seo-sxo

Name: seo-sxo
Author: AgriciDaniel

2.8kinstalls1.8kforks

How to install seo-sxo?

agricidaniel/claude-seo/seo-sxo

$npx -y skills add AgriciDaniel/claude-seo --skill seo-sxo

Installs into the current project.

Summary

Search Experience Optimization: reads Google SERPs backwards to detect page-type mismatches, derives user stories from search intent signals, and scores pages from multiple persona perspectives. Identifies why well-optimized pages fail to rank by analyzing what Google rewards for

Files · 1

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SKILL.md

1	# Search Experience Optimization (SXO)
2
3	SXO bridges the gap between SEO (what Google rewards) and UX (what users need).
4	Traditional SEO audits check technical health. SXO asks: "Does this page deserve
5	to rank for this keyword based on what Google is actually rewarding in the SERP?"
6
7	## Core Insight
8
9	A page can score 95/100 on technical SEO and still fail to rank because it is the
10	wrong page type for the keyword. If Google shows 8 product pages and 2 comparison
11	pages for your keyword, your blog post will never break through -- no matter how
12	well-optimized it is.
13
14	## Commands
15
16	\| Command \| Purpose \|
17	\|---------\|---------\|
18	\| `/seo sxo <url>` \| Full SXO analysis (auto-detect keyword from page) \|
19	\| `/seo sxo <url> <keyword>` \| Full SXO analysis for a specific keyword \|
20	\| `/seo sxo wireframe <url>` \| Generate IST/SOLL wireframe with concrete placeholders \|
21	\| `/seo sxo personas <url>` \| Persona-only scoring (skip SERP analysis) \|
22
23	## Execution Pipeline
24
25	### Step 1: Target Acquisition
26
27	1. Fetch the target URL via `scripts/render_page.py --mode auto` (SPA-aware and SSRF-safe)
28	2. Parse with `scripts/parse_html.py` to extract: title, H1, meta description,
29	headings hierarchy, word count, schema markup, CTAs, media elements
30	3. If no keyword provided, extract primary keyword from title tag + H1 overlap
31	4. Validate keyword is non-empty before proceeding
32
33	### Step 2: SERP Backwards Analysis
34
35	Read `references/page-type-taxonomy.md` for classification rules.
36
37	1. Search Google for the target keyword (WebSearch)
38	2. For each of the top 10 organic results, record:
39	- URL and domain authority tier (brand / niche authority / unknown)
40	- Page type (classify using taxonomy)
41	- Content format (long-form, listicle, how-to, comparison, tool, video)
42	- Word count estimate (from snippet length and page structure)
43	- Schema types present (from currently supported SERP features; exclude FAQ/HowTo)
44	- Media signals (video carousel, image pack, thumbnail presence)
45	3. Record SERP features present:
46	- Featured snippet (paragraph / list / table / video)
47	- People Also Ask (extract all visible questions)
48	- Ads (top and bottom -- count and analyze ad copy themes)
49	- Related searches (extract all)
50	- Knowledge panel / local pack / shopping results
51	- AI Overview presence and source types
52	4. Calculate SERP consensus:
53	- Dominant page type (>60% = strong consensus, 40-60% = mixed, <40% = fragmented)
54	- Content depth expectations (average word count tier)
55	- Schema expectation (most common structured data types)
56	- Media expectations (video required? images critical?)
57
58	### Step 3: Page-Type Mismatch Detection
59
60	This is the core SXO insight. Compare target page type against SERP consensus.
61
62	Mismatch severity levels:
63
64	\| Target Type \| SERP Expects \| Severity \| Recommendation \|
65	\|-------------\|-------------\|----------\|----------------\|
66	\| Blog Post \| Product Pages \| CRITICAL \| Create dedicated product page \|
67	\| Blog Post \| Comparison \| HIGH \| Restructure as comparison with matrix \|
68	\| Product \| Informational \| HIGH \| Add educational content layer \|
69	\| Landing Page \| Tool/Calculator \| HIGH \| Build interactive tool component \|
70	\| Service Page \| Local Results \| MEDIUM \| Add location signals + local schema \|
71	\| Any type match \| - \| ALIGNED \| Focus on content depth and UX \|
72
73	Classification rules:
74	- Classify target page using `references/page-type-taxonomy.md`
75	- Classify each SERP result using the same taxonomy
76	- Flag mismatch if target type differs from SERP dominant type
77	- If SERP is fragmented (no dominant type), note opportunity for differentiation
78
79	### Step 4: User Story Derivation
80
81	Read `references/user-story-framework.md` for the full framework.
82
83	From SERP signals, derive user stories:
84
85	1. PAA questions reveal knowledge gaps and concerns
86	2. Ad copy themes reveal commercial triggers and value propositions
87	3. Related searches reveal the search journey (what comes before/after)
88	4. Featured snippet format reveals the expected answer structure
89	5. AI Overview reveals what Google considers the definitive answer
90
91	For each signal cluster, generate a user story:
92	```
93	As a [persona derived from signal],
94	I want to [goal derived from query intent],
95	because [emotional driver from ad copy / PAA tone],
96	but

