According to AlphaMoat, as of 2026-09, Reddit 主题洞察 has 1,963 downloads and 1 stars — ranked #4,455 of 5,078 AI skills overall, and #299 of 362 in Content.
| Platform | Installs | Downloads | Stars | Comments |
|---|---|---|---|---|
| ClawHub ↗ | 26 | 993 | 1 | - |
| SkillHub ↗ | 460 | 2.0K | 1 | — |
Platforms count differently and update at different times.
It confirms the topic and audience, designs keywords, collects posts and comments, plans angles, and drafts content for X, Xiaohongshu and WeChat Official Accounts with a consolidated source table. Scripts handle collection and merging while subagents handle creative work, with progress files for resuming. It depends on network collection and the documented Python tools and does not guarantee reach.
Profile published 2026-10-27
Generated from the skill’s documentation as examples; not results of actual runs.
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-09 | 2.0K | — | 1 |
| 2026-08 | 1.5K | +8.5% | 1 |
| 2026-07 | 1.4K | +12.4% | 1 |
| 2026-06 | 1.3K | +8.1% | 1 |
| 2026-05 | 1.2K | — | 1 |
| 2026-03 | 860 | — | 1 |
The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.
--- name: reddit-topic-insight description: 输入研究主题,自动采集 Reddit 讨论,提炼爆款角度,产出 X 推文、小红书笔记、公众号文章三平台成品。7 步流水线:需求收集 → 关键词设计 → 数据采集 → 详情获取 → 角度规划 → 成品生产 → 合并输出。 --- # Reddit 主题洞察 Skill 从 Reddit 社区讨论中挖掘爆款内容角度,自动产出三平台(X / 小红书 / 公众号)成品内容。 ## 触发条件
当用户:
- 提及「Reddit 研究」「Reddit 洞察」「Reddit 话题」
- 想从 Reddit 挖掘内容角度或爆款灵感
- 需要基于 Reddit 讨论产出多平台内容
- 使用类似「分析 Reddit 上关于 XX 的讨论」的表述
## 设计原则
| 原则 | 说明 |
|------|------|
| 确定性 → 脚本 | 数据采集、排序去重、内容合并等确定性操作使用 Python 脚本 |
| 创造性 → AI | 关键词设计、角度规划、内容创作等创造性操作由 AI SubAgent 完成 |
| 上下文隔离 | SubAgent 只接收必要信息、只返回极简结果 |
| 主 Agent 保护 | 主 Agent 不读 piece 文件,避免上下文膨胀 |
| 断点恢复 | `progress.json` 记录流水线进度,支持随时中断恢复 |
## 7 步流水线
```
┌─────────────────────────────────────────────────────┐
│ Step 1: 需求收集 │
│ 初始化运行目录 + progress.json 断点恢复 │
├─────────────────────────────────────────────────────┤
│ Step 2: 关键词设计 [AI 创造性] │
│ 5 个英文关键词:核心/同义/教程/经验/痛点 │
├─────────────────────────────────────────────────────┤
│ Step 3: 数据采集 [Python 脚本] │
│ reddit_collector.py → posts_raw.json │
├─────────────────────────────────────────────────────┤
│ Step 4: 详情获取 [Python 脚本] │
│ reddit_detail_fetcher.py → posts_detail.json │
├─────────────────────────────────────────────────────┤
│ Step 5: 角度规划 [AI 创造性] │
│ 单 SubAgent → 10 个角度 × 10 种文章类型 │
├─────────────────────────────────────────────────────┤
│ Step 6: 成品生产 [AI 创造性] │
│ 5 SubAgent × 3 轮(2+2+1)并行 │
│ 每角度 → X 推文 + 小红书笔记 + 公众号文章 │
├─────────────────────────────────────────────────────┤
│ Step 7: 合并输出 [Python 脚本] │
│ content_merger.py → content.md + 来源表格 │
└─────────────────────────────────────────────────────┘
```
## 执行流程
### Step 1: 需求收集
详见 [step-1-requirement.md](workflow/step-1-requirement.md)
1. 检查运行目录下是否存在 `progress.json`
2. 若存在:读取进度,从中断步骤恢复
3. 若不存在:向用户确认研究主题和目标受众
4. 创建 `runs/<topic-slug>/` 目录
5. 初始化 `progress.json`
### Step 2: 关键词设计
详见 [step-2-keywords.md](workflow/step-2-keywords.md)
AI 生成 5 个英文搜索关键词,覆盖:
- 🎯 核心词(锚定词,必须保留)
- 🔄 同义词 / 近义词
- 📚 教程 / How-to 类
- 💡 经验 / 最佳实践类
- 😫 痛点 / 问题类
输出 → `keywords.json`
### Step 3: 数据采集
详见 [step-3-collect.md](workflow/step-3-collect.md)
```bash
python3 scripts/python/reddit_collector.py \
--keywords-file runs/<slug>/keywords.json \
--output runs/<slug>/posts_raw.json \
--config config/default.json
```
### Step 4: 详情获取
详见 [step-4-details.md](workflow/step-4-details.md)
```bash
python3 scripts/python/reddit_detail_fetcher.py \
--input runs/<slug>/posts_raw.json \
--output runs/<slug>/posts_detail.json \
--config config/default.json
```
### Step 5: 角度规划
详见 [step-5-angles.md](workflow/step-5-angles.md)
单 SubAgent 读取帖子摘要 → 规划 10 个不重复角度 → 匹配文章类型 → `angles.json`
### Step 6: 成品生产
详见 [step-6-produce.md](workflow/step-6-produce.md)
5 个 SubAgent 分 3 轮并行(2+2+1),每角度产出三平台成品 → `pieces/angle-{n}.md`
### Step 7: 合并输出
详见 [step-7-merge.md](workflow/step-7-merge.md)
```bash
python3 scripts/python/content_merger.py \
--pieces-dir runs/<slug>/pieces \
--posts-file runs/<slug>/posts_detail.json \
--output runs/<slug>/content.md
```
## 运行目录结构
每次运行产生以下文件:
```
runs/<topic-slug>/
├── progress.json # 流水线进度
├── keywords.json # 5 个关键词
├── posts_raw.json # 原始帖子列表
├── posts_detail.json # 帖子详情 + 评论
├── angles.json # 10 个角度规划
├── pieces/ # 各角度成品
│ ├── angle-01.md
│ ├── angle-02.md
│ └── ...
└── content.md # 最终合并输出
```
## 配置
默认配置文件:`config/default.json`
可选环境变量(用于 Reddit API 认证,提高速率限制):
```
REDDIT_CLIENT_ID=your_client_id
REDDIT_CLIENT_SECRET=your_client_secret
```
## 脚本依赖
仅需 `requests` 库:
```bash
pip install requests
```
## 参考模板
- [10 种文章类型](reference/article-types.md)
- [X 推文模板](reference/x-tweet-template.md)
- [小红书笔记模板](reference/xiaohongshu-template.md)
- [公众号文章模板](reference/wechat-template.md)
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humanizer guides natural expression and style editing for Chinese and English text.
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Bundle a set of marketing playbooks into one skill and route each request to the module that fits.
Data month: 2026-09 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.