Best use case
content-factory is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
> 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
Teams using content-factory should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/content-factory/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How content-factory Compares
| Feature / Agent | content-factory | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
> 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
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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 ```bash # 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
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