Seo SxoSAFE

Universal SEO skill for Claude Code. 26 sub-skills + 19 sub-agents covering technical SEO, E-E-A-T, schema, GEO/AEO, agent readiness (Lighthouse Agentic Browsing, WebMCP, llms.txt), backlinks, local SEO, e-commerce, international SEO, Google APIs, and PDF/Excel reporting. 9 optional extensions for l

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
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: "2.4.2"
  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

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

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pinned

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No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.