x-three-translations

x-three-translations is a skill for Claude Code, Codex from kangarooking/X-growth-skills. It costs 180 tokens per session (3,186 once invoked), scanned A, original, MIT.

A writing method for turning product announcements and feature descriptions into clear explanations of what readers can do. It uses real situations and supporting evidence instead of unsupported claims.

In plain words
What is it for?
Use it to rewrite announcements, feature descriptions, and social posts around reader tasks, concrete examples, and evidence such as comparisons or screenshots.
Why use it?
It helps readers understand the practical value of a product instead of merely hearing what was launched or what it supposedly does.

Skill for Claude CodeCodex

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

Good fit Use it to rewrite announcements, feature descriptions, and social posts around reader tasks, concrete examples, and evidence such as comparisons or screenshots.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/kangarooking/x-growth-skills/x-three-translations
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 kangarooking/X-growth-skills --skill x-three-translations
Clone the repo
git clone --depth 1 https://github.com/kangarooking/X-growth-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 x-three-translations

README.md
[![agentmods](https://agentmods.dev/badge/skills/kangarooking/x-growth-skills/x-three-translations/github.svg)](https://agentmods.dev/skills/kangarooking/x-growth-skills/x-three-translations)
Your own site
<a href="https://agentmods.dev/skills/kangarooking/x-growth-skills/x-three-translations"><img src="https://agentmods.dev/badge/skills/kangarooking/x-growth-skills/x-three-translations/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 x-three-translations

Your own site · 80×15
<a href="https://agentmods.dev/skills/kangarooking/x-growth-skills/x-three-translations"><img src="https://agentmods.dev/badge/skills/kangarooking/x-growth-skills/x-three-translations.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 180 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,186 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.
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 $0.00180 $0.03186
Opus 5 $0.00090 $0.01593
Sonnet 5 $0.00036 $0.00637
Haiku 4.5 $0.00018 $0.00319

Measured 13d ago against content hash e46298800e67, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

x-three-translations 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 13d 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.

x-three-translations/SKILL.md · 153 lines

How it starts

The opening of the file, as written. The whole thing — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.

三次翻译 — 把内部语言翻成外部语言

R — 原文 (Reading)

从内部语言到外部语言。第一次翻译:把发布改成帮助——"我们上线新功能"→"这个功能能帮你把80页报告变成3页提纲"。第二次翻译:把能力改成场景——"支持长上下文"→"一次性读完行业报告并找出竞品变化"。第三次翻译:把结论改成证据——少说"效果很好",多放真实截图、输入输出、步骤、对比。重点是"别人能拿走什么",而非"我说了什么"。

— 向阳乔木 @vista8, X爆款秘籍分享 · 三次翻译


I — 方法论骨架 (Interpretation)

创作者天然用"内部语言"写作——讲自己做了什么(发布)、产品有什么(能力)、结果怎么样(结论)。这些对创作者有意义,对读者毫无抓手。

三次翻译是三步视角转换,每步把焦点从"我说了什么"移向"别人能拿走什么":

  1. 发布→帮助:别告诉读者你做了什么,告诉读者这件事能帮他做什么。"我们上线新功能"是发布,"帮你把80页报告变3页提纲"是帮助。
  2. 能力→场景:别罗列产品能力,把它嵌入一个读者会遇到的真实任务。"支持长上下文"是能力,"一次性读完行业报告找出竞品变化"是场景。
  3. 结论→证据:别只说"效果很好",给出读者能自行验证的证据——截图、输入输出、步骤、前后对比。

三次翻译不是"把话写通顺",而是切换信息接收方视角。判断标准:读者看完能不能直接拿走一个行动?


A1 — 书中的应用 (Past Application)

案例 1: 向阳乔木"公告式→帮助式"改写

  • 问题: AI 产品/工具推文容易写成"我们上线了X功能",像发布公告,读者不知道跟自己有什么关系。
  • 方法论的使用: 对"我们上线新功能"做第一次翻译(发布→帮助),改写成"这个功能能帮你把80页报告变成3页提纲"。焦点从"我们做了什么"变成"你能用它做什么"。
  • 结论: 公告式表达只传递信息,帮助式表达传递行动可能性。后者才有传播力。
  • 结果: 该案例被作为三次翻译的标杆示例,帮助读者理解"内部语言→外部语言"的第一次转换。向阳乔木 3861 帖数据中,带"可行动"信号(资源/步骤/入口)的帖子进入前 10% 概率显著更高。

案例 2: X 官方 Article 指南"Show, don't just tell"

  • 问题: X 官方在 Article 写作指南中指出,创作者常犯的错误是只下结论("效果很好")而不给证据。
  • 方法论的使用: 官方提出"Show, don't just tell"原则——对任何主张,紧跟证据(数据、个人故事、前后对比图)。这本质就是第三次翻译(结论→证据)的官方版。指南原文:"For any claim you make, follow it immediately with evidence of why it's true (stats, personal story, before/after, etc.)"。
  • 结论: 官方指南与向阳乔木的三次翻译独立验证了同一原则——结论必须配证据。
  • 结果: 该指南作为 X 官方 Article 写作的标准方法发布,面向所有 Premium 用户。

案例 3: "长上下文功能"的二次翻译(能力→场景)

  • 问题: 用户问"怎么把'我们上线了长上下文功能'改成强推文?"
  • 方法论的使用: 第二次翻译(能力→场景)——把"支持长上下文"这个能力嵌入读者真实任务:"一次性读完行业报告并找出竞品变化"。再接第三次翻译(结论→证据):放前后对比截图,展示用长上下文前后的效率差异。
  • 结论: 能力是产品视角,场景是读者视角。读者不为能力付费,为解决自己的问题付费。
  • 结果: 这是 V2 验证阶段构造的新问题,证明三次翻译框架能处理原始案例之外的变体。

A2 — 触发场景 (Future Trigger) ★

用户会在什么情境下需要这个 skill?

  1. 已有初稿但读起来像公告:用户写了一条推文/产品发布文案,内容是"我们发布了X/支持Y/升级了Z",感觉没人会转发,想改得更有吸引力。
  2. 功能介绍写不吸引人:用户要把一个产品能力写成推文,但写出来像功能列表,不知道怎么让读者觉得"跟我有关"。
  3. 推文发了没人理,自查原因:用户发了一条推文互动很低,怀疑是表达方式的问题(实际原因是只讲自己不讲读者)。
  4. 把"效果很好"变成可信内容:用户写了"效果很好/非常强大/体验极佳"等结论性表达,需要补证据。
  5. 长上下文/新功能上线的推文改写:用户要把技术性功能描述翻译成读者能感知的场景。

Read the full file on GitHub · 153 lines

Files

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.

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. 13d ago First seen · 153 lines · 180 tokens per session scan A e46298800e67

Subscribe to this mod's changes

x-three-translations is a skill published in the GitHub repository kangarooking/X-growth-skills (62 stars, last pushed 2mo ago), licensed MIT. It adds 180 tokens to every session and 3,186 once invoked, about $0.0009 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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