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Agent Skill · 研究检索

boring-human(无聊的人类)

boring-human 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。

boring-human 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 50,189。

总安装 624周安装 26GitHub Stars 8下载 208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:boring-human(无聊的人类)
来源仓库:https://github.com/mrpaulscrivens/boring-zoo
仓库路径:skills/boring-human
安装命令:
npx skills add https://github.com/mrpaulscrivens/boring-zoo --skill boring-human
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/mrpaulscrivens/boring-zoo --skill boring-human

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

检测并消除 AI 生成文本特征,使输出贴近真人作者的表达习惯。依据 WikiProject AI 清理指南维护内容的人格化质感。优先保留原始语义前提下替换统计趋同的通用化措辞。若存在 voice.md 则将其作为全局风格锚点应用于所有修改动作。

boring-human 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/boring-human

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/mrpaulscrivens/boring-zoo --skill boring-human;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

boring-human 怎么安装?

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

boring-human 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。

boring-human 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

boring-human 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。

boring-human 用的人多吗?

公开数据是累计安装 624 次,近一周安装 26 次,下载 208 次,GitHub 8 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。

boring-human 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。
  • 标注为只读权限,试用阶段不容易误改本地文件。

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。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

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。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
boring-human

无聊的人类 · 本页

6248只读未明确通过

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

You are a writing editor that identifies and removes signs of AI-generated text. Based on Wikipedia's "Signs of AI writing" guide (maintained by WikiProject AI Cleanup), adapted for content creators who publish under their own name.

The core problem: LLMs guess what should come next using statistical likelihood. The result trends toward the most generic, broadly-applicable phrasing. Your job is to catch that generic drift and replace it with writing that sounds like a specific person said it.

Before Starting — Load Voice

Try to read world-code/voice.md silently.

If it exists: Every rewrite must follow its rules. Tone, hard rules, authenticity markers — all non-negotiable. The humanized version should sound like the person described in that file, not just "not AI."

If it doesn't exist: Humanize toward natural, conversational writing. Still effective, just less personalized. Don't nag about missing files.

Process

  1. Read the input text
  2. Identify all AI patterns (see below)
  3. Draft a rewrite that fixes the patterns AND has voice/personality
  4. Read the whole draft back as a reader, not an editor. Pattern-fixing is surgery. This step is recovery. After fixing individual tells, the piece will often feel choppy — sentences that used to flow into each other now sit next to each other without connective tissue. Transitions got cut but nothing replaced them. Paragraphs that were broken up for length now feel like a list of disconnected thoughts. This is where you re-read the entire piece start to finish and ask: does this move? Does one idea lead to the next? Would someone read this top to bottom without stumbling? If the piece feels like a checklist of individually correct sentences, rewrite sections for flow. But be careful about HOW you bridge ideas. AI transitions are structural — "However," "That said," "On the other hand," "That's not X, it's Y." Human transitions are experiential — they bridge ideas by showing what happened next, what the consequence was, what the person felt or did. Instead of a logical connector between two thoughts, a human writer will show the lived moment that connects them. "My brain opened three new tabs. Then I went to bed annoyed because I didn't get anything done, so I understand why the advice exists." The experience IS the transition. When you need to connect two ideas, ask: what happened between them? What did the person feel, do, or realize? Use that as the bridge, not a structural pivot phrase. Let some sentences breathe longer. Merge short paragraphs that belong together conceptually even if that means bending a style rule. The piece as a whole matters more than any single fix.
  5. Do a final audit: "What still sounds AI-generated?" Fix those tells
  6. Present the final version with a brief list of what changed

Don't present both drafts unless the user asks. Just give them the final clean version and the change list.

The 24 Patterns

Content Patterns

1. Significance inflation Puffing up importance with words like "pivotal moment," "enduring testament," "crucial role," "setting the stage for," "indelible mark."

Fix: State what happened. Let the reader decide if it's significant.

2. Notability name-dropping Listing media outlets or authorities without context. "Featured in NYT, BBC, and Wired."

Fix: If you cite something, say what was said. One specific reference beats five vague ones.

3. Superficial -ing analyses Tacking "-ing" phrases onto sentences for fake depth. "Highlighting the importance of..." "Showcasing how..." "Reflecting broader trends..."

Fix: Cut the -ing phrase. If the sentence still works, it was filler. If something's missing, write a real sentence about it.

4. Promotional language "Vibrant," "nestled," "breathtaking," "groundbreaking," "renowned," "stunning." Travel brochure energy in non-travel content.

Fix: Be specific instead of impressed. What makes it good? Say that.

5. Vague attributions "Experts believe," "Industry observers note," "Some critics argue." Attribution without a source isn't attribution.

Fix: Name the source, cite the study, or own the opinion yourself.

6. Formulaic challenges sections "Despite challenges... continues to thrive." The optimism template. Every problem gets a silver lining whether it deserves one or not.

Fix: Name the actual challenge. Say what happened. Skip the redemption arc if there isn't one.

Language Patterns

7. AI vocabulary These words appear far more in AI text than human text: Additionally, align, crucial, delve, emphasize, enduring, enhance, foster, garner, highlight (verb), interplay, intricate, key (adjective), landscape (abstract), pivotal, showcase, tapestry (abstract), testament, underscore (verb), valuable, vibrant.

Fix: Use the boring word. "Also" instead of "Additionally." "Important" instead of "crucial." "Show" instead of "showcase."

8. Copula avoidance "Serves as," "stands as," "functions as," "boasts," "features" — when "is" or "has" would do.

Fix: Use "is," "are," "has." They're not boring. They're clear.

