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Search Experience Optimization (SXO)

搜索体验优化（SXO）

SXO bridges the gap between SEO (what Google rewards) and UX (what users need). Traditional SEO audits check technical health. SXO asks: "Does this page deserve to rank for this keyword based on what Google is actually rewarding in the SERP?"
SXO 搭建起SEO（Google的奖励机制）与UX（用户需求）之间的桥梁。传统的SEO审核侧重于检查技术健康度，而SXO则提出问题：“根据Google在SERP中实际奖励的内容，这个页面是否值得为该关键词排名？”

Core Insight

核心洞察

A page can score 95/100 on technical SEO and still fail to rank because it is the wrong page type for the keyword. If Google shows 8 product pages and 2 comparison pages for your keyword, your blog post will never break through -- no matter how well-optimized it is.
一个页面在技术SEO上可能拿到95/100的高分，但仍无法排名，原因在于它是该关键词的错误页面类型。如果针对你的关键词，Google展示了8个产品页面和2个对比页面，那么你的博客文章无论优化得多好，都无法突破排名——这是无法改变的事实。

Commands

命令

CommandPurpose
/seo sxo <url>
Full SXO analysis (auto-detect keyword from page)
/seo sxo <url> <keyword>
Full SXO analysis for a specific keyword
/seo sxo wireframe <url>
Generate IST/SOLL wireframe with concrete placeholders
/seo sxo personas <url>
Persona-only scoring (skip SERP analysis)
命令用途
/seo sxo <url>
完整SXO分析（从页面自动检测关键词）
/seo sxo <url> <keyword>
针对特定关键词的完整SXO分析
/seo sxo wireframe <url>
生成带有具体占位符的IST/SOLL线框图
/seo sxo personas <url>
仅进行用户角色评分（跳过SERP分析）

Execution Pipeline

执行流程

Step 1: Target Acquisition

步骤1：目标获取

  1. Fetch the target URL via 
    scripts/fetch_page.py
    (SSRF-safe)
  2. Parse with 
    scripts/parse_html.py
    to extract: title, H1, meta description, headings hierarchy, word count, schema markup, CTAs, media elements
  3. If no keyword provided, extract primary keyword from title tag + H1 overlap
  4. Validate keyword is non-empty before proceeding
  1. 通过
    scripts/fetch_page.py
    获取目标URL（SSRF-safe）
  2. 使用
    scripts/parse_html.py
    解析页面，提取：标题、H1、元描述、标题层级、字数、Schema标记、CTA、媒体元素
  3. 如果未提供关键词，从标题标签和H1的重叠部分提取主关键词
  4. 确认关键词非空后再继续

Step 2: SERP Backwards Analysis

步骤2：反向SERP分析

Read 
references/page-type-taxonomy.md
for classification rules.
  1. Search Google for the target keyword (WebSearch)
  2. For each of the top 10 organic results, record:
    • URL and domain authority tier (brand / niche authority / unknown)
    • Page type (classify using taxonomy)
    • Content format (long-form, listicle, how-to, comparison, tool, video)
    • Word count estimate (from snippet length and page structure)
    • Schema types present (from SERP features: ratings, FAQ, HowTo)
    • Media signals (video carousel, image pack, thumbnail presence)
  3. Record SERP features present:
    • Featured snippet (paragraph / list / table / video)
    • People Also Ask (extract all visible questions)
    • Ads (top and bottom -- count and analyze ad copy themes)
    • Related searches (extract all)
    • Knowledge panel / local pack / shopping results
    • AI Overview presence and source types
  4. Calculate SERP consensus:
    • Dominant page type (>60% = strong consensus, 40-60% = mixed, <40% = fragmented)
    • Content depth expectations (average word count tier)
    • Schema expectation (most common structured data types)
    • Media expectations (video required? images critical?)
阅读
references/page-type-taxonomy.md
获取分类规则。
  1. 针对目标关键词在Google上搜索（WebSearch）
  2. 对前10个自然搜索结果，记录以下信息：
    • URL和域名权威层级（品牌/ niche authority/未知）
    • 页面类型（使用分类法进行分类）
    • 内容格式（长篇内容、列表文、指南文、对比文、工具、视频）
    • 字数估算（基于摘要长度和页面结构）
    • 存在的Schema类型（从SERP功能判断：评分、FAQ、HowTo）
    • 媒体信号（视频轮播、图片包、缩略图存在情况）
  3. 记录存在的SERP功能：
    • 精选摘要（段落/列表/表格/视频）
    • 相关问题（提取所有可见问题）
    • 广告（顶部和底部——统计数量并分析广告文案主题）
    • 相关搜索（提取所有内容）
    • 知识面板/本地包/购物结果
    • AI概述的存在情况及来源类型
  4. 计算SERP共识：
    • 主导页面类型（>60%=强共识，40-60%=混合共识，<40%=分散共识）
    • 内容深度预期（平均字数层级）
    • Schema预期（最常见的结构化数据类型）
    • 媒体预期（是否需要视频？图片是否关键？）

