seo-sxo

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

1. If no keyword provided, extract primary keyword from title tag + H1 overlap
2. 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)

1. 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

1. 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

1. 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?

1. Output persona cards with scores and specific improvement recommendations
2. 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

1. Use ultra-concrete placeholders:

• NOT: "Add a CTA here"
• YES: "Add pricing CTA with annual savings badge below hero, linking to /pricing#enterprise"

1. 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