Example output: examples/seo-sxo-bigin-com-20260514/SXO-REPORT.md

SEO SXO — Search Experience Optimization

Diagnose why a "well-optimized" page doesn't rank. Reads the actual SERP for the target keyword, infers the page type Google is rewarding, scores the candidate page against that pattern from multiple persona perspectives, and recommends the page format that would win the SERP.

Acknowledgements: SXO-as-a-skill framework originated in claude-seo by AgriciDaniel (with the original concept credited to Florian Schmitz, Pro Hub Challenge). MIT-licensed both directions; this implementation is independent but the framing is theirs.

Prerequisites

• SE Ranking MCP server connected.
• Claude's WebFetch tool available.
• User provides: (a) target page URL, (b) target keyword the page is meant to rank for, optionally (c) target country (default us).

Process

1. Validate inputs. Both URL and keyword are required. If keyword missing, ask the user — don't infer.
2. Pull the SERP DATA_getSerpResults and DATA_getSerpTaskAdvancedResults
   • Top 10 organic results with URL, title, snippet.
   • SERP features: AI Overview presence, People Also Ask, image carousel, video carousel, shopping pack, Twitter pack, Featured Snippet, etc.
   • Mode selection (cost driver — read this). SERP feature data (AIO/PAA/carousels) only comes back when the task runs with result_type=advanced. That is also the most expensive single call this skill makes (≈ 700 credits per keyword on heavily-trafficked terms in the 2026-04 validation run).
     • Default — mode=full: runs result_type=advanced. Returns features + organic. Use when persona scoring needs PAA / AIO / pack signals (most cases).
     • mode=lite (result_type=standard): organic top-10 only, no SERP features, ≈ 50–100 credits. Use when (a) the user is screening many keywords and SERP features aren't load-bearing, (b) credits are constrained, (c) the user explicitly asks for a cheap pass. The persona scoring still runs but the SERP-features row in SXO-REPORT.md will read (skipped — lite mode) and the dominant-pattern detection will rely on URL/title heuristics alone.
     • Surface the chosen mode + estimated cost up front. If the user didn't specify and the keyword looks ad-heavy or commercial-high-volume, recommend mode=lite first and re-run with mode=full only if dominant-pattern confidence is low.
3. Pull AIO context DATA_getAiOverview
   • If AIO is present for the keyword, capture the answer text and citation list.
   • Note which top-10 organic results are also cited in the AIO.
4. Fetch user's page + top 3 winners WebFetch (always) + mcp__firecrawl-mcp__firecrawl_scrape (when available)
   • WebFetch first (free): pull markdown for the user's page + top 3 winners. Extract <title>, all H-tags, primary content structure (numbered list / table / prose / Q&A), word count, image mentions, comparison-table presence, CTA mentions.
   • Firecrawl second (4 Firecrawl credits typical — 1 per page) — recovers what WebFetch can't show:
     • JSON-LD @types per page (Product, FAQPage, BreadcrumbList, Article, Review, ItemList, etc.) — these are load-bearing for page-type classification in step 5. WebFetch's markdown can't see schema.
     • og:title / og:image / twitter:card from metadata.
     • Real <title> length (the markdown first-heading is sometimes wrong).
   • --screenshots flag (opt-in, +4 Firecrawl credits): when passed, also call firecrawl_scrape with formats: ["screenshot"] on the user's page + top 3 winners. Save as screenshots/{page}.png. Reference in the wireframe (step 8) to ground recommendations in the visual layout, not just the text outline.
   • If Firecrawl unavailable (or --no-firecrawl passed): WebFetch portion runs. Page-type classification in step 5 falls back to URL/title heuristics + content-structure heuristics only — schema-based classification is skipped. Note in 02-page-type-classification.md: Schema-based classification: skipped — Firecrawl required. Confidence in dominant-pattern detection drops accordingly.
5. Classify each top-10 result by page type
   • Use the heuristics in references/page-type-patterns.md.
   • For each: assign one of {comparison, alternatives, listicle, how-to, definition, product, editorial, forum, video}.
   • Note signals that informed the classification (URL pattern, title pattern, schema, content structure).
6. Detect the dominant pattern
   • Count types in top 10. If one type ≥ 6, that's dominant.
   • If two tie at 4–4, the SERP is "split intent" — both work; commercial vs informational angle determines which to choose.
   • Cross-reference with SERP features: video carousel → expect ≥ 2 video results; PAA → expect informational results; shopping pack → commercial intent dominant; AIO → informational consensus.
7. Score the user's page against the dominant pattern × 4 personas
   • Use the rubrics in references/persona-rubrics.md.
   • 4 personas: Skimmer, Researcher, Buyer, Validator.
   • 0–10 per persona. Apply the intent-weighting profile (also in persona-rubrics.md) to get a single 0–100 SXO score.
8. Synthesise verdict and wireframe
   • If user's page type matches dominant: SXO score reflects how well it executes the pattern. Recommend specific persona-targeted improvements.
   • If user's page type does NOT match dominant: this is the "page-type mismatch" case. Output a wireframe for the dominant page type, anchored in observed patterns from the top 3 winners.
   • Write SXO-REPORT.md.

