SERP Analysis: [Topic]【免费下载链接】seomachineA specialized Claude Code workspace for creating long-form, SEO-optimized blog content for any business. This system helps you research, write, analyze, and optimize content that ranks well and serves your target audience.项目地址: https://gitcode.com/GitHub_Trending/se/seomachineDate: [YYYY-MM-DD]Keyword: [target keyword]Search Intent: [informational/commercial/transactional]Top Ranking Articles1. [Article Title] - [Domain]URL: [url]Word Count: ~[count]Structure: [H2 headings list]Strengths: [what they do well]Gaps: [what they miss]Outdated: [old info found][Repeat for top 5]Google-Validated StructureBased on whats ranking, these sections appear essential:[Common H2 found across multiple articles][Another common section] ...Competitor Gap BlueprintMUST-FILL GAPS (found in 3 competitors)[Gap 1]: [How to address][Gap 2]: [How to address]DIFFERENTIATION OPPORTUNITIES[Unique angle 1][Unique angle 2]DATA NEEDED[Specific statistic to find][Expert quote needed]OUTDATED INFO TO UPDATE[Old stat] → Need 2025 version### 2.3 源码佐证自动化的 SERP 分析脚本 人工 WebSearch/WebFetch 之外仓库还提供了可脚本化的 SERP 分析入口。命令文档 [.claude/commands/research-serp.md](https://link.gitcode.com/i/a75fd8f1edf4b81db10c3a8562b3adfa) 记录了其用法 bash python3 research_serp_analysis.py your target keyword从该命令文档看这条命令会通过 DataForSEO 拉取 Top 20 自然结果分析 Top 10 的内容模式与类型抓取各结果字数识别 SERP 特性featured snippet、PAA、视频等评估搜索意图与竞争难度最终生成内容简报并输出到research/serp-analysis-[keyword].md。命令文档还标注了该脚本的成本量级约每关键词 1–2 分钟、API 花费约 $0.02可作为预算参考但具体数据以实际运行环境为准。三、STEP 2社交研究强制——最好的洞察藏在 Reddit 帖子和 YouTube 教程里命令文档的原话点明了这一步的价值The best insights arent in SEO content - theyre in Reddit threads and YouTube tutorials.最好的洞察不在 SEO 内容里而在 Reddit 帖子和 YouTube 教程中。这对应仓库中的 data_sources/modules/social_research_aggregator.py 模块方向。3.1 Reddit 研究访问 5 个真实帖子搜索 RedditWebSearch: site:reddit.com [topic] industry WebSearch: site:reddit.com r/industrying [topic]访问 5 个有潜力的帖子对每个帖子 URL 用 WebFetch提取要素要找什么OPs Question具体的问题Top Comments高赞解决方案与建议Pain Points用户表达的挫败点Success Stories带细节的真实结果Debates不同观点Recommendations社区背书的东西Real Language真实用户如何谈论这个话题提取可引用洞察摘出可以写入或直接引用进文章的具体引语。3.2 YouTube 研究分析 5 个视频搜索 YouTubeWebSearch: site:youtube.com [topic] industry tutorial WebSearch: site:youtube.com [topic] industry review分析 5 个视频页面对每个视频页用 WebFetch提取要素要找什么Title Description覆盖了什么View Count互动信号Topics Covered讨论的主要观点Gaps没讲好的部分Comments观众在问什么3.3 输出模板social-research 报告结果保存到research/social-research-[topic-slug]-[YYYY-MM-DD].md# Social Research: [Topic] **Date**: [YYYY-MM-DD] ## Reddit Insights ### Thread 1: [Title] - URL: [url] - OPs Question: [quote] - Key Insight: [summary] - Quotable: [specific quote that could inform article] [Repeat for 5 threads] ### Pain Points Identified - [Pain point 1] - [Pain point 2] ### Success Stories Found - [Story with specific details] ### Real User Language - Users say [phrase] instead of [what competitors say] ## YouTube Insights ### Video 1: [Title] - [Channel] - URL: [url] - Views: [count] - Topics Covered: [list] - Gaps: [what they miss] - Top Comment Theme: [what viewers ask] [Repeat for 5 videos] ### Content Gaps in Video - [Topic tutorials dont cover well] ### Expert Takes - [Notable opinion from creator] ## Synthesis: Unique Insights for Article ### Insights NOT Available in SEO Content 1. [Unique insight from social research] 2. [Another unique insight] ### Questions to Answer (from real users) 1. [Real question from Reddit/YouTube] 2. [Another real question] ### Story Seeds (for mini-stories) - [Story possibility based on real user experience] ### Language to Use - Use [real user phrase] instead of [generic SEO phrase]模板中的Real User Language、Questions to Answer、Story Seeds三块直接为第四步的真实用户语言与mini-story写作提供弹药。四、STEP 3文章规划——写之前先做章节级作战计划第三步要求在动笔前创建逐节section-by-section计划。这一步的源码实体是 data_sources/modules/article_planner.py其中ArticlePlanner类的注释明确写着 Used by the /article command during the planning phase由 /article 命令在规划阶段使用。4.1 规划进程合并研究组合四类输入SERP 分析能排名的结构竞品缺口可以打败的机会社交研究独特洞察品牌上下文features、brand voice创建 Google 验证过的结构包含多篇 Top 文章共有的章节新增填补缺口章节按逻辑阅读顺序排列。为每个章节分配细节要素用途Typeintro / body-how-to / body-comparison / body-explanation / faq / conclusionWord Target基于竞品深度 缺口填补Strategic Angle我们带来的独特视角Engagement Hook该章节如何抓住注意力Knowledge Gaps填补哪些竞品缺口Unique Data要包含的社交研究洞察Internal Links链接哪些品牌页面CTAsoft / medium / strong如适用Mini-Story是否在此放故事规划参与度分布Engagement DistributionMini-stories开头、中部、接近结尾共 2–3 个CTAs前 500 词soft、中部medium、结尾strongFeatured snippet 机会FAQ、定义类章节4.2 输出模板article-plan 报告保存到research/article-plan-[topic-slug]-[YYYY-MM-DD].md# Article Plan: [Topic] **Date**: [YYYY-MM-DD] **Total Word Target**: [count] **Primary Keyword**: [keyword] **Secondary Keywords**: [list] ## Meta Elements - **Title Options**: 1. [Option 1] 2. [Option 2] 3. [Option 3] - **Meta Description**: [150-160 chars — must directly answer the target query, not just tease] - **URL Slug**: /blog/[slug] ## Section Plan ### 1. Introduction - **Type**: intro - **Word Target**: 200 - **Hook Strategy**: [question / scenario / statistic / bold statement] - **APP Elements**: [Agree point, Promise, Preview] - **Mini-Story**: [Yes - place opening scenario here] - **CTA**: soft (within first 500 words) - **Unique Data**: [Insight from social research to include] ### 2. [H2 Title] - **Type**: body-explanation - **Word Target**: 300 - **Strategic Angle**: [What unique perspective] - **Knowledge Gap**: [Which competitor gap this fills] - **Internal Links**: [your brand page to link] - **Unique Data**: [Social insight to include] ### 3. [H2 Title] - **Type**: body-how-to - **Word Target**: 400 - **Strategic Angle**: [Unique angle] - **Knowledge Gap**: [Gap being filled] - **Mini-Story**: [Yes - real user scenario] [Continue for all sections...] ### N. FAQ - **Type**: faq - **Word Target**: 200 - **Questions from Research**: 1. [Real question from Reddit] 2. [Another real question] 3. [Question competitors dont answer] 4. [Featured snippet opportunity] - **Featured Snippet**: Yes ### N1. Conclusion - **Type**: