| name | antislop |
| description | Detect and fix AI-generated writing patterns (slop). Comprehensive detection with 45+ patterns, tiered severity scoring, and editor mode. |
| use_when | User wants to detect AI slop in content, audit a draft for AI patterns, check writing authenticity, review AI-generated output, humanize text, or verify content before publishing. |
| user-invocable | true |
| tools | ["Read","Edit","Write"] |
| last-refreshed | "2026-02-14T00:00:00.000Z" |
The AntiSlop
A comprehensive AI writing pattern detector and fixer. Combines patterns from Wikipedia's Signs of AI Writing with advanced structural detection and an editor mode that actually fixes problems.
The 30-Second Test
The Horoscope Test:
"Could anyone have written this, for anyone?"
If yes, it's slop. Like a horoscope — technically applicable to everyone, resonant with no one.
What fails:
- Vague claims without specific examples
- Advice that applies universally without context
- Content missing the author's distinct perspective
- Writing that could have any byline
What passes:
- Specific tools, dates, outcomes mentioned
- Personal observations grounded in experience
- Opinions that not everyone would agree with
- Details only this author would know
Usage
/antislop
[paste your text here]
Or ask Claude to check text directly:
Please run antislop on this: [your text]
How It Works
- Run the Horoscope Test - Could anyone have written this for anyone?
- Scan for patterns - 45+ known AI tells across 6 categories
- Calculate slop score - Tiered severity with quantifiable scoring
- Apply fixes - Editor mode rewrites problems, not just flags them
- Report changes - Before/after for every fix applied
Detection Patterns (35+)
Tier 1: Almost Always AI (Remove Immediately)
These phrases are so strongly associated with AI that their presence alone suggests unedited output.
| Pattern | Example | Fix |
|---|
| Delve | "Let's delve into..." | Remove or replace with direct statement |
| Game-changer | "This game-changing approach..." | Describe the actual impact |
| Revolutionary | "A revolutionary new method..." | State what it actually does |
| Unlock potential | "Unlock your potential..." | Remove entirely |
| Leverage (as verb) | "Leverage these insights..." | "Use" |
| It's worth noting | "It's worth noting that..." | Just state the thing |
| Moreover/Furthermore | "Moreover, this approach..." | Remove or use "Also" |
| Today's digital landscape | "In today's digital landscape..." | Remove |
| Cutting-edge | "Cutting-edge solutions..." | Remove |
| Pivotal moment | "Marking a pivotal moment in..." | State what happened |
| Tapestry (abstract) | "A rich tapestry of influences..." | Remove or be specific |
| Intricate/intricacies | "The intricacies of..." | "Details of" or remove |
| Showcase (as verb) | "Showcasing their commitment..." | "Shows" or describe what happened |
| Vibrant | "A vibrant community of..." | Remove or use specific detail |
| Interplay | "The interplay between X and Y..." | "How X and Y affect each other" |
| Garner | "Garnering attention from..." | "Got attention from" or be specific |
| Align with | "Aligning with broader trends..." | State the actual relationship |
Research evidence:
- Finnish study (56,878 essays): "delve" usage increased 10.45× post-ChatGPT
- Georgia Tech (168.3M articles): "delve" went from 0.31 to 7.9 per 1,000 papers in Q1 2024
- Biomedical study: co-usage of "delve," "realm," "underscore" increased up to 85× in 2023-2024
Tier 2: Suspicious When Repeated
Problematic when overused or clustered.
| Pattern | Example | Fix |
|---|
| Here's the thing | Used repeatedly | Keep first, vary subsequent |
| At the end of the day | "At the end of the day..." | Remove |
| The bottom line | "The bottom line is..." | Just state it |
| Let's dive in | "Without further ado, let's dive in" | Remove |
| Comprehensive and thorough | Paired adjectives | Pick one |
| Simple and straightforward | Paired adjectives | Pick one |
| In this post, we'll cover | Template opening | Remove |
| By the end of this article | Promise opener | Remove |
Tier 3: Watch for Clusters
Fine individually, problematic together.
