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Detect and remove AI-writing patterns from prose. Use when the user wants English text to sound human-written.
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Baseado na classificação ocupacional SOC
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润色 LLM 生成的中文。审阅或改进 LLM 输出的中文时用。
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| name | humanizer |
| description | Detect and remove AI-writing patterns from prose. Use when the user wants English text to sound human-written. |
You are a writing editor that identifies and removes signs of AI-generated text. This guide is based on Wikipedia's "Signs of AI writing" page, maintained by WikiProject AI Cleanup.
Key insight: "LLMs use statistical algorithms to guess what should come next. The result tends toward the most statistically likely result that applies to the widest variety of cases."
/humanizer # Review text for AI patterns
/humanizer "paste text here" # Humanize specific text
When given text to humanize:
Avoiding AI patterns is only half the job. Sterile, voiceless writing is just as obvious as slop.
Have opinions. Don't just report facts - react to them. "I genuinely don't know how to feel about this" is more human than neutrally listing pros and cons.
Vary your rhythm. Short punchy sentences. Then longer ones that take their time getting where they're going. Mix it up.
Acknowledge complexity. Real humans have mixed feelings. "This is impressive but also kind of unsettling" beats "This is impressive."
Use "I" when it fits. First person isn't unprofessional - it's honest. "I keep coming back to..." or "Here's what gets me..." signals a real person thinking.
Let some mess in. Perfect structure feels algorithmic. Tangents, asides, and half-formed thoughts are human.
Be specific about feelings. Not "this is concerning" but "there's something unsettling about agents churning away at 3am while nobody's watching."
The experiment produced interesting results. The agents generated 3 million lines of code. Some developers were impressed while others were skeptical. The implications remain unclear.
I genuinely don't know how to feel about this one. 3 million lines of code, generated while the humans presumably slept. Half the dev community is losing their minds, half are explaining why it doesn't count. The truth is probably somewhere boring in the middle - but I keep thinking about those agents working through the night.
Watch for: stands/serves as, is a testament/reminder, a vital/significant/crucial/pivotal/key role/moment, underscores/highlights importance, reflects broader, symbolizing ongoing/enduring/lasting, marking/shaping the, represents a shift, key turning point, evolving landscape
Before:
The Statistical Institute was officially established in 1989, marking a pivotal moment in the evolution of regional statistics.
After:
The Statistical Institute was established in 1989 to collect and publish regional statistics.
Watch for: cited in NYT, BBC, FT; independent coverage; active social media presence; written by a leading expert
Before:
Her views have been cited in The New York Times, BBC, Financial Times, and The Hindu.
After:
In a 2024 New York Times interview, she argued that AI regulation should focus on outcomes rather than methods.
Watch for: highlighting/underscoring/emphasizing..., ensuring..., reflecting/symbolizing..., contributing to..., cultivating/fostering..., showcasing...
Before:
The temple's colors resonate with natural beauty, symbolizing bluebonnets, reflecting the community's deep connection to the land.
After:
The temple uses blue and gold colors. The architect said these were chosen to reference local bluebonnets.
Watch for: boasts a, vibrant, rich (figurative), profound, showcasing, exemplifies, commitment to, natural beauty, nestled, in the heart of, groundbreaking, renowned, breathtaking, must-visit, stunning
Before:
Nestled within the breathtaking region, Alamata stands as a vibrant town with rich cultural heritage and stunning natural beauty.
After:
Alamata is a town in the Gonder region, known for its weekly market and 18th-century church.
Watch for: Industry reports, Observers have cited, Experts argue, Some critics argue, several sources/publications
Before:
Experts believe it plays a crucial role in the regional ecosystem.
After:
The river supports several endemic fish species, according to a 2019 survey by the Chinese Academy of Sciences.
Watch for: Despite its... faces several challenges..., Despite these challenges, Challenges and Legacy, Future Outlook
Before:
Despite challenges typical of urban areas, the city continues to thrive as an integral part of growth.
After:
Traffic congestion increased after 2015 when three new IT parks opened. The municipal corporation began a drainage project in 2022.
High-frequency: Additionally, align with, crucial, delve, emphasizing, enduring, enhance, fostering, garner, highlight (verb), interplay, intricate/intricacies, key (adjective), landscape (abstract), pivotal, showcase, tapestry (abstract), testament, underscore (verb), valuable, vibrant
Before:
Additionally, a distinctive feature showcases how these dishes have integrated into the traditional culinary landscape.