conclusion - **Word Target**: 200 - **CTA**: strong - **Mini-Story**: [Optional - reinforcing story] ## Engagement Map | Element | Location | |---------|----------| | Mini-Story 1 | Introduction | | Mini-Story 2 | Section [X] | | Mini-Story 3 | Conclusion (optional) | | CTA (soft) | Section 1 or 2 | | CTA (medium) | Section [X] | | CTA (strong) | Conclusion | ## Gap-to-Section Mapping | Competitor Gap | Section Addressing It | |----------------|----------------------| | [Gap 1] | Section [X] | | [Gap 2] | Section [Y] | ## Social Insight Mapping | Unique Insight | Where Used | |----------------|------------| | [Insight 1] | Section [X] | | [Insight 2] | Section [Y] |4.3 源码级原理ArticlePlanner 如何把规划变成可计算逻辑data_sources/modules/article_planner.py 将上述模板的每一项沉淀为数据类与算法章节类型枚举SectionType定义了INTRO / BODY_HOW_TO / BODY_COMPARISON / BODY_EXPLANATION / BODY_LIST / FAQ / CONCLUSION七种类型CTAType定义了SOFT / MEDIUM / STRONG三档强度源码注释分别对应 Learn more, explore、Try it, start free、Convert, sign up now。默认字数目标DEFAULT_WORD_TARGETS为每种章节设定了基准字数——intro 200、body-how-to 350、body-comparison 400、body-explanation 300、body-list 400、faq 250、conclusion 200。自动分类章节classify_section_type(heading)通过关键词表把 H2 标题映射为类型例如含 how to/steps/guide/tutorial/process 判为 how-to含 vs/compare/versus 判为 comparison含 top/best/tips/ways 判为 list无法匹配时默认 explanation。动态字数计算calculate_word_target(section_type, competitor_avg, has_gap)的规则是——如果该章节要填补竞品缺口字数在默认值基础上上浮 30%如果竞品平均深度更深则按竞品均值 × 1.1反超 10%。这正是规划模板中 Word Target: Based on competitor depth gap filling 的实现。参与度分布算法plan_engagement_distribution(num_sections)自动决定 mini-story 位置第 1 节必放≥4 节时中部放一个≥6 节时倒数第二节再放一个与 CTA 位置soft 在第 2 节或更早、medium 在中部、strong 在结尾。报告格式化format_article_plan(plan)直接生成上述article-plan-*.md的 Markdown 文本包括章节汇总表、Engagement Map、Gap-to-Section 与 Insight-to-Section 映射。另外create_default_structure(topic)提供了一个通用起步骨架Introduction → What is X → Why X Matters → How to Get Started → Best Practices → Common Mistakes → FAQ → Conclusion命令文档强调这只是起点须按竞品研究修改。五、STEP 4逐节写作——每种章节有独立的写作与编辑标准第四步是整个流水线的产出环节核心原则是每节单独写、单独编辑Write each section individually to maintain quality。5.1 为什么要逐节写长文 AI 写作越写到后面质量越差degrades in quality toward the end每节获得聚焦注意力每节有自己的编辑轮次全程保持质量一致5.2 各章节类型与专项要求Introduction引言要求直接回答优先AI Search Optimization任何 best/top/how 类查询前 1–2 句必须直接给出答案再上叙事钩子。原因很直白AI 爬虫ChatGPT、Perplexity、Gemini从页面顶部抓取内容。钩子Hook不得用泛泛开场用问题/场景/数据/大胆陈述。APP 公式Agree认同读者处境、Promise承诺收获、Preview预览结构。主关键词出现在前 100 词内。信任信号Trust signal。150–250 词。禁止的开场[Product category] is...When it comes to...If youre looking for...In todays world...Key Takeaways 块紧随引言、第一个 H2 之前要求3–5 条要点概括文章真实结论每条是独立的、带具体细节的主张数字、名字、结果不是目录——这是前置结论用带加粗 Key Takeaways 标题的 blockquote 格式整篇初稿完成后撰写再放到这里Body: How-To操作指南体要求顺序流程用编号步骤每步可执行、具体必要时给出耗时估算列出常见错误每节 250–400 词Body: Comparison对比体要求立场平衡承认竞品优势关键指标用数据表具体价格/功能Best for 推荐每节 300–400 词Body: Explanation解释体要求复杂度渐进简单 → 进阶用类比讲复杂概念带具体细节的例子在正文某节嵌入至少一个相关 YouTube 视频优先自家频道其次权威第三方以补充语境每节 250–400 词FAQ要求4–6 个来自研究的问题真实用户问题40–60 词答案面向 featured snippet 优化先直接回答再给语境总计 200–300 词Conclusion结论要求不只是总结——要增加价值3–5 条可执行的关键要点明确的下一步This week:、This month:带风险逆转risk reversal的强 CTA赋能式、向前看的收尾150–250 词5.3 每节的写作流程草稿 → 编辑 → 验证1. 