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-article-outlinegit 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-article-outline)<a href="https://agentmods.dev/skills/zju-real/easel/skill-article-outline"><img src="https://agentmods.dev/badge/skills/zju-real/easel/skill-article-outline/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-article-outline"><img src="https://agentmods.dev/badge/skills/zju-real/easel/skill-article-outline.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 | $0.00089 | $0.01464 |
| Opus 5 | $0.00044 | $0.00732 |
| Sonnet 5 | $0.00018 | $0.00293 |
| Haiku 4.5 | $0.00009 | $0.00146 |
Grade A, and why
skill-article-outline 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 10d 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
长文大纲生成
基于搜索分析，生成结构化长文大纲（标题层级、段落字数、图表位置、FAQ），适用于微信公众号、知乎专栏、博客等平台。
输入
用户 prompt 中提供：
- 主题或目标关键词（必填）
- 目标关键词 — 要优化的精确短语（若与主题不同）
- 搜索意图 — 科普/商业/教程（可选，未提供则根据上下文推断）
- 目标平台 — 公众号/知乎/博客（可选，有 Profile 时自动识别）
输出
Markdown 大纲文件，包含：标题建议、目标参数、H2/H3 层级结构（含段落字数目标）、图表位置标记、FAQ 规划、内容差异化分析。
保存至 outputs/文章主题/outline.md。
执行步骤
1. 主题与意图确认
从用户输入中提取：
- 主题或目标关键词（必填）
- 目标关键词 — 要排名/覆盖的精确短语（若与主题不同）
- 搜索意图 — 科普、商业、教程
仅提供主题时，根据上下文推断关键词和意图。
2. 搜索分析
使用 WebSearch 分析目标关键词的前 5 条结果：
- 搜索目标关键词
- 对每条结果记录：
- 标题结构 — 覆盖了哪些 H2/H3 话题
- 内容篇幅 — 大致字数
- 视觉元素 — 图表、配图、信息图
- FAQ — 是否有常见问题板块
- 独特视角 — 各结果的差异化切入点
- 缺失 — 哪些方面薄弱或遗漏
- 对排名前 2-3 的结果使用 WebFetch 提取详细标题结构（搜索摘要不够时）
- 汇总共性模式与空白机会
3. 生成大纲
按以下模板生成结构化大纲：
# 大纲：[主题]
## 标题建议
1. [主标题 — 15-25 字，关键词前置，有力度]
2. [备选标题 — 不同切入角度]
3. [备选标题 — 提问式]
## 目标参数
- **核心关键词**：[关键词]
- **搜索意图**：[科普/商业/教程]
- **目标字数**：[X,XXX] 字
- **H2 段落数**：[6-8]
- **目标平台**：[公众号/知乎/博客/通用]
---
## 正文大纲
### H2：[段落标题 — 推荐使用提问式]（~400-600 字）
- **开篇要点**：[用什么事实或数据引入这一段？]
- **要点覆盖**：
- [要点 1]
- [要点 2]
- [要点 3]
- **H3：[子段落]**（如有必要）
- [子段落内容方向]
- **关键数据**：[需要查找什么数据来增强说服力？]
- **图表建议**：[柱状图/折线图/饼图/无] — [可视化什么数据]
- **配图位置**：[是/否] — [建议配图描述]
### H2：[段落标题]（~400-600 字）
[... 重复 6-8 个段落 ...]
### FAQ 板块（3-5 题）
1. [来自搜索联想的问题] — [回答方向]
2. [来自搜索联想的问题] — [回答方向]
3. [来自搜索联想的问题] — [回答方向]
4. [来自搜索分析的问题] — [回答方向]
### 结尾（~100-200 字）
- 核心要点总结
- 行动号召方向
---
## 内链规划
- **本文应引用**：[已有内容中可链接的相关文章]
- **应链向本文**：[已有内容中应加链接指向本文的文章]
## 内容差异化
1. [竞品普遍遗漏而本文应覆盖的内容]
2. [可纳入的独特视角或原创观点]
3. [格式优势 — 更好的可视化、更深的覆盖、更清晰的结构]
标题生成原则：
- 60-70% 的 H2 标题使用提问式
- 每个 H2 段落都有明确的"开篇要点"提示
- H3 子段落仅在话题确实需要细分时使用
- 各段字数目标之和应接近总目标字数
- 图表类型建议应多样化（避免重复同一类型）
- 配图位置应均匀分布
4. 内容差异化分析
大纲生成后，补充差异化分析：
- 列出 3-5 个所有头部竞品都遗漏的话题或视角
- 标识可加入原创数据、案例或观点的机会
- 指出本文可利用的格式优势（更多图表、更好结构、更深覆盖）
5. 保存
将大纲保存至 outputs/文章主题/outline.md。
outputs/ 目录或子目录不存在时自动创建。
Profile 感知
- 检测
=== EASEL ACCOUNT PROFILE ===标记 - 有 Profile 时：
- 根据
platform字段适配平台特征（公众号长文排版惯例、知乎专栏深度偏好、博客 SEO 侧重） - 根据
tone/style字段调整标题风格和用语 - 根据
audience字段匹配受众认知水平，调整深度和术语密度
- 根据
- 无 Profile 时：
- 生成通用大纲，
目标平台设为通用 - 附注"如提供账号 Profile 可获得平台定制化大纲"
- 生成通用大纲，
What ships with it
1 file 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.
- 10d ago First seen · 143 lines · 89 tokens per session scan A b12d2790f099
skill-article-outline is a skill published in the GitHub repository ZJU-REAL/Easel (710 stars, last pushed today), licensed Apache-2.0. It adds 89 tokens to every session and 1,464 once invoked, about $0.0004 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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