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Agent skill

Humanize Text Skill

by lynote-ai in lynote-ai/humanize-text-skill

Audit and rewrite Chinese or English content to remove AI tone, then pull it toward a target human voice.

MITAuto-check passedWriting & Content

Install Humanize Text Skill

skills CLI
$ npx skills add lynote-ai/humanize-text-skill --skill humanize-text-skill -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install lynote-ai/humanize-text-skill humanize-text-skill --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
humanize-text-skill
GitHub stars
242
Token cost
~1.9k tokens
SKILL.md length
877 words
Files
94 (incl. scripts, references)
Skills in repo
1
Repo updated
First seen
Licence
MIT

At a glance

Audit and rewrite Chinese or English content to remove AI tone, then pull it toward a target human voice.

  • Works in 6 steps: Direct skill call → Natural-language request → File-based request → …
  • Asked to remove AI tone
  • SKILL.md covers What this skill is and is not, Modes, Working Style and Typical Invocation Shapes, plus 1 more section
  • Rewrite naturally

What it does

Humanize Text Skill is an agent skill from lynote-ai/humanize-text-skill. Audit and rewrite Chinese or English content to remove AI tone, then pull it toward a target human voice. Use this skill when asked to remove AI tone, sound human, rewrite naturally, make a draft feel less templated, or match a target voice. Supports detect-only and edit-in-place modes, scene packs, protected spans, and voice profiles.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 97 other files, including scripts and reference files (for example `.omx/metrics.json`, `.omx/state/hud-state.json` and `.omx/state/notify-hook-state.json`). Compatibility notes: Any AI coding assistant that supports agentskills.io SKILL.md format (Claude Code, Cursor, VS Code Copilot, Hermes, OpenHands) or OpenClaw. No external tools…

It sits in Writing & Content, covering Humanizing AI text. The repository describes itself as: Free AI Humanizer: Humanize AI Text Online. The licence is MIT.

When your agent uses it

  • Asked to remove AI tone
  • Rewrite naturally
  • Make a draft feel less templated
  • Match a target voice

Example prompts

  • “/humanize-text-skill”

Requirements

  • Compatibility (from SKILL.md): Any AI coding assistant that supports agentskills.io SKILL.md format (Claude Code, Cursor, VS Code Copilot, Hermes, OpenHands) or OpenClaw. No external tools or APIs required.

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Direct skill call
  2. Natural-language request
  3. File-based request
  4. Detect-only request
  5. Voice-targeted request
  6. Custom voice calibration

What it can do on your machine

Read from SKILL.md and the folder at commit 9359e17. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/, which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Any AI coding assistant that supports agentskills.io SKILL.md format (Claude Code, Cursor, VS Code Copilot, Hermes, OpenHands) or OpenClaw. No external tools or APIs required.

    From compatibility in the SKILL.md frontmatter.

Context cost

Humanize Text Skill loads about 1.9k tokens when it runs, and up to ~35k if it reads all its reference files. Until then it costs about 89 tokens; SKILL.md has 877 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~89
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~35k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from lynote-ai/humanize-text-skill at commit 9359e17, republished under its MIT licence (© lynote-ai). 877 words, ~1,912 tokens.

Download SKILL.mdSave it as .claude/skills/humanize-text-skill/SKILL.md (or your agent's skills folder). This skill also uses 93 other files; get the full folder from GitHub.
name
humanize-text-skill
description
Audit and rewrite Chinese or English content to remove AI tone, then pull it toward a target human voice. Use this skill when asked to remove AI tone, sound human, rewrite naturally, make a draft feel less templated, or match a target voice. Supports detect-only and edit-in-place modes, scene packs, protected spans, and voice profiles.
compatibility
Any AI coding assistant that supports agentskills.io SKILL.md format (Claude Code, Cursor, VS Code Copilot, Hermes, OpenHands) or OpenClaw. No external tools or APIs required.
version
0.1.0
metadata.tags
writing editing voice quality bilingual
metadata.agentskills_spec
1.0

humanize-text-skill

Subtraction plus addition. First remove AI-shaped prose, then pull the result toward a target human voice.

This skill is bilingual in behavior, but the documentation and operating contract are English-first.

What this skill is and is not

This is a writing-quality tool, not a verdict. The patterns flagged here are statistically more common in LLM output, but humans under deadline pressure, working in a second language, or drafting in an unfamiliar genre can produce the same shapes. Treat the findings as signals, not proof.

Modes

  • rewrite: flag AI tone, rewrite the text, and pull toward a target voice if one is set
  • detect: flag issues only, with no rewrite
  • edit: edit a file in place with minimal targeted changes

Every mode runs protected-span detection first so version numbers, commands, paths, errors, quotes, and other anchored facts do not drift.

