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solo-humanize独奏人性化

Agent Skill

solo-humanize 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。

总安装

744

周安装

31

GitHub Stars

15

下载量

248
CodexClaudeCursorGemini CLI

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:solo-humanize(独奏人性化)
来源仓库:https://github.com/fortunto2/solo-factory
仓库路径:skills/solo-humanize
安装命令:
npx skills add https://github.com/fortunto2/solo-factory --skill solo-humanize
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/fortunto2/solo-factory --skill solo-humanize

简介

solo-humanize 用于查找、检索和筛选相关信息,支持基于关键词或任务场景定位内容。

  • 适用于需要快速获取候选结果的 Codex、Claude、Cursor 等宿主环境。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装,建议结合原始 README 验证具体用法。
  • 安装前应确认权限范围和维护状态,避免触发联网或文件操作等敏感行为。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

/humanize

Strip AI writing patterns from user-facing text. Takes a file or pasted text and rewrites it to read like a human wrote it, without losing meaning or structure.

Why this exists

LLM output has recognizable tells — em dashes, stock phrases, promotional inflation, performed authenticity. Readers (and Google) notice. This skill catches those patterns and rewrites them.

When to use

  • After /content-gen, /landing-gen, /video-promo — polish the output
  • Before publishing any user-facing prose (blog posts, landing pages, emails)
  • When editing CLAUDE.md or docs that will be read by humans
  • Standalone: /humanize path/to/file.md

Input

  • File path from $ARGUMENTS — reads and rewrites in place
  • No argument — asks to paste text, outputs cleaned version
  • Works on .md, .txt, and text content in .tsx/.html (string literals only)

Pattern Catalog

1. Em Dash Overuse (—)

The most obvious AI tell. Replace with commas, periods, colons, or restructure the sentence.

BeforeAfter
"The tool — which is free — works great""The tool (which is free) works great"
"Three features — speed, security, simplicity""Three features: speed, security, simplicity"
"We built this — and it changed everything""We built this. It changed everything."

Rule: Max 1 em dash per 500 words. Zero is better.

2. Stock Phrases

Phrases that signal "AI wrote this." Remove or replace with specific language.

Filler phrases (delete entirely):

  • "it's worth noting that" → (just state the thing)
  • "at the end of the day" → (cut)
  • "in today's world" / "in the modern landscape" → (cut)
  • "without further ado" → (cut)
  • "let's dive in" / "let's explore" → (cut)

Promotional inflation (replace with specifics):

  • "game-changer" → what specifically changed?
  • "revolutionary" → what's actually new?
  • "cutting-edge" → describe the technology
  • "seamless" → "works without configuration" (or whatever it actually does)
  • "leverage" → "use"
  • "robust" → "handles X edge cases" (specific)
  • "streamline" → "cut steps from N to M"
  • "empower" → what can the user now do?
  • "unlock" → what's the actual capability?

Performed authenticity (rewrite):

  • "to be honest" → (if you need to say this, the rest wasn't honest?)
  • "let me be frank" → (just be frank)
  • "I have to say" → (just say it)
  • "honestly" → (cut)
  • "the truth is" → (cut, state the truth directly)

3. Rule of Three

AI loves triplets: "fast, secure, and scalable." Real writing varies list length.

BeforeAfter
"Fast, secure, and scalable""Fast and secure" (if scalable isn't proven)
"Build, deploy, and iterate""Build and ship" (if that's what you mean)
Three bullet points that all say the same thingOne clear bullet

Rule: If you find 3+ triplet lists in one document, break at least half of them.

4. Structural Patterns

Every section has the same shape: AI tends to write: heading → one-sentence intro → 3 bullets → transition sentence. Real writing varies section length and structure.

Hedging sandwich: "While X has limitations, it offers Y, making it Z." → Pick a side. State it.

False balance: "On one hand X, on the other hand Y." → If one side is clearly better, say so.

5. Sycophantic Openers

  • "Great question!" → (cut)
  • "That's a fantastic idea!" → (cut, or say what's specifically good about it)
  • "Absolutely!" → (cut if not genuine agreement)
  • "I'd be happy to help!" → (just help)

6. Passive Voice / Weak Verbs

  • "It should be noted that" → (cut, just note it)
  • "There are several factors that" → name the factors
  • "It is important to" → say why
  • "This can be achieved by" → "Do X"

Process

  1. Read the input — file path or pasted text.
  2. Scan for patterns — check each category above. Count violations per category.
  3. Rewrite — fix each violation while preserving:

- Technical accuracy (don't change code, commands, or technical terms) - Structure (headings, lists, code blocks stay) - Tone intent (if the original was casual, keep it casual) - Length (aim for same or shorter, never longer)

  1. Report what changed: Humanized: {file or "pasted text"} Changes: Em dashes: {N} removed Stock phrases: {N} replaced Inflation: {N} deflated Triplets: {N} broken Sycophancy: {N} cut Total: {N} patterns fixed Before: {word count} After: {word count}
  2. If file path: write the cleaned version back. Show a diff summary. If pasted text: output the cleaned version directly.

What NOT to change

  • Code blocks and inline code
  • Technical terms, library names, CLI commands
  • Quotes from other people (attributed quotes stay verbatim)
  • Numbers, dates, URLs
  • Headings structure (don't merge or split sections)
  • Content meaning — only rephrase, never add or remove ideas

Edge Cases

  • Short text (<50 words): just apply stock phrase filter, skip structural analysis
  • Already clean: report "No AI patterns found. Text looks human."
  • Code-heavy docs: skip code blocks entirely, only process prose sections
  • Non-English text: apply em dash and structural rules (they're universal), skip English stock phrases

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

34.96%
按下载量换算87

Claude

30.67%
按下载量换算76

Cursor

21.46%
按下载量换算53

Gemini CLI

9.63%
按下载量换算24

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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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