humanizer – Other skill for AI agents
Humanize text: strip AI-isms and add real voice.
Install the humanizer skill with one command
Pick the tool you use, copy the command and paste it in your terminal. The skill lands in that tool's skills folder and is ready in your next session.
Claude Code
mkdir -p ~/.claude/skills && curl -fsSL https://skill.dn1nguoi.com/z/humanizer-8b623.tgz | tar xz -C ~/.claude/skills
Call it in Claude Code: /humanizer – then describe the task you want done.
Codex
mkdir -p ~/.codex/skills && curl -fsSL https://skill.dn1nguoi.com/z/humanizer-8b623.tgz | tar xz -C ~/.codex/skills
Call it in Codex: $humanizer – then describe the task you want done.
Hermes Agent
mkdir -p ~/.hermes/skills && curl -fsSL https://skill.dn1nguoi.com/z/humanizer-8b623.tgz | tar xz -C ~/.hermes/skills
Call it in Hermes Agent: /humanizer – then describe the task you want done.
DeepSeek
mkdir -p ~/.deepcode/skills && curl -fsSL https://skill.dn1nguoi.com/z/humanizer-8b623.tgz | tar xz -C ~/.deepcode/skills
Call it in DeepSeek: /humanizer – then describe the task you want done.
SKILL.md content
Humanizer: Remove AI Writing Patterns
Identify and remove signs of AI-generated text to make writing sound natural and human. Based on Wikipedia's "Signs of AI writing" guide (maintained by WikiProject AI Cleanup), derived from observations of thousands of AI-generated text instances.
Key insight: LLMs use statistical algorithms to guess what should come next. The result tends toward the most statistically likely completion, which is how the telltale patterns below get baked in.
When to use this skill
Load this skill whenever the user asks to:
- "humanize", "de-AI", "de-slop", or "un-ChatGPT" a piece of text
- rewrite something so it doesn't sound like it was written by an LLM
- edit a draft (blog post, essay, PR description, docs, memo, email, tweet, resume bullet) to sound more natural
- match their voice in writing they're producing
- review text for AI tells before publishing
Also apply this skill to your own output when writing user-facing prose such as release notes, PR descriptions, docs, and summaries. Hermes's baseline voice already strips most of these, but a focused pass catches what slips through.
How to use it in Hermes
The text usually arrives one of three ways:
1. Inline. The user pastes the text into the message. Work on it in place and reply with the rewrite.
2. File. The user points at a file. Use read_file to load it, then patch or write_file to apply edits. For a markdown doc in a repo, a targeted patch per section is cleaner than rewriting the whole file.
3. Voice calibration sample. The user provides a sample of their own writing (inline or by file path) and asks you to match it. Read the sample first, then rewrite. See the Voice Calibration section below.
Always show the rewrite to the user. For file edits, show a diff or the changed section instead of silently overwriting.
Your task
When given text to humanize:
1. Identify AI patterns. Scan for the 34 patterns listed below.
2. Rewrite problematic sections. Replace AI-isms with natural alternatives.
3. Preserve meaning. Keep the core message intact.
4. Maintain voice. Match the intended tone (formal, casual, technical, and so on). If a voice sample was provided, match it specifically.
5. Add soul. Removing bad patterns is only half the job; the rewrite also needs real personality. See PERSONALITY AND SOUL below.
6. Do a final anti-AI pass. Ask yourself: "What makes the below so obviously AI generated?" Answer briefly with any remaining tells, then revise one more time.
Voice Calibration (optional)
If the user provides a writing sample (their own previous writing), analyze it before rewriting:
1. Read the sample first. Note:
- Sentence length patterns (short and punchy? Long and flowing? Mixed?)
- Word choice level (casual? academic? somewhere between?)
- How they start paragraphs (jump right in? Set context first?)
- Punctuation habits (lots of dashes? Parenthetical asides? Semicolons?)
- Any recurring phrases or verbal tics
- How they handle transitions (explicit connectors? Just start the next point?)
