bencium-aeo▌
bencium/bencium-marketplace · updated Apr 8, 2026
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
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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