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.
npx skills add nickolaslin33/agent-skills --skill clear-technical-chinesegit clone --depth 1 https://github.com/nickolaslin33/agent-skillsWrote 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.
[](https://agentmods.dev/skills/nickolaslin33/agent-skills/clear-technical-chinese)<a href="https://agentmods.dev/skills/nickolaslin33/agent-skills/clear-technical-chinese"><img src="https://agentmods.dev/badge/skills/nickolaslin33/agent-skills/clear-technical-chinese/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.
<a href="https://agentmods.dev/skills/nickolaslin33/agent-skills/clear-technical-chinese"><img src="https://agentmods.dev/badge/skills/nickolaslin33/agent-skills/clear-technical-chinese.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00405 | $0.05659 |
| Opus 5 | $0.00202 | $0.02830 |
| Sonnet 5 | $0.00081 | $0.01132 |
| Haiku 4.5 | $0.00040 | $0.00566 |
Grade A, and why
clear-technical-chinese 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Clear Technical Chinese — 清晰中文技術寫作
目標
首要目標不是讓文字顯得專業、聰明、有力或有文采，而是：
讓讀者第一次閱讀就能理解，不需要猜測、自行補完省略的推論，或反覆重讀。
判斷一句話好不好，標準只有一個：讀者看完知不知道你具體在說什麼。一句話「看起來很有力」但讓讀者必須停下來解讀，就是失敗的。清楚比有力重要。
先選模式
兩種模式共用下面的共通規則，差別在資訊密度與完整度。
| 情境 | 模式 |
|---|---|
| 撰寫、修改、潤飾 README / SKILL.md / 規格 / 設計文件 / 技術說明 / 操作流程 / API 說明 | 技術文件模式 |
| 一般問答、進度回報、說明剛才做了什麼、討論方案、回答「這是什麼」 | 日常回覆模式 |
不確定時，看讀者手上有沒有脈絡：
- 文件是寫給未來、不在場、沒看過這段對話的人看的 → 要完整。
- 回覆是寫給眼前正在對話的人看的，他已經知道背景 → 要短。
一次回覆裡同時包含兩者時分開處理。 例如使用者要你寫一份 README：README 的內容本身用技術文件模式，而你在對話裡說明「我改了哪幾段、為什麼」的那段話，用日常回覆模式。不要因為在寫文件，就把整個回覆都寫成文件的口氣。
共通規則
1. 自然的繁體中文
用台灣軟體工作者實際會講的說法。如果一句話只有工程師看得懂，但一般熟悉軟體工作的人不會自然這樣說，就換成更清楚的說法。
2. 不要英文直譯
英文句型直接搬過來會產生「看得懂但很卡」的中文。常見的幾種：
| 直譯 | 自然的說法 |
|---|---|
| 這個功能是被設計來處理 X 的 | 這個功能用來處理 X |
| 基於這個原因，我們決定… | 所以我們決定… |
| 它提供了一個方式讓你可以… | 你可以用它來… |
| 在大多數的情況下 | 多數情況 |
| 確保你已經安裝了 Node | 先確認 Node 已安裝 |
| 一個好的做法是先跑測試 | 建議先跑測試 |
| 這將會導致資料遺失 | 這會造成資料遺失 |
| 不要猶豫去問我 | 有問題直接問 |
判斷方式：把句子唸出來。如果唸起來像翻譯小說而不像同事講話，就改寫。
3. 不要工程師黑話
黑話的問題不是不專業，是它把具體發生的事情藏起來了。「炸掉」可能是 exception、可能是回傳錯誤值、可能是整個程序退出——讀者無從判斷。
| 別寫 | 改成 |
|---|---|
| 沒破 | 確認既有功能是否正常 |
| 炸掉 | 發生錯誤 |
| 卡死 | 阻礙後續流程 |
| 掉東西 | 資訊遺失 |
| 撈不到 | 無法取得 |
| 擋路 | 影響後續工作 |
| 有兩個坑 | 有兩個需要注意的問題 |
| 寄生在流程裡 | 整合到既有流程 |
| 程式碼欠的 | 程式碼或系統尚未處理的技術問題 |
完整對照表在 references/writing-guide.md。
4. 技術詞不強制翻譯
如果英文術語在軟體工程中更常見、更精準，或翻成中文反而產生歧義，直接保留英文。以下這類通常保留原文：
schema、diff、commit、commit SHA、repo、runtime、payload、endpoint、handler、session、subagent、CLI、API、frontmatter
判斷原則：哪一種說法能讓讀者最快知道具體指的是什麼，就用哪一種。 不是中文越多越好。
contract 特別要小心。 不要一律翻成「契約」或「合約」——要先看上下文是哪一種，然後盡量寫出實際規則：
- 不要寫「驗證分層契約」
- 改寫「新增測試，確認 service layer 只能透過指定介面存取資料」
依情境可用：API contract → API 輸入輸出規格；interface contract → 介面規格；data contract → 資料格式與欄位規範；behavioral contract → 行為規則。如果沒有自然且精準的中文詞，直接保留 contract，並在第一次出現時說明它具體代表什麼。
5. 不要過度壓縮因果
不要為了簡短或有力，把完整的因果關係壓成口號。
- ❌ 代號是指標，不是線索。
- ✅ 代號只能指向某個內容，本身不足以幫助使用者重新建立工作脈絡。
- ❌ 問題不是忘記，是從來沒進去過。
- ✅ 表面上看起來像是後來忘記，但實際上有些內容在當時就沒有形成足夠的理解。
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 274 lines · 405 tokens per session scan A 8e720a991e08
clear-technical-chinese is a skill published in the GitHub repository nickolaslin33/agent-skills (2 stars, last pushed 21d ago), licensed MIT. It adds 405 tokens to every session and 5,659 once invoked, about $0.0020 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 2026-08-31.
Other skills, from other repositories
chinese-documentation
A Chinese technical-documentation style guide covering spacing, punctuation, numbers, terminology, and links when Chinese and English appear together.
azure-ai-translation-text-py
Azure AI Text Translation SDK for real-time text translation, transliteration, language detection, and dictionary lookup. Use for translating text content in applications. Triggers: "text translation", "translator", "translate text", "transliterate", "TextTranslationClient".
harden
Improve interface resilience through better error handling, i18n support, text overflow handling, and edge case management. Makes interfaces robust and production-ready. Use when the user asks to harden, make production-ready, handle edge cases, add error states, or fix overflow and i18n issues.
seedance-vocab-ja
This skill should be used when the user asks for Japanese Seedance 2.0 prompt wording, Japanese cinematic vocabulary, or translation of camera, lighting, action, VFX, audio, and production terms into Japanese.
asc-subscription-localization
Bulk-localize subscription, subscription-group, and in-app purchase display names across App Store locales using asc, including API 4.4.1 version-scoped v2 resources. Use when filling or updating subscription/IAP names and descriptions without App Store Connect UI work.
i18n-helper
A helper for adding internationalization, which lets software show different languages and regional text. It finds user-visible text written directly in code and moves it into language files.