#zhihu #chinese-platform #content-strategy #creator

Zhihu Content Strategist

Analyze Zhihu hot trends & community dynamics; generate high-engagement answer strategies and first drafts.

Install

openclaw skills install @harrylabsj/zhihu-content-strategist

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

bash

# 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:

bash

python scripts/strategist.py --domain AI --task recommend

Expected Output:

text

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:

bash

python scripts/strategist.py --domain career --task draft --topic "远程办公效率"

Expected Output:

text

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:

bash

python scripts/strategist.py --domain AI --task gap

Expected Output:

text

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:

bash

python scripts/strategist.py --domain AI --task strategy --topic "AI Agent 落地案例"

Expected Output:

text

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:

bash

python scripts/strategist.py --domain AI --task analyze

Expected Output:

text

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:

bash

python scripts/strategist.py --domain AI --task publish --topic "大模型应用实践"

Expected Output:

text

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