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zhihu-hot-article-engine

Write Zhihu articles that rank on the hot list and earn genuine upvotes, using a research-driven methodology with Phase 0 knowledge reconnaissance, structured credibility building, and Zhihu algorithm optimization. Covers everything from question selection to opening hooks, argument architecture, data citation, and title optimization for the Zhihu ecosystem. Skill: zhihu-hot-article-engine Owner: aiepco Summary: Write Zhihu articles that rank on the hot list and earn genuine upvotes, using a research-driven methodology with Phase 0 knowledge reconnaissance, structured credibility building, and Zhihu algorithm optimization. Covers everything from question selection to opening hooks, argument architecture, data citation, and title optimization for the Zhihu ecosystem. Tags

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Rank

Safety

Downloads

2.5k

Updated

Oct 9, 2026

Version

0.1.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 2.5K downloads reported by the source. Last updated 10/9/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 9, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 9, 2026
Adoption signal: 2.5K downloadsadoption · observed Oct 9, 2026
Latest release: 0.1.0release · observed Aug 4, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17avgahjzaspfym28ppyjbws98awakk:zhihu-hot-article-engine

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-aiepco-zhihu-hot-article-engine/snapshot"

Documentation

CLAWHUB

19,772 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: zhihu-hot-article-engine
description: >-
  Write Zhihu articles that rank on the hot list and earn genuine upvotes,
  using a research-driven methodology with Phase 0 knowledge reconnaissance,
  structured credibility building, and Zhihu algorithm optimization.
  Covers everything from question selection to opening hooks, argument
  architecture, data citation, and title optimization for the Zhihu ecosystem.
license: MIT-0
compatibility:
  - claude-code
  - cursor
  - cline
metadata:
  author: AiEPCO
  tags:
    - zhihu
    - chinese-content
    - social-media
    - content-creation
    - long-form
  category: writing-content-creation
  version: 1.0.0
---

# Zhihu Hot Article Engine

Write Zhihu articles that rank on the hot list and earn genuine upvotes.

## When to use

- Writing a comprehensive Zhihu answer that needs to stand out among hundreds
- Creating a long-form Zhihu article (专栏文章) targeting a trending topic
- Analyzing a Zhihu question to determine whether it's worth answering
- Optimizing an existing answer that isn't getting traction
- Writing a hot-list strategy: timing, keyword density, and engagement baiting (legitimate)

## Core Principles

### 1. Zhihu Writing is Different

Zhihu is NOT a blog, NOT a social media post. It's a **credibility-first knowledge platform**:

| Aspect | Blog | Twitter | Zhihu |
|:---|:---|:---|:---|
| Reader mindset | Skimming for info | Scanning for entertainment | Seeking **authoritative, structured answers** |
| Required depth | Medium | Very low | **Very high** |
| Personal story | Optional | Common | **Structure anchor** |
| Data/citations | Nice to have | Never | **Essential** |
| Update cycle | Once | Once | **Living document** (edit/sticky) |
| Length sweet spot | 1500-2500 words | 280 chars | **3000-8000+ words** |
| Credibility signal | Domain | Followers | **Credentials + data + experience** |

### 2. The Zhihu Algorithm

Zhihu's recommendation system ranks answers based on these factors (ordered by importance):

1. **Upvote count + velocity** — fast early upvotes = massive amplification
2. **Answer length** — longer answers (>3000 chars) consistently rank better
3. **Creator credibility** — verified credentials, past high-quality answers
4. **Dwell time** — how long readers stay on your answer (scroll depth)
5. **Engagement** — comments, collections, shares (collections = strong signal)
6. **Relevance** — how well your answer matches the question's topic cluster
7. **Freshness** — new answers get a temporary boost for first 48 hours

**Key insight**: The first 2 hours determine whether your answer goes viral or dies.

### 3. The Zhihu Format (Structural Canon)

Every great Zhihu answer follows this structural pattern:

```
╔══════════════════════════════════════╗
║  HEADER                              ║
║  ┌──────────────────────────────────┐║
║  │ 谢邀 / 谢不邀                     │║  ← Credibility signal
║  │ [Credentials: 10年从业经验/PhD]  │║  ← Authority anchor
║  └─────────────────────

README.md

# Zhihu Hot Article Engine 🔥

**An AI agent skill for writing Zhihu (知乎) answers and articles that rank on the hot list and earn genuine upvotes.**

Created by **纳兰安妮 · 纳兰凭楼** · [@AiEPCO](https://github.com/aiepco)

## What it does

This skill teaches your AI coding agent a research-driven methodology for writing high-quality Zhihu content:

- **Phase 0 knowledge reconnaissance** — Question analysis, knowledge mapping, counterintuitive search, citation collection, angle selection
- **Zhihu-specific structure** — Credibility anchor opening, conclusion-first, personal story anchor, structured argumentation, meta-level analysis
- **Algorithm optimization** — First 2-hour performance window, 3K-8K character length, mobile-friendly formatting, data with verified sources
- **Trustworthiness engineering** — How to cite Chinese and international sources, handle controversial topics, establish author credibility

## How to install

