Content Factory

高质量内容创作标准（2026-02-20 更新）

爆款文章核心公式

标题 + 开头 = 80%的阅读量

核心原则

文章必须给用户带来真正价值：

1. 学到具体的方法论 - 不是泛泛而谈，有可操作的方法
2. 能实操的指南 - 照着做就能成，给提示词、给代码、给步骤
3. 开拓视野提升认知 - 看到以前没想过的东西，认知升级

标题写法

• 争议/对比词：「手撕Sora，脚踢Veo」「黑奴」「智商税」
• 数字吸睛：「13个行业实战案例」
• 疑问句引发好奇：「真的值吗？」
• 颠覆认知：挑战传统观点

开头写法

• 用观察/痛点切入：「绝大多数人对AI的耐心，正在消失」
• 回应读者期待：「测评和教学终于来了」
• 3句话内抓住注意力

正文结构

2-3个具体案例，层层递进：

每个案例包含：

• 具体场景（扔给AI什么任务）
• 真实过程（提示词、报错、改Bug）
• 结果展示（截图、链接、数据）
• 优缺点分析（不藏着掖着）

干货要求

• 给可直接抄作业的提示词
• 完整代码示例
• 真实项目名和案例
• 工作流拆解

真诚原则

• 报错经历也分享
• 槽点大方承认
• 效果对比（Before/After）
• 提供测试链接和在线Demo

结尾升华

• 方法论总结
• 未来趋势展望
• Agent自动化想象空间

三篇文章对照学习

Overview

八、高阅读量文章共同点（来自饼干哥哥、藏师傅分析）

1. 选题精准

• 蹭热点 + 独特视角
• 解决具体问题
• 目标人群明确

2. 结构清晰

• 层层递进
• 案例丰富
• 重点突出

3. 语言生动

• 有情绪（有态度）
• 有细节（不说正确的废话）
• 有对话感（像在跟朋友聊天）

4. 利他思维

• 读者能得到什么？
• 看完能做什么？
• 为什么转发？

5. 平台友好

• 关键词布局
• 开头有关键词
• 结尾引导互动

Generate complete WeChat Official Account "viral-style" articles using YouTube research, user confirmation, and professional writing with self-iteration.
Use this skill when given a topic/title and asked to research, create, and write a complete WeChat article with both MD and HTML outputs.

CRITICAL: Tool Dependency Check (MUST DO FIRST)

Before starting any research, MANDATORY tool check:

Step 1: Check yt-dlp Installation

yt-dlp --version

If not installed or command fails:

• STOP immediately
• Inform user: "yt-dlp tool is required for YouTube content extraction"
• Provide installation command: pip install yt-dlp
• Wait for user to install before proceeding
• DO NOT skip to alternative methods without checking tools first

Step 2: Verify Script Files

ls scripts/yt_dlp_search.py
ls scripts/yt_dlp_captions.py

If scripts missing:

• Check skill base directory: ~/.claude/skills\content-factory\scripts\
• Inform user if files are missing
• DO NOT proceed without verifying script availability

Step 3: Tool Status Communication

Always inform user of tool status:

• ✅ "yt-dlp installed (version X.X.X), proceeding with YouTube content extraction"
• ❌ "yt-dlp not found, please install with: pip install yt-dlp"
• ⚠️ "Scripts found but yt-dlp missing, installation required"

IMPORTANT PRINCIPLE:

• Tool checking is a PREREQUISITE, not optional
• "Optionally run scripts" means "use if available", NOT "skip if inconvenient"
• Local scripts are PRIMARY method, WebFetch is FALLBACK
• Never assume tools are unavailable without checking first

Workflow

1) Intake and clarify

• Require: user-provided title/topic.
• Ask for missing essentials: target audience, industry/region, tone (serious vs. punchy), desired length, and recency constraints.
• Confirm language preference; default to Chinese output unless user requests otherwise.

2) Web Research (Multi-source)

IMPORTANT: Research from diverse web sources - YouTube is a KEY source but not the ONLY source.

Primary Research Sources (in priority order):

1. YouTube Videos (Key source for case studies and expert insights)

   CRITICAL: YouTube Content Extraction Priority Order

   PRIMARY METHOD (Try First):

   • Run scripts/yt_dlp_search.py to search and collect video metadata

     python scripts/yt_dlp_search.py "your search query" --max-results 10

   • Use scripts/yt_dlp_captions.py to download subtitles/transcripts

     python scripts/yt_dlp_captions.py VIDEO_URL --whisper-if-missing

   • These scripts provide: title, channel, view count, upload date, duration, URL, full transcripts

   FALLBACK METHOD (If scripts fail):

   • Try WebFetch on YouTube video pages
   • If WebFetch blocked, request user to provide video URLs manually
   • Use WebSearch to find YouTube video links, then extract metadata

   LAST RESORT (If all YouTube access fails):

   • Proceed with other sources (articles, reports, blogs)
   • Clearly document in article that YouTube content was unavailable
   • Compensate with more industry reports and expert blogs

   Content Extraction Guidelines:

   • Load references/youtube_research_checklist.md for detailed extraction steps
   • Select 2-4 high-view, highly relevant videos (preferably top 20% by view count)
   • Extract verifiable data from: transcripts, descriptions, video chapters
   • Capture: title, channel, view count, upload date, duration, URL, transcript excerpts, relevance notes

2. Official Documentation & Announcements

   • Company blogs (e.g., anthropic.com/news, openai.com/blog)
   • Official product documentation
   • Press releases and official statements
   • GitHub repositories (README, release notes)

3. Industry Analysis & Reports

   • Gartner, Forrester, McKinsey reports
   • TechCrunch, VentureBeat, The Verge news articles
   • Industry-specific publications
   • Research papers and whitepapers

4. Expert Blogs & Technical Content

   • Medium, Dev.to, Hacker News discussions
   • Personal blogs of industry experts
   • LinkedIn articles from thought leaders
   • Technical tutorials and case studies

5. Community Discussions & Reviews

   • Reddit threads (r/MachineLearning, r/artificial, etc.)
   • Stack Overflow technical discussions
   • Product Hunt reviews
   • Twitter/X threads from verified experts

Research Strategy:

• Build 5-10 diverse search queries covering:

  • Core topic keywords (e.g., "Claude Cowork enterprise deployment")
  • Use case scenarios (e.g., "AI agent workflow automation case study")
  • Problem/solution pairs (e.g., "reduce report generation time AI")
  • Industry trends (e.g., "AI agent adoption 2026 forecast")
  • Comparison queries (e.g., "Claude Cowork vs Copilot")

• Use WebSearch and WebFetch tools to gather content from multiple sources

• Diversify source types: Mix videos, articles, reports, and discussions

• Prioritize recent content: Focus on material from the last 3-6 months unless topic requires historical context

Content Extraction Guidelines:

• Summarize only from verifiable sources - Extract actual content, not speculation
• Explicitly label source type and URL for each data point:
  • "根据Anthropic官方博客 (anthropic.com/news/...)"
  • "YouTube视频《...》中提到 (youtube.com/watch?v=...)"
  • "Gartner 2026年报告指出 (...)"
  • "TechCrunch报道称 (techcrunch.com/...)"
• Cross-verify key statistics from multiple sources when possible
• Capture publication dates to ensure data freshness
• Note credibility indicators: author credentials, publication reputation, data sources cited

When web access is unavailable:

• Ask for user-provided URLs or content excerpts
• Request permission to enable web search tools
• Use available references and documentation

3) Synthesize angles

• Extract 3-7 cross-source insights: recurring pain points, surprising data, repeated mistakes, or actionable tactics found across multiple sources (videos, articles, reports, discussions)
• Identify consensus patterns: What do multiple credible sources agree on?
• Spot unique insights: What does only one highly credible source reveal?
• Note conflicting viewpoints: Where do sources disagree? (can become article angles)
• Translate insights into shareable WeChat angles:
  • Beginner-friendly guides
  • Myth-busting content
  • Actionable checklists
  • Trend analysis + predictions
  • Real case studies with verified data

4) Produce topic ideas and confirm with user

• Load references/wechat_viral_frameworks.md to diversify angles and hooks.

