How does the Anti-Slop dispatcher work?

It automatically analyzes the file extension you are working on (e.g., .py, .R, .md) and invokes the corresponding domain-specific quality rules without requiring manual selection.

Can I use specific language rules directly?

Yes, you can directly call sub-skills like python/anti-slop or r/anti-slop if you want to focus exclusively on a specific domain's deep-dive requirements.

Is this skill suitable for automated pipelines?

Yes, it includes a toolkit with detection scripts (e.g., detect_slop.py) designed for CI/CD integration to gate-keep code and documentation quality.

Does it support data science workflows?

Absolutely. It includes specialized rules for Quarto, R, Julia, and design visualizations to ensure reproducible and professional research output.

What is 'slop' in the context of AI development?

'Slop' refers to generic, verbose, or repetitive patterns typical of AI models, such as using 'delve into', generic variable names like 'df' or 'result', and missing type hints.

Anti-Slop Quality Guard

Name: Anti-Slop Quality Guard
Author: emraher

byemraher

Developer Tools

Enforces high-quality standards and eliminates generic AI-generated patterns across codebases, documentation, and design files.

About

Anti-Slop acts as a central meta-skill for quality control, automatically detecting file types and applying domain-specific best practices to ensure your output is production-ready. It bridges the gap between raw AI generation and professional human-grade results by enforcing strict naming conventions, type safety, and direct language while purging common AI buzzwords and 'slop' across R, Python, C++, Julia, and technical documentation. Whether you are refactoring a complex repository or polishing a research paper, this skill ensures your work maintains a professional, intentional, and non-generic character.

Key Features

Automatic domain detection and routing based on file extension
Multi-language code quality enforcement for Python, R, C++, and Julia
Prevention of generic AI 'slop' and repetitive language patterns
Technical writing optimization for clarity, directness, and precision
0 GitHub stars
Integrated toolkit for automated quality checks and CI/CD integration

Use Cases

Auditing multi-language projects for consistent quality and reproducibility standards
Refactoring AI-generated code to follow production-grade type-safety and naming conventions
Cleaning technical documentation to remove AI buzzwords and meta-commentary

About

Key Features

Automatic domain detection and routing based on file extension
Multi-language code quality enforcement for Python, R, C++, and Julia
Prevention of generic AI 'slop' and repetitive language patterns
Technical writing optimization for clarity, directness, and precision
0 GitHub stars
Integrated toolkit for automated quality checks and CI/CD integration

Use Cases

Auditing multi-language projects for consistent quality and reproducibility standards
Refactoring AI-generated code to follow production-grade type-safety and naming conventions
Cleaning technical documentation to remove AI buzzwords and meta-commentary