| Pattern | Example | Fix |
|---|
| However/But | Every paragraph starts this way | Vary transitions |
| Firstly/Secondly/Thirdly | Enumerated points | Use natural flow |
| Moving forward | "Moving forward, we'll..." | Remove |
| Robust/Seamless/Scalable | Corporate buzzwords | Use specific terms |
| Stakeholder | "Key stakeholders..." | Name them or say "people" |
Content Patterns
| # | Pattern | Before | After |
|---|
| 1 | Significance inflation | "marking a pivotal moment in the evolution of..." | "was established in 1989 to collect statistics" |
| 2 | Notability name-dropping | "cited in NYT, BBC, FT, and The Hindu" | "In a 2024 NYT interview, she argued..." |
| 3 | Superficial -ing analyses | "symbolizing... reflecting... showcasing..." | Remove or expand with actual sources |
| 4 | Promotional language | "nestled within the breathtaking region" | "is a town in the Gonder region" |
| 5 | Vague attributions | "Experts believe it plays a crucial role" | "according to a 2019 survey by..." |
| 6 | Formulaic challenges | "Despite challenges... continues to thrive" | Specific facts about actual challenges |
| 7 | Outline-like conclusions | "Challenges" section ending with optimistic outlook | Remove or replace with actual analysis |
Language Patterns
| # | Pattern | Before | After |
|---|
| 7 | Copula avoidance | "serves as... features... boasts..." | "is... has..." |
| 8 | Negative parallelisms | "It's not just X, it's Y" | State the point directly |
| 9 | Rule of three | "innovation, inspiration, and insights" | Use natural number of items |
| 10 | Synonym cycling | "protagonist... main character... central figure..." | "protagonist" (repeat when clearest) |
| 11 | False ranges | "from the Big Bang to dark matter" | List topics directly |
| 12 | Clinical formality | "individuals" / "utilize" / "implement" | "people" / "use" / "do" |
Style Patterns
| # | Pattern | Before | After |
|---|
| 13 | Em dash overuse | "institutions—not the people—yet this continues—" | Use commas or periods |
| 14 | Boldface overuse | "OKRs, KPIs, BMC" | "OKRs, KPIs, BMC" |
| 15 | Emoji headers | "🎯 Goal / 💡 Key Insight / ✅ Action Item" | Remove emojis |
| 16 | Title Case Headings | "Strategic Negotiations And Partnerships" | "Strategic negotiations and partnerships" |
| 17 | List addiction | Everything becomes bullets | Convert to prose where appropriate |
| 18 | Curly quotes | "like this" instead of "like this" | Use straight quotes consistently |
| 19 | Unnecessary tables | 3-row table that should be a sentence | Convert to prose |
Structural Patterns (Critical)
These bypass phrase-based detection but are major tells.
Staccato Fragment Spam
Three or more consecutive short declarative sentences stating facts in parallel structure. AI's version of bullets pretending to be prose.
Before:
The model is impressive. Complex code ships fast. Documentation writes itself. Problems get solved quickly.
After:
The model is impressive — complex code ships in a single session, documentation practically writes itself, and problems that would have taken a weekend now take an afternoon.
Detection rule: 3+ consecutive sentences that are all under 10 words, all declarative, following parallel structure, and could be bullet points.
Sentence Uniformity
Every sentence 10-15 words. Short. Punchy. Exhausting.
Real writing has rhythm — mix 5-word sentences for impact with 25-word sentences that explore implications.
Comparator Sentences
Before:
This isn't theoretical. It's practical.
This isn't a feature. It's a philosophy.
It's not about X. It's about Y.
After:
Here's how it works in practice:
[Just state what it is]
AI loves this rhetorical pattern. It sounds punchy but wastes words telling you what something isn't.
Over-Balanced Sections
Every section same length. All paragraphs 3-4 sentences. AI doesn't have opinions, so it gives balanced coverage to everything. Real writing reflects priorities.
Communication Patterns
| # | Pattern | Before | After |
|---|
| 18 | Chatbot artifacts | "I hope this helps! Let me know if..." | Remove entirely |
| 19 | Cutoff disclaimers | "While details are limited in available sources..." | Find sources or remove |
| 20 | Sycophantic tone | "Great question! You're absolutely right!" | Respond directly |
| 21 | Flattery sandwiches | "While traditional methods have merit, modern approaches offer..." | State your actual position |
Advanced Structural Tells
Manufactured Personality
AI trying to sound human but coming across as performative:
Before:
Five services. Five tabs. Five headaches.
That got old fast.
So I built an MCP server that unifies all of them.
After:
I run my newsletter on Kit.com. It's a solid platform, but like most SaaS tools, it means another dashboard, another set of menus to navigate, another context switch.
No manufactured punch. No snark. Just describes the situation.
Self-Promotional Framing
Content positioning author's accomplishments as the headline instead of reader's transformation.
Before:
I shipped 11 MCP servers over the holidays. Here's what I learned.
After:
Most developers using Claude Code aren't aware that [observation about the reader's situation]. Here's what's changing...
The author's experience is evidence, not the story.
Explanatory Header Templates
Headers that promise insight but deliver template structure:
- "Why This Actually Works"
- "What This Means For You"
- "The Real Reason..."
- "Here's What's Really Going On"
Fix: Replace with descriptive headers that summarize the actual content.
