Zhihu Content Strategist (知乎内容策略师)
Analyze Zhihu trending topics and high-performing answers to generate data-driven content strategies, topic recommendations, and ready-to-publish answer drafts tailored to your niche.
Scripts
| Path | Description |
|---|---|
scripts/strategist.py |
Main CLI script — domain analysis, gap detection, draft generation |
schemas/input.schema.json |
JSON Schema for workflow input |
schemas/output.schema.json |
JSON Schema for workflow output |
references/engagement_patterns.json |
Zhihu engagement pattern reference (hooks, structures, timing) |
references/topic_templates.json |
Topic recommendation templates and cold-start roadmap |
CLI Usage
# Recommend topics with gap analysis
python scripts/strategist.py --domain AI --task recommend
# Generate answer strategy for a specific topic
python scripts/strategist.py --domain career --task strategy --topic "远程办公效率"
# Generate full answer draft
python scripts/strategist.py --domain AI --task draft --topic "AI Agent 落地案例"
# Pattern analysis of high-performing answers
python scripts/strategist.py --domain AI --task analyze
# Publishing optimization
python scripts/strategist.py --domain AI --task publish --topic "大模型应用实践"
# JSON output for programmatic use
python scripts/strategist.py --domain AI --task recommend --output json
🚀 First-Success Path (3 Steps)
- Step 1: Run
python scripts/strategist.py --domain AI --task recommend - Step 2: Review the gap matrix and pick one topic
- Step 3: Run
python scripts/strategist.py --domain AI --task draft --topic "AI Agent 落地案例"to receive a complete answer draft with hooks and golden lines in <30 seconds
Core Capabilities
- Hot trend analysis: Scrape Zhihu hot list (热榜) and extract trending topics in your domain
- High-engagement answer dissection: Analyze top-20 answers for structural patterns, emotional hooks, data usage, and opening techniques
- Content gap detection: Identify subtopics with high question volume but low answer quality
- Topic recommendation engine: Suggest topics with competition level, estimated exposure, and recommended angles
- Draft generation: Produce complete answer drafts with hook → argument → golden lines → CTA structure
- Post timing optimization: Recommend best publishing windows based on historical engagement data
- Competitor analysis: Analyze why similar creators succeed, extract replicable patterns
Workflow (8 Steps)
Step 1: Define Target Domain
Input: User specifies target area (e.g., AI, career, psychology, finance) OR provides a specific Zhihu hot-list URL.
Output: Domain definition with sub-topic taxonomy.
Logic: If user gives a URL, extract the topic directly. If a broad domain, generate 5-8 sub-topics for exploration. Verify domain has sufficient question volume on Zhihu.
Step 2: Scrape Hot List & Top Answers
Input: Target domain or topic URL.
Action: Scrape Zhihu hot list + top 20 answers under the target domain/topic.
Output: Dataset of trending questions and high-engagement answers with metrics (upvotes, comments, publish time, author follower count).
Logic: Prioritize answers with 1K+ upvotes from the past 6 months. Respect Zhihu's rate limits.
Step 3: Pattern Analysis of High-Performing Answers
Input: Top-20 answer dataset.
Analysis dimensions:
- Opening hooks: Question-based, story-based, counter-intuitive claim, data drop
- Structure patterns: Problem-solution, timeline narrative, listicle, deep-dive analysis
- Emotional tone: Empathetic, authoritative, humorous, contrarian
- Data usage: Academic citations, personal experience, statistics, case studies
- Length sweet spot: Character count distribution of top answers
- Golden lines: Extract most-upvoted sentences and analyze why they resonate
Output: Pattern report with examples and replicable templates.
Step 4: Content Gap Detection
Input: Target domain question corpus + existing answers.
Analysis: For each sub-topic, compute:
- Question volume: how many related questions exist
- Answer quality: average upvotes of top answers
- Competition index: number of established creators in this niche
- Gap score = question volume × (1 - answer quality percentile)
Output: Gap matrix sorted by opportunity score.
| Sub-topic | Questions | Avg. Upvotes | Competition | Gap Score |
|---|---|---|---|---|
| AI Agent 落地案例 | 230 | 42 | Low | ⭐⭐⭐⭐⭐ |
| Prompt 工程技巧 | 1500 | 180 | High | ⭐⭐ |
Step 5: Topic Recommendation
Input: Gap matrix + user's expertise level.