写草稿套用该节专项要求放入规划好的独特数据/洞察贴合字数目标应用规划的参与度钩子。2. 编辑轮次删除 AI 套话In todays、Its important to note、When it comes to用具体数字替换模糊词many → 73%检查段落长度最多 4 句变换句子节奏混用 5–10 词短句与 15–25 词长句加入口语化手段缩约、设问、括号插话验证主动语态3. 验证要求该节专项标准是否达标规划的洞察是否写入字数是否在目标 ±10% 内。5.4 源码级支撑SectionWriter 的章节指南引擎data_sources/modules/section_writer.py 把上述所有章节要求做成了可编程的指南系统文件头部注释同样标注 Used by the /article command during the section-by-section writing phaseWritingGuidelines 数据结构每种SectionType对应一份包含requirements要求、dos该做、donts不该做、quality_checks质量自检的指南get_writing_guidelines()按类型路由。AI 短语黑名单AI_PHRASES_TO_REMOVE列出 16 条要删除的套话如 In todays、When it comes to、Its important to note、First and foremost、Without further ado 等与content_scorer.py的检测模式相互印证。模糊词替换表VAGUE_WORDS字典给出 17 组模糊词 → 具体化映射例如 many → 具体数字或百分比、significant → 具体百分比或金额、often → 具体频率。通用编辑清单get_editing_checklist()返回 8 项通用检查移除 AI 短语、替换模糊词、段落 ≤4 句、长短句混排、加缩约、主动语态 80%、加括号插话/设问、品牌一致性再加各类型专属检查。提示词生成器format_writing_prompt()与format_editing_prompt()把上述指南渲染成可直接喂给模型的写作/编辑提示词内含字数目标、战略角度、独特洞察、内链、mini-story 与 CTA 要求。5.5 组装与 Meta 元素所有章节写完并编辑后合并章节按计划顺序组装检查节间过渡核对内链位置确认 CTA 分布。添加 Meta 元素frontmatter 格式--- Meta Title: [50-60 chars] Meta Description: [150-160 chars] Primary Keyword: [keyword] Secondary Keywords: [list] URL Slug: /blog/[slug] Word Count: [count] Internal Links: [list] External Links: [list] ---生成检查清单共 4 份SEO Checklist:Primary keyword in H1Primary keyword in first 100 wordsPrimary keyword in 2 H2 headingsKeyword density 1-2%3-5 internal links2-3 external authority linksMeta title 50-60 charsMeta description 150-160 chars2000 wordsAI Search Optimization Checklist:Direct answer in first 1-2 sentences (not buried behind narrative)Key Takeaways block with 3-5 specific bullet points after introductionMeta description directly answers the target queryAt least one relevant YouTube video embeddedFAQ questions written in natural prompt languageOne idea per section (each H2/H3 focuses on single concept)Author attribution in frontmatterEngagement Checklist:Hook (not generic opening)APP Formula in intro2-3 mini-stories with names/details/outcomes2-3 contextual CTAsFirst CTA within 500 wordsNo paragraphs 4 sentencesVaried sentence rhythmResearch Integration Checklist:Addresses 3 competitor gapsIncludes 5 social research insightsUses real user languageAnswers questions from Reddit/YouTubeUpdates outdated info with 2025 data输出位置保存到drafts/[topic-slug]-[YYYY-MM-DD].md。六、写后质量闭环去 AI 痕迹 → 多维评分 → 自动修订 → 优化 Agent初稿落盘并不等于结束命令文档规定了四段式质量闭环每一段都有仓库源码背书。6.1 第一步/scrub 清除 AI 痕迹/scrub drafts/[filename].md/scrub的完整规范见 .claude/commands/scrub.md其实现位于 data_sources/modules/content_scrubber.py 的ContentScrubber类对外暴露scrub_file(file_path, output_path, verbose)。它做四类清理移除隐形 Unicode 水印WATERMARK_CHARS列表列出 15 个字符包括零宽空格U200B、BOMUFEFF、零宽非连接符U200C、零宽连接符U200D、词连接符U2060、软连字符U00AD、窄不换行空格U202F等随后_remove_format_control_chars()再按 Unicode 类别Cf兜底清除所有格式控制字符。