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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","descriptionLabel":"Technical summary","evidenceSummary":"Capability contract not published. No trust telemetry is available yet. 2.5K downloads reported by the source. 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Covers everything from question selection to opening hooks, argument architecture, data citation, and title optimization for the Zhihu ecosystem.\n\nTags: latest:0.1.0\n\nVersion history:\n\nv0.1.0 | 2026-08-04T07:55:33.465Z | auto\n\nZhihu Hot Article Engine 0.1.0\n\n- Initial release with a comprehensive framework for writing Zhihu articles that rank on the hot list and attract genuine upvotes.\n- Introduces a multi-phase research-driven methodology, including knowledge reconnaissance, credibility building, and Zhihu algorithm optimization.\n- Provides detailed structural templates and writing rules tailored specifically for the Zhihu platform.\n- Covers every step from question analysis and data citation to opening hooks, argument architecture, and title optimization.\n- Includes best practices for maximizing engagement, leveraging platform algorithms, and establishing author authority.\n\nArchive index:\n\nArchive v0.1.0: 6 files, 12732 bytes\n\nFiles: README.md (2146b), skill-card.md (2298b), SKILL.md (12344b), tests (0b), tests/test-zhihu-answer.md (8072b), _meta.json (143b)\n\nFile v0.1.0:SKILL.md\n\n---\nname: zhihu-hot-article-engine\ndescription: >-\n  Write Zhihu articles that rank on the hot list and earn genuine upvotes,\n  using a research-driven methodology with Phase 0 knowledge reconnaissance,\n  structured credibility building, and Zhihu algorithm optimization.\n  Covers everything from question selection to opening hooks, argument\n  architecture, data citation, and title optimization for the Zhihu ecosystem.\nlicense: MIT-0\ncompatibility:\n  - claude-code\n  - cursor\n  - cline\nmetadata:\n  author: AiEPCO\n  tags:\n    - zhihu\n    - chinese-content\n    - social-media\n    - content-creation\n    - long-form\n  category: writing-content-creation\n  version: 1.0.0\n---\n\n# Zhihu Hot Article Engine\n\nWrite Zhihu articles that rank on the hot list and earn genuine upvotes.\n\n## When to use\n\n- Writing a comprehensive Zhihu answer that needs to stand out among hundreds\n- Creating a long-form Zhihu article (专栏文章) targeting a trending topic\n- Analyzing a Zhihu question to determine whether it's worth answering\n- Optimizing an existing answer that isn't getting traction\n- Writing a hot-list strategy: timing, keyword density, and engagement baiting (legitimate)\n\n## Core Principles\n\n### 1. Zhihu Writing is Different\n\nZhihu is NOT a blog, NOT a social media post. It's a **credibility-first knowledge platform**:\n\n| Aspect | Blog | Twitter | Zhihu |\n|:---|:---|:---|:---|\n| Reader mindset | Skimming for info | Scanning for entertainment | Seeking **authoritative, structured answers** |\n| Required depth | Medium | Very low | **Very high** |\n| Personal story | Optional | Common | **Structure anchor** |\n| Data/citations | Nice to have | Never | **Essential** |\n| Update cycle | Once | Once | **Living document** (edit/sticky) |\n| Length sweet spot | 1500-2500 words | 280 chars | **3000-8000+ words** |\n| Credibility signal | Domain | Followers | **Credentials + data + experience** |\n\n### 2. The Zhihu Algorithm\n\nZhihu's recommendation system ranks answers based on these factors (ordered by importance):\n\n1. **Upvote count + velocity** — fast early upvotes = massive amplification\n2. **Answer length** — longer answers (>3000 chars) consistently rank better\n3. **Creator credibility** — verified credentials, past high-quality answers\n4. **Dwell time** — how long readers stay on your answer (scroll depth)\n5. **Engagement** — comments, collections, shares (collections = strong signal)\n6. **Relevance** — how well your answer matches the question's topic cluster\n7. **Freshness** — new answers get a temporary boost for first 48 hours\n\n**Key insight**: The first 2 hours determine whether your answer goes viral or dies.