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Type

seo-sxo

2.8kinstalls1.8kforks

View on GitHub

How to install seo-sxo?

agricidaniel/claude-seo/seo-sxo

$npx -y skills add AgriciDaniel/claude-seo --skill seo-sxo

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Search Experience Optimization (SXO)
2
3	SXO bridges the gap between SEO (what Google rewards) and UX (what users need).
4	Traditional SEO audits check technical health. SXO asks: "Does this page deserve
5	to rank for this keyword based on what Google is actually rewarding in the SERP?"
6
7	## Core Insight
8
9	A page can score 95/100 on technical SEO and still fail to rank because it is the
10	wrong page type for the keyword. If Google shows 8 product pages and 2 comparison
11	pages for your keyword, your blog post will never break through -- no matter how
12	well-optimized it is.
13
14	## Commands
15
16	\| Command \| Purpose \|
17	\|---------\|---------\|
18	\| `/seo sxo <url>` \| Full SXO analysis (auto-detect keyword from page) \|
19	\| `/seo sxo <url> <keyword>` \| Full SXO analysis for a specific keyword \|
20	\| `/seo sxo wireframe <url>` \| Generate IST/SOLL wireframe with concrete placeholders \|
21	\| `/seo sxo personas <url>` \| Persona-only scoring (skip SERP analysis) \|
22
23	## Execution Pipeline
24
25	### Step 1: Target Acquisition
26
27	1. Fetch the target URL via `scripts/render_page.py --mode auto` (SPA-aware and SSRF-safe)
28	2. Parse with `scripts/parse_html.py` to extract: title, H1, meta description,
29	headings hierarchy, word count, schema markup, CTAs, media elements
30	3. If no keyword provided, extract primary keyword from title tag + H1 overlap
31	4. Validate keyword is non-empty before proceeding
32
33	### Step 2: SERP Backwards Analysis
34
35	Read `references/page-type-taxonomy.md` for classification rules.
36
37	1. Search Google for the target keyword (WebSearch)
38	2. For each of the top 10 organic results, record:
39	- URL and domain authority tier (brand / niche authority / unknown)
40	- Page type (classify using taxonomy)
41	- Content format (long-form, listicle, how-to, comparison, tool, video)
42	- Word count estimate (from snippet length and page structure)
43	- Schema types present (from currently supported SERP features; exclude FAQ/HowTo)
44	- Media signals (video carousel, image pack, thumbnail presence)
45	3. Record SERP features present:
46	- Featured snippet (paragraph / list / table / video)
47	- People Also Ask (extract all visible questions)
48	- Ads (top and bottom -- count and analyze ad copy themes)
49	- Related searches (extract all)
50	- Knowledge panel / local pack / shopping results
51	- AI Overview presence and source types
52	4. Calculate SERP consensus:
53	- Dominant page type (>60% = strong consensus, 40-60% = mixed, <40% = fragmented)
54	- Content depth expectations (average word count tier)
55	- Schema expectation (most common structured data types)
56	- Media expectations (video required? images critical?)
57
58	### Step 3: Page-Type Mismatch Detection
59
60	This is the core SXO insight. Compare target page type against SERP consensus.
61
62	Mismatch severity levels:
63
64	\| Target Type \| SERP Expects \| Severity \| Recommendation \|
65	\|-------------\|-------------\|----------\|----------------\|
66	\| Blog Post \| Product Pages \| CRITICAL \| Create dedicated product page \|
67	\| Blog Post \| Comparison \| HIGH \| Restructure as comparison with matrix \|
68	\| Product \| Informational \| HIGH \| Add educational content layer \|
69	\| Landing Page \| Tool/Calculator \| HIGH \| Build interactive tool component \|
70	\| Service Page \| Local Results \| MEDIUM \| Add location signals + local schema \|
71	\| Any type match \| - \| ALIGNED \| Focus on content depth and UX \|
72
73	Classification rules:
74	- Classify target page using `references/page-type-taxonomy.md`
75	- Classify each SERP result using the same taxonomy
76	- Flag mismatch if target type differs from SERP dominant type
77	- If SERP is fragmented (no dominant type), note opportunity for differentiation
78
79	### Step 4: User Story Derivation
80
81	Read `references/user-story-framework.md` for the full framework.
82
83	From SERP signals, derive user stories:
84
85	1. PAA questions reveal knowledge gaps and concerns
86	2. Ad copy themes reveal commercial triggers and value propositions
87	3. Related searches reveal the search journey (what comes before/after)
88	4. Featured snippet format reveals the expected answer structure
89	5. AI Overview reveals what Google considers the definitive answer
90
91	For each signal cluster, generate a user story:
92	```
93	As a [persona derived from signal],
94	I want to [goal derived from query intent],
95	because [emotional driver from ad copy / PAA tone],
96	but

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Type