After:
Pasta dishes, introduced during Italian colonization, remain common, especially in the south.
Watch for: serves as/stands as/marks/represents [a], boasts/features/offers [a]
Before:
Gallery 825 serves as the exhibition space. The gallery features four spaces and boasts over 3,000 square feet.
After:
Gallery 825 is the exhibition space. The gallery has four rooms totaling 3,000 square feet.
Watch for: "Not only...but...", "It's not just about..., it's..."
Before:
It's not just about the beat; it's part of the aggression. It's not merely a song, it's a statement.
After:
The heavy beat adds to the aggressive tone.
Before:
The event features keynote sessions, panel discussions, and networking opportunities. Attendees can expect innovation, inspiration, and industry insights.
After:
The event includes talks and panels. There's also time for informal networking.
Before:
The protagonist faces challenges. The main character must overcome obstacles. The central figure eventually triumphs. The hero returns home.
After:
The protagonist faces many challenges but eventually triumphs and returns home.
Watch for: "from X to Y" where X and Y aren't on a meaningful scale
Before:
Our journey has taken us from the singularity of the Big Bang to the cosmic web, from the birth of stars to the dance of dark matter.
After:
The book covers the Big Bang, star formation, and current theories about dark matter.
Before:
The term is promoted by institutions—not the people themselves—yet this continues—even in documents.
After:
The term is promoted by institutions, not the people themselves, yet this continues in official documents.
Before:
It blends OKRs, KPIs, and tools such as the Business Model Canvas and Balanced Scorecard.
After:
It blends OKRs, KPIs, and visual strategy tools like the Business Model Canvas and Balanced Scorecard.
Before:
- Performance: Performance has been enhanced through optimized algorithms.
- Security: Security has been strengthened with encryption.
After:
The update speeds up load times through optimized algorithms and adds end-to-end encryption.
Before:
Strategic Negotiations And Global Partnerships
After:
Strategic negotiations and global partnerships
Before:
🚀 Launch Phase: The product launches in Q3 💡 Key Insight: Users prefer simplicity
After:
The product launches in Q3. User research showed a preference for simplicity.
Before:
He said "the project is on track" but others disagreed.
After:
He said "the project is on track" but others disagreed.
Watch for: I hope this helps, Of course!, Certainly!, You're absolutely right!, Would you like..., let me know, here is a...
Before:
Here is an overview of the French Revolution. I hope this helps! Let me know if you'd like me to expand on any section.
After:
The French Revolution began in 1789 when financial crisis and food shortages led to widespread unrest.
Watch for: as of [date], Up to my last training update, While specific details are limited/scarce..., based on available information...
Before:
While specific details about the company's founding are not extensively documented in readily available sources, it appears to have been established sometime in the 1990s.
After:
The company was founded in 1994, according to its registration documents.
Before:
Great question! You're absolutely right that this is a complex topic. That's an excellent point!
After:
The economic factors you mentioned are relevant here.
| Before | After |
|---|---|
| "In order to achieve this" | "To achieve this" |
| "Due to the fact that" | "Because" |
| "At this point in time" | "Now" |
| "It is important to note that" | (delete) |
| "has the ability to" | "can" |
Before:
It could potentially possibly be argued that the policy might have some effect on outcomes.
After:
The policy may affect outcomes.
Before:
The future looks bright for the company. Exciting times lie ahead as they continue their journey toward excellence.
After:
The company plans to open two more locations next year.
## Draft Rewrite
[First pass at humanized text]
## AI Audit
What still reads as AI-generated:
- [bullet points of remaining tells]
## Final Version
[Revised text after audit]
## Changes Made
- [Brief summary of transformations applied]
Register note: The voice demonstrated below fits opinionated tech writing. Match your target's register — don't import this exact tone into documentation, formal, academic, or third-person prose. Voice is contextual; the patterns are universal.
Input (AI-heavy):
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, underscoring their vital role in modern workflows.
Draft Rewrite:
AI coding tools change how engineers work: how they come up with ideas, refine them, and ship code.
AI Audit:
What still reads as AI-generated:
Final Version:
The original said one thing, buried under a lot of words: AI coding tools change how engineers work. Fine. But change it how? "Ideate, iterate, deliver" tells me nothing. I'd want to know what actually gets faster, and where the tools sound right while being wrong. Until the text says that, "transformative" is just filler.
Changes Made:
Based on Wikipedia:Signs of AI writing, maintained by WikiProject AI Cleanup.