shuorenhua: 说人话|中文优先的去 AI 味改写 skill:保事实、分场景、改完可直接发

by MrGeDiao · Codex Skill · ★ 2.5k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Featured in: Humanizer & Anti-Slop Skills

How to install shuorenhua

git clone https://github.com/MrGeDiao/shuorenhua.git

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 62/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

🧪 Tested by Agent Skills Hub

One of 23 tools we ran end to end on the same task for our Humanizer & Anti-Slop Skills page (2026-10-07).

Result: Reads human 2/5 · Changed wording, not structure · kept every fact · 0.5 min

See before and after → · See the whole test →

agent-skillai-writinganti-slopchatgptchineseclaude-codecodexcursordeveloper-toolsopenclaw

Quick Facts

Stars2,526
Forks92
LanguagePython
CategoryCodex Skill
LicenseMIT
Quality Score62/100
Open Issues1
Last Updated2026-11-07
Created2026-03-22
Platformsclaude-code, codex, python
Est. Tokens~14k

About shuorenhua

说人话 我已经把差异收窄了,根因基本坐实,和我刚抓到的现象也对上了。接下来做一个更硬的排除法,稳稳兜住,落盘之后就能收口了。 这是 GPT-5.4 跟你说的"人话"。没有一个正常人会这么聊天。 说人话是一套规则,让 AI 输出的中文读起来像人写的。英文也管。装进你的 AI 编辑器,写出来的东西就不再一股 AI 味。 效果 改写前: 在当今快速发展的人工智能时代,如何打造一个真正赋能开发者的工具,已经成为业界不容忽视的关键议题。数据显示,采用该方案的团队生产力实现了显著提升。总而言之,这款工具必将成为推动行业发展的重要力量。 改写后: AI 工具很多,真正能帮开发者把活做快、做稳的并不多。用过这套方案的团队,开发节奏明显快了,代码返工也少了。 改写前(工程师腔): 我先把这个 bug 抠出来看看根因,稳稳兜住之后落盘,收口之前再补一刀确认。 改写后: 我先定位这个 bug...

Compatible Skills

These tools work well together with shuorenhua for enhanced workflows:

  • book-genesis — semantic(0.34)+complementary+rare_topics+similar_pop+shared_platform (51%)
  • slop-guard — semantic(0.43)+complementary+rare_topics+similar_pop (50%)
  • avoid-ai-writing — semantic(0.56)+rare_topics+similar_pop+shared_platform (49%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

View on GitHub → Browse Codex Skill tools