• Generate 3-5 potential article topics based on multi-source web research (YouTube, blogs, reports, discussions).
  <<<<<<< HEAD

🚨 CRITICAL: Topic Direction - Application-Focused (2026-03-17 更新)

⚠️ 禁止生成纯技术类选题！以下方向禁止出现：

• ❌ 模型对比（如：DeepSeek vs GPT-4）
• ❌ API 更新（如：OpenAI 新 API 发布）
• ❌ 纯技术解读（如：MoE 架构解析）
• ❌ 论文解读（如：最新 AI 论文分析）

✅ 必须生成应用导向选题！方向优先级：

1. Skills - Agent Skills 使用技巧

   • 如何用 AI Agent 提升工作效率
   • 特定技能的使用秘笈和最佳实践
   • 自动化工作流的搭建经验

2. 应用场景 - AI Agent 实际案例

   • AI 在具体工作中的落地案例
   • 行业场景的 AI 解决方案
   • 真实用户的使用体验分享

3. 商业模式分析 - AI 创业与盈利

   • AI 产品/服务如何赚钱
   • 成功的 AI 商业案例分析
   • AI 创业机会和赛道分析

4. 工具评测 - 真实体验对比

   • 多款 AI 工具的实际使用对比
   • 优缺点分析和使用建议
   • 适合不同场景的工具推荐

5. 案例分享 - AI 落地实录

   • 企业/个人 AI 部署的真实案例
   • 部署成本、效果和经验教训
   • 可复制的 AI 实施方法论

6. OpenClaw 专题 - Agent 编排实战

   • OpenClaw 技巧教程
     • OpenClaw 实际使用技巧
     • 多个 Chat App 如何配置
     • Agent 编排最佳实践
   • OpenClaw 应用场景
     • 用 OpenClaw 做自动化工作流
     • OpenClaw + 飞书/微信/Telegram 集成
     • 自定义 Agent 开发
   • OpenClaw 商业模式
     • 基于 OpenClaw 的 SaaS 服务
     • OpenClaw 企业部署案例

选题评估标准：

• 是否对读者有实际帮助？
• 读者能否立即应用到工作中？
• 是否有具体的案例和数据支撑？

🚨 CRITICAL: Topic Freshness Requirement (2026-03-09)

• MUST only select topics from past 24-48 hours
• For AI/Tech topics: Must verify the news date, no outdated热点
• Use search with time filter: after:2026-03-08 or similar
• If researching DeepSeek, OpenAI, Anthropic etc - check the LATEST developments
• Reject any topic where the news is older than 48 hours

8d2abf78b8490403831aae82052e8e107054b856

• For each topic, provide:
  • Title (Chinese by default)
  • Target reader
  • Core promise/value
  • Outline with 3-5 sections (short section headers + 1-2 key points each)
  • Opening hook (1-2 sentences)

5) User confirmation - MANDATORY

CRITICAL: Before proceeding to write the full article, MUST confirm with user:

• Present the proposed topic and outline clearly
• Use AskUserQuestion tool to ask: "Which topic would you like me to develop into a full article?" or "Is this outline approved for full article writing?"
• Wait for explicit user approval before proceeding
• Allow user to request modifications to the outline

6) Write complete article with self-iteration

Once the topic and outline are confirmed:

Phase 1: Initial Draft

• Write complete article following the approved outline
• Target length: 2000-4000 Chinese characters
• MANDATORY: Include real cases and practical implementation:
  • At least 2-3 real enterprise cases with specific company names, metrics, and outcomes
  • Step-by-step implementation guide for readers to follow
  • Actionable checklists or frameworks that can be immediately applied
  • Concrete examples with numbers, timeframes, and results
• CRITICAL: NO FAKE NUMBERS - Data Integrity Requirements:
  • ❌ NEVER fabricate or invent specific metrics, percentages, or statistics
  • ❌ DO NOT make up company names, dates, or case study details
  • ✅ Use verifiable data from YouTube research (transcripts, descriptions, published reports)
  • ✅ When exact numbers unavailable, use professional qualifying language:
    • "预计可达..." (expected to reach...)
    • "通常情况下可提升..." (typically improves by...)
    • "根据行业经验，一般..." (based on industry experience, generally...)
    • "类似案例显示约..." (similar cases show approximately...)
    • "预期节省..." (projected to save...)
  • ✅ Cite data sources when using specific numbers: "根据Gartner报告...", "McKinsey研究显示..."
  • ✅ Use ranges instead of precise fake numbers: "提升30-50%" not "提升47.3%"
  • ✅ Focus on qualitative improvements when quantitative data unavailable: "显著提升效率" rather than inventing "93%效率提升"
  • Example of WRONG approach: "我见证了北京某公司效率提升93%"（如果这个93%是编造的）
  • Example of RIGHT approach: "类似部署预计可提升30-50%的效率，具体取决于现有流程复杂度"
• IMPORTANT: NO EMOJIS in professional versions - Do not use any emoji characters in the full MD/HTML article content (emojis are only allowed in Xiaohongshu version)
• IMPORTANT: First-person narrative - Use "我" (I/me) perspective throughout the article:
  • Write as if sharing personal insights and experiences
  • Use phrases like: "我发现...", "我观察到...", "让我分享...", "我建议..."
  • Create connection with reader through: "你可能会问...", "这让我想起..."
  • Professional yet warm tone - not overly casual, but approachable and authentic
  • 禁止废话表达: 避免"我认为"、"我觉得"、"我相信"等无信息量表达，直接陈述观点
• IMPORTANT: 精炼写作原则:
  • 每句话必须包含新信息，不重复已说过的内容
  • 删除所有冗余修饰词和过渡句
  • 如果删掉某句话不影响理解，就必须删掉
  • 用数据和事实说话，不用主观判断词
• Follow the professional tone and style from references/reference.html:
  • Use structured headings (h1, h2, h3, h4)
  • Include blockquotes for emphasis
  • Professional yet engaging narrative style
  • Clear section separation with proper spacing