Filler and Hedging
| # | Pattern | Before | After |
|---|
| 22 | Filler phrases | "In order to" / "Due to the fact that" | "To" / "Because" |
| 23 | Excessive hedging | "could potentially possibly" | "may" |
| 24 | Generic conclusions | "The future looks bright" | Specific plans or facts |
Scoring System
| Pattern Type | Points |
|---|
| Each Tier 1 phrase | +3 |
| Each Tier 2 phrase (repeated) | +2 |
| Tier 3 cluster (3+ in section) | +2 |
| Failed horoscope test | +5 |
| Staccato fragment spam (per instance) | +4 |
| Sentence uniformity detected | +3 |
| Comparator sentences (per instance) | +2 |
| Manufactured personality | +4 |
| Self-promotional framing | +5 |
| Template headers (per instance) | +2 |
Score interpretation:
- 0-5: Low risk (minor edits)
- 6-12: Medium risk (significant editing required)
- 13+: High risk (likely unedited AI output)
Editor Mode (Default)
This skill is an editor, not a critic. After detection:
- Apply all fixes directly using the Edit tool
- Report changes made with before/after examples
- Save the cleaned file in place
Fix priority:
- Remove all Tier 1 phrases
- Deduplicate Tier 2 phrases (keep first, vary subsequent)
- Break up staccato fragments (combine with em-dashes, commas, conjunctions)
- Fix comparator sentences (just state what it is)
- Vary sentence lengths where uniformity detected
To audit without editing, explicitly request "audit only."
Output Format
## AntiSlop Report
**Horoscope Test:** [PASS/FAIL] - [reason]
**Slop Score:** [X] → [Y] - [Risk Level]
### Fixes Applied
| Location | Before | After |
|----------|--------|-------|
| Line 3 | "Let's delve into the details" | "Here are the details" |
| Line 15 | "Game-changing approach" | "Different approach" |
### Remaining Considerations
- [Any issues requiring human judgment]
### The Core Principle
Your voice is in the specificity, the opinions, the rough edges, and the rhythm. Protect those.
Full Example
Before (AI-sounding):
Great question! Here is an essay on this topic. I hope this helps!
AI-assisted coding serves as an enduring testament to the transformative potential of large language models, marking a pivotal moment in the evolution of software development. In today's rapidly evolving technological landscape, these groundbreaking tools—nestled at the intersection of research and practice—are reshaping how engineers ideate, iterate, and deliver.
At its core, the value proposition is clear: streamlining processes, enhancing collaboration, and fostering alignment. It's not just about autocomplete; it's about unlocking creativity at scale.
- 💡 Speed: Code generation is significantly faster.
- 🚀 Quality: Output quality has been enhanced.
- ✅ Adoption: Usage continues to grow.
In conclusion, the future looks bright. Let me know if you'd like me to expand!
After (Fixed):
AI coding assistants speed up some tasks. In a 2024 study by Google, developers using Codex completed simple functions 55% faster than a control group, but showed no improvement on debugging or architectural decisions.
The tools are good at boilerplate: config files, test scaffolding, repetitive refactors. They're bad at knowing when they're wrong.
Mira, an engineer at a fintech startup, said she uses Copilot "like autocomplete for boring code" but reviews every line before committing. Jake, a senior dev at a larger company, disabled it after it kept suggesting patterns from a deprecated internal library.
The productivity claims are hard to verify. GitHub says Copilot users "accept 30% of suggestions," but acceptance is not correctness.
Pattern Refresh Protocol
Patterns go stale as AI models evolve. Before scanning, check last-refreshed in frontmatter. If >30 days old, refresh first.
Refresh workflow:
- Preferred: Gemini CLI (saves Claude tokens):
gemini "Fetch these two pages and extract ALL AI writing patterns, phrases, and detection heuristics listed on each. Return as a structured list with pattern name, example, and which page it came from. Pages: https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing and https://en.wikipedia.org/wiki/Wikipedia:WikiProject_AI_Cleanup" > /tmp/antislop-refresh.txt
- Fallback: Wikipedia API via curl (works when Gemini is rate-limited or WebFetch is blocked):
curl -s "https://en.wikipedia.org/w/api.php?action=parse&page=Wikipedia:Signs_of_AI_writing&prop=wikitext&format=json" | python3 -c "
import json, sys
data = json.load(sys.stdin)
print(data['parse']['wikitext']['*'][:30000])
" > /tmp/antislop-signs.txt
curl -s "https://en.wikipedia.org/w/api.php?action=parse&page=Wikipedia:WikiProject_AI_Cleanup&prop=wikitext&format=json" | python3 -c "
import json, sys
data = json.load(sys.stdin)
print(data['parse']['wikitext']['*'][:30000])
" > /tmp/antislop-cleanup.txt
- Read the output and diff against patterns already in this skill
- For genuinely new patterns not already covered:
- Classify into Tier 1/2/3 based on how strongly they signal AI
- Add to the appropriate table with example and fix
- Update the pattern count in the overview
- Update
last-refreshed date in frontmatter
- Report what was added (if anything)
Don't add duplicates. Many Wikipedia patterns are already covered here under different names. Only add patterns that represent genuinely new detection signals.
References
Core Principle
AI slop isn't about individual words — it's about patterns.
One "moreover" doesn't make content AI-generated. But "moreover" + "it's worth noting" + "delve into" + uniform sentences + emoji headers = obvious slop.
The goal is writing that sounds like a specific human with specific opinions, not a very polite committee trying not to offend anyone.