Output: Top 5-10 topic recommendations with:
- Topic title: The question to answer
- Competition level: Low / Medium / High
- Estimated exposure: Views range
- Suggested angle: Unique perspective to stand out
- Difficulty: Quick answer vs deep research required
Logic: Balance high-gap (low competition) with high-volume (high exposure) topics. Consider user's stated expertise.
Step 6: Answer Strategy Generation
Input: 1-3 selected topics.
Output: Per-topic strategy brief:
- Hook (first 3 sentences): 2-3 opening variants to A/B test
- Argument structure: Logical flow with sub-points
- Evidence plan: What data/stories/citations to include
- Golden lines: 2-3 memorable statements to embed
- CTA (call to action): Follow, comment bait, profile link strategy
- Visual suggestions: Where to add images, charts, or code blocks
- Style profile: Tone and complexity level
Step 7: Draft Generation
Input: Strategy brief.
Output: Complete answer draft in markdown.
Logic: Optionally mimic a specified KOL's writing style (requires providing 3+ sample answers from that creator). Include SEO optimization for Zhihu's internal search.
Step 8: Publishing Optimization
Input: Completed draft.
Output: Publishing recommendations:
- Best publish time: Day of week + hour (based on domain analysis)
- Tag strategy: Primary + secondary tags for maximum reach
- Promotion hooks: 1-sentence share text for WeChat/Weibo cross-promotion
- Engagement plan: How to respond to first 10 comments to boost algorithm ranking
Sample Prompts
Prompt 1: Topic Recommendation (AI Domain)
User:
python scripts/strategist.py --domain AI --task recommend
Expected Output:
Zhihu Content Strategist v1.0.0
Domain: AI | Task: recommend
[Step 5/8] Recommending topics in AI
Topic Score Competition Difficulty Angle
------------------------------------------------------------------------------------------------------------------------
AI Agent 落地案例 ⭐⭐⭐⭐⭐ Low Quick answer (1-2 hours) Tell a specific story with measurable results; include code/data snippets
大模型应用实践 ⭐⭐⭐⭐ Medium Moderate research (3-6 hours) Don't just list resources — share your personal learning path with specific mistakes and breakthroughs
AI 在行业中的应用 ⭐⭐⭐⭐⭐ Low Quick answer (1-2 hours) Start with a counter-intuitive claim or personal story to hook readers
AI 创业 ⭐⭐⭐⭐⭐ Low Quick answer (1-2 hours) Start with a counter-intuitive claim or personal story to hook readers
AI 产品经理 ⭐⭐⭐⭐ Low Moderate research (3-6 hours) Don't just list resources — share your personal learning path with specific mistakes and breakthroughs
------------------------------------------------------------------------------------------------------------------------
Task 'recommend' complete!
Prompt 2: Answer Draft Generation
User:
python scripts/strategist.py --domain career --task draft --topic "远程办公效率"
Expected Output:
Zhihu Content Strategist v1.0.0
Domain: CAREER | Task: draft
[Step 7/8] Generating draft for: 远程办公效率
Draft generated (1210 chars, ~3-5 min)
============================================================
# 远程办公效率
> 我分析了远程办公效率领域的100个案例，发现…
## 先说说背景
在过去的半年里，我花了大量时间研究远程办公效率。这篇文章不讲大道理，只分享真实经历和可复用的方法。
## 核心观点
**1. 远程办公效率的核心在于理解本质** — 大多数人只看到表面
**2. 实践中的三个关键发现** — 每一项都有数据支持
...
## 写在最后
如果这篇文章对你有帮助，欢迎**点赞+关注**，我会持续分享更多实战经验。
评论区说说你的想法：你在这方面的经历是怎样的？
---
*本文由 Zhihu Content Strategist 辅助生成。内容仅供参考，请结合实际体验调整。*
============================================================
Task 'draft' complete!
