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

Command

Purpose

/seo sxo

Full SXO analysis (auto-detect keyword from page)

/seo sxo

Full SXO analysis for a specific keyword

/seo sxo wireframe

Generate IST/SOLL wireframe with concrete placeholders

/seo sxo personas

Persona-only scoring (skip SERP analysis)

Execution Pipeline

Step 1: Target Acquisition

Fetch the target URL via

scripts/fetch_page.py

(SSRF-safe)

Parse with

scripts/parse_html.py

to extract: title, H1, meta description,

headings hierarchy, word count, schema markup, CTAs, media elements

If no keyword provided, extract primary keyword from title tag + H1 overlap

Validate keyword is non-empty before proceeding

Step 2: SERP Backwards Analysis

Read

references/page-type-taxonomy.md

for classification rules.

Search Google for the target keyword (WebSearch)

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)

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

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:

PAA questions

reveal knowledge gaps and concerns

Ad copy themes

reveal commercial triggers and value propositions

Related searches

reveal the search journey (what comes before/after)

Featured snippet format

reveals the expected answer structure

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:

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.

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

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? Output persona cards with scores and specific improvement recommendations 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. Generate IST (current state) wireframe from parsed page structure Generate SOLL (target state) wireframe based on: SERP consensus page type Gap analysis findings Persona scoring weaknesses Use ultra-concrete placeholders: NOT: "Add a CTA here" YES: "Add pricing CTA with annual savings badge below hero, linking to /pricing#enterprise" Output as semantic HTML section outline with annotations DataForSEO Integration If DataForSEO MCP tools are available: Before any API call , run cost estimate and confirm with user Use google_organic_serp for precise SERP data (positions, features, snippets) Use keyword_data for search volume and competition metrics 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

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 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 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