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X Five Piece Checklist

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kangarooking
x-five-piece-checklist

用户写完推文初稿、准备发布前做完备性自检时;或纠结"这条推文发出去会有人收藏吗 / 值得收藏吗"时;或感觉初稿"差点什么"但说不清缺什么时。关键 trigger: 检查推文还缺什么 / 五件套 / 完备性检查 / 收藏理由 / 证据够不够 / 三个可 / checklist / is this tweet complete / worth saving。不适用于:还没写初稿时(先用起草工具);评估值不值得发(那是四省模型)。本 skill 把推文当小型信息产品逐件自检五要素:价值承诺 / 使用场景 / 低门槛入口 / 证据(三个可:可见可点可算)/ 收藏理由,缺任一件读者在该环节流失。

Overview

Publisherkangarooking
RepositoryX-growth-skills
Skill namex-five-piece-checklist
Stars
72
Forks
11
Bundled files
2
LicenseMIT
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by kangarooking on GitHub. Read the source before you install it.

Installation

Install the X Five Piece Checklist AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/kangarooking/X-growth-skills/tree/main/x-five-piece-checklist
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/kangarooking/X-growth-skills.git /tmp/X-growth-skills
mkdir -p .claude/skills
cp -r /tmp/X-growth-skills/x-five-piece-checklist .claude/skills/x-five-piece-checklist
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable X Five Piece Checklist in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads X Five Piece Checklist on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so X Five Piece Checklist is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

五件套检查清单 — 内容完备性 + 证据标准

R — 原文 (Reading)

"一条强X内容往往像一个小型信息产品:①第一眼能看懂的价值承诺——这和我有什么关系;②一个具体使用场景——我什么时候会用到;③降低门槛的步骤、入口或解释——我现在能不能开始;④截图、数字、案例这样的证据——我凭什么相信;⑤一个值得收藏或转发的理由——我为什么要留着它。"证据须满足"三个可":可见(截图/录屏)、可点(链接/工具名)、可算(数字/成本/步骤)。带"地址见评论"进前10%概率约40%。

— 向阳乔木, X爆款秘籍分享 · 五件套检查清单 / 三个可

I — 方法论骨架 (Interpretation)

把一条推文当"小型信息产品"做发布前自检。五件套是五个读者决策点,每件回答一个问题:

  1. 价值承诺 → "这和我有什么关系?" — 第一眼能看懂能拿走什么,不是标题党而是价值预览
  2. 使用场景 → "我什么时候会用到?" — 具体场景而非抽象功能描述
  3. 低门槛入口 → "我现在能不能开始?" — 步骤/链接/搜索路径,降低行动成本
  4. 证据(三个可) → "我凭什么相信?" — 可见(截图/录屏/对比图)、可点(链接/工具名/搜索路径)、可算(数字/时间/成本/步骤),至少满足一个;数据表明带"地址见评论"进前10%概率约40%,带资源词约35%,带"免费/简单"低门槛信号约30%
  5. 收藏理由 → "我为什么要留着它?" — 读者未来还会用到才会收藏,收藏>点赞是信任信号

核心洞察:五件套是检查工具不是写作模板 — 先写完初稿,再逐件查缺哪补哪。缺任一件不是"少了一点",而是读者在该决策环节直接流失。

A1 — 书中的应用 (Past Application)

案例 1: 飞书博物馆文档帖 (26.3 万浏览)

  • 问题: 如何让一个资源分享帖获得高传播
  • 方法论的使用: 逐件对照——价值承诺="全球博物馆155万份藏品整理进飞书,直接可用入口"(第一眼看懂);场景=查博物馆藏品;入口="网址见评论区"(地址见评论→进前10%概率约40%);证据=155万份(可算)+"直接可用"(可点);收藏理由=未来查藏品要用
  • 结论: 五件齐全,尤其第4件满足"可算+可点",第3件用"地址见评论"降低门槛
  • 结果: 26.3 万浏览,资源入口型中位互动 2965 的代表案例

