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  4. Stripping AI Clichés from LLM Outputs with Stop-Slop Skill Files
Vibe coding workflow

Stripping AI Clichés from LLM Outputs with Stop-Slop Skill Files

Stop Slop is an open-source system prompt skill file designed to eradicate typical AI-sounding phrases from generated text. Keep your documentation, UI copy, and changelogs completely natural.

May 28, 2026· 4 min read
OKCurated by Oleksandr Kuzmenko, AI Product Engineer·Updated May 28, 2026·Sources cited on every story
AI-assisted · editor-reviewed·How we use AI
Stripping AI Clichés from LLM Outputs with Stop-Slop Skill Files

Why it matters

It instantly purges cheesy AI jargon from your generated code comments, pull request descriptions, and user documentation.

TL;DR

  • 01Copy the Stop Slop rules list into your .cursorrules or Claude system instructions
  • 02Add your own custom domain-specific jargon words to the negative prompt blacklist
  • 03Use few-shot rewrite templates to show the AI exactly how you prefer sentences to be structured

Patterns of AI Prose

AI-generated writing is riddled with predictable patterns like 'in today's digital landscape' or excessive use of adverbs. The Stop Slop project provides a structured set of rules to identify and strip these clichés. It covers structural issues, such as dramatic fragmentation, as well as sentence-level rules like banning specific filler phrases.

Integration Strategies

  • Claude Code: Add the skill folder directly.
  • Claude Projects: Upload the SKILL.md file to your project knowledge.
  • API Calls: Include the rule definitions directly within your system prompt.
  • Custom Instructions: Copy the core rules to your environment configuration.

By enforcing active voice and banning specific tropes, developers can ensure that generated documentation and commit messages maintain a professional, human-like tone.

#Stop Slop#Cursor#Claude Code#CI/CD
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← Previous storyExploring Ethical Constraints and Policy Safeguards in Commercial Language ModelsNext story →Analyzing Prompt-Engineering Exhaustion and the Limits of Multi-Pass Image Generation

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