| name | antislop |
| description | Detect and fix AI-generated writing patterns (slop). Comprehensive detection with 50+ patterns across 3 severity tiers, structure weighted above lexical tells, scoring system, and editor mode that directly fixes problems. Use when scanning any content for AI tells, auditing drafts before publishing, checking if writing "sounds like AI", humanizing AI-generated text, or verifying content authenticity. Trigger on "check for slop", "does this sound like AI", "humanize this", "AI audit", "slop check", "clean up AI writing", or any request to detect/remove artificial-sounding patterns. Also use proactively before publishing any AI-assisted content. |
| use_when | User wants to detect AI patterns, audit content authenticity, humanize AI text, check for slop, or verify writing doesn't sound AI-generated before publishing. |
| user-invocable | true |
| tools | ["Read","Edit","Write"] |
| last-refreshed | 2026-08-18T00:00:00.000Z |
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 - 50+ known AI tells across 6 categories, structure weighted above vocabulary
- 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 (50+, structure-weighted)
Tier 1: AI-vocabulary density (weight the cluster, not the word)
These words were near-certain AI tells in the 2023–2024 GPT-3.5/4 era. Frontier models in 2025–2026 emit the loudest of them far less, so a single hit now means little — treat this as a density probe, not a blocklist. Three or more clustered in a passage is still a reliable tell; one "leverage" in isolation usually is not. Score the density (see Scoring), not each word.
Legacy signals — weakened, still worth flagging in clusters:
| 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 |
| Today's digital landscape | "In today's digital landscape..." | Remove |
| Cutting-edge | "Cutting-edge solutions..." | Remove |
| Tapestry (abstract) | "A rich tapestry of influences..." | Remove or be specific |
| 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 |
Current signals — the vocabulary today's models still lean on:
| Pattern | Example | Fix |
|---|
| Underscore/underscores | "This underscores the importance of..." | "This shows" or state it plainly |
| Testament to | "A testament to their dedication..." | State the evidence directly |
| Stands as / serves as | "Stands as a reminder that..." | "is" — or describe what happened |
| Boasts | "The platform boasts 40 integrations" | "has" |
| Navigate (metaphorical) | "Navigating the complexities of..." | "Dealing with" or be specific |
| Realm | "In the realm of..." | "In" |
| Foster | "Fostering a culture of..." | "Building" or "encouraging" |
| Ever-evolving / ever-changing | "The ever-evolving landscape of AI" | Remove; name the actual change |
| Seamless / seamlessly | "A seamless integration" | Describe what it actually does |
| Landscape (abstract) | "The competitive landscape" | Name the actual field or players |
Research evidence (lexical tells peaked 2023–2024; density-based detection ages better than any word list):
- Finnish study (56,878 essays): "delve" usage increased 10.45x 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 85x in 2023-2024
- 2025–2026 shift: frontier models were tuned against the loudest 2023 tells, so detection now leans on structure (below), where the signal has held.
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 |
| 8 | Fabricated precision | "cut deploy time by 47%" with no source | Cite the source, or use an honest range, or cut the number |
Fabricated precision is a current-model tell: a suspiciously exact figure (47%, 3.2x, "over 10,000 hours") presented without a citation, to manufacture authority. Flag any hard statistic that has no source attached.
Language Patterns
| # | Pattern | Before | After |
|---|
| 7 | Copula avoidance | "serves as... features... boasts..." | "is... has..." |
| 8a | Negative parallelism — "Not X, but Y" | "It's not about the tools, it's about the process" | State the point directly |
| 8b | Negative parallelism — "Not just X, but also Y" | "Not just faster, but also cheaper" | Say what it is; drop the setup |
| 8c | Negative parallelism — "X rather than Y" | "By removing tools rather than adding them" | State the action plainly |
| 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 | "Target Goal / Lightbulb Key Insight / Check 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 | Use straight quotes consistently | |
| 19 | Unnecessary tables | 3-row table that should be a sentence | Convert to prose |
| 20 | Inline-header vertical lists | "The problem: X. The cause: Y. The fix: Z." | Write as prose; bolded-lead bullets are a strong current tell |
| 21 | Heading-level anomalies | Skipped levels, a wall of H1s, or thematic breaks (---) dropped between every section | Use a normal, nested heading structure |
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 use five different services for my workflow. They're solid platforms, but like most SaaS tools, each 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 projects over the holidays. Here's what I learned.
After:
Most developers 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.
Engagement-Bait Openers
Rhetorical hooks that fake a conversation to manufacture interest:
- "What if I told you..."
- "Ever wondered why...?"
- "Let's be honest:"
- "Picture this:"
- "Here's the thing —"
Fix: Open with the actual claim or the specific detail. Trust the reader to keep reading because the content is worth it.
Trailing Affirmations
A short reassurance tacked onto a sentence to soften or self-approve:
Before:
Automation frees people to do what they do best. And that's a good thing.
The tool handles the boring parts — which is exactly what you want.
After:
Automation frees people from the boring parts.
The affirmation adds no information. Cut it.
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
Structure is weighted above vocabulary: modern models were tuned against the loudest lexical tells, so a clean-sounding passage can still be structurally slop.
| Pattern Type | Points |
|---|
| Each Tier 1 word (isolated) | +1 |
| Tier 1 cluster (3+ in a passage) | +4 |
| 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 / negative parallelism (per instance) | +3 |
| Inline-header vertical list | +3 |
| Manufactured personality | +4 |
| Engagement-bait opener | +2 |
| Trailing affirmation | +1 |
| Fabricated precision (unsourced stat) | +2 |
| 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:
- Fetch the latest patterns from Wikipedia:
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.
