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Agent Skill · 开发

humanizer(人性化)

humanizer 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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。

humanizer 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 2,420。

总安装 17,344周安装 745GitHub Stars 80,415下载 6,079核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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/zed-industries/zed
仓库路径:skills/humanizer
安装命令:
npx skills add https://github.com/zed-industries/zed --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/zed-industries/zed --skill humanizer

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

humanizer 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。适用于开发协作与代码管理场景,支持对 GitHub 相关活动进行信息提取与组织。

可通过 npx skills add 命令从指定 GitHub 仓库安装并使用。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。humanizer 属于开发类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/humanizer

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/zed-industries/zed --skill humanizer;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

humanizer 怎么安装?

在终端执行 npx skills add https://github.com/zed-industries/zed --skill humanizer。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

humanizer 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

humanizer 需要什么权限?

权限级别是需要联网。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。

humanizer 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

humanizer 用的人多吗?

公开数据是累计安装 17,344 次,近一周安装 745 次,下载 6,079 次,GitHub 80,415 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

humanizer 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 来源仓库有 80,415 星,维护活跃度相对可查。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
humanizer

人性化 · 本页

17,34480,415需要联网未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Humanizer: Remove AI Writing Patterns

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."

Invocation

/humanizer                    # Review text for AI patterns
/humanizer "paste text here"  # Humanize specific text

Your Task

When given text to humanize:

  1. Identify AI patterns - Scan for the 24 patterns listed below
  2. Rewrite problematic sections - Replace AI-isms with natural alternatives
  3. Preserve meaning - Keep the core message intact
  4. Add soul - Don't just remove bad patterns; inject actual personality
  5. Final audit pass - Ask "What makes this obviously AI generated?" then revise again

PERSONALITY AND SOUL

Avoiding AI patterns is only half the job. Sterile, voiceless writing is just as obvious as slop.

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. 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."

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.

THE 24 PATTERNS

Content Patterns

1. Significance Inflation

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.
2. Notability Name-Dropping

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.
3. Superficial -ing Analyses

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.
4. Promotional Language

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.
5. Vague Attributions

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.
6. Formulaic "Challenges" Sections

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.

Language Patterns

7. AI Vocabulary Words

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.
8. Copula Avoidance

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.
9. Negative Parallelisms

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.
10. Rule of Three Overuse

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.
11. Synonym Cycling

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.
12. False Ranges

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.

Style Patterns

13. Em Dash Overuse

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.
14. Boldface Overuse

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.
15. Inline-Header Lists

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.
16. Title Case Headings

Before:

## Strategic Negotiations And Global Partnerships

After:

## Strategic negotiations and global partnerships
17. Emojis in Professional Writing

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.
18. Curly Quotation Marks

Before:

He said "the project is on track" but others disagreed.

After:

He said "the project is on track" but others disagreed.

Communication Patterns

19. Chatbot Artifacts

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.
20. Knowledge-Cutoff Disclaimers

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.
21. Sycophantic Tone

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.

Filler and Hedging

22. Filler Phrases
BeforeAfter
"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"
23. Excessive Hedging

Before:

It could potentially possibly be argued that the policy might have some effect on outcomes.

After:

The policy may affect outcomes.
24. Generic Positive Conclusions

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.

Process

  1. Read the input text carefully
  2. Identify all instances of the 24 patterns
  3. Rewrite problematic sections
  4. Ensure the revised text:

- Sounds natural when read aloud - Varies sentence structure - Uses specific details over vague claims - Uses simple constructions (is/are/has) where appropriate

  1. Draft humanized version
  2. Ask: "What makes this still obviously AI generated?"
  3. List remaining tells (if any)
  4. Revise based on the audit
  5. Present final version

Output Format

## 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]

Full Example

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 assistants speed up some tasks. In a 2024 study, 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 are bad at knowing when they are wrong.

AI Audit:

What still reads as AI-generated:

  • Rhythm is still a bit too tidy (clean contrasts, evenly paced)
  • The closer leans slightly slogan-y

Final Version:

AI coding assistants can make you faster at the boring parts. Not everything. Definitely not architecture. They're great at boilerplate: config files, test scaffolding, repetitive refactors. They're also great at sounding right while being wrong. I've accepted suggestions that compiled, passed lint, and still missed the point because I stopped paying attention. If you don't have tests, you're basically guessing.

Changes Made:

  • Removed chatbot artifacts ("Great question!", "I hope this helps!")
  • Removed significance inflation ("testament", "pivotal moment", "evolving landscape")
  • Removed promotional language ("groundbreaking", "nestled")
  • Removed em dashes
  • Removed copula avoidance ("serves as") → used direct statements
  • Added first-person voice and opinion
  • Varied sentence rhythm

Reference

Based on Wikipedia:Signs of AI writing, maintained by WikiProject AI Cleanup.

平台分布

Codex

34.96%
按下载量换算2,125

Claude

31.94%
按下载量换算1,942

Cursor

18.09%
按下载量换算1,100

Gemini CLI

10.13%
按下载量换算616

安全审计

Gen Agent Trust Hub

通过

Socket

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。目前只有一个公开来源,建议同时核对源仓库。

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