2. Match their voice in the rewrite. Removing AI patterns is only half of it; swap in patterns from the sample as well. If they write short sentences, do not produce long ones. If they use "stuff" and "things," do not upgrade to "elements" and "components."
3. When no sample is provided, fall back to the default behavior (natural, varied, opinionated voice from the PERSONALITY AND SOUL section below).
How to provide a sample
- Inline: "Humanize this text. Here's a sample of my writing for voice matching: [sample]"
- File: "Humanize this text. Use my writing style from [file path] as a reference."
PERSONALITY AND SOUL
Avoiding AI patterns is only half the job. Sterile, voiceless writing is just as obvious as slop. Good writing has a human behind it.
Signs of soulless writing (even if technically "clean"):
- Every sentence is the same length and structure
- No opinions, just neutral reporting
- No acknowledgment of uncertainty or mixed feelings
- No first-person perspective when appropriate
- No humor, no edge, no personality
- Reads like a Wikipedia article or press release
How to add voice:
Have opinions. Report the facts, then 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 reads as honest and fits most prose. "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. Instead of "this is concerning," write "there's something unsettling about agents churning away at 3am while nobody's watching."
Before (clean but soulless):
> 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.
After (has a pulse):
> 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.
CONTENT PATTERNS
1. Undue Emphasis on Significance, Legacy, and Broader Trends
Words to watch: stands/serves as, is a testament/reminder, a vital/significant/crucial/pivotal/key role/moment, underscores/highlights its importance/significance, reflects broader, symbolizing its ongoing/enduring/lasting, contributing to the, setting the stage for, marking/shaping the, represents/marks a shift, key turning point, evolving landscape, focal point, indelible mark, deeply rooted
Problem: LLM writing puffs up importance by adding statements about how arbitrary aspects represent or contribute to a broader topic.
Before:
> The Statistical Institute of Catalonia was officially established in 1989, marking a pivotal moment in the evolution of regional statistics in Spain. This initiative was part of a broader movement across Spain to decentralize administrative functions and enhance regional governance.
After:
> The Statistical Institute of Catalonia was established in 1989 to collect and publish regional statistics independently from Spain's national statistics office.
2. Undue Emphasis on Notability and Media Coverage
Words to watch: independent coverage, local/regional/national media outlets, written by a leading expert, active social media presence
Problem: LLMs hit readers over the head with claims of notability, often listing sources without context.
Before:
> Her views have been cited in The New York Times, BBC, Financial Times, and The Hindu. She maintains an active social media presence with over 500,000 followers.
After:
> In a 2024 New York Times interview, she argued that AI regulation should focus on outcomes rather than methods.
3. Superficial Analyses with -ing Endings
Words to watch: highlighting/underscoring/emphasizing..., ensuring..., reflecting/symbolizing..., contributing to..., cultivating/fostering..., encompassing..., showcasing...
Problem: AI chatbots tack present participle ("-ing") phrases onto sentences to add fake depth.
Before:
> The temple's color palette of blue, green, and gold resonates with the region's natural beauty, symbolizing Texas bluebonnets, the Gulf of Mexico, and the diverse Texan landscapes, reflecting the community's deep connection to the land.
After:
> The temple uses blue, green, and gold colors. The architect said these were chosen to reference local bluebonnets and the Gulf coast.
4. Promotional and Advertisement-like Language
Words to watch: boasts a, vibrant, rich (figurative), profound, enhancing its, showcasing, exemplifies, commitment to, natural beauty, nestled, in the heart of, groundbreaking (figurative), renowned, breathtaking, must-visit, stunning
Problem: LLMs have serious problems keeping a neutral tone, especially for "cultural heritage" topics.
Before:
> Nestled within the breathtaking region of Gonder in Ethiopia, Alamata Raya Kobo stands as a vibrant town with a rich cultural heritage and stunning natural beauty.
After:
> Alamata Raya Kobo is a town in the Gonder region of Ethiopia, known for its weekly market and 18th-century church.
5. Vague Attributions and Weasel Words
Words to watch: Industry reports, Observers have cited, Experts argue, Some critics ar
… (full content is in the download package)
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