bencium-aeo▌
bencium/bencium-marketplace · updated Apr 8, 2026
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Answer Engine Optimization - Optimize content for AI citations, not traditional search rankings.
AEO Content Optimization Skill
Answer Engine Optimization - Optimize content for AI citations, not traditional search rankings.
When to Use This Skill
Use this skill when:
- User asks to optimize content for AI search/citations
- User mentions ChatGPT, Claude, Gemini visibility
- User wants FAQ schema, JSON-LD, or structured data for AI
- User asks about GEO (Generative Engine Optimization)
- User wants to analyze content for AI extraction readiness
- User mentions "AI Overviews" or "answer engines"
NOT for traditional SEO - This is specifically for AI/LLM citation optimization.
Core Reference
Full templates and guidelines: Read prd.md in this directory for complete implementation details.
Quick Reference: Key Principles
The 18-Token Extraction Rule
LLMs extract self-contained sentences of ~18 tokens (~15-20 words). Key claims must be complete, quotable statements requiring zero surrounding context.
Good: "Eight-API synthesis reduces property analysis errors by 67%." (9 tokens) Bad: "Our system is incredibly fast and delivers amazing results." (vague)
Single-Topic Focus Pages
Single-concept pages vastly outperform multi-topic content. Create focused URLs like domain.com/specific-concept rather than comprehensive guides.
Citations + Statistics = 30-40% More Visibility
Every major claim needs:
- Verifiable data with methodology
- Date of data collection
- Expert attribution (Name + Credentials + Org)
Freshness is Critical
95% of AI citations come from content updated in last 10 months. Static content dies.
Authority Level Determines Strategy
| Authority Level | Optimization Approach |
|---|---|
| Challenger (new sites, low authority) | Aggressive: 5-7 extraction points per page, heavy citations, weekly micro-updates |
| Established (top-ranked, well-known) | Light touch: 1-2 strategic points, trust existing credibility, avoid over-optimization |
Princeton finding: Rank-5 sites gained 115% visibility with aggressive optimization. Rank-1 sites that over-optimized lost 30%.
What to Generate
When user requests AEO content, generate:
1. Product Overview (50 words)
- What it is (one clause)
- Scope/timeframe context
- Why it matters (value proposition)
- "Last updated" date
2. 15 FAQs with Schema
- Questions: 7-12 words, natural language
- Answers: 30-50 words (sweet spot for AI extraction)
- FAQPage JSON-LD schema with
datePublishedanddateModified - Persistent anchor IDs (#faq-slug)
3. Evidence Panels
For every important claim:
- Claim statement
- Methodology
- Data source + URL
- Date of data collection
- Limitations
- Contact for questions
4. JSON-LD Schema
- FAQPage (most important)
- HowTo (for guides)
- Product (for product pages)
- Organization (for About page)
Anti-Patterns (What to Avoid)
Traditional SEO Tactics Harm GEO
- Keyword stuffing
- Generic listicles without original insight
- Vague hedged language ("may help", "could potentially")
- Multi-topic comprehensive guides
- Over-optimization on established sites
Content Structure Errors
- FAQ answers over 50 words
- Buried answers (put conclusion first)
- Pronoun ambiguity ("it" instead of "the product")
- Missing dates and freshness signals
- No schema markup
Assessment Framework
When analyzing content for AEO readiness, score (0-10):
| Dimension | What to Check |
|---|---|
| Extraction | How many citation-ready sentences under 18 tokens? |
| Focus | Single topic or sprawling multi-topic? |
| Authority | Expert attribution with credentials? Citations? |
| Freshness | Updated within 90 days? Dated content? |
Quick test: Can you copy-paste 3 sentences that fully answer a question without context?
Implementation Checklist
- Product overview: 50 words, dated, under H1
- 15 FAQs: 30-50 words each, natural questions
- Evidence panels: method, data, date, limitations
- "Last updated" dates on every section
- FAQPage JSON-LD schema in
<head> - Persistent anchor IDs for FAQs
- Validated with Google Rich Results Test
Testing Protocol
After implementation, test with:
- Recognition: "What is [Product]?" (ChatGPT, Claude, Gemini)
- Comparison: "Compare [Product] to [Competitor]"
- Best for: "What's the best [category] for [use case]?"
- How-to: "How do I [task with product]?"
Track: Mentioned? Linked? Accurate? Evidence quoted?
Full Documentation
For complete templates, examples, and detailed guidelines, read:
prd.md- Full AEO content generation guide with HTML templatesstory-structured.md- Framework summary from Princeton study
How to use bencium-aeo 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 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 bencium-aeo
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches bencium-aeo from GitHub repository bencium/bencium-marketplace and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate bencium-aeo. Access the skill through slash commands (e.g., /bencium-aeo) 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
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
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 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▌
- 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
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★44 reviews- ★★★★★Dhruvi Jain· Dec 24, 2024
Keeps context tight: bencium-aeo is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Layla Wang· Dec 24, 2024
bencium-aeo is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Pratham Ware· Dec 20, 2024
Useful defaults in bencium-aeo — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Diego Choi· Dec 20, 2024
I recommend bencium-aeo for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Benjamin Chawla· Dec 12, 2024
Useful defaults in bencium-aeo — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Valentina Torres· Nov 27, 2024
Useful defaults in bencium-aeo — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Oshnikdeep· Nov 15, 2024
Registry listing for bencium-aeo matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Layla Nasser· Nov 15, 2024
bencium-aeo reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Valentina Diallo· Oct 18, 2024
I recommend bencium-aeo for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Ganesh Mohane· Oct 6, 2024
bencium-aeo reduced setup friction for our internal harness; good balance of opinion and flexibility.
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