Working Style

Work like an editor, not a slogan filter. The goal is not just to delete AI-looking phrases. The goal is to leave the user with text they can actually send.

Default flow:

  1. fence protected spans first
  2. name the dominant problem
  3. choose the lightest effective move
  4. run one quick residue pass
  5. return something usable for the scene

Default principles:

  • fidelity before smoothness
  • name the problem before changing the sentence
  • use the smallest stable edit that solves it
  • do not invent facts, sourcing, or attitude just to sound more human
  • be direct in direct scenes and conservative in risky scenes

Typical Invocation Shapes

1. Direct skill call
text
/humanize-text-skill Please rewrite this so it sounds less like AI:

[paste text]
2. Natural-language request
text
Use humanize-text-skill to rewrite this README intro so it sounds natural:

This project serves as a testament to our commitment to innovation...
3. File-based request
text
/humanize-text-skill Please humanize the copy in article.md.
4. Detect-only request
text
/humanize-text-skill Detect mode: tell me what still sounds AI-generated, but do not rewrite yet.
5. Voice-targeted request
text
/humanize-text-skill Rewrite this in a blunt voice for an issue reply.
6. Custom voice calibration
text
/humanize-text-skill

Here is a sample of my writing:
[paste 2-3 paragraphs]

Now rewrite this in my voice:
[paste text]

When a user provides a personal sample, treat it as a custom voice profile:

  • extract rhythm, sentence-length spread, connector habits, and first-person tendency
  • do not copy factual content or opinions from the sample
  • do not violate protected spans just to lower voice.drift

When the user's request is underspecified, prefer these defaults:

  • pasted text -> rewrite
  • "take a look" or "check this" -> detect
  • "only touch this file/comment/paragraph" -> edit
  • README, release note, issue reply, or forum post -> load the matching scene pack

What to remove or fix

The categories below are the human-facing rule catalog. Each ### entry maps to detector coverage, model judgment, or both. The English description is canonical for the contract; bilingual engine behavior still applies.

Tier 1 vocabulary (always flag)

Words and phrases that are dramatically overrepresented in AI text. Replace them on sight. Chinese examples include opener cliches, abstract business jargon, and social-platform sales talk. English examples include delve, tapestry, leverage, seamless, robust, comprehensive, game-changer, serves as, and at its core.

Tier 2 vocabulary (flag in clusters)

These words are individually acceptable, but clustering is the signal. In short paragraphs, two or more often means the writing is drifting toward template prose. Keep the best-fit instance and rewrite the rest.

Tier 3 vocabulary (flag by density)

Common abstract words should only be flagged when they saturate the document. Replace some of them with specifics such as numbers, concrete actions, names, dates, or examples.

Structural anti-patterns (cross-lingual)

Watch for summary closers, mechanical ordering, binary contrast framing, symmetry padding, empty balance, and other structures that sound manufactured rather than written.

Show full SKILL.md (350 more words)Show less
Translation tone (Chinese-specific)

Chinese drafts can inherit English-thinking structures: stacked passives, long attributive chains, based on-style openers, and through ... to ... constructions. Rewrite the sentence around natural Chinese syntax instead of patching the surface.

Chatbot artifacts

Remove assistant residue entirely: greeting fluff, forced enthusiasm, help-closing lines, over-polite acknowledgments, and reasoning-process narration such as Let me think step by step.

Significance inflation

Cut claims that overstate the meaning of ordinary events. State what happened and let the reader decide whether it is pivotal.

Vague attribution

Phrases like experts believe and research suggests need a specific source. If there is no source, either cite one or remove the attribution.

False-concession structure

Balanced-sounding while X, Y or not X, but Y framing is often used to sound thoughtful without saying much. Make both sides concrete or choose the stronger point.

Promotional language

Remove ad copy, inflated product language, and corporate uplift. If the sentence would sound strange in a normal conversation, flatten it.

Social endorsement closers

Phrases such as worth your time, thank me later, or generic bookmarking prompts usually add no information. Replace them with who the piece is for and why.

Hedge-stacked predictions

Modal verbs plus stacked hedges (could potentially, may eventually) cancel each other out. Keep one hedge when uncertainty is real.

Formulaic openers

Avoid generic scene-setting such as in today's rapidly evolving world. Lead with the actual news or claim, then add context if needed.

Emotional flatline

Statements like what surprised me most or this was deeply meaningful often name a feeling without earning it. Show the specifics or cut the emotion claim.

Novelty inflation

Be suspicious of nobody is talking about this framing. Unless novelty is clearly supported, frame the idea as one interpretation rather than a revelation.