Step 3: Page-Type Mismatch Detection

步骤3：页面类型不匹配检测

This is the core SXO insight. Compare target page type against SERP consensus.
Mismatch severity levels:
Target TypeSERP ExpectsSeverityRecommendation
Blog PostProduct PagesCRITICALCreate dedicated product page
Blog PostComparisonHIGHRestructure as comparison with matrix
ProductInformationalHIGHAdd educational content layer
Landing PageTool/CalculatorHIGHBuild interactive tool component
Service PageLocal ResultsMEDIUMAdd location signals + local schema
Any type match-ALIGNEDFocus on content depth and UX
Classification rules:
  • Classify target page using 
    references/page-type-taxonomy.md
  • Classify each SERP result using the same taxonomy
  • Flag mismatch if target type differs from SERP dominant type
  • If SERP is fragmented (no dominant type), note opportunity for differentiation
这是SXO的核心洞察。将目标页面类型与SERP共识进行对比。
不匹配严重程度等级：
目标类型SERP预期类型严重程度建议
博客文章产品页面严重创建专门的产品页面
博客文章对比页面高重构为带矩阵的对比页面
产品页面资讯页面高添加教育内容层
着陆页工具/计算器高构建交互式工具组件
服务页面本地结果中添加位置信号+本地Schema
类型匹配-匹配专注于内容深度和UX
分类规则：
  • 使用
    references/page-type-taxonomy.md
    对目标页面进行分类
  • 使用相同分类法对每个SERP结果进行分类
  • 如果目标类型与SERP主导类型不同，标记为不匹配
  • 如果SERP分散（无主导类型），记录差异化机会

Step 4: User Story Derivation

步骤4：用户故事推导

Read 
references/user-story-framework.md
for the full framework.
From SERP signals, derive user stories:
  1. PAA questions reveal knowledge gaps and concerns
  2. Ad copy themes reveal commercial triggers and value propositions
  3. Related searches reveal the search journey (what comes before/after)
  4. Featured snippet format reveals the expected answer structure
  5. AI Overview reveals what Google considers the definitive answer
For each signal cluster, generate a user story:
As a [persona derived from signal],
I want to [goal derived from query intent],
because [emotional driver from ad copy / PAA tone],
but I'm blocked by [barrier derived from PAA questions / related searches].
Generate 3-5 user stories covering the primary intent angles.
阅读
references/user-story-framework.md
获取完整框架。
从SERP信号中推导用户故事：
  1. 相关问题（PAA） 揭示知识缺口和关注点
  2. 广告文案主题 揭示商业触发点和价值主张
  3. 相关搜索 揭示搜索旅程（之前/之后的搜索内容）
  4. 精选摘要格式 揭示预期的答案结构
  5. AI概述 揭示Google认为的权威答案
针对每个信号集群，生成用户故事：
作为[从信号中推导的用户角色],
我想要[从查询意图中推导的目标],
因为[从广告文案/PAA语气中推导的情感驱动力],
但我被[从PAA问题/相关搜索中推导的障碍]所阻碍。
生成3-5个覆盖主要意图角度的用户故事。

Step 5: Gap Analysis

步骤5：差距分析

Compare the target page against SERP expectations across 7 dimensions:
DimensionWhat to CompareScore
Page TypeTarget type vs SERP dominant type0-15
Content DepthWord count, heading depth, topic coverage0-15
UX SignalsCTA clarity, above-fold content, mobile layout0-15
Schema MarkupPresent vs expected structured data types0-15
Media RichnessImages, video, interactive elements vs SERP norm0-15
Authority SignalsE-E-A-T markers, social proof, credentials0-15
FreshnessLast updated, date signals, content recency0-10
Total: 0-100 SXO Gap Score (lower = larger gap, higher = better alignment)
从7个维度将目标页面与SERP预期进行对比：
维度对比内容分值
页面类型目标类型与SERP主导类型对比0-15
内容深度字数、标题层级、主题覆盖范围0-15
UX信号CTA清晰度、首屏内容、移动端布局0-15
Schema标记现有标记与预期结构化数据类型对比0-15
媒体丰富度图片、视频、交互元素与SERP标准对比0-15
权威信号E-E-A-T标记、社交证明、资质认证0-15
新鲜度最后更新时间、日期信号、内容时效性0-10
总分：0-100分SXO差距得分（分值越低差距越大，分值越高匹配度越好）

