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Get Started Free →内容创作与发布全流程技能，整合网页采集、Markdown格式化、智能配图、多平台发布（微信公众号、X/Twitter）功能，实现从内容获取到发布的一站式解决方案
.claude/skills/anbeime-content-creation-publisher/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 419% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 385% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 212% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 82% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 74% | 0% |
功能：将任意网页转换为干净的Markdown格式
使用场景：
操作步骤：
技术实现：
功能：格式化和优化Markdown文档
核心特性：
使用场景：
功能：分析文章内容，在需要视觉辅助的位置生成插图
配图类型：
配图风格（6种类型 × 8种风格）：
操作步骤：
功能：自动发布内容到微信公众号
发布模式：
操作方式：
操作步骤：
功能：自动发布内容到X（Twitter）
发布类型：
技术特点：
操作步骤：
1. 网页URL
↓
2. baoyu-url-to-markdown（提取内容）
↓
3. baoyu-format-markdown（格式优化）
↓
4. article-illustrator（智能配图）
↓
5a. baoyu-post-to-wechat（发布到微信）
↓
5b. baoyu-post-to-x（发布到X）1. 创作Markdown文章
↓
2. baoyu-format-markdown（格式优化）
↓
3. article-illustrator（智能配图）
↓
4. 多平台发布1. 论文网页URL
↓
2. baoyu-url-to-markdown（提取内容）
↓
3. paper-analysis-assistant（论文分析）
↓
4. baoyu-format-markdown（格式优化）
↓
5. ppt-generator（生成PPT）
↓
6. baoyu-post-to-wechat（发布解读）1. product-marketing-copywriter（生成文案）
↓
2. baoyu-format-markdown（格式优化）
↓
3. pop-up-book-illustration（3D插图）
↓
4. baoyu-post-to-x（推广发布）bash# 检查Node.js node --version # 检查Chrome chrome --version # 安装依赖（如需要） npm install
bash# 创建配置文件 ~/.baoyu-skills/.env # 配置内容（可选） WECHAT_APP_ID=your_app_id WECHAT_APP_SECRET=your_app_secret
用户："采集这篇文章并发布到微信公众号"
URL: https://example.com/article
执行流程：
1. baoyu-url-to-markdown 提取内容
2. baoyu-format-markdown 格式优化
3. article-illustrator 智能配图
4. baoyu-post-to-wechat 发布到微信用户："优化这篇文章并发布到微信和X"
输入：article.md
执行流程：
1. baoyu-format-markdown 格式优化
2. article-illustrator 智能配图
3. baoyu-post-to-wechat 发布到微信
4. baoyu-post-to-x 发布到X用户："采集这篇论文并生成PPT"
URL: https://arxiv.org/abs/xxxxx
执行流程：
1. baoyu-url-to-markdown 提取内容
2. paper-analysis-assistant 论文分析
3. baoyu-format-markdown 格式优化
4. ppt-generator 生成PPT
5. baoyu-post-to-wechat 发布解读批量采集多个URL
↓
批量格式优化
↓
批量智能配图
↓
定时发布到多平台用户可以根据需求组合不同模块：
解决方案：
解决方案：
解决方案：
解决方案：
本技能整合了以下子技能：
以及可协同的44个现有技能。
🚀 一站式内容创作与发布解决方案！
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 7,186 | 19,123 | +166% | 1 | 1 | 0% | 1,238 | 6,429 | +419% | 0 | 0 | — |
case-02 | fail→pass | 5,026 | 8,203 | +63% | 1 | 1 | 0% | 858 | 4,158 | +385% | 0 | 0 | — |
case-03 | fail→fail | 25,979 | 17,927 | -31% | 1 | 1 | 0% | 3,434 | 6,420 | +87% | 0 | 0 | — |
case-04 | pass→pass | 14,936 | 8,388 | -44% | 1 | 1 | 0% | 2,578 | 4,327 | +68% | 0 | 0 | — |
case-05 | fail→pass | 15,560 | 8,640 | -44% | 1 | 1 | 0% | 1,153 | 3,594 | +212% | 0 | 0 | — |
case-06 | fail→pass | 12,744 | 5,391 | -58% | 1 | 1 | 0% | 2,209 | 4,016 | +82% | 0 | 0 | — |
case-07 | fail→pass | 12,292 | 5,281 | -57% | 1 | 1 | 0% | 2,348 | 4,092 | +74% | 0 | 0 | — |
case-08 | fail→pass | 13,656 | 7,650 | -44% | 1 | 1 | 0% | 2,463 | 4,430 | +80% | 0 | 0 | — |
case-09 | fail→pass | 15,867 | 6,509 | -59% | 1 | 1 | 0% | 1,714 | 4,510 | +163% | 0 | 0 | — |
case-10 | pass→pass | 9,357 | 4,990 | -47% | 1 | 1 | 0% | 1,658 | 3,933 | +137% | 0 | 0 | — |
case-11 | pass→pass | 11,597 | 4,562 | -61% | 1 | 1 | 0% | 1,469 | 3,844 | +162% | 0 | 0 | — |
case-12 | pass→pass | 15,891 | 6,460 | -59% | 1 | 1 | 0% | 2,863 | 4,082 | +43% | 0 | 0 | — |
case-13 | pass→pass | 19,222 | 8,325 | -57% | 1 | 1 | 0% | 2,533 | 4,603 | +82% | 0 | 0 | — |
case-14 | fail→pass | 5,699 | 5,493 | -4% | 1 | 1 | 0% | 973 | 4,112 | +323% | 0 | 0 | — |
case-15 | pass→pass | 8,893 | 2,789 | -69% | 1 | 1 | 0% | 1,683 | 3,566 | +112% | 0 | 0 | — |
case-16 | fail→pass | 11,771 | 7,915 | -33% | 1 | 1 | 0% | 2,085 | 4,513 | +116% | 0 | 0 | — |
case-17 | fail→pass | 14,802 | 13,091 | -12% | 1 | 1 | 0% | 2,392 | 5,660 | +137% | 0 | 0 | — |
case-18 | fail→pass | 12,696 | 9,755 | -23% | 1 | 1 | 0% | 2,009 | 4,875 | +143% | 0 | 0 | — |
case-19 | fail→pass | 10,302 | 6,303 | -39% | 1 | 1 | 0% | 1,839 | 4,387 | +139% | 0 | 0 | — |
case-20 | fail→fail | 16,099 | 18,173 | +13% | 1 | 1 | 0% | 2,921 | 6,596 | +126% | 0 | 0 | — |
case-21 | fail→fail | 9,824 | 10,720 | +9% | 1 | 1 | 0% | 1,871 | 5,109 | +173% | 0 | 0 | — |
case-22 | fail→fail | 14,249 | 13,261 | -7% | 1 | 1 | 0% | 2,911 | 6,124 | +110% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +55 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.