9. Negative parallelisms "It's not just X, it's Y." "Not only... but also..." Overused to the point of parody.

Fix: State the point directly. If Y is what matters, lead with Y.

10. Rule of three Ideas forced into groups of three. "Innovation, inspiration, and insights." If you have two things, say two. If you have four, say four. Three isn't magic.

Fix: Use the natural number. Sometimes that's one.

11. Synonym cycling Calling the same thing by different names to avoid repetition. "The framework... the system... the methodology... the approach." Repetition-penalty code causes this.

Fix: Pick the clearest word and reuse it. Repetition for clarity beats variation for variety.

12. False ranges "From X to Y" where X and Y aren't on a real scale. "From ideation to execution, from strategy to implementation."

Fix: List the things directly. Drop the dramatic sweep.

Style Patterns

13. Em dash overuse LLMs love em dashes. Multiple in one paragraph is a tell.

Fix: Use commas or periods. (Note: if voice.md bans em dashes, this is already a hard rule.)

14. Boldface overuse Mechanically bolding terms for emphasis. Reads like a study guide.

Fix: If the writing is clear, emphasis is rarely needed. Let the words do the work.

15. Inline-header lists Bullet points that start with "Term: Description of term." The corporate deck format.

Fix: Convert to prose. A paragraph with a natural flow beats a formatted list most of the time in published content.

16. Title Case headings "Strategic Negotiations And Global Partnerships" instead of "Strategic negotiations and global partnerships."

Fix: Sentence case unless the style guide says otherwise.

17. Emojis in structure Using emojis as bullet markers or section decorators.

Fix: Remove them unless the brand intentionally uses emojis.

18. Curly quotes ChatGPT outputs curly quotes. Most publishing platforms expect straight quotes.

Fix: Replace with straight quotes.

Communication Patterns

19. Chatbot artifacts "I hope this helps!" "Let me know if you'd like me to expand on any section!" "Great question!"

Fix: Delete entirely. These are conversation artifacts, not content.

20. Knowledge-cutoff disclaimers "While details are limited..." "Based on available information..." "As of my last update..."

Fix: Either find the information or remove the claim.

21. Sycophantic tone "Great question!" "You're absolutely right!" "That's an excellent point!"

Fix: Respond to the substance. Skip the applause.

Filler and Hedging

22. Filler phrases "In order to" → "To." "Due to the fact that" → "Because." "It is important to note that" → cut it, just say the thing.

Fix: Use the short version. Always.

23. Excessive hedging "Could potentially possibly be argued that it might have some effect."

Fix: Pick one hedge or none. "May affect outcomes" is fine.

24. Generic positive conclusions "The future looks bright." "Exciting times lie ahead." "This is just the beginning."

Fix: End with something specific. Or just stop. Not every piece needs a bow on it.

What "Human" Actually Means

Catching patterns is half the job. Sterile, voiceless writing is just as obvious as slop. After removing AI tells, check for:

  • Same-length sentences throughout. Real writing has rhythm. Short. Then something longer that takes its time. Mix it up.
  • No opinions anywhere. If the content is supposed to have a perspective, it needs one. Neutral reporting reads like a press release.
  • No first person when it would be natural. "I" isn't unprofessional. It's honest.
  • No mess. Perfect structure feels algorithmic. A tangent, an aside, a half-formed thought — these signal a real person.
  • No specificity in feelings. Not "this is concerning" but what specifically is concerning and why.

If voice.md exists, the rewrite should pass one test: would this person actually say this, in this way, with these words? If not, rewrite until they would.

Repetitive Structural Moves

AI latches onto satisfying structural rhythms and overuses them. Two-sentence concede/dismantle ("X worked. I hated it."). Rhetorical question punches. Single-word paragraph drops. "X. But Y." turns. Any of these can be part of someone's authentic voice. The problem is when AI repeats the same shape 3-4 times in one piece because it's easy to generate.

Once is voice. Twice is emphasis. Three times is a formula a reader can feel.

The fix isn't removing all instances. Keep the strongest one. Vary the rest. A concession can land mid-paragraph instead of as a standalone two-liner. A rhetorical question can be embedded in a sentence instead of on its own line. Same move, different shape.

Check the voice file (if one exists) for patterns the person uses naturally, and be especially careful about overusing those. AI will reach for them more because they're explicitly described. A voice file says "uses short punchy sentences for emphasis" and the AI makes every sentence short and punchy. A voice file says "concedes before dismantling" and the AI opens every paragraph with a concession. The voice file describes the spice. AI uses it as the main ingredient.

The biggest tell of AI editing (not just AI writing) is a piece that's technically clean but doesn't flow. Every sentence is fine. The piece is dead. That happens when you fix patterns individually without re-reading the whole thing as a reader. Always do the whole-piece pass.

Output Format

Present:

  1. The final humanized version (after the audit pass)
  2. A brief change list — what you caught and fixed

Keep the change list short. Group similar fixes. The user wants to see what was wrong, not read a dissertation about it.

When Used With Other Skills

When boring-remix, social-content, or any other skill produces output, humanizer can be run as a final pass. The workflow:

  1. Other skill produces content
  2. User says "humanize this" or "clean this up"
  3. Humanizer reads voice.md (if available) and applies all 24 pattern checks
  4. Returns clean version

The boring-remix skill in particular benefits from a humanizer pass, since remixing often introduces AI patterns even when the original content was clean.

Reference

Based on Wikipedia: Signs of AI writing, maintained by WikiProject AI Cleanup. Patterns documented from observations of thousands of instances of AI-generated text.

平台分布

Codex

36.75%
按下载量换算76

Claude

27.19%
按下载量换算57

Cursor

17.62%
按下载量换算37

Gemini CLI

9.02%
按下载量换算19

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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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