Step 6: Persona-Based Scoring

步骤6：基于用户角色的评分

Read 
references/persona-scoring.md
for methodology.
  1. Derive 4-7 personas from SERP intent signals:
    • Cluster PAA questions by theme
    • Segment ad copy by target audience
    • Map related searches to journey stages
  2. For each persona, score the target page on 4 dimensions (25 pts each):
    • Relevance: Does the page address this persona's need?
    • Clarity: Can this persona find their answer within 10 seconds?
    • Trust: Are there adequate trust signals for this persona?
    • Action: Is there a clear next step for this persona?
  3. Output persona cards with scores and specific improvement recommendations
  4. Sort recommendations by weakest persona first (biggest opportunity)
阅读
references/persona-scoring.md
获取评分方法。
  1. 从SERP意图信号中推导4-7个用户角色：
    • 按主题对PAA问题进行聚类
    • 按目标受众对广告文案进行细分
    • 将相关搜索映射到旅程阶段
  2. 针对每个用户角色，从4个维度评分（各25分）：
    • 相关性：页面是否满足该用户角色的需求？
    • 清晰度：该用户角色能否在10秒内找到答案？
    • 信任度：是否有足够的信任信号满足该用户角色？
    • 行动引导：是否有明确的下一步行动引导该用户角色？
  3. 输出带有评分和具体改进建议的用户角色卡片
  4. 按用户角色的薄弱程度排序建议（优先处理最大机会点）

Step 7: Wireframe Generation (Optional)

步骤7：线框图生成（可选）

Only execute when 
/seo sxo wireframe
is invoked.
Read 
references/wireframe-templates.md
for templates.
  1. Generate IST (current state) wireframe from parsed page structure
  2. Generate SOLL (target state) wireframe based on:
    • SERP consensus page type
    • Gap analysis findings
    • Persona scoring weaknesses
  3. Use ultra-concrete placeholders:
    • NOT: "Add a CTA here"
    • YES: "Add pricing CTA with annual savings badge below hero, linking to /pricing#enterprise"
  4. Output as semantic HTML section outline with annotations
仅在调用
/seo sxo wireframe
时执行。
阅读
references/wireframe-templates.md
获取模板。
  1. 从解析的页面结构生成IST（当前状态）线框图
  2. 基于以下内容生成SOLL（目标状态）线框图：
    • SERP共识页面类型
    • 差距分析结果
    • 用户角色评分的薄弱点
  3. 使用极其具体的占位符：
    • 错误示例：“在此处添加CTA”
    • 正确示例：“在Hero区域下方添加带有年度优惠标识的定价CTA，链接至/pricing#enterprise”
  4. 输出带注释的语义化HTML章节大纲

DataForSEO Integration

DataForSEO集成

If DataForSEO MCP tools are available:
  1. Before any API call, run cost estimate and confirm with user
  2. Use 
    google_organic_serp
    for precise SERP data (positions, features, snippets)
  3. Use 
    keyword_data
    for search volume and competition metrics
  4. Fall back to WebSearch if DataForSEO unavailable -- note reduced precision in output
如果有DataForSEO MCP工具可用：
  1. 在进行任何API调用前，估算成本并获得用户确认
  2. 使用
    google_organic_serp
    获取精准的SERP数据（排名、功能、摘要）
  3. 使用
    keyword_data
    获取搜索量和竞争指标
  4. 如果DataForSEO不可用，退而使用WebSearch——在输出中注明精度降低

SXO Score vs SEO Health Score

SXO得分与SEO健康得分

The SXO score is separate from the main SEO Health Score.
  • SEO Health Score = technical compliance (crawlability, speed, schema, etc.)
  • SXO Gap Score = alignment between page and SERP expectations
  • A page can score 95 SEO + 30 SXO = technically perfect but strategically misaligned
  • Both scores should be reported together when both are available
SXO得分与主SEO健康得分相互独立。
  • SEO健康得分 = 技术合规性（可抓取性、速度、Schema等）
  • SXO差距得分 = 页面与SERP预期的匹配度
  • 一个页面可能获得95分SEO + 30分SXO = 技术完美但策略错位
  • 当两者数据都可用时，应同时报告两个得分

