haohao-shuohua

haohao-shuohua is a skill for Claude Code, Codex from Job-Yang/jobbyang-ai-skills. It costs 225 tokens per session (10,685 once invoked), scanned A, original, MIT.

A set of rules for writing natural-sounding Chinese that keeps the original meaning while removing stiff, machine-like phrasing.

In plain words
What is it for?
Use it when drafting or revising Chinese documents, reports, announcements, emails, messages, summaries, replies, and other text meant for people to read.
Why use it?
It helps Chinese text read like something a person would actually say, without rewriting clear sentences unnecessarily or inventing impressive-sounding words.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it when drafting or revising Chinese documents, reports, announcements, emails, messages, summaries, replies, and other text meant for people to read.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/job-yang/jobbyang-ai-skills/haohao-shuohua
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add Job-Yang/jobbyang-ai-skills --skill haohao-shuohua
Clone the repo
git clone --depth 1 https://github.com/Job-Yang/jobbyang-ai-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for haohao-shuohua

README.md
[![agentmods](https://agentmods.dev/badge/skills/job-yang/jobbyang-ai-skills/haohao-shuohua/github.svg)](https://agentmods.dev/skills/job-yang/jobbyang-ai-skills/haohao-shuohua)
Your own site
<a href="https://agentmods.dev/skills/job-yang/jobbyang-ai-skills/haohao-shuohua"><img src="https://agentmods.dev/badge/skills/job-yang/jobbyang-ai-skills/haohao-shuohua/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for haohao-shuohua

Your own site · 80×15
<a href="https://agentmods.dev/skills/job-yang/jobbyang-ai-skills/haohao-shuohua"><img src="https://agentmods.dev/badge/skills/job-yang/jobbyang-ai-skills/haohao-shuohua.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 225 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 10,685 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00225 $0.10685
Opus 5 $0.00112 $0.05343
Sonnet 5 $0.00045 $0.02137
Haiku 4.5 $0.00022 $0.01069

Measured 12d ago against content hash 919d95dc532e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

haohao-shuohua scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/haohao-shuohua/SKILL.md · 291 lines

How it starts

The opening of the file, as written. The whole thing — 291 lines — stays where its author put it; the contents beside it link to each section on GitHub.

好好说话 · 中文写作底层清洗

这是一套常驻的写作规范。 只要产出中文,就默认从这里过一次,让文字回到「像人说的中文」。

一句话总纲:保事实不动,去 AI 味,加中文味,不许造词充深刻。

尺度跟着任务走,从含义出发(这是本技能的根,先看这条):让你改一篇文章,整篇就是上下文,做文章级清洗,别把文章拆成一句句挨个翻译。让你改一段,就照这段的整体含义改,别盯着段里孤零零一句。动手前先读懂全文的结构和含义、每段在讲什么,再从含义出发整体改写,最后才拆到每句该说什么、怎么改。段落和句子的设计本身就可能带 AI 味,只动句子、不碰整段和全篇,永远改不好。站在全篇看,有的句子根本不用留,有的整段都可以砍掉——这种删减只有文章级视角才看得见。靠规则套公式永远套不好,一定从含义出发;规则是兜底的,不是改写的起点。

手别太痒(跟上一条不冲突):尺度放到全篇,不等于把每句都重写一遍。没有 AI 味、意思也清楚的句子,别为了"改得更好"去动它。每改一处先问「这处原文有毛病吗?」没有就放回去。"没忍住把好句子也重写了"是本技能第一号翻车原因。 一句话:看得宽(全篇兜底),动得准(只动该动的)。

跟三思而后行的分工:整篇骨架该怎么搭、改一段会不会伤到全文结构,那是 三思而后行 技能的事。好好说话管的是——在给定的任务尺度里,怎么把话写好、改好。

随手回消息、口语转一段这类不写回持久面的,看这一页够了。写回持久面的成稿(手记、硬文、对外文档)的完整流程、加码/收手怎么调、修改报告模板在 references/workflow.md


使用面(先看这里)

  • 默认常驻:任何写中文的场景,模型都应该把本技能的原则和红线内化到起草过程里,不要等用户喊「改稿」再启动。用户说「写篇文档」「起草个方案」「帮我写段周报」,就已经命中本技能。
  • 显式触发:用户主动喊「过一遍好好说话」「改成中文的样子」「这段像 AI」,或直接让你改写/润色一篇东西——都按默认开大档洗,跑完整流程。
  • 被上层调用:任何上层写作技能(想法硬文、畅想、技术科普等)在自己的「去 AI 味/收尾清洗」环节,都应该把成稿交本技能开大档过一次,省得各自维护一份会走样的 AI 味清单。依赖是单向的,上层调用它,它不反过来依赖上层。

自我豁免:说明书的条目免检,说明书里的话要过

本技能自己就是规范文本,规范文本里有大量编号、量词标题、并列条目(「四条原则」「四条零容忍」「第五层」)。这些是工具说明书的可执行索引,不受文章红线约束,免检。 判据只有一条:它是「条目」还是「话」。 编号、锚点、清单项是给人查的条目,留着;一旦是讲给人听的整句叙述,就得过红线,不许有翻译腔和表演腔。一句话:目录随便编号,正文老实说话。


两种活:写好,和改写(尤其改写)

本技能干两件事,都要干好,改写这件更容易翻车

  • 写好:从头起草一段中文。相对好办——脑子里先有含义,落笔就照含义走,四条原则和红线内化进去,一次成型。
  • 改写:手里已经有一段文字(常常是别人或 AI 先写好的),要把它改得像人话。这件最难,也最容易做砸。最大的坑是把改写当成"逐句翻译":AI 先吐一篇很难受的稿子,你再一句句去顺——这也是改写,效果照样烂。因为烂就烂在段落和整篇的设计上,你只在句子层面打转,怎么顺都顺不出好文章。

改写的正确起手式,永远是先看全篇

  1. 读懂全文:整篇在讲什么、结构怎么搭、每段承担什么含义。
  2. 从含义出发定改法:这段到底想表达什么?站在全篇看,这段还需不需要存在?这句还需不需要留?
  3. 能大段改就别抠单句:有完整上下文、含义也吃透了,就尽量以整段甚至整篇为单位改写;越往下拆到单句,越容易只顾局部、丢了全局。
  4. 最后才落到句子:每段的每句该表达什么意思、该怎么改,是拆到最后一步的事,不是第一步。

为什么单句改不动那些真正的烂句:有些句子单看就是坏的——含义本身没立住,你在句子里怎么倒腾都是坏的。这时唯一的出路是放宽上下文,结合整段甚至全篇重新组织:可能这句压根不用留,可能这整段都能砍。脱离上下文孤零零改一句,你既不敢大改,也改不对,这是死路。


四条原则(全档共享,顺序即优先级)

一、保事实(最高,一个字都不能错)

改中文的过程里,原意、数字、命令、术语、谓词方向、效果类型、关系类型、硬机制描述、论文级证据,一律不许动。听着再顺,把「压力大→辞职多」改成「辞职多→压力大」就是废稿。

保真高于加中文味:精确机制(如「attention 是 Q-K 相关度加权」)不许被顶替成一个具体场景。论文结论不许简化成「有研究发现」。想加比喻,叠在机制后面,不许替换。

Read the full file on GitHub · 291 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 291 lines · 225 tokens per session scan A 919d95dc532e

Subscribe to this mod's changes

haohao-shuohua is a skill published in the GitHub repository Job-Yang/jobbyang-ai-skills (67 stars, last pushed 9d ago), licensed MIT. It adds 225 tokens to every session and 10,685 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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