seo-sxo

Compare original and translation side by side

🇺🇸

Original

English

🇨🇳

Translation

Chinese

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

Command	Purpose
`/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

Fetch the target URL via
```
scripts/fetch_page.py
```
(SSRF-safe)
Parse with
```
scripts/parse_html.py
```
to extract: title, H1, meta description, headings hierarchy, word count, schema markup, CTAs, media elements
If no keyword provided, extract primary keyword from title tag + H1 overlap
Validate keyword is non-empty before proceeding

通过
```
scripts/fetch_page.py
```
获取目标URL（SSRF-safe）
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
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
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.

Search Google for the target keyword (WebSearch)
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)
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
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。

针对目标关键词在Google上搜索（WebSearch）
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）
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
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 Type	SERP Expects	Severity	Recommendation
Blog Post	Product Pages	CRITICAL	Create dedicated product page
Blog Post	Comparison	HIGH	Restructure as comparison with matrix
Product	Informational	HIGH	Add educational content layer
Landing Page	Tool/Calculator	HIGH	Build interactive tool component
Service Page	Local Results	MEDIUM	Add location signals + local schema
Any type match	-	ALIGNED	Focus 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:

PAA questions reveal knowledge gaps and concerns
Ad copy themes reveal commercial triggers and value propositions
Related searches reveal the search journey (what comes before/after)
Featured snippet format reveals the expected answer structure
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：

相关问题（PAA） 揭示知识缺口和关注点
广告文案主题 揭示商业触发点和价值主张
相关搜索 揭示搜索旅程（之前/之后的搜索内容）
精选摘要格式 揭示预期的答案结构
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:

Dimension	What to Compare	Score
Page Type	Target type vs SERP dominant type	0-15
Content Depth	Word count, heading depth, topic coverage	0-15
UX Signals	CTA clarity, above-fold content, mobile layout	0-15
Schema Markup	Present vs expected structured data types	0-15
Media Richness	Images, video, interactive elements vs SERP norm	0-15
Authority Signals	E-E-A-T markers, social proof, credentials	0-15
Freshness	Last updated, date signals, content recency	0-10

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.

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
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?
Output persona cards with scores and specific improvement recommendations
Sort recommendations by weakest persona first (biggest opportunity)

阅读

references/persona-scoring.md

获取评分方法。

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
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？
输出带有评分和具体改进建议的用户角色卡片
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.

Generate IST (current state) wireframe from parsed page structure
Generate SOLL (target state) wireframe based on:
- SERP consensus page type
- Gap analysis findings
- Persona scoring weaknesses
Use ultra-concrete placeholders:
- NOT: "Add a CTA here"
- YES: "Add pricing CTA with annual savings badge below hero, linking to /pricing#enterprise"
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。

从解析的页面结构生成IST（当前状态）线框图
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
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”
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:

Before any API call, run cost estimate and confirm with user
Use
```
google_organic_serp
```
for precise SERP data (positions, features, snippets)
Use
```
keyword_data
```
for search volume and competition metrics
Fall back to WebSearch if DataForSEO unavailable -- note reduced precision in output

如果有DataForSEO MCP工具可用：

在进行任何API调用前，估算成本并获得用户确认
使用
```
google_organic_serp
```
获取精准的SERP数据（排名、功能、摘要）
使用
```
keyword_data
```
获取搜索量和竞争指标
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

Finding	Hand 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]

undefined

[无法评估的内容、数据源说明]

undefined

Error Handling

错误处理

Error	Action
URL fetch fails	Report error, suggest checking URL accessibility
No keyword provided or detected	Ask user to provide target keyword
WebSearch returns <5 results	Proceed 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-rendered	Note limitation, use available HTML content
DataForSEO cost exceeds threshold	Fall 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