// Generate AEO-optimized content (Answer Engine Optimization) for AI search visibility - ChatGPT, Claude, Gemini, AI Overviews. Use when optimizing websites for AI citations, creating FAQ schemas, evidence panels, or analyzing content for LLM extraction readiness.
Grassroots-first campaign design for anyone being outspent — startups vs. incumbents, NGOs vs. corporate comms, movements vs. state-backed machines, solo brands vs. big-budget competitors. Ideates awareness, launch, fundraising, mobilization, community-build, counter-narrative, referral, founder-story, and coalition campaigns. Triggers on "campaign plan", "marketing strategy", "ad budget", "should I advertise", "paid vs organic", "launch plan", "grassroots", "low budget marketing", "NGO campaign", "outspent", "competitor has bigger budget", "how do I compete without money". Also trigger on any spend asymmetry, collapsing organic reach, rising CPAs, or a trust/credibility problem — even without the word "campaign". Nudge activation when the user debates buying ads, boosting posts, or hiring influencers; they are likely about to burn money on a channel that will not persuade.
Reviews codebases, architectures, PRs, and technical plans for vanity engineering — code and systems built for the developer's ego, resume, or intellectual pleasure rather than delivering user or business value. Triggers on: "review this code", "is this over-engineered", "code review", "architecture review", "complexity audit", "vanity check", "is this necessary", "simplify this", "tech debt review", or any request to evaluate whether code or architecture is justified by actual requirements. Also trigger when the user shares a codebase and asks for feedback, when discussing framework/library choices, when reviewing PRs, or when someone is debating whether to refactor or rebuild. Nudge activation when you detect patterns of unnecessary abstraction, premature optimization, or resume-driven technology choices in code the user shares — even if they haven't asked for a vanity review.
Systematic architectural thinking for irreplaceable human capabilities - domain modeling, systems thinking, constraint navigation, and AI-aware problem decomposition. Use proactively when detecting architectural decisions, system design discussions, or multi-component planning.
Use when detecting ambiguous user intent, hedging language, open-ended framing, personal context before requests, or when unsure whether user wants exploration vs direct answer. Applies to all conversations.
Bence's code style, tech stack, and workflow conventions
Expert UI/UX design guidance for unique, accessible interfaces. Use for visual decisions, colors, typography, layouts. Always ask before making design decisions. Use this skill when the user asks to build web components, pages, or applications.
| name | bencium-aeo |
| description | Generate AEO-optimized content (Answer Engine Optimization) for AI search visibility - ChatGPT, Claude, Gemini, AI Overviews. Use when optimizing websites for AI citations, creating FAQ schemas, evidence panels, or analyzing content for LLM extraction readiness. |
Answer Engine Optimization - Optimize content for AI citations, not traditional search rankings.
Use this skill when:
NOT for traditional SEO - This is specifically for AI/LLM citation optimization.
Full templates and guidelines: Read prd.md in this directory for complete implementation details.
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-concept pages vastly outperform multi-topic content. Create focused URLs like domain.com/specific-concept rather than comprehensive guides.
Every major claim needs:
95% of AI citations come from content updated in last 10 months. Static content dies.
| 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%.
When user requests AEO content, generate:
datePublished and dateModifiedFor every important claim:
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?
<head>After implementation, test with:
Track: Mentioned? Linked? Accurate? Evidence quoted?
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