Phase 2: Self-Iteration

• Review the draft for:
  • Tone consistency: Professional, confident, not arrogant; avoid complex jargon
  • First-person voice: Ensure consistent use of "我" perspective, warm and authentic
  • Structure flow: Logical progression from problem → insight → solution
  • Hook strength: Opening must grab attention within 3 seconds
  • Key points: Each section delivers clear value
  • Call-to-action: Natural and compelling conclusion
  • Case quality: Verify all cases have specific metrics and are credible
  • Implementation practicality: Ensure action steps are clear and executable
  • 精炼检查 ⚠️ MANDATORY:
    • 删除所有"我认为"、"我觉得"、"我相信"等废话表达
    • 检查是否有重复表达相同意思的句子，合并或删除
    • 确保每个段落都有新信息，不是前文的重复
    • 删除所有不影响理解的冗余句子
  • Data integrity verification ⚠️ CRITICAL:
    • Check every number, percentage, and metric in the article
    • Verify each can be traced back to YouTube research or cited source
    • Replace any invented numbers with qualifying language ("预计...", "通常...", "约...")
    • Ensure company names and case details are verifiable or appropriately qualified
    • Remove overly precise fake statistics (e.g., "47.3%" → "约45-50%" or "显著提升")
    • 禁止杜撰案例: 所有案例必须来自研究阶段的真实来源
• Make 2-3 rounds of self-improvement focusing on clarity and impact
• Ensure transitions between sections are smooth

Phase 3: Quality Review & Scoring (MANDATORY)

After completing the MD draft, perform a structured quality review and scoring:

Scoring Criteria (100 points total):

Scoring Process:

1. Complete the MD article draft
2. Perform self-review against each criterion
3. Calculate total score (0-100)
4. Output score report in this format:

📊 文章质量评分报告
━━━━━━━━━━━━━━━━━━━━━
精炼度:      XX/20  [具体问题说明]
数据真实性:  XX/20  [具体问题说明]
结构清晰度:  XX/15  [具体问题说明]
标题吸引力:  XX/15  [具体问题说明]
可执行性:    XX/15  [具体问题说明]
语言质量:    XX/15  [具体问题说明]
━━━━━━━━━━━━━━━━━━━━━
总分: XX/100

Decision Logic:

• Score < 85: 自动进行修改，修复扣分项，重新评分，循环直到≥85
• Score ≥ 85: 进入"飞书文档确认"流程

Auto-fix priorities for score < 85:

1. 删除所有"我认为/我觉得/我相信"
2. 合并或删除重复表达
3. 核实并修正可疑数据
4. 加强开头吸引力
5. 补充具体可执行建议

⏸️ 飞书文档确认流程 (CRITICAL - 2026-03-20 新增)

在生成4种格式之前，必须先写入飞书云文档等待确认！

Step 1: 创建飞书云文档

• 使用 feishu_doc 工具创建新文档
• 文档标题格式：[待确认] {文章标题}
• 内容为 Markdown 格式
• 写入后用 read 验证内容

Step 2: 通知用户确认

• 发送消息给用户，包含：
  • 文档链接
  • "请确认内容是否OK，回复确认后生成4种格式"
• 等待用户明确确认（用户回复"确认"、"OK"、"可以"等）

Step 3: 用户确认后

• 用户确认 → 进入 Phase 4 生成4种格式
• 用户有修改意见 → 根据意见修改后重新写入飞书，循环确认流程

⚠️ 重要：禁止跳过此步骤直接生成4种格式！

Phase 4: Final Polish

• Check all examples and data points are accurate
• Verify Chinese grammar and punctuation
• Ensure visual hierarchy is clear for mobile reading
• Add emphasis with bold for key insights
• Remove all emoji characters - Article must be emoji-free for professional publication
• Add business conversion ending - Replace "参考来源" with engagement and consultation call-to-action (see below)
• No horizontal rules in HTML - Do NOT use <hr/> tags between paragraphs in HTML output (markdown --- is acceptable)
• ⚠️ VERIFY XIAOHONGSHU CHARACTER COUNT - Before finalizing Format 3, count characters (excluding hashtags) and ensure 800-1000. If under 800, add more detail; if over 1000, aggressively cut while keeping core message.

Phase 5: Generate Infographics for Key Data (HTML Only)

CRITICAL: If article contains core data visualizations, generate infographics using NotebookLM

When to generate infographics:

• Article has 2+ key statistics or data comparisons
• Market growth trends with specific numbers
• Before/after comparisons with metrics
• Multi-point data that benefits from visual representation

Infographic generation workflow:

1. Identify data visualization opportunities in the article:

   • Market size/growth data (e.g., "$315B → $1.1T")
   • Percentage trends (e.g., "36.3% of companies")
   • Before/after comparisons (e.g., "Traditional: $25k/mo vs Solo: $500/mo")
   • Multi-metric comparisons (e.g., "3 key factors")

2. Use notebooklm-api skill to generate infographics:

   # For each key data point, generate an infographic
   notebooklm generate infographic "Create a professional infographic showing [data description].
   Use blue color scheme (#1a5490, #2980b9, #3498db) to match article theme.
   Modern, clean design with clear data visualization. Include: [specific metrics]"

3. Color scheme requirements (MUST match HTML theme):

   • Primary blue: #1a5490
   • Accent blue: #2980b9
   • Light blue: #3498db
   • Background: #ebf5fb (light blue)
   • Text: #2c3e50 (dark gray)
   • Use blue gradients and professional business style

4. Wait for generation and download:

   # Wait for infographic completion
   notebooklm artifact wait <artifact_id> --timeout 900
   
   # Download to output directory
   notebooklm download infographic "D:\AI\contents\CCoutput\YYYY-MM-DD-[slug]-[data-name].png"

5. CRITICAL: Compress image for WeChat upload:

   # NotebookLM generates large PNG files (5-10MB) that timeout during WeChat upload
   # ALWAYS compress to JPEG before publishing
   python scripts/compress_image.py "D:\AI\contents\CCoutput\YYYY-MM-DD-[slug]-[data-name].png"
   
   # This creates: YYYY-MM-DD-[slug]-[data-name]-compressed.jpg (typically < 1MB)
   # Compression: PNG 5.93MB → JPEG 0.68MB (88% reduction, quality=85)

6. Insert into HTML output:

   • Use the COMPRESSED image filename (not the original PNG)
   • Replace CSS-based visualizations with actual infographic images
   • Use proper <figure> structure with caption and source
   • Example:

   <figure style="margin: 40px 0;">
     <img src="2026-01-18-article-slug-market-growth-compressed.jpg"
          alt="Market Growth Visualization"
          class="article-image">
     <figcaption class="image-caption">
       SaaS市场增长趋势 (2025-2032)
     </figcaption>
     <p class="image-source">
       数据来源：<a href="[source_url]" target="_blank">[Source Name]</a>
     </p>
   </figure>

7. Typical infographics to generate (2-4 per article):

   • Market growth chart (if article discusses market trends)
   • Comparison infographic (traditional vs new approach)
   • Statistics visualization (key percentages/numbers)
   • Process/workflow diagram (if explaining steps)

Example prompts for common data types:

• Market Growth:

  "Create a professional infographic showing SaaS market growth from $315B (2025) to $1.13T (2032).
  Use ascending bar chart or line graph. Blue color scheme (#1a5490, #2980b9, #3498db).
  Highlight 20%+ CAGR. Modern business style."

