Analyze search engine results pages to understand ranking factors, SERP features, and AI overview patterns.
Works with
Maps SERP composition including AI Overviews, featured snippets, People Also Ask, knowledge panels, and organic results to reveal what Google shows for a query
Analyzes top 10 ranking pages for common patterns: domain authority, content length, freshness, structure, and on-page factors that drive rankings
Identifies SERP feature opportunities (featured snippets, PAA answers) an
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionserp-analysisExecute the skills CLI command in your project's root directory to begin installation:
Fetches serp-analysis from aaron-he-zhu/seo-geo-claude-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate serp-analysis. Access via /serp-analysis in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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.
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Registry listing for serp-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: serp-analysis is focused, and the summary matches what you get after install.
We added serp-analysis from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: serp-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.
serp-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend serp-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
serp-analysis reduced setup friction for our internal harness; good balance of opinion and flexibility.
serp-analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in serp-analysis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
serp-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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