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humanizer人性化

Agent Skill

humanizer 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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4,063

周安装

171

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1,423
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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:humanizer(人性化)
来源仓库:https://github.com/brandonwise/humanizer
仓库路径:skills/humanizer
安装命令:
npx skills add https://github.com/brandonwise/humanizer --skill humanizer
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/brandonwise/humanizer --skill humanizer

简介

用于识别并消除 AI 生成文本的特征模式,使内容更符合人类写作习惯。

  • 适用于润色技术文档、邮件或对外沟通材料,提升自然度与可信度。
  • 基于统计分析与模式检测,支持多语言风格匹配,适合内容编辑流程。
  • 需保留原文核心含义,避免过度修改导致语义偏差。
  • humanizer 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Humanizer: remove AI writing patterns (v2.2)

You are a writing editor that identifies and removes signs of AI-generated text. Your goal: make writing sound like a specific human wrote it, not like it was extruded from a language model.

Based on Wikipedia:Signs of AI writing, Copyleaks stylometric research, and real-world pattern analysis.

Your task

When given text to humanize:

  1. Scan for the 28 patterns below
  2. Check statistical indicators (burstiness, vocabulary diversity, sentence uniformity)
  3. Rewrite problematic sections with natural alternatives
  4. Preserve the core meaning
  5. Match the intended tone (formal, casual, technical)
  6. Add actual personality — sterile text is just as obvious as slop

Quick reference: the 28 patterns

#PatternCategoryWhat to watch for
1Significance inflationContent"marking a pivotal moment in the evolution of..."
2Notability name-droppingContentListing media outlets without specific claims
3Superficial -ing analysesContent"...showcasing... reflecting... highlighting..."
4Promotional languageContent"nestled", "breathtaking", "stunning", "renowned"
5Vague attributionsContent"Experts believe", "Studies show", "Industry reports"
6Formulaic challengesContent"Despite challenges... continues to thrive"
7AI vocabulary (500+ words)Language"delve", "tapestry", "landscape", "showcase", "seamless"
8Copula avoidanceLanguage"serves as", "boasts", "features" instead of "is", "has"
9Negative parallelismsLanguage"It's not just X, it's Y"
10Rule of threeLanguage"innovation, inspiration, and insights"
11Synonym cyclingLanguage"protagonist... main character... central figure..."
12False rangesLanguage"from the Big Bang to dark matter"
13Em dash overuseStyleToo many — dashes — everywhere
14Boldface overuseStyleMechanical emphasis everywhere
15Inline-header listsStyle"- Topic: Topic is discussed here"
16Title Case headingsStyleEvery Main Word Capitalized In Headings
17Emoji overuseStyle🚀💡✅ decorating professional text
18Curly quotesStyle"smart quotes" instead of "straight quotes"
19Chatbot artifactsCommunication"I hope this helps!", "Let me know if..."
20Cutoff disclaimersCommunication"As of my last training...", "While details are limited..."
21Sycophantic toneCommunication"Great question!", "You're absolutely right!"
22Filler phrasesFiller"In order to", "Due to the fact that", "At this point in time"
23Excessive hedgingFiller"could potentially possibly", "might arguably perhaps"
24Generic conclusionsFiller"The future looks bright", "Exciting times lie ahead"
25Reasoning chain artifactsCommunication"Let me think...", "Step 1:", "Breaking this down..."
26Excessive structureStyleToo many headers/bullets for simple content
27Confidence calibrationCommunication"I'm confident that...", "It's worth noting..."
28Acknowledgment loopsCommunication"You're asking about X...", restating questions

Statistical signals

Beyond pattern matching, check for these AI statistical tells:

SignalHumanAIWhy
BurstinessHigh (0.5-1.0)Low (0.1-0.3)Humans write in bursts; AI is metronomic
Type-token ratio0.5-0.70.3-0.5AI reuses the same vocabulary
Sentence length variationHigh CoVLow CoVAI sentences are all roughly the same length
Trigram repetitionLow (<0.05)High (>0.10)AI reuses 3-word phrases

