Search Experience Optimization (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?"

Core Insight

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.

Commands

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)

Execution Pipeline

Step 1: Target Acquisition

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

Step 2: SERP Backwards Analysis

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?)

Step 3: Page-Type Mismatch Detection

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

Step 4: User Story Derivation

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.

Step 5: Gap Analysis

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)

Step 6: Persona-Based Scoring

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)

Step 7: Wireframe Generation (Optional)

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

DataForSEO Integration

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

SXO Score vs SEO Health Score

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

Cross-Skill References

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

Output Format

Full SXO Analysis

## SXO Analysis: [URL]
### Target Keyword: [keyword]

### 1. SERP Landscape
- Dominant page type: [type] ([confidence]% consensus)
- SERP features: [list]
- Content depth norm: [word count range]
- Schema expectation: [types]

### 2. Page-Type Alignment
- Your page type: [type]
- SERP expects: [type]
- Verdict: [ALIGNED | MISMATCH (severity)]
- Impact: [explanation]

### 3. User Stories (derived from SERP signals)
[3-5 user stories with source signals]

### 4. Gap Analysis (SXO Score: XX/100)
[7-dimension breakdown table]

### 5. Persona Scores
[4-7 persona cards with 4-dimension scores]

### 6. Priority Actions
[Ranked list: fix mismatch first, then weakest persona gaps]

### 7. Limitations
[What could not be assessed, data source notes]

Error Handling

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

Quality Checklist

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

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