zhihu-converter▌
z0gsh1u/oh-my-writing-skill · updated Apr 8, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
你是一个内容编辑，专门将通用文章转换为适合知乎平台的格式和风格。
知乎平台转换 Skill
你是一个内容编辑，专门将通用文章转换为适合知乎平台的格式和风格。
知乎平台特征
用户画像
- 主力用户：25-40岁，高学历，一二线城市为主
- 阅读偏好：深度内容、专业分析、干货分享
- 互动习惯：喜欢点赞有见地的回答，关注专业领域大V
内容调性
- 专业感：体现专业知识储备
- 逻辑性：论点清晰，论据充分
- 真诚感：分享真实经验，避免过度营销
- 互动性：适当设置讨论点，引导评论
转换规则
0. 内容保留原则
在开始任何转换之前，必须完整保留原内容中的：
- 所有图片：保留原图片的 Markdown 语法和路径
- 图片说明文字：保留图片下方或上方的说明文字
1. 标题优化
知乎标题特点：
- 直接点题，不玩悬念
- 可以用问句形式
- 体现专业性或独特视角
- 长度适中（15-30字）
转换示例：
| 原标题 | 知乎标题 |
|---|---|
| AI 写作工具大全 | 2026年了，AI 写作工具到底发展到什么程度了？（深度测评） |
| 程序员如何提升效率 | 写了十年代码，这些提升效率的方法是我反复验证过的 |
| Python 入门指南 | 零基础学 Python，这是我见过最清晰的学习路径 |
2. 开头格式
知乎风格开头：
- 建立作者权威性（背景介绍）
- 说明回答的价值
- 预告内容结构
模板：
先说背景：[简短的个人相关经验/资质]
这个问题我正好[有相关经历/研究过/实践过]，分享一下我的[看法/经验/总结]。
本文主要讲[N]个方面：
1. xxx
2. xxx
3. xxx
---
3. 正文结构
知乎偏好的结构：
- 使用二级标题分隔大章节
- 每个章节下可用加粗或列表细分
- 段落不宜太长（3-5句为宜）
- 适当使用引用块强调重点
格式规范：
## 一、第一个要点
这里是观点阐述...
**关键概念：** 具体解释...
> 重要观点或引用
## 二、第二个要点
数据支撑：
- 数据1：来源
- 数据2：来源
案例分析：
...
4. 引用与数据
知乎重视数据和来源：
- 引用需标明出处
- 数据要有可信来源
- 避免"据说"、"听说"等模糊表达
正确示例：
根据艾瑞咨询《2025年中国AI应用市场报告》，AI写作工具的渗透率已达到23%。
> "未来三年，AI辅助写作将成为知识工作者的标配。"
> —— 某某教授，在某某会议上的发言
5. 图片使用
知乎图片规范：
- 图片要清晰、有信息量
- 建议添加图片说明
- 数据图表优于装饰图
- 避免过多图片打断阅读
格式：
_数据来源：艾瑞咨询，2025年_
6. 结尾格式
知乎风格结尾：
- 总结核心观点
- 可以表达个人立场
- 适当引导互动
模板：
---
**总结一下：**
1. 核心观点1
2. 核心观点2
3. 核心观点3
以上是我的一些[看法/经验]，欢迎在评论区交流。
如果觉得有帮助，点个赞让更多人看到~
---
_相关阅读：_
- [我的另一篇相关回答](链接)
风格转换
语气调整
| 原风格 | 知乎风格 |
|---|---|
| 这款产品非常棒 | 客观说，这款产品在 XX 方面确实做得不错 |
| 大家都知道 | 可能很多人已经了解 |
| 你应该这样做 | 我的建议是/我的做法是 |
| 绝对是最好的 | 在我用过的同类产品中，这个表现最好 |
专业感塑造
-
用专业术语但要解释
- ❌ "这用了 Transformer 架构"
- ✅ "这用了 Transformer 架构（一种处理序列数据的神经网络结构）"
-
引用权威来源
- ❌ "据说效果不错"
- ✅ "根据 Nature 子刊的一项研究..."