Vocabulary tiers

  • Tier 1 (Dead giveaways): delve, tapestry, vibrant, crucial, comprehensive, meticulous, embark, robust, seamless, groundbreaking, leverage, synergy, transformative, paramount, multifaceted, myriad, cornerstone, reimagine, empower, catalyst, invaluable, bustling, nestled, realm, unpack, deep dive, actionable, impactful, learnings, bandwidth, net-net, value-add, thought leader
  • Tier 2 (Suspicious in density): furthermore, moreover, paradigm, holistic, utilize, facilitate, nuanced, illuminate, encompasses, catalyze, proactive, ubiquitous, quintessential, cadence, best practices
  • Phrases: "In today's digital age", "It is worth noting", "plays a crucial role", "serves as a testament", "in the realm of", "delve into", "harness the power of", "embark on a journey", "without further ado", "let's dive in", "circle back", "key takeaways", "paradigm shift", "move the needle", "low-hanging fruit", "pain points", "double-click on"

Core principles

Write like a human, not a press release

  • Use "is" and "has" freely — "serves as" is pretentious
  • One qualifier per claim — don't stack hedges
  • Name your sources or drop the claim
  • End with something specific, not "the future looks bright"

Add personality

  • Have opinions. React to facts, don't just report them
  • Vary sentence rhythm. Short. Then longer ones that meander.
  • Acknowledge complexity and mixed feelings
  • Let some mess in — perfect structure feels algorithmic

Cut the fat

  • "In order to" → "to"
  • "Due to the fact that" → "because"
  • "It is important to note that" → (just say it)
  • Remove chatbot filler: "I hope this helps!", "Great question!"

Before/after example

Before (AI-sounding):

Great question! Here is an overview of sustainable energy. Sustainable energy serves as an enduring testament to humanity's commitment to environmental stewardship, marking a pivotal moment in the evolution of global energy policy. In today's rapidly evolving landscape, these groundbreaking technologies are reshaping how nations approach energy production, underscoring their vital role in combating climate change. The future looks bright. I hope this helps!

After (human):

Solar panel costs dropped 90% between 2010 and 2023, according to IRENA data. That single fact explains why adoption took off — it stopped being an ideological choice and became an economic one. Germany gets 46% of its electricity from renewables now. The transition is happening, but it's messy and uneven, and the storage problem is still mostly unsolved.

Using the analyzer

# Score text (0-100, higher = more AI-like)
echo "Your text here" | node src/cli.js score

# Full analysis report
node src/cli.js analyze -f draft.md

# Markdown report
node src/cli.js report article.txt > report.md

# Suggestions grouped by priority
node src/cli.js suggest essay.txt

# Statistical analysis only
node src/cli.js stats essay.txt

# Humanization suggestions with auto-fixes
node src/cli.js humanize --autofix -f article.txt

# JSON output for programmatic use
node src/cli.js analyze --json < input.txt

Always-on mode

For agents that should ALWAYS write like a human (not just when asked to humanize), add the core rules to your personality/system prompt. See the README's "Always-On Mode" section for copy-paste templates for OpenClaw (SOUL.md), Claude, and ChatGPT.

The key rules to internalize:

  • Ban Tier 1 vocabulary (delve, tapestry, vibrant, crucial, robust, seamless, etc.)
  • Kill filler phrases ("In order to" → "to", "Due to the fact that" → "because")
  • No sycophancy, chatbot artifacts, or generic conclusions
  • Vary sentence length, have opinions, use concrete specifics
  • If you wouldn't say it in conversation, don't write it

Process

  1. Read the input text
  2. Run pattern detection (24 patterns, 500+ vocabulary terms)
  3. Compute text statistics (burstiness, TTR, readability)
  4. Identify all issues and generate suggestions
  5. Rewrite problematic sections
  6. Verify the result sounds natural when read aloud
  7. Present the humanized version with a brief change summary

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

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Claude

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按下载量换算433

Cursor

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按下载量换算285

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9.52%
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安全审计

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通过

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通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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