Prompt 3: Content Gap Detection
User:
python scripts/strategist.py --domain AI --task gap
Expected Output:
Zhihu Content Strategist v1.0.0
Domain: AI | Task: gap
[Step 4/8] Detecting content gaps in AI
Sub-topic Questions Avg Upvotes Competition Gap
------------------------------------------------------------------------------
AI Agent 落地案例 230 42 Low ⭐⭐⭐⭐⭐
AI 在行业中的应用 1200 35 Low ⭐⭐⭐⭐⭐
AI 创业 320 28 Low ⭐⭐⭐⭐⭐
AI 产品经理 280 38 Low ⭐⭐⭐⭐
AI 编程助手 680 55 Medium ⭐⭐⭐⭐
大模型应用实践 890 65 Medium ⭐⭐⭐⭐
AI 绘画与生成 1800 120 High ⭐⭐
Prompt 工程技巧 1500 180 High ⭐⭐
------------------------------------------------------------------------------
Task 'gap' complete!
Prompt 4: Answer Strategy Generation
User:
python scripts/strategist.py --domain AI --task strategy --topic "AI Agent 落地案例"
Expected Output:
Zhihu Content Strategist v1.0.0
Domain: AI | Task: strategy
[Step 6/8] Generating strategy for: AI Agent 落地案例
Topic: AI Agent 落地案例
Hook Variants:
- 数据切入: 我分析了AI Agent 落地案例领域的100个案例，发现…
- 故事切入: 去年我亲身经历了一个AI Agent 落地案例项目，结果让我…
- 反直觉: 你可能不相信，但AI Agent 落地案例的真相和大部分人想的恰恰相反
Structure:
- 1. 开篇：用数据或故事建立信任
- 2. 核心论点：分3-4个方面展开
- 3. 个人经验：为什么我的观点值得信
- 4. 可操作建议：读者能立刻做的事
- 5. 金句收尾：值得转发的浓缩总结
Golden Lines:
- 关于AI Agent 落地案例，大多数人看到的是XX，但真正重要的是YY
- 短期来看AI Agent 落地案例是ZZ，但长期来看…
Task 'strategy' complete!
Prompt 5: High-Performing Answer Pattern Analysis
User:
python scripts/strategist.py --domain AI --task analyze
Expected Output:
Zhihu Content Strategist v1.0.0
Domain: AI | Task: analyze
[Steps 2-3/8] Analyzing high-performing answers in AI
Sample size: 20
Opening hook types: ['data_drop', 'counter_intuitive', 'story_based']
Common structures: Problem-Solution, Step-by-step tutorial, Comparative analysis
Avg length: 2500 chars
Golden line pattern: 短期XX vs 长期XX 的反转认知
Task 'analyze' complete!
Prompt 6: Publishing Optimization
User:
python scripts/strategist.py --domain AI --task publish --topic "大模型应用实践"
Expected Output:
Zhihu Content Strategist v1.0.0
Domain: AI | Task: publish
[Step 8/8] Publishing optimization for: 大模型应用实践
Best time: Tuesday 10:00 AM (tech audience peak) or Thursday 8:00 PM (evening readers)
Tags: primary=['AI'], secondary=['大模型应用实践', '经验分享', '实战']
Promotion hooks:
- 写了一篇关于大模型应用实践的深度回答，分享一些真实经验
- 这可能是你今年看过最实用的分享
Comment plan:
- 前30分钟务必回复每条评论（算法权重最高）
- 对已赞评论点赞表示认可
- 1小时后回复较长的评论（展示深度）
- 第2天补充1-2条高质量回复（推动二次曝光）
Task 'publish' complete!
Boundary Conditions
[Boundary conditions unchanged from design doc]
Error Handling
[Error handling unchanged from design doc]
Security Requirements
- Original content only: Never plagiarize; generate original content inspired by patterns, not copies
- No misinformation: Flag when generating content on medical, legal, or financial topics; include disclaimer
- Zhihu ToS compliance: Respect robots.txt; don't automate posting (this is strategy + draft only)
- Data privacy: Discard scraped answer content after analysis; don't store full answer texts
- Content safety: Refuse to generate content violating Chinese internet regulations
