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 kangarooking/X-growth-skills --skill x-content-archetypesgit clone --depth 1 https://github.com/kangarooking/X-growth-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/kangarooking/x-growth-skills/x-content-archetypes)<a href="https://agentmods.dev/skills/kangarooking/x-growth-skills/x-content-archetypes"><img src="https://agentmods.dev/badge/skills/kangarooking/x-growth-skills/x-content-archetypes/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/kangarooking/x-growth-skills/x-content-archetypes"><img src="https://agentmods.dev/badge/skills/kangarooking/x-growth-skills/x-content-archetypes.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 | $0.00162 | $0.02884 |
| Opus 5 | $0.00081 | $0.01442 |
| Sonnet 5 | $0.00032 | $0.00577 |
| Haiku 4.5 | $0.00016 | $0.00288 |
Grade A, and why
x-content-archetypes 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 11d 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
四类内容原型 — 按读者行动分类
R — 原文 (Reading)
不看主题标签,看读者拿到能做什么。资源入口型(51%,中位互动2965)、工具教程型(39%,中位2035)、AI工具发现型(24%,中位94)、普通表达型(9%,中位16)。爆款不是主题,是功能。按"读者拿到后能做什么"分类,差异比主题更明显。
— 向阳乔木 @vista8, X爆款秘籍分享 · 四类内容原型
I — 方法论骨架 (Interpretation)
这个框架把内容分成四类,不按主题(AI/创业/编程)而按"读者拿到后能做什么":
- 资源入口型 — 读者获得平时难搜到的入口(工具/文档/网站),中位互动 2965,进前 10% 概率约 40%。读者行动:点击进入、收藏备用。
- 工具教程型 — 读者学会用某个工具(有步骤有路径),中位互动 2035,概率约 24%。读者行动:跟着做、收藏回看。
- AI工具发现型 — 读者看到新工具被演示(能力翻译成任务),中位互动 94,概率约 16%。读者行动:了解可能性、收藏待试。
- 普通表达型 — 读者只读到观点/感叹/故事,中位互动 16,概率约 9%。读者行动:点赞(或划走)。
核心洞察:原型决定传播上限。资源入口型的中位互动是普通表达型的 185 倍。如果你的内容过度集中在普通表达型(观点/感叹),传播天花板会被锁死在低位。用法:审计内容结构——各类占比是多少?普通表达型是否过多?把感叹转化为资源入口或工具教程。
A1 — 书中的应用 (Past Application)
案例 1: 飞书博物馆文档帖(资源入口型)
- 问题: 如何让"帮读者省搜索"的内容获得高传播
- 方法论的使用: 向阳乔木将此帖归为资源入口型——提供平时难搜到的入口(全球博物馆 155 万份藏品整理进飞书文档,直接可用),入口放评论区
- 结论: 资源入口型是四类中中位互动最高的原型(2965),进前 10% 概率约 40%
- 结果: 26.3 万浏览,是资源入口型的代表案例
案例 2: ClaudeCode 新手指南帖(工具教程型)
- 问题: 如何让工具教程降低读者门槛
- 方法论的使用: 归为工具教程型——"不会编程的人也能看懂"降低试错成本,有明确工具名(ClaudeCode)+ 完整步骤路径,入口放评论区
- 结论: 工具教程型中位互动 2035,是第二高传播原型;关键在于"从新手视角写"降低门槛
- 结果: 21.4 万浏览,254 转发,1319 收藏
案例 3: NotebookLM Skill 帖(AI工具发现型)
- 问题: 如何把抽象 AI 能力翻译成可观察结果
- 方法论的使用: 归为 AI 工具发现型——展示"一句话生成导图/播客/PPT"的具体能力,把工具能力翻译成可观察的任务结果
- 结论: AI 工具发现型中位互动 94,低于前两类但靠"发现叙事"(我去,这个更牛逼了)获得单帖高曝光
- 结果: 28.6 万浏览,1399 收藏
A2 — 触发场景 (Future Trigger) ★
用户会在什么情境下需要这个 skill?
- 用户审计自己的内容结构,发现互动低,想知道是不是内容类型的问题
- 用户发了纯感叹/观点帖(如"AI太强了")没人理,想知道为什么
- 用户规划内容策略,想知道哪种类型传播上限最高,该往哪个方向发力
- 用户判断一条待发帖子的传播潜力,想知道它属于哪类原型
语言信号 (用户的话里出现这些就应激活)
- "为什么没人互动/为什么没人理/why no engagement"
- "哪种内容容易爆/哪种内容传播好/which content goes viral"
- "我的内容是不是方向有问题/content direction wrong"
- "内容原型/content archetype"
- "传播上限/viral ceiling"
- "资源入口型/工具教程型/AI工具发现型/普通表达型"
- "我的内容结构/内容占比"
与相邻 skill 的区分
- 与
x-short-content-craft的区别: 本 skill 做分类与预测(你的内容属于哪类原型,传播上限多高),后者做生产(已选好类型后怎么写 Hook-Body-CTA)。先分类再生产。 - 与
x-five-piece-checklist的区别: 本 skill 看"原型类型"决定传播天花板,后者看"要素完备性"决定单条地板。原型选错,五件套再齐也上不去。 - 与
x-four-saves的区别: 本 skill 按"读者能做什么"分四类预测传播,四省模型按"帮读者省几步路"评估价值。可组合:先定原型再验四省。 - 与
x-longtail-strategy的区别: 本 skill 提供分类框架选高概率原型,长尾策略提供心态框架接受中位数常态。 - 与
x-positioning-tradeoff的区别: 本 skill 关注内容分类与传播预测,后者关注账号定位取舍(IP vs 泛流量)。
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.
- 11d ago First seen · 157 lines · 162 tokens per session scan A 1a84554230fd
x-content-archetypes is a skill published in the GitHub repository kangarooking/X-growth-skills (62 stars, last pushed 1mo ago), licensed MIT. It adds 162 tokens to every session and 2,884 once invoked, about $0.0008 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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gr-competitor-research
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optimize
A self-contained improvement loop for reviewing Threads skill failures and proposing rule changes based on recorded user feedback.