• Percentage Trend:

  "Create a professional infographic showing 36.3% of new companies are solo-founded in 2026,
  compared to <10% in 2025. Show dramatic 3x+ growth. Blue color scheme matching corporate theme.
  Use pie chart or comparison bars."

• Cost Comparison:

  "Create a professional side-by-side comparison infographic: Traditional (5-person team, $25k/month,
  $50k+ MRR to break even) vs Solo+AI ($500/month, $5k MRR profitable). Blue color scheme.
  Clear visual contrast."

Important notes:

• Generate infographics AFTER writing full article content
• Only generate for Format 2 (HTML) - not needed for MD/Xiaohongshu/Tweet
• Infographics should enhance, not replace, written content
• Always include proper attribution and source links
• File naming: YYYY-MM-DD-[article-slug]-[data-description].png

7) Output format - Quad Format Required

⚠️ 仅在用户确认飞书文档后生成！

MANDATORY: Always generate ALL FOUR formats

Format 1: Markdown (.md) - 完整专业版

• Save to D:\AI\contents\CCoutput\YYYY-MM-DD-[article-title-slug].md
• Clean markdown with proper heading hierarchy
• DO NOT include metadata/frontmatter - Start directly with # Article Title
• No YAML header, no version info, no date header

Format 2: HTML (.html) - 完整专业版

• Save to D:\AI\contents\CCoutput\YYYY-MM-DD-[article-title-slug].html
• ⚠️ INFOGRAPHIC GENERATION REQUIRED: If article contains core data (market trends, statistics, comparisons), use Phase 4 workflow to generate infographics via notebooklm-api skill BEFORE finalizing HTML
• Use the enhanced HTML structure and styling:
  • Color scheme: Blue-based theme (#1a5490, #2980b9, #3498db) - UNIFIED COLOR SYSTEM
  • Font family: -apple-system, BlinkMacSystemFont, "Helvetica Neue", "PingFang SC", "Microsoft YaHei"
  • Line height: 1.75 (reduced from 1.9 for tighter spacing)
  • Max width: 860px, centered
  • Headings: Blue color scheme (h1: #1a5490, h2: #1a5490 with #2980b9 border and text-shadow for depth, h3: #2c5282)
  • Blockquote: Blue theme with shadow (#ebf5fb background, #3498db border, box-shadow)
  • Enhanced text styles:
    • .highlight - Blue highlight background (linear-gradient)
    • .highlight-blue - Blue text with blue highlight background
    • .highlight-green - DEPRECATED: Use .highlight-blue instead (unified blue theme)
    • .highlight-orange - DEPRECATED: Use .highlight-blue instead (unified blue theme)
    • .underline-dotted - Dotted underline for emphasis (2px dotted #3498db)
    • .data-box - Data highlight box with shadow
    • .key-number - Key numbers in blue, bold, larger font
  • Image styles:
    • .article-image - Responsive images with shadow and border-radius
    • .image-caption - Centered, italic caption text
    • .image-source - Source attribution with clickable links
  • Responsive design with proper viewport meta
• Include complete HTML structure (DOCTYPE, head, body)
• Apply consistent spacing and typography
• IMPORTANT: Do NOT use <hr/> tags between paragraphs - Use spacing and headings for visual separation instead
• DO NOT include metadata - No date, version info, or author metadata in HTML output

🚨 微信公众号排版规范 (CRITICAL - 必须遵守)

1. 段落行间距 (必须只有一行)

• <p> 标签之间不能有额外的空行
• CSS: p { margin: 4px 0; line-height: 1.4; } (紧凑间距，一行)

2. 标题样式 (必须有背景阴影和左侧下划线)

• # 标题：紫色渐变背景 + 白色字 + 左侧白线
  • CSS: h1 { background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: #fff; border-left: 5px solid #fff; padding: 10px 14px; border-radius: 6px; }
• ## 二级标题：黄橙渐变背景 + 左侧红线
  • CSS: h2 { background: linear-gradient(120deg, #f6d365 0%, #fda085 100%); border-left: 4px solid #ff6b6b; padding: 8px 12px; border-radius: 5px; }
• ### 三级标题：蓝色渐变背景 + 白色字 + 左侧白线
  • CSS: h3 { background: linear-gradient(120deg, #4facfe 0%, #00f2fe 100%); color: #fff; border-left: 3px solid #fff; padding: 6px 10px; border-radius: 4px; }

3. 重点字词背景阴影

• 使用黄色渐变背景突出重点词汇
• CSS类: .key-point { background: linear-gradient(120deg, #fff3cd 0%, #fff8e1 100%); padding: 2px 6px; border-radius: 3px; }
• 应用场景: 核心概念、关键数据、行动词

4. 重点句点下划线

• 使用点状下划线强调重要句子
• CSS类: .important { border-bottom: 2px dotted #ff6b6b; padding-bottom: 2px; }
• 应用场景: 核心观点、关键结论、重要提醒

5. 完整CSS样式示例 (必须使用)

body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; font-size: 16px; line-height: 1.4; padding: 16px; }
h1 { font-size: 20px; font-weight: bold; margin: 16px 0 8px; color: #fff; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 10px 14px; border-radius: 6px; border-left: 5px solid #fff; }
h2 { font-size: 18px; font-weight: bold; margin: 14px 0 6px; color: #333; background: linear-gradient(120deg, #f6d365 0%, #fda085 100%); padding: 8px 12px; border-radius: 5px; border-left: 4px solid #ff6b6b; }
h3 { font-size: 16px; font-weight: bold; margin: 12px 0 4px; color: #fff; background: linear-gradient(120deg, #4facfe 0%, #00f2fe 100%); padding: 6px 10px; border-radius: 4px; border-left: 3px solid #fff; }
p { margin: 4px 0; line-height: 1.4; }
ul, ol { margin: 4px 0; padding-left: 18px; }
li { margin: 2px 0; line-height: 1.4; }
.key-point { background: linear-gradient(120deg, #fff3cd 0%, #fff8e1 100%); padding: 2px 6px; border-radius: 3px; }
.important { border-bottom: 2px dotted #ff6b6b; padding-bottom: 2px; }
.cta-box { background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 16px; border-radius: 10px; text-align: center; color: #fff; margin: 16px 0; }

6. HTML中使用方式

• 标题: 使用标准 Markdown # ## ###
• 重点词汇: <span class="key-point">核心概念</span>
• 重点句子: <span class="important">关键结论</span>
• 引用块: <blockquote>引用的重点内容</blockquote>

7. 禁止事项

• ❌ 禁止在文章结尾添加 "本文由AI Agent编写" 等说明
• ❌ 禁止添加日期、版本号等元信息
• ❌ 禁止添加非必要的版权声明

8. 结尾CTA (必须添加)

<div class="cta-box">
<p>如果你觉得这篇文章有帮助，欢迎关注我们</p>
<p>📱 微信公众号：搜索「AI进化社」</p>
<p>🔗 官网：https://example.com</p>
<p>💬 评论区聊聊：你用的是什么方案？</p>
</div>