\n\n### 3. The Zhihu Format (Structural Canon)\n\nEvery great Zhihu answer follows this structural pattern:\n\n```\n╔══════════════════════════════════════╗\n║  HEADER                              ║\n║  ┌──────────────────────────────────┐║\n║  │ 谢邀 / 谢不邀                     │║  ← Credibility signal\n║  │ [Credentials: 10年从业经验/PhD]  │║  ← Authority anchor\n║  └──────────────────────────────────┘║\n╠══════════════════════════════════════╣\n║  HOOK (First 3 lines)                ║\n║  ┌──────────────────────────────────┐║\n║  │ 直接回答：「答案是：……」          │║  ← Direct answer first\n║  │ or                              │║\n║  │ 先讲结论：「先说结论：是的。」     │║  ← Thesis statement\n║  └──────────────────────────────────┘║\n╠══════════════════════════════════════╣\n║  STORY / Personal Anchor             ║\n║  ┌──────────────────────────────────┐║\n║  │ \"我是怎么知道这个的\"               │║  ← Personal credibility\n║  │ [Concrete personal experience]   │║  ← Lived example\n║  └──────────────────────────────────┘║\n╠══════════════════════════════════════╣\n║  STRUCTURED ARGUMENT                  ║\n║  ┌──────────────────────────────────┐║\n║  │ 1. 核心原理                        │║  ← First principle\n║  │    [Data/citation + explanation]  │║\n║  │ 2. 常见误区                        │║  ← Refute against false theses\n║  │    [Counter-argument + why wrong] │║\n║  │ 3. 实用建议                        │║  ← Actionable framework\n║  │    [Step-by-step methodology]     │║\n║  │ 4. 深层思考                        │║  ← Meta-analysis (the X factor)\n║  │    [Unique insight]               │║\n║  └──────────────────────────────────┘║\n╠══════════════════════════════════════╣\n║  CLOSURE                              ║\n║  ┌──────────────────────────────────┐║\n║  │ 总结一句话：「……。」               │║  ← One-line summary\n║  │ 延伸阅读：[book/paper/link]       │║  ← Bonus value\n║  │ \"如果觉得有用，点个赞/关注\"        │║  ← Soft CTA\n║  └──────────────────────────────────┘║\n╚══════════════════════════════════════╝\n```\n\n## Phase 0: Knowledge Reconnaissance\n\nBEFORE writing, you MUST complete this research phase. Each item is mandatory.\n\n### Step 1: Question Analysis\nScan the question:\n- Is it a **hot question**? (check Zhihu hot list relevance)\n- Is it **well-formulated**? (clear, specific, debatable)\n- What's the **question type**?\n  - 事实类 (\"What is X?\" ) → data-heavy, authoritative sources\n  - 观点类 (\"Do you think X?\") → personal experience + reasoning\n  - 经验类 (\"How to do X?\") → step-by-step, credibility via results\n  - 评价类 (\"How do you evaluate X?\") → balanced, multi-perspective\n- Who are the **top answerers**? (read 3-5 top answers to understand the landscape)\n\n### Step 2: Knowledge Mapping\nSearch for:\n- **Core concepts**: definitions, principles, academic references\n- **Data points**: statistics, studies, surveys with specific numbers\n- **Case studies**: real-world examples, historical analogues\n- **Expert opinions**: what credible figures have said on this topic\n\n### Step 3: Counter-Intuition Hunt\nMUST find at least one counter-intuitive point:\n- \"Commonly believed X, but actually Y\"\n- \"Most people think A, but research shows B\"\n- \"The mainstream advice is wrong because...\"\n\n### Step 4: Citation Harvesting\nCollect minimum 3 high-credibility citations:\n- Academic papers (preferred)\n- Official data/statistics\n- Authoritative media reports\n- Expert quotes with named attribution\n\n### Step 5: Angle Selection\nChoose your UNIQUE angle:\n- [ ] **Contrarian angle**: Challenge the mainstream view\n- [ ] **Expert insider**: Reveal what people don't know from the inside\n- [ ] **Systematizer**: Provide a framework/step-by-step no one else has\n- [ ] **Deep-diver**: Go 10x deeper than existing answers\n- [ ] **Connector**: Connect two seemingly unrelated fields\n\n## Phase 1: Structure Building\n\nAfter Phase 0, build the answer structure:\n\n```\n## Structure Template\n\n### Opening (150-250 words)\n【谢邀/直接回答】\n[Thesis: one sentence that answers the question directly]\n[Credentials: why YOU should be trusted on this]\n[Map: \"我将从三个方面回答这个问题：……\"]\n\n### Body Section 1: Core Insight (400-800 words)\n[概念定义 + 原理说明]\n[数据/研究支持]\n[个人经历或案例印证]\n【要点总结：一个小绿框/加粗句】\n\n### Body Section 2: Counter Arguments & Refinements (300-600 words)\n[常见错误认知]\n[为什么这些是错的]\n[正确理解应该是什么]\n\n### Body Section 3: Practical Framework (400-800 words)\n[可操作的步骤/方法]\n[每个步骤的具体应用场景]\n[注意事项和边界条件]\n\n### Meta-Angle: The Deep Insight (200-500 words)\n[这个问题的本质是什么？]