Output format

Create a folder seo-sxo-{target-slug}-{YYYYMMDD}/ with:

seo-sxo-{target-slug}-{YYYYMMDD}/
├── 01-serp-snapshot.md            (top 10 + features + AIO)
├── 02-page-type-classification.md (each top-10 result classified)
├── 03-user-page-fingerprint.md    (the candidate page's structure)
├── 04-persona-scores.md           (4 personas × current page)
├── 05-recommendation.md           (verdict + page-type-winning wireframe)
├── screenshots/                   (only if --screenshots ran: candidate.png + winner-1/2/3.png)
└── SXO-REPORT.md                  (executive summary deliverable)

SXO-REPORT.md shape:

# SXO Report: {URL} for keyword "{keyword}"

> Snapshot dated {YYYY-MM-DD} · Country: {country}

## SERP profile
- Top 10 page types: {comparison: 4, listicle: 3, editorial: 2, video: 1}
- Dominant pattern: **{pattern}** ({n} of 10)
- SERP features: AIO ✓ ({n} citations), PAA ✓ ({n} questions), Image carousel ✗, Video carousel ✗, Shopping pack ✗
- Intent: {informational | commercial-investigation | transactional | navigational}

## Your page
- Page type: **{detected type}**
- Page-type match with dominant: **{✓ match | ✗ MISMATCH — see Verdict}**
- Word count: {n}
- Primary content structure: {prose | numbered-list | table | step-blocks | Q&A | mixed}

## SXO score: **{score}/100**

| Persona | Weight | Score | Notes |
|---|---|---|---|
| Skimmer | {%} | {n}/10 | {1-line note} |
| Researcher | {%} | {n}/10 | {1-line note} |
| Buyer | {%} | {n}/10 | {1-line note} |
| Validator | {%} | {n}/10 | {1-line note} |

## Verdict

{One paragraph. If page type matches: "Your page is the right type for this SERP. The score gap is {X} points — see persona-specific gaps below." If MISMATCH: "Your page is a {your type} but the SERP rewards {dominant type}. No amount of on-page optimization will close the gap; ship a {dominant type} page instead. Wireframe below."}

## If MISMATCH — wireframe for the winning page type

\`\`\`
{Page title pattern — e.g., "{Brand A} vs {Brand B}: 2026 Comparison"}

[Hero / TL;DR — first 200 words answer the comparative question]
[Comparison table — must be visually dominant]
[Section per dimension — each with H2 named after the dimension]
[Verdict / recommendation — explicit, justified]
[FAQ — top 3–5 PAA questions]
[Schema — Product (×2) + BreadcrumbList + FAQPage]
\`\`\`

## If MATCH — top 3 changes by persona

1. {Skimmer}: {specific change}
2. {Researcher}: {specific change}
3. {Buyer or Validator}: {specific change}

## Raw data
- 02-page-type-classification.md — every top-10 result, classified
- 03-user-page-fingerprint.md — your page's signals
- 04-persona-scores.md — full persona-by-persona breakdown

Tips

• Respect rate limit: 10 req/sec. The SERP calls in step 2/3 are fast; WebFetch calls in step 4 dominate latency, not API.
• Cost is mode-dependent. mode=full is ~750–900 SE Ranking credits per run (the SERP-advanced call dominates). mode=lite is ~80–150 SE Ranking credits. Always call DATA_getCreditBalance before running and surface the estimate against remaining balance. Step 4 adds 4 Firecrawl credits when Firecrawl is available, +4 more if --screenshots is passed. Pass --no-firecrawl to skip both.
• result_type=advanced is the only way to get AIO / PAA / pack data. The standard SERP endpoint returns organic-only. Don't try to reconstruct SERP features from organic results — that's the cost the user is paying for.
• Page-type classification is a heuristic — references/page-type-patterns.md documents the signals so users can override. If the heuristic gets a result wrong, edit that file with the correction.
• The 4 personas are opinionated. They come from the framework's original source — don't invent more without good reason.
• The SXO score is directional. An 85/100 doesn't guarantee ranking; a 35/100 strongly suggests the page won't break through. Treat as a diagnostic, not a forecast.
• When the dominant pattern is split-intent (4-4), ship two pages — one per intent — rather than trying to make one page serve both. Google's SERPs reflect this split for a reason.
• The wireframe in MISMATCH mode is a starting point. The user still needs to write the content. This skill diagnoses; seo-content-brief produces the writer-ready brief.