• Apply styling classes to key content:
  • Wrap important numbers in <span class="key-number">$XXX</span>
  • Wrap key concepts in <span class="highlight-blue">text</span> 或 <span class="key-point">text</span>
  • Wrap emphasized terms in <span class="underline-dotted">text</span> 或 <span class="important">text</span>
  • Wrap data comparisons in <div class="data-box">...</div>
• Content structure optimization:
  • REDUCE bullet points - convert to flowing narrative paragraphs
  • INCREASE first-person narrative ("我发现...", "我观察到...", "在我看来...")
  • Use bullet points ONLY for tool lists or step-by-step instructions
  • Transform data lists into prose with inline highlights
• WeChat list formatting (CRITICAL for publishing):
  • Remove ALL blank lines between <li> elements to prevent WeChat from adding extra bullets
  • Format lists compactly: <ul><li>Item 1</li><li>Item 2</li><li>Item 3</li></ul> (no newlines between items)
  • Apply to both <ul> and <ol> lists
  • WeChat's parser treats blank lines as new list items, causing visual clutter
  • This is handled automatically by wechat_publish.py script during upload
• Visual data presentation:
  • PREFER NotebookLM-generated infographics for key data (see Phase 4)
  • Use CSS-based visualizations only as fallback if infographic generation fails
  • Add inline data visualizations using CSS gradients and flexbox
  • Create visual comparison cards for before/after scenarios
  • Use colored boxes with statistics for key data points
  • Include source attribution links for all data visualizations
  • Example: Create gradient boxes showing market growth trends
  • Example: Use side-by-side comparison cards for cost structures
  • Example: Design stat cards with large numbers and context
• Information source images:
  • Search for charts/graphs from original sources (Fortune Business Insights, Scalable.news, etc.)
  • If images unavailable, create CSS-based data visualizations
  • Always include <figure> with <figcaption> and source links
  • Use <p class="image-source"> with clickable attribution links

Format 3: Markdown (.md) - 小红书版 (Xiaohongshu Style)

• Save to D:\AI\contents\CCoutput\YYYY-MM-DD-[article-title-slug]-小红书.md
• 🚨 STRICT CHARACTER COUNT (CRITICAL):
  • Target: 800-1000 characters (Chinese chars + English words)
  • MUST use Python script to verify before sending to user
  • Verification command:

    python3 -c "import re; c=re.sub(r'#+\s*|---+|^-\s+|#+\s*$|\n\n+','',open('file.md').read()); print(len(re.findall(r'[\u4e00-\u9fa5]',c))+len(re.findall(r'[a-zA-Z]+',c)))"

  • If count < 800: Add more content/expansion
  • If count > 1000: Aggressively cut while keeping core message
  • DO NOT rely on wc -c - it counts bytes not displayable characters
• Casual, engaging style WITH emojis (opposite of professional versions)
• STRICT CHARACTER LIMIT: 800-1000 Chinese characters (excluding hashtags)
  • ⚠️ CRITICAL: Count characters BEFORE finalizing - must be ≥800 AND ≤1000
  • Use Python to verify: 800 <= len(content_without_hashtags) <= 1000
  • If under 800, add more detail to case study or tips
  • If over 1000, aggressively cut content while keeping core message
  • Priority: Keep case study + key data + actionable tips
• MUST include at least 1 complete case study with specific metrics
• Content structure:
  • Eye-catching opening with emojis (2-3 sentences)
  • 1-2 key insights or problems
  • 1 complete real case with specific numbers/results
  • 3-5 actionable tips or takeaways (bullet points with emojis)
  • Engaging conclusion with call-to-action
  • Hashtags at the end (5-10 relevant hashtags)
• Tone: Conversational, exciting, relatable - like sharing a discovery with friends
• Emoji usage: Use emojis strategically for emphasis and visual appeal (✨💡🚀📊💪 etc.)
• Hashtag format examples:

  #ClaudeCowork #AI工具 #企业效率 #人工智能 #职场提升 #AI代理 #效率翻倍 #科技趋势

• DO NOT include business conversion section - Keep it short and focused for Xiaohongshu platform
• Character count verification (MANDATORY):

  # After writing, verify character count
  content = """[your xiaohongshu content]"""
  lines = content.split('\n')
  hashtag_line = [l for l in lines if l.startswith('#')]
  if hashtag_line:
      content_without_hashtags = content.replace(hashtag_line[0], '').strip()
  else:
      content_without_hashtags = content.strip()
  
  char_count = len(content_without_hashtags)
  assert 800 <= char_count <= 1000, f"Character count {char_count} must be between 800-1000!"

Format 4: Text (.txt) - X/Twitter 英文长推文版 (X Pro)

• Save to D:\AI\contents\CCoutput\YYYY-MM-DD-[article-title-slug]-tweet.txt
• English language only - Professional, data-driven long-form tweet
• Maximum length: 1000 characters (optimized for readability and engagement)
• MUST include 2-3 complete cases with specific metrics from the article
• Content structure:
  • Strong hook (2-3 sentences defining the core shift/insight)
  • Case 1: Problem → Solution → Results (3-5 bullet points with specific numbers)
  • Case 2: Problem → Solution → Results (3-5 bullet points with specific numbers)
  • Optional Case 3 or security/challenge lesson (if space permits)
  • Paradigm shift summary (3-5 transformation points using →)
  • Industry prediction or trend (Gartner data, market forecast)
  • Brief actionable framework or next steps (optional, 2-3 points)
  • Compelling call-to-action (question or statement)
  • 5-8 relevant hashtags at the end
• Tone: Professional yet engaging, authoritative but accessible, Twitter-style brevity
• Visual structure: Use separators (━━━), bullet points (•), arrows (→) for clarity
• Emoji usage: Minimal - only if it enhances readability (max 2-3 total)
• Data density: Include 6-9 specific metrics (percentages, time reductions, ROI)
• Hashtag format: 5-8 tags covering technology, industry, and application

  #AI #ClaudeCowork #EnterpriseAI #Automation #AIAgents #DigitalTransformation #Productivity #FutureOfWork

• Focus: Extract 2-3 most compelling cases with concrete results, maintain Twitter's scannable format while providing depth

File Naming Convention:

• Use ISO date format: YYYY-MM-DD (e.g., 2026-01-14)
• Followed by article title slug in lowercase with hyphens
• Xiaohongshu version adds -小红书 suffix before extension
• Tweet version adds -tweet suffix before extension
• Examples:
  • 2026-01-14-claude-cowork-enterprise-guide.md (Professional MD)
  • 2026-01-14-claude-cowork-enterprise-guide.html (Professional HTML)
  • 2026-01-14-claude-cowork-enterprise-guide-小红书.md (Xiaohongshu MD)
  • 2026-01-14-claude-cowork-enterprise-guide-tweet.txt (Twitter/X English)

Output Directory Structure:

D:\AI\contents\CCoutput\
├── 2026-01-14-[article-title-slug].md
├── 2026-01-14-[article-title-slug].html
├── 2026-01-14-[article-title-slug]-小红书.md
└── 2026-01-14-[article-title-slug]-tweet.txt

Create the output directory if it doesn't exist.

8) WeChat Cover Photo Generation (GLM-Image API)

IMPORTANT: Before publishing, generate a professional WeChat cover photo using GLM-Image API.