\n[大多数回答没有触及到的层面]\n[——这是让你的回答被收藏的段落]\n\n### Closing (100-200 words)\n[一句话总结]\n[延伸阅读推荐]\n[互动的邀请]\n```\n\n## Phase 2: Writing Execution\n\n### Writing Rules for Zhihu\n\n| Rule | Explanation |\n|:---|:---|\n| **Direct answer first** | Never bury the lead. First 3 lines must answer the question |\n| **分段≤5句** | Short paragraphs for mobile readability |\n| **加粗关键句** | Bold the thesis statement in each section |\n| **数据必须有来源** | Every number needs a citation |\n| **每300字一个\"钩子\"** | A surprising fact, a question, or a provocative statement |\n| **专业但不学院** | Use plain language, but show depth |\n| **例子≥1个** | Every abstract point needs a concrete example |\n| **字数3000+** | Short answers don't rank. Aim for 3000-8000 chars |\n| **不要纯讲道理** | Mix personal story with data |\n| **留白** | Not every point needs exhaustive explanation. Let readers fill gaps |\n\n### Title Rules\n\nFor **专栏文章** (not answers), the title is critical:\n\n- **含关键词**: Include the topic's core search keyword\n- **数字 + 结果**: \"3个方法\"，\"10年经验\"，\"5步做到\"\n- **悬念/冲突**: \"为什么专业的人都不说真话？\"\n- **具体而非抽象**: \"我如何从零学会XX\" vs \"学习XX的方法\"\n- **情绪触发**: 好奇、紧迫、认同、优越\n- **A/B测试**: Write 3-5 title variants and pick the best\n\n### Engagement Optimization\n\n- **Asking questions**: End sections with a rhetorical question to boost comment engagement\n- **Comments bait**: \"有不同意见欢迎评论区讨论\"\n- **Collection candy**: \"建议先收藏，慢慢看\" (at a logical mid-point)\n- **Upvote ask**: Subtle, at the end, with justification (\"如果对你有帮助\")\n\n## Quality Checklist\n\n- [ ] Phase 0 completed (question analysis, knowledge mapping, citations found)\n- [ ] Counter-intuitive point identified and woven in\n- [ ] Direct answer in first 3 lines\n- [ ] Minimum 3 high-credibility citations\n- [ ] Personal story/experience anchor\n- [ ] At least 1 concrete example per abstract point\n- [ ] 3000+ characters length confirmed\n- [ ] Short paragraphs (≤5 sentences each)\n- [ ] Key clauses bolded for mobile scanning\n- [ ] Every number has a source\n- [ ] Title optimized (for 专栏) or opening optimized (for 回答)\n- [ ] Comment/engagement hooks placed strategically\n- [ ] Soft CTA at the end\n- [ ] No self-promotion unless explicitly relevant\n- [ ] Read aloud test: sounds natural, not translated from English\n\n## Advanced Tips\n\n### Hot-List Timing\n- **Post timing**: Weekday evenings 19:00-22:00 (maximum active users)\n- **Weekend**: Saturday 10:00-14:00 (information-seeking mode)\n- **Avoid**: Late night (00:00-06:00) and working hours (09:00-12:00)\n- **Boost window**: First 2 hours after posting — engage with every comment\n\n### Building Credibility on a New Account\n- Answer 3-5 smaller questions FIRST to build answer history\n- Get your first 100 upvotes on easy questions before tackling big ones\n- Credentials: Verify your real identity (知乎认证) if applicable\n- Consistency: Answer 1-2 questions per week minimum\n\n### Handling Controversial Topics\n- Acknowledge opposing views: \"很多人可能觉得……\"\n- Present data, not opinion: \"根据研究显示……\"\n- Stay calm: never use aggressive language\n- \"我可能不对，但目前的理解是……\"\n- Invite counter-arguments: \"欢迎补充或反驳\"\n\nFile v0.1.0:README.md\n\n# Zhihu Hot Article Engine 🔥\n\n**An AI agent skill for writing Zhihu (知乎) answers and articles that rank on the hot list and earn genuine upvotes.