seo-sxo

name: seo-sxo description: > Search Experience Optimization: reads Google SERPs backwards to detect page-type mismatches, derives user stories from search intent signals, and scores pages from multiple persona perspectives. Identifies why well-optimized pages fail to rank by analyzing what Google rewards for each keyword. Use when user says "SXO", "search experience", "page type mismatch", "SERP analysis", "user story", "persona scoring", "why isn't my page ranking", "intent mismatch", or "wireframe". user-invocable: true argument-hint: "<url> [keyword]" license: MIT metadata: author: AgriciDaniel original_author: "Florian Schmitz (Pro Hub Challenge)" version: "2.2.0" category: seo

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

Execution Pipeline

Step 1: Target Acquisition

1. Fetch the target URL via scripts/render_page.py --mode auto (SPA-aware and 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

Step 2: SERP Backwards Analysis

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?)

Step 3: Page-Type Mismatch Detection

This is the core SXO insight. Compare target page type against SERP consensus.

Mismatch severity levels:

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:

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.

Step 5: Gap Analysis

Compare the target page against SERP expectations across 7 dimensions:

Total: 0-100 SXO Gap Score (lower = larger gap, higher = better alignment)

Step 6: Persona-Based Scoring

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)

Step 7: Wireframe Generation (Optional)

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

DataForSEO Integration

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

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

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

Quality Checklist

Before delivering results, verify:

• Target URL was fetched via scripts/render_page.py --mode auto (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