WeChat Cover Specifications:

• Size: 900x386 pixels (WeChat Official Account standard)
• Aspect Ratio: 21:9 (ultra-wide landscape)
• Style: Professional magazine style with minimalist flat vector graphics
• Format: PNG or JPEG (compress if > 1MB)

Cover Photo Generation Workflow:

1. Extract article title from the finalized content

2. Generate cover photo using GLM-Image API:

   # Generate cover photo using GLM-Image API
   python scripts/generate_cover_photo.py \
     --title "[article title]" \
     --theme "[article theme]" \
     --output "D:\AI\contents\CCoutput\YYYY-MM-DD-[article-slug]-cover.png"

3. Image will be automatically generated at 21:9 ratio (900x386 pixels)

4. Compress if needed:

   # Check file size
   ls -lh "D:\AI\contents\CCoutput\YYYY-MM-DD-[article-slug]-cover.png"
   
   # If > 1MB, compress to JPEG
   python scripts/compress_image.py "D:\AI\contents\CCoutput\YYYY-MM-DD-[article-slug]-cover.png"
   # Creates: YYYY-MM-DD-[article-slug]-cover-compressed.jpg

5. Include cover photo reference in publishing workflow

Prompt Engineering Guidelines for Cover Photos:

Core Principles:

• Title-focused: Graphics should complement, not compete with the title
• Minimalist: Less is more - avoid cluttered designs
• Professional: Magazine-quality aesthetics
• White background: Pure white (#ffffff) - MANDATORY for all covers
• Dark blue primary: Navy blue (藏青色 #1a3a52 or #1a5490) for text and main graphics
• Accent colors: Light blue (#3498db), gray (#7f8c8d) for subtle elements only
• 21:9 Ultra-wide: Design for 21:9 aspect ratio (900x386 pixels)

Example Prompts by Article Type:

1. Tech/AI Articles:

   Create a professional WeChat cover image (900x386 pixels, 21:9 ultra-wide landscape).
   
   Title: [AI/Tech article title]
   
   Style: Professional magazine cover with minimalist flat vector graphics.
   - Pure white background (#ffffff)
   - Title centered in dark blue (藏青色 #1a3a52)
   - Subtle geometric shapes: circuits, nodes, or abstract tech patterns in dark blue
   - Optional accent: light blue (#3498db) for highlights
   - Clean, modern typography
   - High contrast for mobile readability
   - 21:9 ultra-wide composition

2. Business/Enterprise Articles:

   Create a professional WeChat cover image (900x386 pixels, 21:9 ultra-wide landscape).
   
   Title: [Business article title]
   
   Style: Professional magazine cover with minimalist design.
   - Pure white background (#ffffff)
   - Title centered in navy blue (藏青色 #1a5490)
   - Subtle business graphics: charts, graphs, or geometric patterns in dark blue
   - Optional accent: gray (#7f8c8d) for subtle elements
   - Professional, trustworthy aesthetic
   - Balanced composition

3. Tutorial/Guide Articles:

   Create a professional WeChat cover image (900x386 pixels, 21:9 ultra-wide landscape).
   
   Title: [Tutorial/Guide title]
   
   Style: Professional magazine cover with clear hierarchy.
   - Pure white background (#ffffff)
   - Title centered in dark blue (藏青色 #1a3a52)
   - Minimalist icons or step indicators in dark blue
   - Optional accent: light blue (#3498db) for visual interest
   - Clean, educational aesthetic
   - Easy to scan on mobile

4. General Template (Recommended):

   Create a professional WeChat Official Account cover image (900x386 pixels, 21:9 ultra-wide landscape).
   
   Title: [article title - use actual title text]
   
   Design requirements:
   - Professional magazine style
   - Minimalist flat vector graphics based ONLY on the title keywords
   - Pure white background (#ffffff) - MANDATORY
   - Title text centered, large, and prominent in dark blue (藏青色 #1a3a52 or #1a5490)
   - Graphics and shapes in dark blue/navy (藏青色)
   - Optional accent colors: light blue (#3498db), gray (#7f8c8d) for subtle elements only
   - Clean geometric shapes that relate to title content
   - High contrast for mobile readability
   - Professional typography (sans-serif, bold for title)
   - **21:9 ultra-wide composition** - use horizontal space effectively
   - Suitable for business/professional audience
   
   Layout:
   - Title occupies center 60% of image
   - Background graphics subtle and non-distracting
   - Adequate white space around title
   - Professional, magazine-quality finish

Color Palette (Consistent with Article Theme):

• Background: Pure white (#ffffff) - MANDATORY for all covers
• Primary: Dark blue/Navy (藏青色) #1a3a52 or #1a5490 - for title text and main graphics
• Accent 1: Light blue #3498db or #2980b9 - for highlights and visual interest
• Accent 2: Gray #7f8c8d - for subtle elements
• Text: Dark blue for title, dark gray #2c3e50 for subtitles

Quality Checklist:

• Pure white background (#ffffff)
• Title and graphics in dark blue (藏青色)
• Title is clearly readable on mobile devices
• Background graphics don't compete with title
• Color scheme matches article theme (dark blue primary)
• Image dimensions are 900x386 pixels (21:9)
• File size < 1MB (compress if needed)
• Professional, magazine-quality aesthetic
• Suitable for business/professional audience

Integration with Publishing Workflow:

• Generate cover photo AFTER finalizing article content
• Compress to < 1MB if needed (use scripts/compress_image.py)
• Include cover photo path in WeChat publishing API call
• Verify cover photo meets WeChat specifications (size, format, content)

Error Handling:

• If GLM generation fails, retry with simplified prompt
• If generation takes too long, check API quota
• If file size > 1MB, compress to JPEG with quality=85
• Notify user if cover generation is skipped

9) WeChat Official Account Auto-Publishing

IMPORTANT: After generating Format 2 (HTML) and cover photo, automatically publish to WeChat Official Account for preview.

WeChat Official Account Credentials:

• Credentials are stored in .env file (see Configuration section below)
• AppID: Set WECHAT_APP_ID in .env
• AppSecret: Set WECHAT_APP_SECRET in .env
• Get credentials from: https://mp.weixin.qq.com/ → 设置与开发 → 基本配置

Publishing Workflow:

1. Generate all 4 formats (MD, HTML, Xiaohongshu, Tweet)
2. Generate WeChat cover photo using GLM-Image API (see section 8)
3. Compress cover photo and infographics if > 1MB
4. Extract content from HTML file for WeChat API
5. Call publishing script with UTF-8 mode (CRITICAL on Windows)
6. Verify encoding and images in WeChat draft preview
7. Return preview URL to user

Publishing Script Usage (CRITICAL - Windows UTF-8 Mode):

# ALWAYS use -X utf8 flag on Windows to prevent encoding errors
python -X utf8 scripts/wechat_publish.py --html "D:\AI\contents\CCoutput\YYYY-MM-DD-[article-title-slug].html"

# WITHOUT -X utf8, Chinese characters will show as \uXXXX escape sequences
# This is a Windows-specific issue due to GBK default encoding

Expected Output:

✓ Access token obtained
✓ Content image uploaded: [filename] -> http://mmbiz.qpic.cn/...
✓ Draft article created
✓ Media ID: WOr7ZIAYNpv...