**\n\nCreated by **纳兰安妮 · 纳兰凭楼** · [@AiEPCO](https://github.com/aiepco)\n\n## What it does\n\nThis skill teaches your AI coding agent a research-driven methodology for writing high-quality Zhihu content:\n\n- **Phase 0 knowledge reconnaissance** — Question analysis, knowledge mapping, counterintuitive search, citation collection, angle selection\n- **Zhihu-specific structure** — Credibility anchor opening, conclusion-first, personal story anchor, structured argumentation, meta-level analysis\n- **Algorithm optimization** — First 2-hour performance window, 3K-8K character length, mobile-friendly formatting, data with verified sources\n- **Trustworthiness engineering** — How to cite Chinese and international sources, handle controversial topics, establish author credibility\n\n## How to install\n\n```bash\n# Claude Code\nclaude plugin install zhihu-hot-article-engine\n\n# Manual (works with any agent)\n# Clone this repo to your agent's skills directory:\ngit clone https://github.com/aiepco/zhihu-hot-article-engine.git ~/.claude/skills/zhihu-hot-article-engine/\n```\n\n## When to use\n\nThis 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.\n\n## Key differentiators\n\n| Aspect | Generic writing | This skill |\n|:-------|:----------------|:-----------|\n| **Research phase** | Optional or skipped | **Mandatory Phase 0 reconnaissance** |\n| **Platform fit** | Generic structure | Native Zhihu format (opening, voice, argumentation) |\n| **Algorithm awareness** | None | First-2-hour window, length optimization, vote-to-view ratio |\n| **Credibility** | Vague claims | Every paragraph must have a named source, statistic, or named example |\n| **Counterintuition** | Rare | Mandatory \"counter-angle\" search before writing |\n\n## License\n\nMIT-0 — free to use, modify, and share.\n\n---\n\n*Part of the [AiEPCO](https://github.com/aiepco) skill ecosystem.*\n\nFile v0.1.0:_meta.json\n\n{\n  \"ownerId\": \"kn7cztjgny85jpdcsch9smje5x8awstn\",\n  \"slug\": \"zhihu-hot-article-engine\",\n  \"version\": \"0.1.0\",\n  \"publishedAt\": 1785830133465\n}\n\nFile v0.1.0:skill-card.md\n\n## Description:\n\nWrite 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.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[aiepco](https://clawhub.ai/user/aiepco)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nExternal 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.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: The skill is optimized for Zhihu-specific engagement and formatting patterns that may be inappropriate for other Chinese platforms or publication contexts.\n\nMitigation: Review the final draft against the target platform's rules, audience expectations, and editorial standards before publishing.\n\nRisk: Drafts may include citations, statistics, or claims that require source verification before publication.\n\nMitigation: Verify cited sources, numbers, and claims independently, especially for controversial or time-sensitive topics.\n\n## Reference(s):\n\n- [Server-resolved source repository](https://github.com/aiepco/zhihu-hot-article-engine)\n- [ClawHub skill page](https://clawhub.ai/aiepco/skills/zhihu-hot-article-engine)\n- [Publisher profile](https://clawhub.ai/user/aiepco)\n- [Artifact README](artifact/README.md)\n- [Example Zhihu answer test](artifact/tests/test-zhihu-answer.md)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, guidance]\n\n**Output Format:** [Markdown prose, outlines, checklists, and structured Chinese long-form drafts]\n\n**Output Parameters:** [1D]\n\n**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.]\n\n## Skill Version(s):\n\n0.1.0 (source: server release metadata and changelog)\n\n## Ethical Considerations:\n\nUsers 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.\n\nFile v0.1.0:tests/test-zhihu-answer.md\n\n# Test: Zhihu Answer using zhihu-hot-article-engine\n\n**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？\n\n## Phase 0: Knowledge Reconnaissance\n\n### Step 1: Question Analysis\n- **Type**: 经验类 + 观点类\n- **Hot level**: High — 职场焦虑是中文互联网长盛不衰的话题\n- **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\n- **Counter-intuitive angle available?**: YES — \"不是能力倒退了，是你的衡量标准变了\"\n\n### Step 2: Knowledge Mapping\n- **Dunning-Kruger Effect**: 新手高估自己 → 三年后意识到差距 → \"好像退步了\"\n- **Skill acquisition curve**: 快速上升期(0-1年) → 平台期(2-3年) → 感觉停滞\n- **Impostor syndrome spikes**: 通常在2-4年经验时出现峰值\n- **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\n\n### Step 3: Counter-Intuition Hunt\n**反直觉点**: \"你的能力没有倒退，是你的雷达升级了。\"\n- 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。