案例 2: Claude Code 爬虫抓取 Paul Graham 文章帖 (171 万浏览,最高)

  • 问题: 如何演示 AI 工具能力同时获得最高传播
  • 方法论的使用: 逐件对照——价值承诺="提示词抓取 PG 所有文章做成 epub";场景=想把作者全集合做成电子书;入口=提示词直接给出(可复制);证据=三个可全满足(可见=结果截图,可点=提示词可复制,可算="4 分钟"时间数字);收藏理由=提示词可反复用
  • 结论: 五件齐全且第4件"三个可"全满足,是全部案例中曝光最高的
  • 结果: 171 万浏览,400 转发,2293 收藏

A2 — 触发场景 (Future Trigger) ★

用户会在什么情境下需要这个 skill?

  1. 初稿写完、发布前做完备性检查 — "帮我看看这条推文还缺什么"
  2. 纠结"这条推文发出去会有人收藏吗" — 需要逐件查哪件缺导致不收藏
  3. 帖子已写好但感觉"差点什么",说不清缺什么 — 需要结构化检查工具定位
  4. 想确认证据是否够强 — 需要用"三个可"检验证据质量

语言信号 (用户的话里出现这些就应激活)

  • "帮我检查这条推文还缺什么" / "checklist for tweet"
  • "这条推文发出去会有人收藏吗" / "worth saving"
  • "这条内容够不够完整" / "is this tweet complete"
  • "证据够不够" / "need more evidence" / "三个可"
  • "五件套" / "five-piece checklist"

与相邻 skill 的区分

  • 与 x-four-saves(四省模型) 的区别: 四省模型是估值工具——"值不值得发"(帮读者省几步路);五件套是检查工具——"发之前查缺补漏"(五件是否齐全)。先用四省估值决定发,再用五件套查完备性。若用户还在纠结"值不值得发",应触发四省而非五件套。
  • 与 x-short-content-craft(短内容工艺) 的区别: 短内容工艺是起草工具——"怎么写"(选类型 + Hook-Body-CTA);五件套是检查工具——"起草完检查"(五件是否齐全)。若用户还没写初稿,应触发短内容工艺而非五件套。
  • 与 x-three-translations(三次翻译) 的区别: 三次翻译是改写工具——把公告式语言翻译成读者语言;五件套第4件(证据)是三次翻译第3次(结论→证据)的检验标准。若用户的问题是"怎么把公告改成帮助式表达",应触发三次翻译。

E — 可执行步骤 (Execution)

当 skill 被激活后, agent 应按以下步骤执行:

  1. 逐件检查初稿

    • 对五件套逐一标注 ✓ 或 ✗: ①价值承诺 ②使用场景 ③低门槛入口 ④证据 ⑤收藏理由
    • 完成标准: 五件均标注完毕,明确哪件缺

  2. 对第4件(证据)做"三个可"检验

    • 逐一查: 可见?(有截图/录屏/对比图) 可点?(有链接/工具名/搜索路径) 可算?(有数字/时间/成本/步骤)
    • 完成标准: 三个可逐一标注,至少一个 ✓;若三个都没有,标记为"弱主张"
    • 判停条件: 若初稿已有数字 + 截图(可算 + 可见),证据充分,直接跳到步骤 4

  3. 缺件补件

    • 缺价值承诺 → 第一句重写为"读者能拿走什么",而非"我发布了什么"
    • 缺场景 → 加一个"当你想 X 时"的具体使用场景
    • 缺入口 → 加链接/工具名/搜索路径,或用"地址见评论"(进前10%概率约40%)
    • 缺证据 → 加截图/数字/对比,优先补"可算"(数字/对比最缺也最有效)
    • 缺收藏理由 → 问"读者未来什么时候会回来找这条",把答案写进结尾
    • 完成标准: 每件 ✗ 都有具体补件建议

