de-slopify

name	de-slopify
description	Removes AI writing artifacts from documentation and code. Use when editing LLM-generated prose, reviewing READMEs, polishing docs before publishing, or cleaning up AI-generated code. Use for emdash cleanup, formulaic phrase removal, tone calibration, over-commented code, verbose naming, and AI code smell detection.
license	MIT
metadata	{"author":"oakoss","version":"1.0"}

De-Slopify

Overview

De-slopify is a methodology for removing telltale signs of AI-generated content from documentation, prose, and code. LLMs produce statistically regular output with characteristic vocabulary, punctuation habits, and structural patterns that make text and code feel inauthentic. Some patterns appear over 1,000x more frequently in LLM output than human writing.

When to use: Before publishing READMEs, after AI-assisted writing sessions, during documentation reviews, when reviewing AI-generated code for over-engineering, before committing prose or code that an LLM touched.

When NOT to use: On code logic or algorithms where correctness matters more than style. On technical specifications where precision outweighs voice. On content that was already human-written and reads naturally.

Quick Reference

Category	Pattern	Fix
Punctuation	Emdash overuse	Semicolons, commas, colons, or split into two sentences
Phrase	"Here's why" / "Here's why it matters"	Explain why directly without the lead-in
Phrase	"It's not X, it's Y"	"This is Y" or restate the distinction
Phrase	"Let's dive in" / "Let's get started"	Delete; just start the content
Phrase	"It's worth noting" / "Keep in mind"	Delete the hedge; state the fact
Phrase	"At its core" / "In essence" / "Fundamentally"	Delete; say the thing directly
Vocabulary	"delve", "tapestry", "landscape", "nuanced"	Replace with plain, specific language
Vocabulary	"revolutionize", "cutting-edge", "game-changer"	Replace with concrete claims or delete
Structure	Uniform sentence length throughout	Mix short (5-word) and long (20+ word) sentences
Structure	Perfectly balanced lists of exactly 3 items	Vary list length; humans use 2, 4, or odd counts
Structure	Generic claims without specifics	Add names, dates, numbers, or first-person detail
Sycophancy	"Great question!" / "Absolutely!"	Delete; answer the question directly
Meta	"Let me break this down..." / "Let me explain"	Delete the preamble; just break it down
Structure	Numbered lists where a sentence suffices	Use a sentence; reserve lists for genuinely parallel items
Closer	"In conclusion" / "To summarize"	Delete or replace with a specific takeaway
Code	Over-commented trivial functions	Remove comments that restate the code
Code	Unnecessary abstractions and design patterns	Flatten to the simplest working solution
Code	Verbose or overly descriptive variable names	Use domain-appropriate concise names
Code	Defensive error handling on every operation	Handle errors only where failure is realistic

Common Mistakes

Mistake	Correct Pattern
Replacing every emdash mechanically	Evaluate context; sometimes an emdash is the right choice
Editing code blocks for style	Focus on prose; leave code examples and technical syntax untouched
Removing all structure to sound casual	Keep headers, tables, and lists intact; rewrite prose only
Over-correcting into choppy fragments	Read aloud after editing; recombine sentences that lost flow
Applying fixes without defining target voice	Set persona, tone, and audience before starting edits
Running regex replacements instead of reading	Manual line-by-line review is required; context determines fixes
Ignoring AI code smells	Review AI-generated code for over-engineering, verbose names, and unnecessary abstractions
Removing all LLM-typical words unconditionally	Some flagged words are perfectly natural in context; use judgment

Delegation

• Scan a repository for documentation files that need de-slopifying: Use Explore agent
• Rewrite an entire documentation site to remove AI artifacts: Use Task agent
• Plan a documentation voice guide and editorial workflow: Use Plan agent
• Review AI-generated code for slop patterns: Use code-reviewer agent

For systematic quality auditing across 12 dimensions (architecture, security, testing, performance, etc.), use the quality-auditor skill.

References

• Prose patterns: emdash alternatives, phrase replacements, and voice calibration
• Before-and-after examples of common AI writing fixes
• AI slop vocabulary: words and phrases that signal LLM authorship
• Code slop: detecting and fixing AI-generated code smells
• Review workflow: prompts, checklists, and integration