\n- 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\n\n### Step 4: Citation Harvesting\n1. **Dunning & Kruger (1999)** — \"Unskilled and Unaware of It\" — 低能力者高估自己，高能力者低估自己\n2. **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\n3. **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\n\n### Step 5: Angle Selection\n**Selected: Connector** — 将能力感知的\"倒退\"重新定义为\"雷达升级\"\n\n## Phase 1: Structure\n\n### Opening\n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。\n\n### Body 1: 达克效应的反面\nDunning-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。\n\n### Body 2: 职场三年的三道坎\n第一道坎：从\"学习模式\"到\"产出模式\"\n第二道坎：从\"执行者\"到\"思考者\"\n第三道坎：从\"个体贡献\"到\"系统思维\"\n\n### Body 3: 雷达升级 vs 能力退步\n用一个类比来拆解——这个类比本身就是反直觉的\n\n### Meta-Angle\n本质问题不是能力，是反馈机制\n\n### Closing\n总结 + 行动建议\n\n## Phase 2: Writing Execution\n\n---\n\n谢邀。\n\n先说结论：**大概率不是你的能力倒退了，是你的\"雷达\"升级了。**\n\n我知道这听起来像鸡汤。但让我用研究数据来说清楚。\n\n---\n\n### 一、达克效应的暗面，比明面更有价值\n\n1999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。\n\n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*\n\n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。\n\n这就是你现在的状态。\n\n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。**\n\n---\n\n### 二、职场三年的三道隐形坎\n\n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：\n\n**第一道坎：从\"学习模式\"到\"产出模式\"（1-2年）**\n\n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。\n\n**第二道坎：从\"执行者\"到\"思考者\"（2-3年）**\n\n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。\n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。\n\n**第三道坎：从\"个体贡献\"到\"系统思维\"（3-5年）**\n\n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。\n**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。**\n\nSakulku & 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。\n\n你不是一个人。\n\n---\n\n### 三、一个类比：你的雷达升级了\n\n想象一下：\n\n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。\n\n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。\n\n**问题是：花冠变差了吗？**\n\n没有。花冠还是那辆花冠。是你的感知标准升级了。\n\n你职场三年的\"能力倒退感\"，本质就是你的雷达升级了：\n\n- 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\"\n- 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\"\n- 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\"\n\n**每一项\"倒退感\"，都是你能力增长的证据。**\n\n---\n\n### 四、真正的危机不是倒退，是反馈真空\n\n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。\n\n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。**\n\n职场前两年，反馈密集而明确：\n- 有没有按时完成任务 → 有/没有\n- 代码/文档有没有bug → 有/没有\n- 客户满不满意 → 是/否\n\n第三年开始，反馈变得模糊：\n- \"方案再优化一下\"——优化什么？\n- \"你要更有战略思维\"——怎么衡量？\n- \"这个quarter表现不错\"——怎么量化？\n\nKahneman 的峰终定律告诉我们：**人们对\"能力\"的整体判断，主要基于最近几次表现和情绪记忆，而不是长期趋势。** 当你没有清晰的反馈信号时，大脑会自动用\"最近遇到的一件挫折\"来定义自己。\n\n---\n\n### 五、怎么办？\n\n说三件可以做的事，都是经过验证的：\n\n**1. 建立你的\"成果档案\"**\n\n不是简历，是一个文档，每季度记录：\n- 我解决了什么别人解决不了的问题？\n- 我创造了什么之前不存在的价值？\n- 有哪些事情是半年前我做不到的？\n\n**客观记录会打败主观感受。**\n\n**2. 找一个比你高两级的人做参照**\n\n如果你的参照系是同龄人，你容易焦虑或自满。如果你的参照系是对标岗位（你想要的职位），你会清楚地看到差距在哪里——**这种差距不是\"倒退\"，而是路线图。**\n\n**3. 开始\"写\"，无论什么形式**\n\n我观察到一个规律：那些三年后明显成长的人，都有一个共同习惯——**写**。写周报、写复盘、写笔记、写博客。\n\n不是因为写作本身神奇，而是写作强迫你完成一个动作：**把模糊的思考变成清晰的表述。** 这个动作本身就是认知升级的最佳训练。\n\n---\n\n如果觉得有用，点个赞收藏，下次觉得自己能力倒退了就翻出来看看。\n\n**你花冠开完了，该开特斯拉了。**\n\n---\n*延伸阅读：Dunning, D. (2011). \"The Dunning–Kruger Effect: On Being Ignorant of One's Own Ignorance.\" Advances in Experimental Social Psychology, 44, 247-296.*","readmeExcerpt":"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","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"╔══════════════════════════════════════╗\n║  HEADER                              ║\n║  ┌──────────────────────────────────┐║\n║  │ 谢邀 / 谢不邀                     │║  ← Credibility signal\n║  │ [Credentials: 10年从业经验/PhD]  │║  ← Authority anchor\n║  └──────────────────────────────────┘║\n╠══════════════════════════════════════╣\n║  HOOK (First 3 lines)                ║\n║  ┌──────────────────────────────────┐║\n║  │ 直接回答：「答案是：……」          │║  ← Direct answer first\n║  │ or                              │║\n║  │ 先讲结论：「先说结论：是的。」     │║  ← Thesis statement\n║  └──────────────────────────────────┘║\n╠══════════════════════════════════════╣\n║  STORY / Personal Anchor             ║\n║  ┌──────────────────────────────────┐║\n║  │ \"我是怎么知道这个的\"               │║  ← Personal credibility\n║  │ [Concrete personal experience]   │║  ← Lived example\n║  └──────────────────────────────────┘║\n╠══════════════════════════════════════╣\n║  STRUCTURED ARGUMENT                  ║\n║  ┌──────────────────────────────────┐║\n║  │ 1. 