按上下文替换 em-dash_determine_emdash_replacement()分析破折号前后语境决定替换标点——归属/引用场景换逗号独立分句换分号however/therefore/moreover 等连接副词前也换分号句子级强停顿换句号列表或补充说明换逗号。替换 AI 套话AI_PHRASE_REPLACEMENTS内置了 13 组正则替换例如 Its important to note that X → X、leverage → use、utilize → use、dive into → explore、In conclusion, → 删除。空白归一化多空格压为单空格、清理标点前空格、句尾补空格并刻意避免破坏 URL 与文件扩展名、连续 3 换行压为 2。该模块是幂等的对已清理内容重复运行不会产生额外修改因此可以安全地多次执行、纳入自动化流程。运行后会输出统计Unicode 水印移除数、格式控制字符移除数、em-dash 替换数、AI 短语替换数。最佳实践要求发布前必须 scrub且/write、/rewrite保存文件后应立即自动执行。6.2 第二步多维内容评分阈值 70python data_sources/modules/content_scorer.py drafts/[filename].mddata_sources/modules/content_scorer.py 的ContentScorer.score()对文章做五维加权评分复合分 ≥70 才通过维度权重Humanity/Voice人性化/声音30%Specificity具体性25%Structure Balance结构平衡20%SEO ComplianceSEO 合规15%Readability可读性10%各维度的判定逻辑值得展开均可从源码确认Humanity30%检测 25 条 AI 短语正则如 in todays digital/modern、when it comes to、lets dive in、furthermore、leverage、robust、game-changer按每千词密度扣分最高 -30统计被动语态最高 -15奖励对话手段括号插话、设问、缩约最高 15缩约密度过低扣 10 分。Specificity25%以 70 分为基线模糊词many/some/various/significant/very 等 30 个密度过高最高扣 25 分奖励具体性信号百分比、美元金额、年份、日期、带名字的引语、长引号文本最高 30数字密度过低每千词 3扣分。Structure Balance20%按字符统计 prose正文占比目标 50%–75% 得满分低于 50%列表/表格过多或高于 75%纯文字墙都会按偏离度扣分——这与命令文档prose-to-list ratio (target 50-75%)一致。SEO Compliance15%检查 meta title50–60 字符、meta description150–160 字符、主关键词是否在 H1、是否在前 100 词、全文是否 ≥2000 词meta 缺失最高各扣 15 分。Readability10%委托ReadabilityScorer计算 Flesch Reading Ease目标 60–70与年级水平目标 8–10新增两项检查——段落 4 句扣分每处 3 分最高 15、句子节奏单调扣分用 5 句滑动窗口检测长度近似的单调段并结合句子长度标准差评分。评分结果会给出priority_fixes按维度权重 × 分数缺口排序的前 5 条修复建议format_report()则输出人读报告。6.3 第三步低于 70 分自动修订如果复合分 70查看评分器的priority_fixes应用前 3–5 条修复重新评分若仍不足再迭代一次仍 70 则移入review-required/目录并附_REVIEW_NOTES.md含最终分数、各维度分解、剩余修复项与人工复核原因6.4 第四步运行优化 Agent通过质量门槛后依次运行 5 个优化 agent定义于 .claude/agents/ 目录例如 .claude/agents/content-analyzer.mdcontent-analyzeragent综合内容分析search intent、关键词密度、与竞品字数对比、可读性、SEO 质量评分底层调用search_intent_analyzer.py、keyword_analyzer.py、content_length_comparator.py、readability_scorer.py、seo_quality_rater.py五个模块seo-optimizeragentSEO 优化报告meta-creatoragent多个 meta title/description 候选internal-linkeragent内链建议keyword-mapperagent关键词布局分析6.5 完整输出结构一次/article会话最终在命令运行时生成的research/、drafts/目录下产出如下文件集research/ ├── serp-analysis-[topic]-[date].md # SERP research ├── social-research-[topic]-[date].md # Reddit/YouTube insights └── article-plan-[topic]-[date].md # Section-by-section plan drafts/ ├── [topic]-[date].md # Final article ├── content-analysis-[topic]-[date].md # Content analyzer output ├── seo-report-[topic]-[date].md # SEO optimizer output ├── meta-options-[topic]-[date].md # Meta creator output ├── link-suggestions-[topic]-[date].md # Internal linker output └── keyword-analysis-[topic]-[date].md # Keyword mapper output【免费下载链接】seomachineA specialized Claude Code workspace for creating long-form, SEO-optimized blog content for any business. This system helps you research, write, analyze, and optimize content that ranks well and serves your target audience.项目地址: https://gitcode.com/GitHub_Trending/se/seomachine创作声明：本文部分内容由AI辅助生成（AIGC），仅供参考