Womenai cohort cards
Turn WoMen AI Lab founding-cohort survey responses (exported from the cohort "build in public" survey as CSV/JSON, or pasted in) into ready-to-post 小红书 / Xiaohongshu / Instagram carousel cards that follow the WoMen AI Lab visual system, then render them to 2160×2880 PNGs. Use this whenever the user wants to make social-media cards, 小红书图, carousel slides, or "build in public" posts from a member's survey answers — including phrasings like "把某位成员的回答做成卡片", "generate cohort cards", "做几页适合发小红书的内容", or when they hand over a survey export and want it turned into shareable graphics. Also use to tweak copy, change the 栏目名/日期, add/remove pages, or re-render an existing card set.From its SKILL.md
npx -y skills add ruthless-coder-ai/womenai-cohort-cardsAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
5.2 KB, ~1.6k tokens by cl100k_base, as published. Nobody here has run it
WoMen AI Lab 共创社群 · 小红书卡片
把 founding cohort 的问卷回答,做成一组符合社群视觉系统、可直接发布的小红书卡片,并渲染成 2 倍分辨率 PNG。
文案由你(Claude)来写——这是创意活;版式、配色、页眉页脚、页码、固定社群文案都已固化在脚本里,你不用碰。
流程
1. 拿到数据
数据来自社群「build in public」问卷导出表,字段固定为:
Name · What Are You Building · Why It Matters · Progress Since Last Session · One Thing Learned · Support Needed · Content Permission · Social Handle · Anything Else · Submitted At
用户可能:导出 CSV/JSON 丢给你、贴一段、或让你去读。读 CSV/JSON 直接解析即可——不要用浏览器一格格抠,那很慢。
隐私:问卷导出和回答属于个人信息,留在本地,不要提交进这个公开仓库。
2. 按授权过滤(先做,避免白做工)
看 Content Permission,决定能不能对外发、怎么署名。规则见
references/visual-system.md 的「发布授权」一节。Community only 的人直接跳过。
如果用户只点名某一个人(如「只做某位成员的」),就只处理那个人。
3. 写文案
通读这个人的全部回答,按 references/visual-system.md:
- 内容映射:问卷字段是叙事骨架(building→产品页,why→缘起,progress→进展,learned→金句,support/anything else→尾页或副标题)。
- 语言风格(务必读,最容易出错):不用引号强调、不用破折号、不用「不是A而是B」对仗句、第一人称「我们/我」、高亮克制。
- 结构:首页 1 + 中间页 N + 尾页 1,默认 4–6 张。
- 挑最有画面感的真实细节当钩子(比如某句用户原话、某个具体场景),别写成通用宣传。
把文案写进一个 cards.json(schema 见下)。只写内容,页码/页眉/Who We Are 文案脚本会自动加。
4. 渲染
python3 "$SKILL_DIR/scripts/build_cards.py" <cards.json> --out <输出目录>
($SKILL_DIR = 这个 skill 的目录)。需要 Python + Playwright(见 README 的安装说明)。
先 --html-only 预览结构、确认无误再渲染也可以。
5. 交付 + 确认
渲染出的 PNG 是 2160×2880。把图复制到用户方便拿的地方(项目文件夹/桌面),按文件名顺序就是发布顺序。 做尾页前,如果 Social Handle 不是真实小红书 ID,先跟用户确认真实 handle。
cards.json schema
下面是一个虚构示例,演示三种卡片类型和绿色高亮的用法(请换成真实文案):
{
"series": "founding cohort · build in public · 2026.06",
"person": "example",
"output_dir": "/绝对路径/example-cards",
"cards": [
{
"type": "cover",
"name": "cover",
"tag": "— 一个 AI 项目 —",
"title": "我给自己做了一个\n每天写三行的\n<g>记录小工具</g>",
"sub": "一句话副标题,\n点明这组图要讲什么",
"watermark": "AI"
},
{
"type": "middle",
"name": "why",
"title": "为什么\n<g>从这里开始</g>",
"body": "正文,约 42px 米色。讲缘起或动机,用具体的生活场景,不要堆术语。",
"body2": "可选的第二段,暗调,用来补充信息。",
"list": ["可选要点一", "可选要点二", "可选要点三"],
"section_title": "小栏目标题(含英文词时英文标绿)",
"section_body": "加粗白色,最关键一句<g>标绿</g>。"
},
{
"type": "end",
"name": "end",
"whoami": "我是 <g>你的署名</g>。一句话身份 + 正在做的项目。"
}
]
}
字段说明:
- 文案里
<g>...</g>= 薄荷绿高亮(这是唯一的强调方式,别用引号);\n= 换行。 series默认栏目格式:founding cohort · build in public · YYYY.MM。name决定输出文件名(如02_why.png),可省略(默认用 type)。- middle 卡的
body / body2 / list / section_*都可选,按需组合。 - end 卡的 Who We Are 文案和页码由脚本自动注入,不要手写;
cta可覆盖默认行动引导。 - 右下角水印固定为「AI」,无需手写。
- 页码(01 / N)按卡片顺序与总数自动生成。
改版式
配色/间距/字号集中在 assets/style.css,改那里就行,不用动脚本。
完整规范见 references/visual-system.md。
What ships with it: 6 files
19.9 KB alongside SKILL.md, 1 of them executable
assets/
- style.css3.8 KB
references/
- visual-system.md4.2 KB
scripts/
- build_cards.pyruns8.3 KB
- .gitignore217 B
- LICENSE1.0 KB
- README.md2.3 KB
Gives 0 of the 12 instructions most social media skills give in ~1.6k tokens
Counted across 489 of the 492 authors here whose files we hold, read 2026-08-07
- Adapt formats and tone to each platformin 26 of 489, across 14 files
- Build content around three to five pillarsin 25 of 489, across 13 files
- Read product marketing context before asking questionsin 23 of 489, across 13 files
- Respond to all comments on your postsin 21 of 489, across 9 files
- Use the output flag to specify an output directoryin 14 of 489, across 4 files
- Generate output logo images with white backgroundin 13 of 489, across 4 files
- Fix failing generation scripts directlyin 13 of 489, across 4 files
- Ask user about HTML preview after logo generationin 12 of 489, across 3 files
- Run the download script with a URLin 12 of 489, across 3 files
- Implement exponential backoff for 429 responsesin 12 of 489, across 3 files
- Write the hook firstin 12 of 489, across 7 files
- Include a single clear call to actionin 12 of 489, across 9 files
Said here and by no other author read
- parse CSV or JSON survey data directly
- filter responses by content permission before working
- skip community-only responses
- process only the named member if requested
- write card copy in first person
- use real specific details as hooks
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.