核心原理                        │║  ← First principle\n║  │    [Data/citation + explanation]  │║\n║  │ 2. 常见误区                        │║  ← Refute against false theses\n║  │    [Counter-argument + why wrong] │║\n║  │ 3. 实用建议                        │║  ← Actionable framework\n║  │    [Step-by-step methodology]     │║\n║  │ 4. 深层思考                        │║  ← Meta-analysis (the X factor)\n║  │    [Unique insight]               │║\n║  └──────────────────────────────────┘║\n╠══════════════════════════════════════╣\n║  CLOSURE                              ║\n║  ┌──────────────────────────────────┐║\n║  │ 总结一句话：「……。」               │║  ← One-line summary\n║  │ 延伸阅读：[book/paper/link]       │║  ← Bonus value\n║  │ \"如果觉得有用，点个赞/关注\"        │║  ← Soft CTA\n║  └──────────────────────────────────┘║\n╚══════════════════════════════════════╝"},{"language":"text","snippet":"## Structure Template\n\n### Opening (150-250 words)\n【谢邀/直接回答】\n[Thesis: one sentence that answers the question directly]\n[Credentials: why YOU should be trusted on this]\n[Map: \"我将从三个方面回答这个问题：……\"]\n\n### Body Section 1: Core Insight (400-800 words)\n[概念定义 + 原理说明]\n[数据/研究支持]\n[个人经历或案例印证]\n【要点总结：一个小绿框/加粗句】\n\n### Body Section 2: Counter Arguments & Refinements (300-600 words)\n[常见错误认知]\n[为什么这些是错的]\n[正确理解应该是什么]\n\n### Body Section 3: Practical Framework (400-800 words)\n[可操作的步骤/方法]\n[每个步骤的具体应用场景]\n[注意事项和边界条件]\n\n### Meta-Angle: The Deep Insight (200-500 words)\n[这个问题的本质是什么？]\n[大多数回答没有触及到的层面]\n[——这是让你的回答被收藏的段落]\n\n### Closing (100-200 words)\n[一句话总结]\n[延伸阅读推荐]\n[互动的邀请]"},{"language":"bash","snippet":"# Claude Code\nclaude plugin install zhihu-hot-article-engine\n\n# Manual (works with any agent)\n# Clone this repo to your agent's skills directory:\ngit clone https://github.com/aiepco/zhihu-hot-article-engine.git ~/.claude/skills/zhihu-hot-article-engine/"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: zhihu-hot-article-engine\ndescription: >-\n  Write Zhihu articles that rank on the hot list and earn genuine upvotes,\n  using a research-driven methodology with Phase 0 knowledge reconnaissance,\n  structured credibility building, and Zhihu algorithm optimization.\n  Covers everything from question selection to opening hooks, argument\n  architecture, data citation, and title optimization for the Zhihu ecosystem.\nlicense: MIT-0\ncompatibility:\n  - claude-code\n  - cursor\n  - cline\nmetadata:\n  author: AiEPCO\n  tags:\n    - zhihu\n    - chinese-content\n    - social-media\n    - content-creation\n    - long-form\n  category: writing-content-creation\n  version: 1.0.0\n---\n\n# Zhihu Hot Article Engine\n\nWrite Zhihu articles that rank on the hot list and earn genuine upvotes.\n\n## When to use\n\n- Writing a comprehensive Zhihu answer that needs to stand out among hundreds\n- Creating a long-form Zhihu article (专栏文章) targeting a trending topic\n- Analyzing a Zhihu question to determine whether it's worth answering\n- Optimizing an existing answer that isn't getting traction\n- Writing a hot-list strategy: timing, keyword density, and engagement baiting (legitimate)\n\n## Core Principles\n\n### 1. Zhihu Writing is Different\n\nZhihu is NOT a blog, NOT a social media post. It's a **credibility-first knowledge platform**:\n\n| Aspect | Blog | Twitter | Zhihu |\n|:---|:---|:---|:---|\n| Reader mindset | Skimming for info | Scanning for entertainment | Seeking **authoritative, structured answers** |\n| Required depth | Medium | Very low | **Very high** |\n| Personal story | Optional | Common | **Structure anchor** |\n| Data/citations | Nice to have | Never | **Essential** |\n| Update cycle | Once | Once | **Living document** (edit/sticky) |\n| Length sweet spot | 1500-2500 words | 280 chars | **3000-8000+ words** |\n| Credibility signal | Domain | Followers | **Credentials + data + experience** |\n\n### 2. The Zhihu Algorithm\n\nZhihu's recommendation system ranks answers based on these factors (ordered by importance):\n\n1. **Upvote count + velocity** — fast early upvotes = massive amplification\n2. **Answer length** — longer answers (>3000 chars) consistently rank better\n3. **Creator credibility** — verified credentials, past high-quality answers\n4. **Dwell time** — how long readers stay on your answer (scroll depth)\n5. **Engagement** — comments, collections, shares (collections = strong signal)\n6. **Relevance** — how well your answer matches the question's topic cluster\n7. **Freshness** — new answers get a temporary boost for first 48 hours\n\n**Key insight**: The first 2 hours determine whether your answer goes viral or dies.