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 ZJU-REAL/Easel --skill skill-content-repurposinggit clone --depth 1 https://github.com/ZJU-REAL/EaselWrote 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/zju-real/easel/skill-content-repurposing)<a href="https://agentmods.dev/skills/zju-real/easel/skill-content-repurposing"><img src="https://agentmods.dev/badge/skills/zju-real/easel/skill-content-repurposing/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/zju-real/easel/skill-content-repurposing"><img src="https://agentmods.dev/badge/skills/zju-real/easel/skill-content-repurposing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00159 | $0.01291 |
| Opus 5 | $0.00079 | $0.00646 |
| Sonnet 5 | $0.00032 | $0.00258 |
| Haiku 4.5 | $0.00016 | $0.00129 |
Grade A, and why
skill-content-repurposing 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
跨平台内容改编
将一篇长内容拆解为多平台原生素材,根据各平台特性适配格式、长度与语气,实现「一次创作,全域分发」。
输入
用户 prompt 中提供源内容,支持以下形式:
- 文本:直接提供社媒帖子、公众号文章、视频脚本等文本
- URL:提供内容链接(通过 WebFetch 抓取)
- 视频/音频转录稿:提供短视频口播稿或直播文字版
- 混合:源内容 + 指定目标平台
示例 prompt:
Execute /skill-content-repurposing
源内容:<B站视频脚本>
目标平台:小红书、抖音、微博
输出
输出包含两部分:
1. 改编计划
{
"source_type": "blog_post",
"source_word_count": 2000,
"core_elements": {
"thesis": "核心论点",
"key_points": ["要点1", "要点2", "要点3"],
"quotable_lines": ["金句1", "金句2"],
"data_points": ["数据1"]
},
"target_platforms": ["twitter", "linkedin", "xiaohongshu"],
"total_pieces": 8
}
2. 各平台改编内容
每个平台输出独立的、符合该平台原生格式的内容。具体转换规则参见 references/conversion-recipes.md。
内容金字塔
本 SKILL 遵循三层内容金字塔方法论:
| 层级 | 说明 | 示例 |
|---|---|---|
| 支柱内容 (Pillar) | 深度长内容 | B站长视频、公众号长文、播客、直播回放 |
| 衍生内容 (Derivative) | 中等长度,提取支柱内容的子主题 | 小红书图文、知乎回答、抖音口播脚本 |
| 微内容 (Micro) | 短小精悍,单点突破 | 微博热评、抖音15秒、小红书封面金句卡 |
一篇支柱内容可衍生 15–25 个跨平台素材。SKILL 根据源内容类型自动规划金字塔拆解方案。
执行步骤
-
识别源内容类型
- 判断源内容属于支柱层的哪种类型(B站视频 / 公众号文章 / 直播 / 播客)
- 统计字数、段落数,评估内容密度
-
提取核心元素
- 提炼核心论点(thesis)
- 提取 3–5 个关键要点(key points)
- 标记可引用金句(quotable lines)
- 抓取数据点和案例(data points)
-
映射目标平台
- 有 Profile:从 Profile 的
platforms字段读取目标平台列表 - 无 Profile:使用用户指定的平台,或默认为 小红书 + 抖音 + 微博
- 根据
references/platform-specs.md确定每个平台的格式要求
- 有 Profile:从 Profile 的
-
逐平台生成改编内容
- 根据
references/conversion-recipes.md中的转换配方执行改编 - 每条内容必须是该平台的原生内容——不是简单截断或复制粘贴
- 适配长度限制、语气风格、标签策略、格式规范
- 根据
-
输出排期建议
- 建议各平台的发布顺序和时间间隔
- 原则:支柱内容先发 → 衍生内容次日起陆续发 → 微内容穿插填充
改编原则
- 平台原生:每条内容读起来像是专门为该平台写的,不是机械裁剪
- 核心一致:所有改编内容传达同一核心信息,不跑题不矛盾
- 独立成立:每条内容单独阅读也完整有价值,不依赖用户看过源内容
- 格式适配:严格遵守各平台的长度、格式、标签规则(见
references/platform-specs.md)
Profile 感知
- 有 Profile:
- 从
platforms字段读取目标平台列表,自动确定改编方向 - 从
tone/voice字段读取语气风格,统一应用到所有改编内容 - 从
audience字段读取目标受众,调整内容深度和表达方式 - 从
hashtag_strategy字段读取标签偏好
- 从
- 无 Profile:
- 要求用户指定目标平台(未指定则默认 小红书 + 抖音 + 微博)
- 使用通用专业语气
- 提示:"如提供账号 Profile(含平台和受众信息),可获得更精准的改编效果"
What ships with it
3 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 · 112 lines · 159 tokens per session scan A a4bc7efea461
skill-content-repurposing is a skill published in the GitHub repository ZJU-REAL/Easel (494 stars, last pushed 2d ago), licensed Apache-2.0. It adds 159 tokens to every session and 1,291 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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