القائمة
Analyze SERPs: ranking factors, features, intent patterns, AI overviews, featured snippets. SERP分析/搜索结果
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npx skills add https://github.com/MODSetter/SurfSense --skill serp-analysisيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
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This skill encodes Emil Kowalski's philosophy on UI polish, component design, animation decisions, and the invisible details that make software feel great.
Reviews animation and motion code against a high craft bar derived from Emil Kowalski's design engineering philosophy. Default to flagging; approval is earned.
Structured approach to finding and synthesizing information from the user's knowledge base
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عرض SKILL.md
| name | serp-analysis |
| description | Analyze SERPs: ranking factors, features, intent patterns, AI overviews, featured snippets. SERP分析/搜索结果 |
| version | 6.0.0 |
| license | Apache-2.0 |
| compatibility | Claude Code ≥1.0, skills.sh marketplace, ClawHub marketplace, Vercel Labs skills ecosystem. No system packages required. Optional: MCP network access for SEO tool integrations. |
| allowed-tools | WebFetch |
| homepage | https://github.com/aaron-he-zhu/seo-geo-claude-skills |
| when_to_use | Use when analyzing search engine results pages, SERP features, featured snippets, People Also Ask, or understanding ranking patterns for a query. |
| argument-hint | <keyword or query> |
| metadata | {"author":"aaron-he-zhu","version":"6.0.0","geo-relevance":"high","tags":["seo","geo","serp-analysis","serp-features","featured-snippet","ai-overview","people-also-ask","search-intent","SERP分析","検索結果分析","검색결과","analisis-serp"],"triggers":["analyze search results","SERP analysis","what ranks for","SERP features","why does this page rank","featured snippets","AI overviews","what's on page one for this query","who ranks for this keyword","what does Google show for","what shows up for this search","who is on page one","why does this page rank first","what SERP features appear for","SERP分析","搜索结果分析","精选摘要","AI概览","谁排第一","搜索结果长什么样","谁排在前面","検索結果ページ分析","検索結果分析","強調スニペット","검색 결과 분석","SERP 분석","análisis SERP","análisis de resultados de búsqueda","análise de SERP","serp anaylsis"]} |
SEO & GEO Skills Library · 20 skills for SEO + GEO · ClawHub · skills.sh System Mode: This research skill follows the shared Skill Contract and State Model.
This skill analyzes Search Engine Results Pages to reveal what's working for ranking content, which SERP features appear, and what triggers AI-generated answers. Understand the battlefield before creating content.
System role: Research layer skill. It turns market signals into reusable strategic inputs for the rest of the library.
Use this when the conversation involves any of these situations — even if the user does not use SEO terminology:
Use this whenever the task needs reusable market intelligence that should influence strategy, not just an ad hoc answer.
Start with one of these prompts. Finish with a short handoff summary using the repository format in Skill Contract.
Analyze the SERP for [keyword]
What does it take to rank for [keyword]?
Analyze featured snippet opportunities for [keyword list]
Which of these keywords trigger AI Overviews? [keyword list]
Why does [URL] rank #1 for [keyword]?
Expected output: a prioritized research brief, evidence-backed findings, and a short handoff summary ready for memory/research/.
memory/research/.CLAUDE.md, memory/decisions.md, and memory/research/; hand canonical entity work to entity-optimizer.Next Best Skill below when the findings are ready to drive action.Note: All integrations are optional. This skill works without any API keys — users provide data manually when no tools are connected.
See CONNECTORS.md for tool category placeholders.
With ~~SEO tool + ~~search console + ~~AI monitor connected: Automatically fetch SERP snapshots for target keywords, extract ranking page metrics (domain authority, backlinks, content length), pull SERP feature data, and check AI Overview presence using ~~AI monitor. Historical SERP change data and mobile vs. desktop variations can be retrieved automatically.
With manual data only: Ask the user to provide:
Proceed with the full analysis using provided data. Note in the output which metrics are from automated collection vs. user-provided data.
When a user requests SERP analysis:
Understand the Query
Clarify if needed:
Map SERP Composition
Document all elements appearing on the results page: AI Overview, ads, featured snippet, organic results, PAA, knowledge panel, image pack, video results, local pack, shopping results, news results, sitelinks, and related searches.
Analyze Top Ranking Pages
For each of the top 10 results, document: URL, domain, domain authority, content type, word count, publish/update dates, on-page factors (title, meta description, H1, URL structure), content structure (headings, media, tables, FAQ), estimated metrics (backlinks, referring domains), and why it ranks.
Identify Ranking Patterns
Analyze common characteristics across top 5 results: word count, domain authority, backlinks, content freshness, HTTPS, mobile optimization. Document content format distribution, domain type distribution, and key success factors.
Analyze SERP Features
For each present SERP feature: analyze the current holder, content format, and strategy to win. Cover Featured Snippet (type, content, winning strategy), PAA (questions, current answers, optimization approach), and AI Overview (sources cited, content patterns, citation strategy).
Determine Search Intent
Confirm primary intent from SERP composition. Document evidence, intent breakdown percentages, and content format implications (format, tone, CTA).
Calculate True Difficulty
Score overall difficulty (1-100) based on: top 10 domain authority, page authority, backlinks required, content quality bar, and SERP stability. Provide realistic assessments for new, growing, and established sites, plus easier alternatives.
Generate Recommendations
Produce a summary with: Key Findings, Content Requirements to Rank (minimum requirements + differentiators), SERP Feature Strategy, Recommended Content Outline, and Next Steps.
Reference: See references/analysis-templates.md for detailed templates for each step.
Reference: See references/example-report.md for a complete example analyzing the SERP for "how to start a podcast".
Compare SERPs for [keyword 1], [keyword 2], [keyword 3]
How has the SERP for [keyword] changed over time?
Compare SERP for [keyword] in [location 1] vs [location 2]
Analyze mobile vs desktop SERP differences for [keyword]
After delivering findings to the user, ask:
"Save these results for future sessions?"
If yes, write a dated summary to memory/research/serp-analysis/YYYY-MM-DD-<topic>.md containing:
If any findings should influence ongoing strategy, recommend promoting key conclusions to memory/hot-cache.md.