# Zhihu Topic Scout

> Generate Zhihu questions, answer angles, article topics, and evergreen search-driven ideas. Use when the user asks for 知乎选题, 知乎回答方向, 知乎文章选题, 问答选题, 长尾问题, or search-friendly content ideas for Zhihu.

- Skill: `bubu4me/zhihu-topic-scout` (Agent Skill)
- Install (CLI): `npx skillmds@latest add bubu4me/zhihu-topic-scout`
- Raw SKILL.md: https://api.skillmd.com/api/skills/bubu4me/zhihu-topic-scout/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: bubu4me (https://skillmd.com/u/bubu4me)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/bubu4me/zhihu-topic-scout

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# Zhihu Topic Scout

## Goal

Help the user choose Zhihu topics that can accumulate search traffic, credibility, saves, upvotes, and long-tail value.

## Workflow

1. Clarify or infer:
   - Domain and expertise level
   - Target reader and decision stage
   - Whether the output is for answers, articles, columns, or lead generation
   - Whether the content should be beginner-friendly, professional, personal, or argumentative
2. Build topic candidates from:
   - High-intent questions: "怎么选", "值不值得", "有没有必要", "如何判断"
   - Comparison questions: tools, services, schools, jobs, products, strategies
   - Experience questions: lessons, failures, tradeoffs, real cases
   - Explainer questions: concepts, mechanisms, principles, misconceptions
   - Decision questions: what to do before buying, applying, resigning, learning, investing time
   - Long-tail search questions: specific audience + specific problem + specific context
3. Score each topic:
   - Search durability
   - Expertise fit
   - Differentiated perspective
   - Evidence availability
   - Conversion relevance
   - Risk level
4. Recommend answer angles, not just titles.

## Output Format

```markdown
## 知乎选题建议

| 问题/标题 | 搜索意图 | 推荐切入角度 | 适合内容形态 | 优先级 |
|---|---|---|---|---|
| ... | ... | ... | 回答/文章 | 高/中/低 |

## 最值得先写的 3 个

1. [问题]：推荐理由 + 文章骨架
2. [问题]：推荐理由 + 文章骨架
3. [问题]：推荐理由 + 文章骨架
```

## Quality Bar

- Prefer answerable questions over vague "观点表达".
- Avoid making up platform data unless live research was explicitly performed.
- Include evidence hooks: data, personal experience, industry case, framework, or counterexample.
- For sensitive domains, recommend cautious framing and note where expert verification is needed.