geo-fundamentals
Optimization for AI-powered search engines.
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Installation Guide
How to use geo-fundamentals on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
geo-fundamentals
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches geo-fundamentals from davila7/claude-code-templates and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate geo-fundamentals. Access via /geo-fundamentals in your agent's command palette.
Security 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 environment. Always review source, verify the publisher, and test in isolation before production.
Documentation
GEO Fundamentals
Optimization for AI-powered search engines.
1. What is GEO?
GEO = Generative Engine Optimization
| Goal | Platform |
|---|---|
| Be cited in AI responses | ChatGPT, Claude, Perplexity, Gemini |
SEO vs GEO
| Aspect | SEO | GEO |
|---|---|---|
| Goal | #1 ranking | AI citations |
| Platform | AI engines | |
| Metrics | Rankings, CTR | Citation rate |
| Focus | Keywords | Entities, data |
2. AI Engine Landscape
| Engine | Citation Style | Opportunity |
|---|---|---|
| Perplexity | Numbered [1][2] | Highest citation rate |
| ChatGPT | Inline/footnotes | Custom GPTs |
| Claude | Contextual | Long-form content |
| Gemini | Sources section | SEO crossover |
3. RAG Retrieval Factors
How AI engines select content to cite:
| Factor | Weight |
|---|---|
| Semantic relevance | ~40% |
| Keyword match | ~20% |
| Authority signals | ~15% |
| Freshness | ~10% |
| Source diversity | ~15% |
4. Content That Gets Cited
| Element | Why It Works |
|---|---|
| Original statistics | Unique, citable data |
| Expert quotes | Authority transfer |
| Clear definitions | Easy to extract |
| Step-by-step guides | Actionable value |
| Comparison tables | Structured info |
| FAQ sections | Direct answers |
5. GEO Content Checklist
Content Elements
- Question-based titles
- Summary/TL;DR at top
- Original data with sources
- Expert quotes (name, title)
- FAQ section (3-5 Q&A)
- Clear definitions
- "Last updated" timestamp
- Author with credentials
Technical Elements
- Article schema with dates
- Person schema for author
- FAQPage schema
- Fast loading (< 2.5s)
- Clean HTML structure
6. Entity Building
| Action | Purpose |
|---|---|
| Google Knowledge Panel | Entity recognition |
| Wikipedia (if notable) | Authority source |
| Consistent info across web | Entity consolidation |
| Industry mentions | Authority signals |
7. AI Crawler Access
Key AI User-Agents
| Crawler | Engine |
|---|---|
| GPTBot | ChatGPT/OpenAI |
| Claude-Web | Claude |
| PerplexityBot | Perplexity |
| Googlebot | Gemini (shared) |
Access Decision
| Strategy | When |
|---|---|
| Allow all | Want AI citations |
| Block GPTBot | Don't want OpenAI training |
| Selective | Allow some, block others |
8. Measurement
| Metric | How to Track |
|---|---|
| AI citations | Manual monitoring |
| "According to [Brand]" mentions | Search in AI |
| Competitor citations | Compare share |
| AI-referred traffic | UTM parameters |
9. Anti-Patterns
| ❌ Don't | ✅ Do |
|---|---|
| Publish without dates | Add timestamps |
| Vague attributions | Name sources |
| Skip author info | Show credentials |
| Thin content | Comprehensive coverage |
Remember: AI cites content that's clear, authoritative, and easy to extract. Be the best answer.
Script
| Script | Purpose | Command |
|---|---|---|
scripts/geo_checker.py |
GEO audit (AI citation readiness) | python scripts/geo_checker.py <project_path> |
List & Monetize Your Skill
Submit your Claude Code skill and start earning
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
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share 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
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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Reviews
- BBenjamin Bansal★★★★★Dec 20, 2024
geo-fundamentals has been reliable in day-to-day use. Documentation quality is above average for community skills.
- IIsabella Smith★★★★★Dec 16, 2024
Useful defaults in geo-fundamentals — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- AAlexander Park★★★★★Dec 8, 2024
Solid pick for teams standardizing on skills: geo-fundamentals is focused, and the summary matches what you get after install.
- NNoah Thomas★★★★★Dec 4, 2024
geo-fundamentals has been reliable in day-to-day use. Documentation quality is above average for community skills.
- KKiara Perez★★★★★Dec 4, 2024
geo-fundamentals reduced setup friction for our internal harness; good balance of opinion and flexibility.
- MMaya Chawla★★★★★Nov 27, 2024
We added geo-fundamentals from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- KKiara Brown★★★★★Nov 23, 2024
geo-fundamentals fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- IIshan Desai★★★★★Nov 23, 2024
I recommend geo-fundamentals for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- AAma Flores★★★★★Nov 19, 2024
geo-fundamentals is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- RRahul Santra★★★★★Nov 15, 2024
geo-fundamentals is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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