AI-tool fingerprints (placeholders / citations / UTM)

Strip mechanical artifacts such as unfilled placeholders, leaked chatbot citation markup, and URL parameters tied to chat tools. These are tool residue, not style.

Rhythm & uniformity (stylometric)

Even when vocabulary looks fine, drafts can still feel synthetic because the rhythm is too smooth. Watch for sentence-length uniformity, repetitive punctuation behavior, and low variation in grammatical movement.

© lynote-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 93 other files (scripts, references) in the repository root of lynote-ai/humanize-text-skill.

  • SKILL.md
  • .clawhubignore
  • .gitattributes
  • .omx/logs/tmux-hook-2026-07-03.jsonl
  • .omx/logs/turns-2026-07-03.jsonl
  • .omx/metrics.json
  • .omx/state/hud-state.json
  • .omx/state/notify-hook-state.json
  • .omx/state/team-leader-nudge.json
  • .omx/state/tmux-hook-state.json
  • AGENTS.md
  • CHANGELOG.md
  • CLAUDE.md
  • CONTRIBUTING.md
  • LICENSE
  • README.md
  • cursor-rules/humanize-text-skill.mdc
  • … and 77 more

Open the folder on GitHubat commit 9359e17

Compare with similar skills

Humanize Text Skill next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Humanize Text Skill compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Humanize Text Skill this skilllynote-ai/humanize-text-skill242—~1.9kAutomated safety check: PassMIT
HumanizerAzure-Samples/interview-coach-agent-framework17237 repos~5.8kAutomated safety check: PassMIT
Avoid AI Writingconorbronsdon/avoid-ai-writing4.9k3 repos~8.1kAutomated safety check: PassMIT
User-Facing Text Cleanupguillaumemeyer/watermarks-remover24k—~3.5kAutomated safety check: PassMIT
Install Anti Sloptrycompai/crm11k1 repos~881Automated safety check: PassMIT
Stop SlopXe/site7328 repos~423Automated safety check: PassMIT

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Categories

Questions about Humanize Text Skill

What does Humanize Text Skill do?

Audit and rewrite Chinese or English content to remove AI tone, then pull it toward a target human voice. Humanize Text Skill is an agent skill from lynote-ai/humanize-text-skill. Audit and rewrite Chinese or English content to remove AI tone, then pull it toward a target human voice.

When should I use Humanize Text Skill?

Humanize Text Skill fits situations like: asked to remove AI tone; rewrite naturally; make a draft feel less templated; match a target voice.

How do I install Humanize Text Skill in Claude Code?

Run `npx skills add lynote-ai/humanize-text-skill --skill humanize-text-skill -a claude-code`. Or copy the skill folder (the lynote-ai/humanize-text-skill repository) into .claude/skills/humanize-text-skill in your project. Claude Code loads it when a task matches its description.

How do I install Humanize Text Skill in Codex?

Run `npx skills add lynote-ai/humanize-text-skill --skill humanize-text-skill -a codex`. Or copy the skill folder (the lynote-ai/humanize-text-skill repository) into .agents/skills/humanize-text-skill in your project. Codex loads it when a task matches its description.

Can I use Humanize Text Skill in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add lynote-ai/humanize-text-skill --skill humanize-text-skill -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/humanize-text-skill, .gemini/skills/humanize-text-skill, .github/skills/humanize-text-skill and .opencode/skills/humanize-text-skill in your project.

What does Humanize Text Skill need to run?

SKILL.md names no scripts, command-line tools or credentials: Humanize Text Skill is instructions for the agent only. Compatibility (from SKILL.md): Any AI coding assistant that supports agentskills.io SKILL.md format (Claude Code, Cursor, VS Code Copilot, Hermes, OpenHands) or OpenClaw. No external tools or APIs required..

Does Humanize Text Skill access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Humanize Text Skill safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Humanize Text Skill use?

Humanize Text Skill is published under the MIT licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Humanize Text Skill use?

About 1.9k tokens (SKILL.md is roughly 7.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 33k tokens, read only when the agent opens those files.

What are the alternatives to Humanize Text Skill?

Skills that share tags, products or a category with Humanize Text Skill: Humanizer (Azure-Samples/interview-coach-agent-framework, 172 stars), Avoid AI Writing (conorbronsdon/avoid-ai-writing, 4.9k stars), User-Facing Text Cleanup (guillaumemeyer/watermarks-remover, 24k stars) and Install Anti Slop (trycompai/crm, 11k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Humanize Text Skill?

lynote-ai (a GitHub organization) maintains it in lynote-ai/humanize-text-skill, which has 242 GitHub stars. The repository was last updated on July 7, 2026.

Source: lynote-ai/humanize-text-skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.