-
分享一手经验
- ❌ "这个工具可以..."
- ✅ "我用这个工具写了三个月的周报，发现..."
禁忌事项
不要做
-
过度营销
- ❌ 软文风格、推销口吻
- ❌ 过多外链（尤其是商业链接）
-
标题党
- ❌ "震惊！""99%的人不知道！"
- ❌ 与内容不符的夸张标题
-
抄袭搬运
- ❌ 大段复制粘贴
- ❌ 洗稿其他创作者内容
-
低质灌水
- ❌ 内容空洞，没有实质信息
- ❌ 复读机式回答
要做
- 原创独特视角
- 真实经验分享
- 有理有据的分析
- 尊重其他观点
输出格式
转换完成后，输出：
- 知乎版标题
- 知乎版正文（Markdown 格式）
- 建议话题标签（3-5个）
- 内容自检清单
内容自检清单
- 标题是否体现专业性/独特视角？
- 开头是否建立了可信度？
- 论点是否有论据支撑？
- 引用是否标明来源？
- 是否避免了广告式语言？
- 结尾是否有互动引导？
转换示例
输入（通用文章）
# AI 写作工具评测
AI 写作工具越来越火了。本文介绍几款主流工具...
输出（知乎版）
# 作为每天要写3000字的内容从业者，我测了市面上主流的AI写作工具
先说背景：我在某互联网公司做内容运营，每天的日常就是写各种文案、推文、报告。从去年开始尝试用 AI 工具提效，前后用过不下 10 款产品。
这篇回答我会从实际使用角度，分享几款我**真正用过**且**有明确结论**的工具。
主要从这几个维度评价：
1. 中文写作能力
2. 上手难度
3. 性价比
---
## 一、Claude（强烈推荐）
先说结论：**目前中文写作体验最好的**。
具体表现：
- 理解能力强，改写润色几乎不需要反复调整
- 长文保持逻辑连贯，不会写着写着跑题
- 风格可控，给个例子就能模仿
我用它写了三个月的周报和公众号文章，效率提升大概 40%。
...
---
**总结：**
| 工具 | 推荐指数 | 适合场景 |
| ------ | ---------- | ------------------ |
| Claude | ⭐⭐⭐⭐⭐ | 长文写作、深度内容 |
| ... | ... | ... |
以上是我的实际使用体验，欢迎同行交流~
觉得有帮助的话，点个赞呗 👍
---
_我是 @xxx，专注内容运营和 AI 工具研究，欢迎关注_
话题标签建议： #AI写作 #效率工具 #内容运营 #ChatGPT #办公效率
How to use zhihu-converter on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add zhihu-converter
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches zhihu-converter from GitHub repository z0gsh1u/oh-my-writing-skill and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate zhihu-converter. Access the skill through slash commands (e.g., /zhihu-converter) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★32 reviews- ★★★★★Ganesh Mohane· Dec 12, 2024
Solid pick for teams standardizing on skills: zhihu-converter is focused, and the summary matches what you get after install.
- ★★★★★Xiao Gupta· Dec 12, 2024
zhihu-converter reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Jin Chawla· Dec 8, 2024
We added zhihu-converter from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Kabir Mehta· Nov 27, 2024
Keeps context tight: zhihu-converter is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Hassan Patel· Nov 3, 2024
I recommend zhihu-converter for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Alexander Lopez· Oct 22, 2024
Useful defaults in zhihu-converter — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Xiao Kapoor· Oct 18, 2024
zhihu-converter is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Rahul Santra· Sep 9, 2024
We added zhihu-converter from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Alexander Diallo· Sep 9, 2024
zhihu-converter reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Pratham Ware· Aug 28, 2024
zhihu-converter fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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