\n\n### 3. The Zhihu Format (Structural Canon)\n\nEvery great Zhihu answer follows this structural pattern:\n\n```\n╔══════════════════════════════════════╗\n║  HEADER                              ║\n║  ┌──────────────────────────────────┐║\n║  │ 谢邀 / 谢不邀                     │║  ← Credibility signal\n║  │ [Credentials: 10年从业经验/PhD]  │║  ← Authority anchor\n║  └─────────────────────"},{"path":"README.md","content":"# Zhihu Hot Article Engine 🔥\n\n**An AI agent skill for writing Zhihu (知乎) answers and articles that rank on the hot list and earn genuine upvotes.**\n\nCreated by **纳兰安妮 · 纳兰凭楼** · [@AiEPCO](https://github.com/aiepco)\n\n## What it does\n\nThis skill teaches your AI coding agent a research-driven methodology for writing high-quality Zhihu content:\n\n- **Phase 0 knowledge reconnaissance** — Question analysis, knowledge mapping, counterintuitive search, citation collection, angle selection\n- **Zhihu-specific structure** — Credibility anchor opening, conclusion-first, personal story anchor, structured argumentation, meta-level analysis\n- **Algorithm optimization** — First 2-hour performance window, 3K-8K character length, mobile-friendly formatting, data with verified sources\n- **Trustworthiness engineering** — How to cite Chinese and international sources, handle controversial topics, establish author credibility\n\n## How to install\n\n```bash\n# Claude Code\nclaude plugin install zhihu-hot-article-engine\n\n# Manual (works with any agent)\n# Clone this repo to your agent's skills directory:\ngit clone https://github.com/aiepco/zhihu-hot-article-engine.git ~/.claude/skills/zhihu-hot-article-engine/\n```\n\n## When to use\n\nThis 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.\n\n## Key differentiators\n\n| Aspect | Generic writing | This skill |\n|:-------|:----------------|:-----------|\n| **Research phase** | Optional or skipped | **Mandatory Phase 0 reconnaissance** |\n| **Platform fit** | Generic structure | Native Zhihu format (opening, voice, argumentation) |\n| **Algorithm awareness** | None | First-2-hour window, length optimization, vote-to-view ratio |\n| **Credibility** | Vague claims | Every paragraph must have a named source, statistic, or named example |\n| **Counterintuition** | Rare | Mandatory \"counter-angle\" search before writing |\n\n## License\n\nMIT-0 — free to use, modify, and share.\n\n---\n\n*Part of the [AiEPCO](https://github.com/aiepco) skill ecosystem.*"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn7cztjgny85jpdcsch9smje5x8awstn\",\n  \"slug\": \"zhihu-hot-article-engine\",\n  \"version\": \"0.1.0\",\n  \"publishedAt\": 1785830133465\n}"},{"path":"skill-card.md","content":"## Description:\n\nWrite 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.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[aiepco](https://clawhub.ai/user/aiepco)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nExternal 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.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: The skill is optimized for Zhihu-specific engagement and formatting patterns that may be inappropriate for other Chinese platforms or publication contexts.\n\nMitigation: Review the final draft against the target platform's rules, audience expectations, and editorial standards before publishing.\n\nRisk: Drafts may include citations, statistics, or claims that require source verification before publication.\n\nMitigation: Verify cited sources, numbers, and claims independently, especially for controversial or time-sensitive topics.