Rapid Mvp Building

mvp-geo-optimizer

$npx skills add blunotech-dev/agents --skill mvp-geo-optimizer

Generative Engine Optimization (GEO) skill for optimizing landing pages, SaaS websites, product pages, and marketing copy for AI discoverability. Trigger for product-facing web content, schema markup, LLM visibility, or requests to optimize for ChatGPT, Perplexity, Gemini, and similar AI search tools. This skill is essential for MVP builders; trigger it even if the user doesn't explicitly mention GEO, schema, or structured data, as long as they're writing product-facing content.

References

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mvp-geo-optimizerGenerative Engine Optimization (GEO) skill for optimizing landing pages, SaaS websites, product pages, and marketing copy for AI discoverability. Trigger for product-facing web content, schema markup, LLM visibility, or requests to optimize for ChatGPT, Perplexity, Gemini, and similar AI search tools. This skill is essential for MVP builders; trigger it even if the user doesn't explicitly mention GEO, schema, or structured data, as long as they're writing product-facing content.Rapid Mvp Building

MVP GEO Optimizer

Generative Engine Optimization (GEO) ensures your MVP's pages are accurately summarized and surfaced by AI-powered search engines: Perplexity, ChatGPT Search, Gemini, Bing Copilot, and others. Unlike traditional SEO (optimizing for crawlers), GEO optimizes for LLM comprehension: how well a language model can extract, summarize, and confidently cite your product.


Your Job When This Skill Triggers

When writing or reviewing any product/landing page content, you must:

  1. Embed GEO structures inline as you write, not as a post-hoc audit
  2. Generate the JSON-LD schema block appropriate for the product type
  3. Output a GEO Scorecard at the end (see format below)
  4. Flag any GEO anti-patterns found in existing content

Read references/geo-patterns.md for the full pattern library before writing. Read references/schema-templates.md for ready-to-use JSON-LD blocks.


Core GEO Principles

1. Entity Clarity (Most Important)

LLMs build a "knowledge graph" of what your product IS. Be explicit:

  • State the product category in the first sentence: "X is a [category] that..."
  • Name the problem solved in plain language, no metaphors in H1/H2
  • Define any brand-specific terms the first time they appear
  • Include the founding context: who built it, where, what for

Bad: "The future of team collaboration is here." Good: "Nexus is a project management tool for remote software teams that replaces Jira with a no-code interface."

2. Semantic HTML Structure

LLMs weight content by HTML hierarchy. Use the right tags:

<main>
  <article>
    <h1>[Product Name]: [Category] for [Target User]</h1>
    <p class="product-description">[One-sentence definition]</p>
    
    <section aria-label="features">
      <h2>Key Features</h2>
      <!-- Each feature as its own <article> or <section> -->
    </section>
    
    <section aria-label="use-cases">
      <h2>Who Uses [Product Name]</h2>
    </section>
    
    <section aria-label="faq">
      <h2>Frequently Asked Questions</h2>
      <!-- Use <details>/<summary> OR explicit Q&A pairs -->
    </section>
  </article>
</main>

3. JSON-LD Schema (Required on Every Page)

Always inject a <script type="application/ld+json"> block. Minimum viable:

  • SoftwareApplication or Product for the product itself
  • FAQPage if FAQ section exists
  • Organization for the company/brand
  • WebSite with SearchAction if there's a search feature

See references/schema-templates.md for complete templates.

4. Citation-Friendly Prose

LLMs prefer content they can quote directly in answers. Write so that individual sentences are self-contained facts:

  • Use present tense, active voice
  • Avoid pronouns without clear antecedents ("it", "this", "they")
  • Each feature description should be a standalone, quotable claim
  • Include concrete numbers where possible: "reduces setup time by 80%", "supports 14 integrations"

5. FAQ Section (High Impact)

FAQs are the highest-GEO section on any page. LLMs almost always pull from FAQ content when answering "What is X?" or "How does X work?" queries.

Every MVP page needs at minimum 5 FAQs covering:

  1. What is [Product]? (definition + category)
  2. Who is [Product] for? (target user)
  3. How does [Product] work? (mechanism, 2-3 sentences)
  4. How is [Product] different from [top competitor]?
  5. How much does [Product] cost? (even "free tier available" is better than silence)

6. Entity Disambiguation

If your product name is generic or shares a name with other entities, add disambiguation signals:

<meta name="description" content="[ProductName] by [CompanyName], [category] for [niche]">

And in the body: "[ProductName] (by [Company], founded [Year]) is a..."


Output Format

When generating or reviewing a page, output in this order:

A. The Content/Code

Write the full page content or component with all GEO structures embedded. Include the JSON-LD block inside a <head> section comment or at the end as a clearly labeled script block.

B. GEO Scorecard

After the content, always append:

## GEO Scorecard

| Signal                        | Status | Notes                          |
|-------------------------------|--------|--------------------------------|
| Entity definition (H1)        | ✅/⚠️/❌ |                               |
| Semantic HTML structure       | ✅/⚠️/❌ |                               |
| JSON-LD: SoftwareApplication  | ✅/⚠️/❌ |                               |
| JSON-LD: FAQPage              | ✅/⚠️/❌ |                               |
| JSON-LD: Organization         | ✅/⚠️/❌ |                               |
| FAQ section (min 5 Q&As)      | ✅/⚠️/❌ |                               |
| Citation-friendly prose       | ✅/⚠️/❌ |                               |
| Competitor differentiation    | ✅/⚠️/❌ |                               |
| Entity disambiguation         | ✅/⚠️/❌ |                               |
| Concrete numbers/metrics      | ✅/⚠️/❌ |                               |

GEO Score: X/10
Top 3 improvements: ...

Legend: ✅ = implemented, ⚠️ = partial/weak, ❌ = missing


Anti-Patterns to Flag

When reviewing existing content, call out:

  • Vague H1s: Taglines that don't state product category ("Build something people love")
  • Pronoun soup: "It helps them do this with their teams"; LLMs lose the referent
  • Feature-list-only pages: Bullet lists with no prose context; LLMs can't build semantic chains from bullets alone
  • Missing schema: Any product page without JSON-LD is invisible to structured AI search
  • No FAQ: Single highest-impact missing element
  • Jargon without definition: Internal brand terms used before being explained
  • Passive descriptions: "Used by thousands of teams" vs "10,000 teams use [Product] to..."

Workflow for New Pages

  1. Collect: product name, category, target user, top 3 features, main competitor, pricing
  2. Read references/geo-patterns.md for the content type (SaaS, marketplace, tool, API)
  3. Read references/schema-templates.md and select the right JSON-LD template
  4. Draft with all GEO signals embedded from the start
  5. Output content + GEO Scorecard

Workflow for Existing Pages

  1. Read the existing content
  2. Run through the GEO Scorecard checklist
  3. Rewrite or patch weak/missing signals
  4. Output the improved version + before/after GEO Scorecard comparison