serp-analysis

aaron-he-zhu/seo-geo-claude-skills · updated Apr 8, 2026

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$npx skills add https://github.com/aaron-he-zhu/seo-geo-claude-skills --skill serp-analysis
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summary

Analyze search engine results pages to understand ranking factors, SERP features, and AI overview patterns.

  • 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
skill.md

SERP Analysis

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.

When This Must Trigger

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.

  • Before creating content for a target keyword
  • Understanding why certain pages rank #1
  • Identifying SERP feature opportunities (featured snippets, PAA)
  • Analyzing AI Overview/SGE patterns
  • Evaluating keyword difficulty more accurately
  • Planning content format based on what ranks
  • Identifying ranking factors for specific queries

What This Skill Does

  1. SERP Composition Analysis: Maps what appears on the results page
  2. Ranking Factor Identification: Reveals why top results rank
  3. SERP Feature Mapping: Identifies featured snippets, PAA, knowledge panels
  4. AI Overview Analysis: Examines when and how AI answers appear
  5. Intent Signal Detection: Confirms user intent from SERP composition
  6. Content Format Recommendations: Suggests optimal format based on SERP
  7. Difficulty Assessment: Evaluates realistic ranking potential

Quick Start

Start with one of these prompts. Finish with a short handoff summary using the repository format in Skill Contract.

Basic SERP Analysis

Analyze the SERP for [keyword]
What does it take to rank for [keyword]?

Feature-Specific Analysis

Analyze featured snippet opportunities for [keyword list]
Which of these keywords trigger AI Overviews? [keyword list]

Competitive SERP Analysis

Why does [URL] rank #1 for [keyword]?

Skill Contract

Expected output: a prioritized research brief, evidence-backed findings, and a short handoff summary ready for memory/research/.

  • Reads: user goals, target market inputs, available tool data, and prior strategy from CLAUDE.md and the shared State Model when available.
  • Writes: a user-facing research deliverable plus a reusable summary that can be stored under memory/research/.
  • Promotes: durable keyword priorities, competitor facts, entity candidates, and strategic decisions to CLAUDE.md, memory/decisions.md, and memory/research/; hand canonical entity work to entity-optimizer.
  • Next handoff: use the Next Best Skill below when the findings are ready to drive action.

Data Sources

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:

  1. Target keyword(s) to analyze
  2. SERP screenshots or detailed descriptions of search results
  3. URLs of top 10 ranking pages
  4. Search location and device type (mobile/desktop)
  5. Any observations about SERP features (featured snippets, PAA, AI Overviews)

Proceed with the full analysis using provided data. Note in the output which metrics are from automated collection vs. user-provided data.

Instructions

When a user requests SERP analysis:

  1. Understand the Query

    Clarify if needed:

    • Target keyword(s) to analyze
    • Search location/language
    • Device type (mobile/desktop)
    • Specific questions about the SERP
  2. 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.

  3. 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.

  4. 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.

  5. 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).

  6. Determine Search Intent

    Confirm primary intent from SERP composition. Document evidence, intent breakdown percentages, and content format implications (format, tone, CTA).

  7. 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.

  8. 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.

Validation Checkpoints

Input Validation

  • Target keyword(s) clearly specified
  • Search location and device type confirmed
  • SERP data is current (date confirmed)
  • Top 10 ranking URLs identified or provided

Output Validation

  • Every recommendation cites specific data points (not generic advice)
  • SERP composition mapped with all features documented
  • Ranking factors identified from actual top 10 analysis (not assumptions)
  • Content requirements based on observed patterns in current SERP
  • Source of each data point clearly stated (~~SEO tool data, ~~AI monitor data, user-provided, or manual observation)

Example

Reference: See references/example-report.md for a complete example analyzing the SERP for "how to start a podcast".

Advanced Analysis

Multi-Keyword SERP Comparison

Compare SERPs for [keyword 1], [keyword 2], [keyword 3]

Historical SERP Changes

How has the SERP for [keyword] changed over time?

Local SERP Variations

Compare SERP for [keyword] in [location 1] vs [location 2]

Mobile vs Desktop SERP

Analyze mobile vs desktop SERP differences for [keyword]

Tips for Success

  1. Always check SERP before writing - Don't assume, verify
  2. Match content format to SERP - If lists rank, write lists
  3. Identify SERP feature opportunities - Lower competition than #1
  4. Note SERP volatility - Stable SERPs are harder to break into
  5. Study the outliers - Why does a weaker site rank? Opportunity!
  6. Consider AI Overview optimization - Growing importance

Save Results

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:

  • One-line headline finding
  • Top 3-5 actionable items
  • Open loops or blockers
  • Source data references

If any findings should influence ongoing strategy, recommend promoting key conclusions to memory/hot-cache.md.

Reference Materials

  • Analysis Templates — Detailed templates for each analysis step (SERP composition, top results, ranking patterns, features, intent, difficulty, recommendations)
  • SERP Feature Taxonomy — Complete taxonomy of SERP features with trigger conditions, AI overview framework, intent signals, and volatility assessment
  • Example Report — Complete example analyzing the SERP for "how to start a podcast"

Next Best Skill

  • Primary: seo-content-writer — turn SERP patterns into a content brief or page structure.
how to use serp-analysis

How to use serp-analysis on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add serp-analysis
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/aaron-he-zhu/seo-geo-claude-skills --skill serp-analysis

The skills CLI fetches serp-analysis from GitHub repository aaron-he-zhu/seo-geo-claude-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/serp-analysis

Reload or restart Cursor to activate serp-analysis. Access the skill through slash commands (e.g., /serp-analysis) or your agent's skill management interface.

Security & Verification Notice

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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

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Use Cases

User Story & Requirements Generation

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

Competitive Analysis

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

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

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

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ 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.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.634 reviews
  • Pratham Ware· Dec 28, 2024

    Registry listing for serp-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Meera Rao· Dec 24, 2024

    Solid pick for teams standardizing on skills: serp-analysis is focused, and the summary matches what you get after install.

  • Zaid Lopez· Dec 20, 2024

    We added serp-analysis from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Maya Yang· Dec 4, 2024

    Keeps context tight: serp-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Amina Menon· Nov 23, 2024

    serp-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Chen Sethi· Nov 23, 2024

    I recommend serp-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Sakshi Patil· Nov 19, 2024

    serp-analysis reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Kiara Brown· Nov 15, 2024

    serp-analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Noah Lopez· Nov 11, 2024

    Useful defaults in serp-analysis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Naina Sethi· Oct 14, 2024

    serp-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

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