Detect and remove AI-generated slop patterns in code and writing.
npx skills add https://github.com/letxbrace-droid/inrunparis --skill eagle-anti-slop
Please help me install this Agent Skill. Skill: eagle-anti-slop Source: https://github.com/letxbrace-droid/inrunparis/tree/main/.agents/skills/eagle-anti-slop Command: npx skills add https://github.com/letxbrace-droid/inrunparis --skill eagle-anti-slop
Eagle Anti-Slop helps you remove unnecessary, LLM-typical “slop” patterns—over-abstracted code, meta-commentary text, hedgy phrasing, and generic design tropes—so your output feels authored by a human and stays easier to maintain.
Use eagle-anti-slop to run a slop scan on the document you want to humanize (for example, scan your README draft and then apply the cleanup step).
High-intent search queries and answers about installing and using this skill.
To detect and remove AI slop from technical writing and source files, scan your Markdown and codebase for high-risk phrases, hedging, and meta-commentary. The tool produces categorized findings and a weighted slop score to guide cleanup.
A slop score quantifies AI-generated patterns in code by categorizing high-risk phrasing and redundant narration. It provides a weighted breakdown so you can quickly prioritize which slop patterns to fix first during code review.
Remove meta-commentary and hedging phrasing from Markdown documents by applying rule-based replacements. An optional aggressive normalization mode simplifies language structures and strips obvious AI template tropes to reduce AI-smell.
Yes, you can use regular-expression catalogs for deterministic text cleanup without machine learning. The tool satisfies deterministic pattern-matching requirements by scanning source files and applying rule-based replacements to normalize text.
The best way to humanize a README draft that sounds like AI is to run a slop scan to identify template structures and hedgy phrasing, then apply the cleanup step to simplify the language and remove generic design tropes.
When using aggressive normalization to clean up AI-sounding prose, limitations include the risk of over-simplifying language beyond rule-based replacements. It targets obvious template structures but may not catch nuanced context-specific slop patterns.
LayerDynamicsGenerate markdown documentation mirroring source code directory structure.
Pryma-TechClean AI-generated code slop with a bounded, regression-checked workflow.
t-stringsEnforces global naming conventions across files, modules, and artifacts.
lengoctuong2005Remove AI-generated patterns from text to improve natural readability.
