility issues.
Identifying high-priority refactoring targets and large-file bottlenecks in legacy code.
Scanning for duplicate function patterns and similar logic to consolidate code.

关于

主要功能

Multi-language support for Python, Go, and JS/TS with auto-detection.
Structured JSON output optimized for LLM consumption and programmatic workflows.
0 GitHub stars
Automated identification of dead code, legacy markers, and stale exports.
Test coverage gap analysis to find missing test files and quality issues.
AST-based function analysis for high-precision code scanning and signature detection.

使用场景

Auditing public API surfaces for missing documentation and visibility issues.
Identifying high-priority refactoring targets and large-file bottlenecks in legacy code.
Scanning for duplicate function patterns and similar logic to consolidate code.