\n\n## Reference(s):\n\n- [Server-resolved source repository](https://github.com/aiepco/zhihu-hot-article-engine)\n- [ClawHub skill page](https://clawhub.ai/aiepco/skills/zhihu-hot-article-engine)\n- [Publisher profile](https://clawhub.ai/user/aiepco)\n- [Artifact README](artifact/README.md)\n- [Example Zhihu answer test](artifact/tests/test-zhihu-answer.md)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, guidance]\n\n**Output Format:** [Markdown prose, outlines, checklists, and structured Chinese long-form drafts]\n\n**Output Parameters:** [1D]\n\n**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.]\n\n## Skill Version(s):\n\n0.1.0 (source: server release metadata and changelog)\n\n## Ethical Considerations:\n\nUsers 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."},{"path":"tests/test-zhihu-answer.md","content":"# Test: Zhihu Answer using zhihu-hot-article-engine\n\n**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？\n\n## Phase 0: Knowledge Reconnaissance\n\n### Step 1: Question Analysis\n- **Type**: 经验类 + 观点类\n- **Hot level**: High — 职场焦虑是中文互联网长盛不衰的话题\n- **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\n- **Counter-intuitive angle available?**: YES — \"不是能力倒退了，是你的衡量标准变了\"\n\n### Step 2: Knowledge Mapping\n- **Dunning-Kruger Effect**: 新手高估自己 → 三年后意识到差距 → \"好像退步了\"\n- **Skill acquisition curve**: 快速上升期(0-1年) → 平台期(2-3年) → 感觉停滞\n- **Impostor syndrome spikes**: 通常在2-4年经验时出现峰值\n- **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\n\n### Step 3: Counter-Intuition Hunt\n**反直觉点**: \"你的能力没有倒退，是你的雷达升级了。\"\n- 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。\n- 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\n\n### Step 4: Citation Harvesting\n1. **Dunning & Kruger (1999)** — \"Unskilled and Unaware of It\" — 低能力者高估自己，高能力者低估自己\n2. **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\n3. **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\n\n### Step 5: Angle Selection\n**Selected: Connector** — 将能力感知的\"倒退\"重新定义为\"雷达升级\"\n\n## Phase 1: Structure\n\n### Opening\n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。\n\n### Body 1: 达克效应的反面\nDunning-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。\n\n### Body 2: 职场三年的三道坎\n第一道坎：从\"学习模式\"到\"产出模式\"\n第二道坎：从\"执行者\"到\"思考者\"\n第三道坎：从\"个体贡献\"到\"系统思维\"\n\n### Body 3: 雷达升级 vs 能力退步\n用一个类比来拆解——这个类比本身就是反直觉的\n\n### Meta-Angle\n本质问题不是能力，是反馈机制\n\n### Closing\n总结 + 行动建议\n\n## Phase 2: Writing Execution\n\n---\n\n谢邀。\n\n先说结论：**大概率不是你的能力倒退了，是你的\"雷达\"升级了。**\n\n我知道这听起来像鸡汤。但让我用研究数据来说清楚。\n\n---\n\n### 一、达克效应的暗面，比明面更有价值\n\n1999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。\n\n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*\n\n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。\n\n这就是你现在的状态。\n\n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。**\n\n---\n\n### 二、职场三年的三道隐形坎\n\n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：\n\n**第一道坎：从\"学习模式\"到\"产出模式\"（1-2年）**\n\n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。\n\n**第二道坎：从\"执行者\"到\"思考者\"（2-3年）**\n\n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。\n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。\n\n**第三道坎：从\"个体贡献\"到\"系统思维\"（3-5年）**\n\n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。\n**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。**\n\nSakulku & 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。\n\n你不是一个人。\n\n---\n\n### 三、一个类比：你的雷达升级了\n\n想象一下：\n\n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。\n\n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。\n\n**问题是：花冠变差了吗？**\n\n没有。花冠还是那辆花冠。是你的感知标准升级了。\n\n你职场三年的\"能力倒退感\"，本质就是你的雷达升级了：\n\n- 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\"\n- 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\"\n- 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\"\n\n**每一项\"倒退感\"，都是你能力增长的证据。**\n\n---\n\n### 四、真正的危机不是倒退，是反馈真空\n\n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。\n\n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。**\n\n职场前两年，反馈密集而明确：\n- 有没有按时完成任务 → 有/没有\n- 代码/文档有没有bug → 有/没有\n- 客户满不满意 → 是/否\n\n第三年开始，反馈变"}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"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. 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