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home / skills / zephyrwang6 / myskill / topic-agent

topic-agent skill

/topic-agent

This skill orchestrates today's topic workflow by collecting AI hotspots, generating proposals, auditing quality, and saving results to the Obsidian topic

npx playbooks add skill zephyrwang6/myskill --skill topic-agent

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
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---
name: topic-agent
description: 选题系统主控Agent。协调热点采集、选题生成、选题审核三个环节，支持迭代直到产出合格选题。触发方式：(1)"开始今日选题"启动完整流程 (2)"今日AI热点"只采集热点不生成选题 (3)"我有一个选题"进入单个选题分析 (4)"推荐一些好的选题"直接输出推荐。输出保存到Obsidian选题库。
---

# Topic Agent - 选题系统主控

## 触发方式

| 用户说 | 执行动作 |
|--------|----------|
| "开始今日选题" | 完整流程：采集→生成→审核→保存 |
| "今日AI热点" / "看看今天有什么热点" | 仅采集：列出今日热点，不生成选题 |
| "我有一个选题" | 单个分析：用户输入→生成→审核 |
| "推荐一些好的选题" | 快速推荐：基于近期热点推荐3-5个 |

---

## 模式一：今日AI热点（仅采集）

当用户说"今日AI热点"时，执行以下流程：

### 采集步骤

使用WebSearch并行搜索以下内容：

```
# 1. AI博主实践分享
"Claude Code" OR "Cursor" tips tricks workflow 2026

# 2. 创业公司/新产品
Product Hunt AI tools launch 2026

# 3. 模型厂商动态
Anthropic Claude OR OpenAI ChatGPT update January 2026

# 4. AI Agent工作流
AI agent automation n8n workflow 2026

# 5. 社区讨论
Reddit ClaudeAI OR LocalLLaMA hot January 2026

# 6. 研究动态
AI research DeepMind OR Meta AI January 2026
```

### 输出格式

```markdown
## 今日AI热点 - MMDD

---

### 🧑‍💻 AI博主实践分享

1. **[内容摘要]**
   - 作者：@用户名 / 博主名
   - 原文：[文章标题](https://具体URL)
   - 要点：一句话总结

---

### 🚀 创业公司/新产品

1. **[产品名]** - 一句话描述
   - 原文：[Product Hunt](https://producthunt.com/posts/xxx)
   - 热度：⬆️ N upvotes

---

### 🏢 模型厂商动态

1. **[更新内容]**
   - 厂商：Anthropic / OpenAI / Google
   - 原文：[官方博客](https://具体URL)
   - 要点：关键变化

---

### 🔬 AI研究/学术

1. **[论文/博客标题]**
   - 来源：DeepMind / Meta AI / arXiv
   - 原文：[链接](https://具体URL)
   - 要点：核心发现

---

### 💬 社区热议

1. **[讨论标题]**
   - 来源：r/ClaudeAI / Hacker News
   - 原文：[帖子链接](https://具体URL)
   - 热度：⬆️ upvotes | 💬 comments
```

### 关键要求

- **每条热点必须有原文链接**：不是主域名，而是具体文章/帖子URL
- 按5个分类整理：博主分享、新产品、厂商动态、研究、社区
- 优先有实操价值的内容

---

## 模式二：完整选题流程

当用户说"开始今日选题"时：

```
┌─────────────────────────────────────────────┐
│  1. 采集热点（同模式一）                      │
│     输出今日AI热点列表                        │
└─────────────────┬───────────────────────────┘
                  ▼
┌─────────────────────────────────────────────┐
│  2. 筛选TOP10生成选题方案                     │
│     事件描述 + 核心角度 + 标题 + 写作方式      │
└─────────────────┬───────────────────────────┘
                  ▼
┌─────────────────────────────────────────────┐
│  3. 审核评分                                  │
│     热度 + 独特角度 + 国内关注度 → 总分        │
└─────────────────┬───────────────────────────┘
                  ▼
┌─────────────────────────────────────────────┐
│  4. 输出结果                                  │
│     ✅通过：保存到选题库                      │
│     ⚠️待优化：给出修改建议                   │
│     ❌不通过：说明原因                        │
└─────────────────────────────────────────────┘
```