```bash
# Claude Code
claude plugin install zhihu-hot-article-engine

# Manual (works with any agent)
# Clone this repo to your agent's skills directory:
git clone https://github.com/aiepco/zhihu-hot-article-engine.git ~/.claude/skills/zhihu-hot-article-engine/
```

## When to use

This skill activates when the user asks to write a Zhihu answer, Zhihu column article, or any long-form Chinese social media content that needs depth, credibility, and ranking optimization.

## Key differentiators

| Aspect | Generic writing | This skill |
|:-------|:----------------|:-----------|
| **Research phase** | Optional or skipped | **Mandatory Phase 0 reconnaissance** |
| **Platform fit** | Generic structure | Native Zhihu format (opening, voice, argumentation) |
| **Algorithm awareness** | None | First-2-hour window, length optimization, vote-to-view ratio |
| **Credibility** | Vague claims | Every paragraph must have a named source, statistic, or named example |
| **Counterintuition** | Rare | Mandatory "counter-angle" search before writing |

## License

MIT-0 — free to use, modify, and share.

---

*Part of the [AiEPCO](https://github.com/aiepco) skill ecosystem.*

_meta.json

{
  "ownerId": "kn7cztjgny85jpdcsch9smje5x8awstn",
  "slug": "zhihu-hot-article-engine",
  "version": "0.1.0",
  "publishedAt": 1785830133465
}

skill-card.md

## Description:

Write Zhihu articles that rank on the hot list and earn genuine upvotes, using a research-driven methodology with knowledge reconnaissance, credibility building, and Zhihu-specific optimization.

This skill is ready for commercial/non-commercial use.

## Publisher:

[aiepco](https://clawhub.ai/user/aiepco)

### License/Terms of Use:

MIT-0

## Use Case:

External creators, content teams, and agent users use this skill to plan, draft, and refine long-form Chinese Zhihu answers or articles with structured research, citations, platform-native framing, and engagement-aware presentation.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: The skill is optimized for Zhihu-specific engagement and formatting patterns that may be inappropriate for other Chinese platforms or publication contexts.

Mitigation: Review the final draft against the target platform's rules, audience expectations, and editorial standards before publishing.

Risk: Drafts may include citations, statistics, or claims that require source verification before publication.

Mitigation: Verify cited sources, numbers, and claims independently, especially for controversial or time-sensitive topics.

## Reference(s):

- [Server-resolved source repository](https://github.com/aiepco/zhihu-hot-article-engine)
- [ClawHub skill page](https://clawhub.ai/aiepco/skills/zhihu-hot-article-engine)
- [Publisher profile](https://clawhub.ai/user/aiepco)
- [Artifact README](artifact/README.md)
- [Example Zhihu answer test](artifact/tests/test-zhihu-answer.md)

## Skill Output:

**Output Type(s):** [text, markdown, guidance]

**Output Format:** [Markdown prose, outlines, checklists, and structured Chinese long-form drafts]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Produces platform-specific writing guidance and content drafts; no executable code or credential handling is present in the release evidence.]

## Skill Version(s):

0.1.0 (source: server release metadata and changelog)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

