audit-hotspots

Measure file size and responsibility concentration across Python modules in gestor_contable.

Updated Feb 26, 2026
One-click install
npx skills add https://github.com/ElPoot/contabilidad --skill audit-hotspots
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: audit-hotspots
Source: https://github.com/ElPoot/contabilidad/tree/main/.agents/skills/audit-hotspots
Command: npx skills add https://github.com/ElPoot/contabilidad --skill audit-hotspots

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps you identify where the codebase has excessive responsibility concentration, long functions, and monolithic files that are most likely to create regressions.

Core Features & Use Cases

  • File size triage: Measures which Python files under gestor_contable/ exceed 500 lines.
  • Responsibility concentration metrics: Counts methods, classes, and unique imports per file to quantify complexity.
  • Domain-mixing hotspot detection: Uses method/class names to classify domains (UI, I/O, BD, Red, Business Logic) and flags files mixing 3+ domains.

Quick Start

Ask the AI to audit gestor_contable/ and produce an objective hotspot report for files over 500 lines, including method counts, domain mixtures, and the longest method per top-risk file.

Frequently Asked Questions about audit-hotspots

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

FAQPage Schema
How do I identify code hotspots and monolithic Python files in my project?

Code hotspot detection measures file size and responsibility concentration by counting lines, methods, classes, and unique imports in Python files. This objective metrics report flags monolithic files exceeding 500 lines that are likely to create regressions.

What is domain mixing and how does it indicate refactoring risk in Python modules?

Domain mixing occurs when a single Python file handles multiple functional areas like UI, I/O, DB, network, and business logic. Files mixing three or more domains are flagged as refactoring risks because they concentrate excessive responsibility and increase regression likelihood.

How do I audit Python modules for complexity metrics and technical debt?

Auditing Python modules for technical debt involves counting lines, methods, classes, and import diversity per file. By mapping method names to functional domains, you produce an objective complexity metrics report that highlights files with concentrated responsibility and refactoring risk.

Can I use hotspot detection for regression risk triage on an existing Python codebase?

Yes, hotspot detection is designed for maintenance and regression-risk triage on existing Python codebases. By mapping method names to functional domains and measuring responsibility concentration, it identifies overly long functions and monolithic files most likely to break.

What are the limitations of using file size for code audit and hotspot detection?

Using file size for code audits limits hotspot detection to files exceeding 500 lines and focuses specifically on the gestor_contable package. It requires counting methods and mapping domains, meaning smaller files with high complexity but fewer lines might be missed.