Content factory
14 AI executives powered by legendary minds (Musk/Buffett/Simons/Feynman) — deploy your virtual C-Suite in one git clone.From the repository description
npx -y skills add aAAaqwq/AGI-Super-Team --skill content-factoryAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
SKILL.md
1.7 KB, 631 tokens by cl100k_base, as published. Nobody here has run it
content-factory
内容工厂——从热点池批量筛选评分选题,生成内容选题卡片并推送
使用场景
- 每日内容选题自动筛选(从 10+ 平台热点池中 AI 评分 Top 10)
- 选题卡片格式化推送到 Telegram
- 内容管线核心组件,衔接 content-source-aggregator(上游采集)和内容创作(下游)
使用方法
# 评分今天的热点池(默认 Top 10)
python ~/clawd/skills/content-factory/scripts/topic_scorer.py
# 指定日期 / 数量
python topic_scorer.py --date 2026-03-22 --top 5
# 不推送,只输出
python topic_scorer.py --no-send
# Dry run(不调 LLM,用随机分测试)
python topic_scorer.py --dry-run
# 只评分前 N 条热点
python topic_scorer.py --limit 20
# 推送已评分的选题到 Telegram
python ~/clawd/skills/content-factory/scripts/topic_presenter.py --date 2026-03-22 --top 5
python topic_presenter.py --dry-run
评分维度(权重可配)
| 维度 | 权重 | 说明 |
|---|---|---|
| heat | 0.35 | 热度和讨论量 |
| timeliness | 0.25 | 话题新鲜度 |
| creativity | 0.40 | 创作空间(纯新闻搬运低分) |
配置要求
- Python 3.10+, httpx
- LLM API(DeepSeek 或 ZAI,通过
pass show api/deepseek配置) ~/clawd/scripts/newsbot_send.py(推送依赖)- 热点池目录:
~/clawd/workspace/content-pipeline/hotpool/ - 选题输出目录:
~/clawd/workspace/content-pipeline/topics/
相关文件
scripts/topic_scorer.py— 核心评分脚本scripts/topic_presenter.py— 选题格式化与推送scripts/aggregator/— 内容聚合子模块data/topics/— 历史评分数据data/hotpool/— 历史热点池快照
What ships with it: 11 files
913.0 KB alongside SKILL.md, 2 of them executable
data/
- hotpool/2026-03-18.json85.3 KB
- hotpool/2026-03-19.json85.0 KB
- hotpool/2026-03-20.json67.0 KB
- hotpool/2026-03-21.json87.1 KB
- hotpool/2026-03-22.json94.1 KB
- topics/2026-03-18.json122.3 KB
- topics/2026-03-19.json115.4 KB
- topics/2026-03-20.json107.5 KB
- topics/2026-03-22.json132.5 KB
scripts/
- topic_presenter.pyruns3.9 KB
- topic_scorer.pyruns12.9 KB