audit-markers

Audit and fix pytest markers in test files using heuristics and project rules.

1.8k|149|Updated Jul 31, 2025

Search Tags:#test automation #pytest #resource optimization #test suite maintenance #marker analysis

Ready to run

npx skills add https://github.com/generative-computing/mellea --skill audit-markers

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: audit-markers
Source: https://github.com/generative-computing/mellea/tree/main/.agents/skills/audit-markers
Command: npx skills add https://github.com/generative-computing/mellea --skill audit-markers

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines the process of auditing and fixing pytest markers in test files and examples, ensuring that tests are correctly classified and optimized for efficient execution.

Core Features & Use Cases

Automatic Classification: Classifies tests as unit, integration, e2e, or qualitative using heuristics and project-specific rules.
Resource Estimation: Estimates GPU VRAM and RAM requirements for tests to optimize resource allocation.
Quick Fixes: Applies fixes to test markers to ensure they are correct and up-to-date.
Use Case: Before committing changes, use this Skill to audit and fix pytest markers in your test suite, ensuring that tests are correctly classified and optimized for performance.

Quick Start

Run the 'audit-markers' skill on your test files to automatically classify and fix pytest markers.

Frequently Asked Questions about audit-markers

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

FAQPage Schema

How do I automate pytest marker auditing and fixing for optimized test runs?▼

To automate pytest marker auditing, you can run this Skill on your test files to classify tests as unit, integration, e2e, or qualitative using heuristics and project-specific rules. It applies quick fixes to ensure markers are correct and up-to-date for efficient execution.

What is the best way to estimate GPU VRAM and RAM requirements for pytest tests?▼

Estimating GPU VRAM and RAM requirements for pytest tests is handled by analyzing test markers and applying resource estimation heuristics. This classifies your tests and optimizes resource allocation to improve overall test suite performance.

Do I need specific Python libraries to classify and fix pytest markers?▼

Yes, classifying and fixing pytest markers requires pytest and Python libraries for analysis and manipulation. You must have these dependencies installed in your environment to enable the automatic classification and resource estimation processes.

When should I audit pytest markers in my test suite?▼

You should audit pytest markers before committing changes to your repository. Running marker analysis at this stage ensures that tests are correctly classified and optimized for performance, preventing misclassified tests from degrading suite execution efficiency.

Can I use project-specific rules to classify pytest tests as unit or integration?▼

Yes, you can classify pytest tests as unit, integration, e2e, or qualitative using project-specific rules. The auditing process applies these custom rules alongside general heuristics to accurately categorize tests and correct marker usage.