Verification Checklist:

• Check logs for "Content image uploaded" (confirms image upload success)
• Verify Chinese characters display correctly (not \uXXXX sequences)
• Open WeChat draft preview to confirm images display
• Document Media ID for reference

API Endpoints Used:

• Token: https://api.weixin.qq.com/cgi-bin/token
• Draft: https://api.weixin.qq.com/cgi-bin/draft/add
• Preview: https://api.weixin.qq.com/cgi-bin/freepublish/submit

Error Handling:

• If API call fails, save HTML locally and notify user
• Log error details to D:\AI\contents\CCoutput\publish_errors.log
• Provide manual publishing instructions

Security Notes:

• AppSecret is sensitive - consider moving to environment variable in production
• Access tokens expire after 2 hours - script handles refresh automatically
• Preview links are valid for 24 hours

Article Ending - Business Conversion (MANDATORY for Format 1 & 2):

Instead of listing "参考来源" or "References", ALWAYS end Format 1 (MD) and Format 2 (HTML) professional articles with a business conversion section that guides readers to follow and engage for AI consulting services.

NOTE: Do NOT include this section in Format 3 (Xiaohongshu) - that format should end with hashtags only.

Markdown Format (for Format 1):

---

如果你觉得文章对你有所帮助，请关注。让我们一起在AI时代找到您的竞争优势。

HTML Format (for Format 2, add before </body>):

<p>如果你觉得文章对你有所帮助，请关注。让我们一起在AI时代找到您的竞争优势。</p>

Important Notes:

• Do NOT include "参考来源" or "References" sections in any articles
• The business conversion section is MANDATORY for every article
• Customize the contact information placeholder based on user needs
• Keep the tone consistent with first-person narrative: professional, helpful, and approachable
• This section serves as the commercial funnel to convert readers into consulting clients
• Do NOT use <hr/> before the business conversion section - The h2 heading provides sufficient visual separation

Quality bar and safety

• Avoid unverifiable claims about video content; summarize only what is available in transcripts or descriptions.
• Keep summaries concise and clearly attributed to sources.
• Avoid direct copying of long transcript passages.
• Article Writing Standards:
  • Maintain professional, confident tone without arrogance
  • Use concrete examples and data points
  • Ensure logical flow and clear value proposition
  • Mobile-first readability (short paragraphs, clear hierarchy)
  • Include proper attributions for any referenced content
• Writing Quality Rules (MANDATORY):
  • 精炼表达: 不要重复相同意思的话语，每句话必须有新信息
  • 禁止废话: 不要使用"我认为"、"我觉得"、"我相信"等无意义表达，直接陈述观点
  • 禁止杜撰: 所有案例和数据必须来自研究阶段收集的真实来源，不得编造
  • 直接有力: 用"是"而不是"我认为是"，用"数据显示"而不是"我觉得数据可能显示"
  • 删除冗余: 如果删掉某句话不影响理解，就删掉它

Common Mistakes and Lessons Learned

❌ MISTAKE 1: Skipping Tool Dependency Check

What happened:

• Assumed yt-dlp was unavailable without checking
• Jumped directly to WebFetch/WebSearch fallback methods
• Missed opportunity to extract rich YouTube content

Why it's wrong:

• Tool checking is MANDATORY, not optional
• "Optionally run scripts" means "use if available", NOT "skip if inconvenient"
• Local scripts are PRIMARY method, WebFetch is FALLBACK

Correct approach:

# ALWAYS check first
yt-dlp --version
ls scripts/yt_dlp_search.py
ls scripts/yt_dlp_captions.py

# Then decide execution path based on results

❌ MISTAKE 2: Misunderstanding "Optional" in Documentation

What happened:

• Saw "Optionally run scripts" and interpreted as "can skip"
• Didn't realize local scripts are the PRIMARY YouTube extraction method

Why it's wrong:

• "Optional" means "if conditions are met, should use"
• It's about tool availability, not execution priority
• WebFetch is the fallback, not the primary method

Correct interpretation:

• IF yt-dlp installed → MUST use scripts (primary method)
• IF yt-dlp missing → inform user, request installation
• ONLY IF user declines → use fallback methods

❌ MISTAKE 3: Not Reading Reference Documentation

What happened:

• Didn't load references/youtube_research_checklist.md
• Missed detailed instructions on YouTube content extraction

Why it's wrong:

• Reference docs contain critical workflow details
• They provide step-by-step guidance for complex tasks
• Skipping them leads to incomplete execution

Correct approach:

# ALWAYS load reference docs when mentioned
Read("references/youtube_research_checklist.md")

❌ MISTAKE 4: Premature Fallback to Alternative Sources

What happened:

• WebFetch failed → immediately switched to WebSearch for articles
• Didn't exhaust all YouTube extraction methods first

Why it's wrong:

• YouTube content provides unique value (case studies, personal stories, demos)
• Articles/reports can't fully replace video content richness
• Should try ALL methods before giving up on primary source

Correct decision tree:

YouTube needed?
    ↓
Check yt-dlp installed?
    ↓ YES                           ↓ NO
Try scripts                    → Inform user + install
    ↓ SUCCESS    ↓ FAIL              ↓
Continue      → Try WebFetch    → Retry scripts
                    ↓ FAIL              ↓ FAIL
              Request user URLs   → Use fallback sources
                    ↓ FAIL         (document limitation)
              Use fallback sources
              (document limitation)

❌ MISTAKE 5: WeChat Publishing Encoding Errors (Windows)

What happened:

• Published article to WeChat showed Unicode escape sequences instead of Chinese characters
• Example: \u6211\u752872\u5c0f\u65f6 instead of "我用72小时"
• Draft created successfully but content was unreadable

Why it's wrong:

• Windows default encoding is GBK, not UTF-8
• BeautifulSoup HTML parsing inherits system encoding
• JSON serialization with ensure_ascii=False alone is insufficient
• The issue affects the entire Python process, not just file I/O

Root cause:

• Windows console uses GBK encoding by default
• Python's default encoding follows system locale
• HTML parsing and string operations use default encoding
• WeChat API receives incorrectly encoded content

Correct approach:

# ALWAYS use Python UTF-8 mode on Windows for WeChat publishing
python -X utf8 scripts/wechat_publish.py --html "path/to/article.html"

# NOT just PYTHONIOENCODING (insufficient):
# PYTHONIOENCODING=utf-8 python scripts/wechat_publish.py  # ❌ Only fixes I/O

# The -X utf8 flag forces UTF-8 for:
# - File I/O operations
# - String encoding/decoding
# - HTML parsing (BeautifulSoup)
# - JSON serialization
# - HTTP request bodies

Verification:

• Check WeChat draft preview immediately after publishing
• Look for Chinese characters rendering correctly
• If you see \uXXXX sequences, encoding failed
• Re-run with -X utf8 flag

Prevention checklist:

• Always use python -X utf8 for WeChat publishing on Windows
• Test with a short article first to verify encoding
• Check draft preview before announcing success
• Document the Media ID for reference

❌ MISTAKE 6: NotebookLM Infographic Too Large for WeChat Upload

What happened:

• Generated 6.0MB PNG infographic using NotebookLM API
• WeChat image upload timed out: TimeoutError('The write operation timed out')
• Article published successfully but without the infographic
• User reported: "图片没有加载进去" (images not loaded)

Why it's wrong:

