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From agi-super-team
Detects and fixes AI-generated writing patterns (slop) with 45+ patterns, tiered severity scoring, and editor mode. Use /antislop to check text for AI tells.
How this skill is triggered — by the user, by Claude, or both
Slash command
/agi-super-team:the-antislopThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
A comprehensive AI writing pattern detector and fixer. Combines patterns from [Wikipedia's Signs of AI Writing](https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing) with advanced structural detection and an editor mode that actually fixes problems.
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 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:
What passes:
/antislop
[paste your text here]
Or ask Claude to check text directly:
Please run antislop on this: [your text]
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:
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 |
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" |
| # | 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 |
| # | 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" |
| # | 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 |
These bypass phrase-based detection but are major tells.
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.
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.
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.
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.
| # | 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 |
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.
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.
Headers that promise insight but deliver template structure:
Fix: Replace with descriptive headers that summarize the actual content.
| # | 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 |
| 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:
This skill is an editor, not a critic. After detection:
Fix priority:
To audit without editing, explicitly request "audit only."
## 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.
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.
Patterns go stale as AI models evolve. Before scanning, check last-refreshed in frontmatter. If >30 days old, refresh first.
Refresh workflow:
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
# Signs of AI writing - full wikitext
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
# WikiProject AI Cleanup
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
last-refreshed date in frontmatterDon't add duplicates. Many Wikipedia patterns are already covered here under different names. Only add patterns that represent genuinely new detection signals.
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.
npx claudepluginhub aaaaqwq/agi-super-team --plugin agi-super-teamRemoves AI-generated writing patterns like em dash overuse, passive voice, filler phrases, and promotional language from text. Makes prose sound natural and human-written; matches user voice from samples. Use for editing docs or reviews.
Humanizes AI-generated text by removing patterns like inflated symbolism, promotional language, em dash overuse, and rule of three per Wikipedia guide. Use for editing drafts and reviewing content.
Detects AI writing patterns in text and provides revision guidance to sound more natural. Use before finalizing AI-assisted content or when reviewing for undisclosed AI artifacts.