  4. 输出检查结果

    • 格式: 五件 ✓/✗ 清单 + 三个可 ✓/✗ + 补件建议(逐条)
    • 完成标准: 用户拿到结果后知道改什么、怎么改

B — 边界 (Boundary) ★

不要在以下情况使用此 skill

  • 还没写初稿时 — 五件套是检查工具不是写作模板;先写再查。若用户说"我想发一条教人用 Raycast 的推文,怎么写",应触发 x-short-content-craft
  • 用来评估"值不值得发" — 那是 x-four-saves(四省模型)的事;五件套默认你已决定发,只查完备性。若用户说"这条值不值得发",应触发四省模型
  • 对纯观点/故事型内容强求五件齐全 — 五件套最适用资源入口型/工具教程型;普通表达型(纯观点/感叹)不强求五件,应先用 x-content-archetypes 转化原型

作者在书中警告的失败模式

  • ce15(把过拟合经验当通用公式套) — 五件套是检验工具非教条,别机械套用。不同内容原型对五件的需求程度不同:资源入口型/工具教程型五件缺一不可,普通表达型不强求。向阳乔木自注"粗看有点过拟合,仅供参考"

作者的盲点 / 时代局限

  • 五件套来自向阳乔木个人 3.4G 数据(AI/产研赛道、男性为主的华人受众、2023-2026 年特定算法版本),其统计结论(如"地址见评论"40% 概率)是个人样本的过拟合结果,不同赛道/受众/时期数据分布不同
  • "三个可"的统计数据(40%/35%/30%)是时点数据,会随算法版本漂移;具体概率数字仅作参考,核心逻辑(具象证据比抽象观点可信)不依赖具体数字

容易混淆的邻近方法论

  • 四省模型(估值)vs 五件套(检查): 前者是"值不值得发"(省几步路),后者是"发之前查缺补漏"(五件齐全)
  • Hook-Body-CTA(结构)vs 五件套(完备性): 前者是"怎么组织内容"(三段式),后者是"查漏"(五要素是否缺)
  • 三次翻译(改写)vs 五件套第4件(证据): 前者是"怎么把结论变成证据",后者是"检验证据够不够(三个可)"

相关 skills

  • depends-on x-four-saves: 检查结构完备性前先过四省估值——先判断"值不值得发",再查"齐不齐"。四省是估值门槛,五件套是结构审计。
  • composes-with x-three-translations: 三次翻译改写语言质量,五件套检查结构完备性,配合使用——翻译完验证证据是否满足"三个可"。
  • composes-with x-short-content-craft: 起草完用五件套检查,两者一造一检——先生产再查漏。

审计信息

  • 验证通过: V1 ✓ / V2 ✓ / V3 ✓
  • 测试通过率: 待测 (详见 test-prompts.json)
  • 蒸馏时间: 2026-07-14

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the X Five Piece Checklist AI skill do?

用户写完推文初稿、准备发布前做完备性自检时;或纠结"这条推文发出去会有人收藏吗 / 值得收藏吗"时;或感觉初稿"差点什么"但说不清缺什么时。关键 trigger: 检查推文还缺什么 / 五件套 / 完备性检查 / 收藏理由 / 证据够不够 / 三个可 / checklist / is this tweet complete / worth saving。不适用于:还没写初稿时(先用起草工具);评估值不值得发(那是四省模型)。本 skill 把推文当小型信息产品逐件自检五要素:价值承诺 / 使用场景 / 低门槛入口 / 证据(三个可:可见可点可算)/ 收藏理由,缺任一件读者在该环节流失。

Why use X Five Piece Checklist on TypingMind?

Because you install it once and use it with any model. X Five Piece Checklist is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install X Five Piece Checklist in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kangarooking/X-growth-skills/tree/main/x-five-piece-checklist. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use X Five Piece Checklist?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with X Five Piece Checklist?

As many as you like. As long as a model supports skills, you can use X Five Piece Checklist with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the X Five Piece Checklist AI skill free?

Yes. It is published on GitHub by kangarooking under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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