SEO Sxo

Search Experience Optimization (SXO)

Shared Data Cache

Step 0 -- Check shared data cache:

Before gathering, check .seo-cache/ for reusable context from related SEO skills. Reference: ../seo/references/shared-data-cache.md for schemas and dependency map.

Check these cache files when present:

• .seo-cache/site-meta.json for domain, business type, industry, and crawl context

• .seo-cache/audit-scores.json for prior full-audit priorities

• .seo-cache/pages/{url-slug}/page-analysis.json for page-level context when a URL is provided

• If found: parse and use clearly valid fields (note "Using cached [X] from [date]")

• If missing, corrupt, or irrelevant: continue with fresh evidence

• If the user says "refresh" or "re-run": ignore cache reads and overwrite on write

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/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

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/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

Write to shared data cache

After completing all work, write a concise JSON summary to .seo-cache/ when the workflow produced durable findings. Use the schemas and naming rules in ../seo/references/shared-data-cache.md; include at least cache_type, analyzed_at, source URL/domain, key findings, issues, recommendations, and tool limitations. Add .seo-cache/ to .gitignore if it is missing.