Cross-Skill References

跨技能参考

FindingHand Off To
E-E-A-T gaps in persona scoring
/seo content
for deep E-E-A-T audit
Missing schema types
/seo schema
for generation
Local intent detected in SERP
/seo local
for GBP analysis
Content depth gaps
/seo page
for deep page analysis
Technical issues found during fetch
/seo technical
for full audit
Image/media gaps
/seo images
for optimization
发现转交至
用户角色评分中的E-E-A-T差距
/seo content
进行深度E-E-A-T审核
缺失的Schema类型
/seo schema
生成对应标记
SERP中检测到本地意图
/seo local
进行GBP分析
内容深度差距
/seo page
进行深度页面分析
获取页面时发现技术问题
/seo technical
进行完整审核
图片/媒体差距
/seo images
进行优化

Output Format

输出格式

Full SXO Analysis

完整SXO分析

undefined
undefined

SXO Analysis: [URL]

SXO分析: [URL]

Target Keyword: [keyword]

目标关键词: [keyword]

1. SERP Landscape

1. SERP格局

  • Dominant page type: [type] ([confidence]% consensus)
  • SERP features: [list]
  • Content depth norm: [word count range]
  • Schema expectation: [types]
  • 主导页面类型: [类型] ([置信度]%共识)
  • SERP功能: [列表]
  • 内容深度标准: [字数范围]
  • Schema预期: [类型]

2. Page-Type Alignment

2. 页面类型匹配度

  • Your page type: [type]
  • SERP expects: [type]
  • Verdict: [ALIGNED | MISMATCH (severity)]
  • Impact: [explanation]
  • 你的页面类型: [类型]
  • SERP预期: [类型]
  • 结论: [匹配 | 不匹配（严重程度）]
  • 影响: [说明]

3. User Stories (derived from SERP signals)

3. 用户故事（从SERP信号推导）

[3-5 user stories with source signals]
[3-5个带来源信号的用户故事]

4. Gap Analysis (SXO Score: XX/100)

4. 差距分析（SXO得分: XX/100）

[7-dimension breakdown table]
[7维度细分表格]

5. Persona Scores

5. 用户角色评分

[4-7 persona cards with 4-dimension scores]
[4-7个带4维度评分的用户角色卡片]

6. Priority Actions

6. 优先级行动

[Ranked list: fix mismatch first, then weakest persona gaps]
[排序列表：先修复不匹配问题，再处理最薄弱的用户角色差距]

7. Limitations

7. 局限性

[What could not be assessed, data source notes]
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[无法评估的内容、数据源说明]
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Error Handling

错误处理

ErrorAction
URL fetch failsReport error, suggest checking URL accessibility
No keyword provided or detectedAsk user to provide target keyword
WebSearch returns <5 resultsProceed with available data, note limited sample
SERP has no organic results (all ads)Note highly commercial SERP, analyze ad copy only
Target page is JavaScript-renderedNote limitation, use available HTML content
DataForSEO cost exceeds thresholdFall back to WebSearch, notify user
错误操作
URL获取失败报告错误，建议检查URL的可访问性
未提供或未检测到关键词请用户提供目标关键词
WebSearch返回结果<5条使用现有数据继续，注明样本有限
SERP无自然结果（全为广告）注明该SERP商业化程度高，仅分析广告文案
目标页面为JavaScript渲染注明局限性，使用可用HTML内容
DataForSEO成本超过阈值退而使用WebSearch，通知用户

Quality Checklist

质量检查清单

Before delivering results, verify:
  • Target URL was fetched via 
    scripts/fetch_page.py
    (not raw curl/fetch)
  • Page type classification uses taxonomy from references
  • At least 5 SERP results were analyzed
  • User stories cite specific SERP signals as evidence
  • Persona scores include concrete improvement suggestions
  • SXO score is clearly labeled as separate from SEO Health Score
  • Limitations section is present and honest
  • Cross-skill recommendations are included where relevant
交付结果前，验证：
  • 目标URL通过
    scripts/fetch_page.py
    获取（而非原始curl/fetch）
  • 页面类型分类使用参考文档中的分类法
  • 至少分析了5个SERP结果
  • 用户故事引用了具体的SERP信号作为证据
  • 用户角色评分包含具体的改进建议
  • SXO得分明确标注为独立于SEO健康得分
  • 存在局限性章节且内容真实
  • 相关的跨技能建议已包含在内