Agent-Skills.md

Agent Skills: Search Experience Optimization (SXO)

>

seoID: georgekhananaev/claude-skills-vault/seo-sxo

Repository

georgekhananaev/claude-skills-vault
georgekhananaevLicense: MIT
2910

Install this agent skill to your local

pnpm dlx add-skill https://github.com/georgekhananaev/claude-skills-vault/tree/HEAD/.claude/skills/claude-seo/skills/seo-sxo

Skill Files

Browse the full folder contents for seo-sxo.

Download Skill

Loading file tree…

.claude/skills/claude-seo/skills/seo-sxo/SKILL.md

Skill Metadata

Name
seo-sxo
Description
>

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 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:

| 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):
    • 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

| 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