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 w

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 1. Fetch the target URL via "${CLAUDE PLUGIN ROOT}/scripts/claude seo" run render page.py <URL mode auto (SPA aware and SSRF safe) 2. Parse with "${CLAUDE PLUGIN ROOT}/scripts/claude seo" run parse html.py <URL 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 currently supported SERP features; exclude 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: 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: 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: 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. 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 serp organic live advanced for precise SERP data (positions, features, snippets) 3. Use kw data google ads search volume 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 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 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 "${CLAUDE PLUGIN ROOT}/scripts/claude seo" run render page.py <URL 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

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