• NotebookLM generates high-resolution PNG files (often 5-10MB)
• WeChat API has 10MB limit but large files timeout frequently
• Network upload timeout occurs before reaching size limit
• PNG format is uncompressed and inefficient for web delivery

Root cause:

• NotebookLM prioritizes quality over file size
• PNG format preserves all pixel data without compression
• WeChat's upload endpoint has strict timeout limits (60 seconds)
• Large files fail silently - draft created but image missing

Correct approach:

# Step 1: Generate infographic with NotebookLM (as usual)
notebooklm generate infographic "prompt..." --json
notebooklm artifact wait <artifact_id> --timeout 600
notebooklm download infographic ./output.png

# Step 2: ALWAYS compress before WeChat upload
python -c "
from PIL import Image
import os

# Convert PNG to JPEG with quality optimization
img = Image.open('output.png').convert('RGB')
img.save('output-compressed.jpg', 'JPEG', quality=85, optimize=True)

# Verify size reduction
original_mb = os.path.getsize('output.png') / 1024 / 1024
compressed_mb = os.path.getsize('output-compressed.jpg') / 1024 / 1024
print(f'Original: {original_mb:.2f}MB → Compressed: {compressed_mb:.2f}MB')
"

# Step 3: Update HTML to reference compressed image
# Replace: <img src="output.png" ...>
# With:    <img src="output-compressed.jpg" ...>

# Step 4: Publish with compressed image
python -X utf8 scripts/wechat_publish.py --html "article.html"

Compression results:

• Original PNG: 5.93MB → Compressed JPEG: 0.68MB (88% reduction)
• Upload time: Timeout → 21 seconds (successful)
• Visual quality: Minimal degradation at 85% JPEG quality

Prevention checklist:

• Always compress NotebookLM infographics before WeChat upload
• Target file size: < 1MB for reliable upload
• Use JPEG format with quality=85 for good balance
• Verify image uploaded successfully in logs: "Content image uploaded: ... -> http://mmbiz.qpic.cn/..."
• Check WeChat draft preview to confirm image displays
• If upload fails, reduce JPEG quality to 75 or resize dimensions

Image compression script (reusable):

# Save as scripts/compress_image.py
from PIL import Image
import sys
import os

def compress_for_wechat(input_path, output_path=None, quality=85):
    """Compress image for WeChat upload (target < 1MB)"""
    if output_path is None:
        base, ext = os.path.splitext(input_path)
        output_path = f"{base}-compressed.jpg"

    img = Image.open(input_path)
    if img.mode in ('RGBA', 'LA', 'P'):
        img = img.convert('RGB')

    img.save(output_path, 'JPEG', quality=quality, optimize=True)

    original_mb = os.path.getsize(input_path) / 1024 / 1024
    compressed_mb = os.path.getsize(output_path) / 1024 / 1024

    print(f"Original: {original_mb:.2f}MB")
    print(f"Compressed: {compressed_mb:.2f}MB ({100*(1-compressed_mb/original_mb):.1f}% reduction)")
    print(f"Output: {output_path}")

    return output_path

if __name__ == "__main__":
    if len(sys.argv) < 2:
        print("Usage: python compress_image.py <input_image> [output_image] [quality]")
        sys.exit(1)

    input_path = sys.argv[1]
    output_path = sys.argv[2] if len(sys.argv) > 2 else None
    quality = int(sys.argv[3]) if len(sys.argv) > 3 else 85

    compress_for_wechat(input_path, output_path, quality)

Usage:

# Compress with default settings (quality=85)
python scripts/compress_image.py infographic.png

# Compress with custom quality
python scripts/compress_image.py infographic.png output.jpg 75

# Compress and specify output path
python scripts/compress_image.py infographic.png compressed.jpg

✅ BEST PRACTICES

1. Always Check Tools First

   • Run dependency checks before starting research
   • Inform user of tool status
   • Don't assume unavailability

2. Follow Priority Order Strictly

   • Primary method → Fallback method → Last resort
   • Don't skip steps in the decision tree
   • Document why each method failed

3. Be Transparent with User

   • Tell user which tools are available
   • Explain which methods you're using
   • Inform if content quality is degraded due to tool limitations

4. Read All Reference Documentation

   • Load checklist files when mentioned
   • Follow detailed workflows in reference docs
   • Don't improvise when guidance exists

5. Systematic Troubleshooting

   • If A fails, check why before trying B
   • Don't jump from A to D without trying B and C
   • Record failure reasons for future improvement

6. WeChat Publishing on Windows (CRITICAL)

   • ALWAYS use python -X utf8 flag for WeChat publishing
   • Verify encoding in draft preview immediately
   • Check for \uXXXX sequences indicating encoding failure
   • Re-run with UTF-8 mode if Chinese characters don't display

7. NotebookLM Infographic Workflow

   • Generate infographic with NotebookLM as usual
   • ALWAYS compress PNG to JPEG before WeChat upload
   • Target file size: < 1MB for reliable upload
   • Use scripts/compress_image.py for consistent compression
   • Update HTML to reference compressed image filename
   • Verify image upload success in logs before announcing completion

8. Image Compression for WeChat

   • Convert PNG to JPEG with quality=85
   • Check compressed file size (should be < 1MB)
   • If still too large, reduce quality to 75 or resize dimensions
   • Verify image displays correctly in WeChat draft preview
   • Keep original PNG for archival purposes

Configuration

Environment Setup

All API keys and credentials are stored in .env file for security and easy management.

Setup Steps:

1. Copy the example file:

   cd ~/.claude/skills\content-factory
   copy .env.example .env

2. Edit .env and add your credentials:

   # WeChat Official Account (Required for auto-publishing)
   WECHAT_APP_ID=your-wechat-app-id
   WECHAT_APP_SECRET=your-wechat-app-secret

3. Get your credentials:

   • WeChat Credentials: https://mp.weixin.qq.com/ → 设置与开发 → 基本配置
   • NotebookLM: Authenticate with notebooklm login (for cover photo generation)

4. Verify configuration:

   python scripts/check_env.py

Important:

• Never commit .env file to Git (it's in .gitignore)
• Keep .env.example as a template (safe to commit)
• See API_KEY_SETUP.md for detailed setup instructions
• See CONFIGURATION.md for configuration management details

Resources

Research Tools:

• scripts/yt_dlp_captions.py - Download YouTube captions/transcripts
• scripts/yt_dlp_search.py - Search and collect YouTube metadata
• scripts/compress_image.py - Compress images for WeChat upload
• scripts/wechat_publish.py - Auto-publish to WeChat Official Account
• notebooklm-api skill - Generate infographics and cover photos
• WebSearch tool - Search across all web sources
• WebFetch tool - Fetch and extract content from URLs

Reference Guides:

• references/youtube_research_checklist.md - YouTube-specific research workflow
• references/web_research_guide.md - Multi-source web research best practices
• references/wechat_viral_frameworks.md - Article frameworks and hooks
• references/wechat_publishing_guide.md - WeChat publishing documentation
• references/reference.html - HTML template and styling reference for output

Templates:

• assets/wechat_outline_template.md - Outline template (optional)

Output Directory:

• D:\AI\contents\CCoutput\ - Target directory for generated MD, HTML, Xiaohongshu, Tweet files, and cover photos

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