Search Experience Optimization (SXO)

Core Insight

Commands

/seo sxo <url>

/seo sxo <url> <keyword>

/seo sxo wireframe <url>

/seo sxo personas <url>

Execution Pipeline

Step 1: Target Acquisition

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

Step 2: SERP Backwards Analysis

references/page-type-taxonomy.md

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

• 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

references/user-story-framework.md

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

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].

Step 5: Gap Analysis

Step 6: Persona-Based Scoring

references/persona-scoring.md

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)

/seo sxo wireframe

references/wireframe-templates.md

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

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

• 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

/seo content

/seo schema

/seo local

/seo page

/seo technical

/seo images

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

• 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