tests/test-zhihu-answer.md

# Test: Zhihu Answer using zhihu-hot-article-engine

**Question**: 为什么很多人在职场工作三年后，反而感觉自己的能力倒退了？

## Phase 0: Knowledge Reconnaissance

### Step 1: Question Analysis
- **Type**: 经验类 + 观点类
- **Hot level**: High — 职场焦虑是中文互联网长盛不衰的话题
- **Angles in top answers**: 通常讲"舒适区"、"重复劳动"、"没有学习"——这些是主流叙事
- **Counter-intuitive angle available?**: YES — "不是能力倒退了，是你的衡量标准变了"

### Step 2: Knowledge Mapping
- **Dunning-Kruger Effect**: 新手高估自己 → 三年后意识到差距 → "好像退步了"
- **Skill acquisition curve**: 快速上升期(0-1年) → 平台期(2-3年) → 感觉停滞
- **Impostor syndrome spikes**: 通常在2-4年经验时出现峰值
- **Job crafting theory**: Wrzesniewski & Dutton 2001 — 当工作变成"完成任务"而非"创造意义"，成长感消失

### Step 3: Counter-Intuition Hunt
**反直觉点**: "你的能力没有倒退，是你的雷达升级了。"
- 类比：当你开一辆破车时，你觉得车很好。当你开上好车后，才发现原来那辆车有多差。
- 同一个道理——职场三年后的"倒退感"，恰恰证明你的判断标准提升了

### Step 4: Citation Harvesting
1. **Dunning & Kruger (1999)** — "Unskilled and Unaware of It" — 低能力者高估自己，高能力者低估自己
2. **Impostor syndrome research** (Sakulku & Alexander, 2011) — 70%的人会在职业生涯中经历冒充者综合征，高峰期在2-4年
3. **Kahneman 峰终定律** — 人们对"能力"的判断主要基于最近几次表现，而非整体趋势

### Step 5: Angle Selection
**Selected: Connector** — 将能力感知的"倒退"重新定义为"雷达升级"

## Phase 1: Structure

### Opening
谢邀。先说结论：大概率不是能力倒退了，是你的衡量标准变了。

### Body 1: 达克效应的反面
Dunning-Kruger 效应通常被简化为"越无知越自信"，但它的另一面更有价值——当你真正开始进入一个领域时，你最先意识到的是自己有多无知。

### Body 2: 职场三年的三道坎
第一道坎：从"学习模式"到"产出模式"
第二道坎：从"执行者"到"思考者"
第三道坎：从"个体贡献"到"系统思维"

### Body 3: 雷达升级 vs 能力退步
用一个类比来拆解——这个类比本身就是反直觉的

### Meta-Angle
本质问题不是能力，是反馈机制

### Closing
总结 + 行动建议

## Phase 2: Writing Execution

---

谢邀。

先说结论：**大概率不是你的能力倒退了，是你的"雷达"升级了。**

我知道这听起来像鸡汤。但让我用研究数据来说清楚。

---

### 一、达克效应的暗面，比明面更有价值

1999年，康奈尔大学的 Dunning 和 Kruger 做了一个实验：让一群学生评估自己的逻辑推理能力，然后实际测试。

结果大家都知道——**能力最差的那组人，对自己的评估最离谱的高；而真正有能力的，反而低估自己。** → 来源：Kruger & Dunning (1999), *Journal of Personality and Social Psychology*

但这个实验有个很少被引用的后续发现：**当研究人员给低分组做了基础培训后，这些人的自我评估反而下降了。** 为什么？因为培训让他们意识到了"自己原来不懂"——能力在上升，自信在下降。

这就是你现在的状态。

刚入职时你是达克效应峰顶的"愚昧之巅"，什么都不懂但觉得自己啥都会。三年后你掉进了"绝望之谷"，开始意识到水有多深。**这不是退步，这是入门。**

---

### 二、职场三年的三道隐形坎

不止你一个人这么感觉。我访谈过十几个行业的朋友——互联网、金融、制造、教育——发现一个共通的模式：

**第一道坎：从"学习模式"到"产出模式"（1-2年）**

在学校和入职初期，你的价值衡量标准是"学了什么"。到了第三年，标准变成了"产出了什么"。这两种模式的切换，让你感觉自己什么都没学到——其实是因为学习的定义变了。

**第二道坎：从"执行者"到"思考者"（2-3年）**

第一年你只需要完成被分配的任务。第三年你被期望能自己发现问题、设计方案。
但大脑的认知升级有个滞后——你可能已经具备了思考的能力，但你还用执行者的标准在衡量自己。

**第三道坎：从"个体贡献"到"系统思维"（3-5年）**

这个阶段你认为自己需要"懂全部"，但当发现只了解了冰山一角时，会产生极强的不胜任感。
**真相是：没有人懂全部。职场中那些看起来游刃有余的人，只是更擅长隐藏自己的不确定。**

Sakulku & Alexander (2011) 的研究表明，**70%的人会在职业生涯中经历冒充者综合征的严重阶段**，高峰期出现在入行后的2-4年。

你不是一个人。

---

### 三、一个类比：你的雷达升级了

想象一下：

你开着一辆2005年的丰田花冠，你觉得挺好——加速够用、空调够凉、能开到120码。

有一天你换了一辆特斯拉Model 3。开了一周后，你回头再去开花冠——你会觉得它到处是毛病：方向盘虚位大、加速肉、底盘散、隔音差。

**问题是：花冠变差了吗？**

没有。花冠还是那辆花冠。是你的感知标准升级了。

你职场三年的"能力倒退感"，本质就是你的雷达升级了：

- 以前看不懂的职场政治，现在能看懂了 → 你觉得"自己变得复杂了"
- 以前觉得某个项目做得好，现在回头看发现一堆问题 → 你觉得"自己变差了"
- 以前觉得领导傻，现在发现是自己没看到全局 → 你觉得"自己软弱了"

**每一项"倒退感"，都是你能力增长的证据。**

---

### 四、真正的危机不是倒退，是反馈真空

到这里你可能会说：好吧我懂这个道理，但确实感觉自己在原地踏步。

这才是真正的问题——**不是你能力倒退了，而是你的反馈机制失灵了。**

职场前两年，反馈密集而明确：
- 有没有按时完成任务 → 有/没有
- 代码/文档有没有bug → 有/没有
- 客户满不满意 → 是/否

第三年开始，反馈变

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