---

## 模式三：单个选题分析

当用户说"我有一个选题"时：

1. 询问选题内容
2. 搜索相关热点验证热度
3. 生成完整选题方案
4. 审核评分
5. 给出优化建议

---

## 输出位置

```
/Users/ugreen/Documents/obsidian/选题库/每日选题/MMDD-选题名称.md
```

文件命名示例：`0114-Claude-Code进阶技巧.md`

---

## 选题输出模板

```markdown
# [选题标题]

**日期**：YYYY-MM-DD
**状态**：待写 / 写作中 / 已发布
**审核分数**：XX/100

## 事件描述
[一段话说清楚发生了什么]

## 核心角度
[为什么值得写，差异化在哪]

## 标题备选
1.
2.
3.

## 写作方式
[干货教程 / 产品体验 / 观点分享 / 新技术突破]

## 大纲
1.
2.
3.

## 素材链接
- [来源1标题](https://具体URL)
- [来源2标题](https://具体URL)
- [来源3标题](https://具体URL)

## 备注
[其他想法]
```

---

## 快捷指令

| 指令 | 说明 |
|------|------|
| "继续" | 继续上次中断的流程 |
| "跳过" | 跳过当前选题，看下一个 |
| "保存" | 直接保存当前选题到库 |
| "放弃" | 放弃当前选题 |
| "改角度" | 保持热点，换个角度重新生成 |
| "展开写" | 基于选题开始写作 |

---

## 审核标准

| 维度 | 权重 | 评分标准 |
|------|------|----------|
| 热度 | 30% | 社交媒体讨论量、搜索趋势 |
| 独特角度 | 40% | 是否有差异化视角，不是简单搬运 |
| 国内关注 | 30% | 国内用户是否关心这个话题 |

**通过标准**：
- ✅ 80分以上：通过，保存到选题库
- ⚠️ 60-79分：待优化，给出修改建议
- ❌ 60分以下：不通过，说明原因

---

## 每日目标

- 采集：50+条热点（5个分类）
- 生成：10个选题方案
- 通过：3-5个合格选题
- 保存：全部通过的选题存入选题库

Overview

This skill is the topic-agent: a topic-selection controller that coordinates hotspot collection, idea generation, and review until approved topics are produced and stored in an Obsidian topic library. It supports full daily runs, hotspot-only collection, single-topic analysis, and fast recommendations. Outputs are saved as markdown files ready for writing and tracking.

How this skill works

The agent collects daily hotspots across five categories (blogger practices, new products, vendor updates, research, community discussion) via web search, extracts actionable signals, and generates a shortlist of topic proposals with event description, angle, title options, writing style, outline, and source links. Each proposal is scored on heat, uniqueness, and domestic relevance; high-scoring items are saved to the Obsidian topic vault, while lower scores receive concrete improvement suggestions and can iterate until acceptable.

When to use it

  • Start a full daily workflow: collect hotspots → generate → review → save
  • Quick snapshot of today's AI hotspots without creating topics
  • Analyze and validate a single topic you already have
  • Get 3–5 recommended high-potential topics based on recent trends
  • Continue or modify an interrupted workflow or adjust angles

Best practices

  • Prefer primary-source links for every hotspot entry (specific article or post URL)
  • Prioritize content with practical, repeatable value for readers
  • Use the three-dimension scoring (heat, uniqueness, domestic interest) to filter focus
  • Iterate on angles for borderline topics rather than discarding immediately
  • Name and save approved topics with clear metadata and source links for traceability

Example use cases

  • Morning editorial run: produce 10 topic proposals and save 3–5 approved ones into Obsidian
  • Fetch today’s AI product launches and blog tactics to inform a hands-on tutorial
  • Validate and refine a reader-submitted idea into a publishable topic with scoring and improvement steps
  • Quickly recommend evergreen or timely topics for social posts or newsletter pitch
  • Audit a candidate topic’s heat by searching community threads and recent vendor announcements

FAQ

How are hotspots sourced and categorized?

Hotspots are collected via parallel web searches across five categories—blogger practice, new products, vendor updates, research, and community discussion—and organized with direct article/post URLs and concise summaries.

What determines whether a topic is saved?

Topics are scored on heat (30%), unique angle (40%), and domestic relevance (30%). Scores above 80/100 are saved; 60–79 receive revision advice; below 60 are rejected with reasons.