/seo-sxo

name: seo-sxo
description: >
  Diagnose search-experience and intent mismatches using SERP page types, user
  stories, and persona scoring. Use when ranking problems appear intent- or
  layout-driven.
user-invocable: true
argument-hint: "<url> [keyword]"
license: MIT
metadata:
  author: AgriciDaniel
  original_author: "Florian Schmitz (Pro Hub Challenge)"
  version: "2.6.0"
  category: seo

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:

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.

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):

• **Releva