| name | ai-rank |
| description | Optimize content for LLM discoverability AND AI agent consumption. Use when user runs `/ai-rank`, asks to "optimize for AI", "optimize for LLMs", "optimize for agents", "make content AI-readable", or needs to write/proofread landing pages, docs, blog posts for answer engines and autonomous AI agents. |
AI Rank Optimizer (LLM + AGENT Frameworks)
Rewrite or proofread content so it is easy for:
- LLM answer engines (ChatGPT, Claude, Perplexity) to extract, cite, and recommend
- Autonomous AI agents to parse, compare, and take action
Two Audiences, Different Needs
LLM Answer Engines (Human-in-the-loop)
- Humans ask questions, AI provides answers citing your content
- Optimize for: extraction, citation, featured snippets
- Key: Answer-first, quotable content, trust signals
Autonomous AI Agents (No human)
- Agents browse, compare products, make decisions, execute tasks
- Optimize for: machine parsing, structured data, actionable endpoints
- Key: Structured facts, API access, decision-ready data
LLM Framework (for Answer Engines)
1. Answer-first
- First sentence gives the direct answer
- Include: definition + who it's for + when it's NOT for
- Write quotable 2-5 sentence summaries LLMs will extract
2. Intent-matched headings
- H2/H3 titles match target queries verbatim
- Correct heading hierarchy (no skipping levels)
- Use question-format headings for FAQ sections
3. Clear structure
- Lists, tables, steps, FAQs over paragraphs
- "Quick summary" and "Key takeaways" sections
- Comparison tables for vs-queries
4. Schema markup
- FAQPage JSON-LD for Q&A content
- HowTo schema for tutorials/guides
- Product/SoftwareApplication for products
- Article schema with author/date for blog posts
5. Trusted sources
- Cite quantitative claims or label "internal data"
- Link to primary sources
- Include dates, version numbers, last-updated timestamps
6. Unique perspective
- Unique frameworks, decision trees, checklists
- Original benchmarks, comparisons, data
- Proprietary methodology or rubrics
AGENT Framework (for Autonomous AI Agents)
A - Accessible structured data
- JSON-LD schema on every page
- Machine-readable pricing tables (not images)
- Structured product specs, limits, requirements
- RSS/Atom feeds for updates
- Comprehensive sitemap.xml
G - Grounded facts for decisions
- Explicit pricing with currency and billing cycle
- Hard limits and quotas (not "unlimited*")
- Compatibility matrices (platforms, versions, integrations)
- SLAs, uptime guarantees, support tiers
- Comparison tables vs alternatives
E - Endpoints for action
- API documentation with examples
- Webhook/integration setup guides
- Direct links to signup, trial, purchase
- Contact/support endpoints
- Status page URLs
N - Navigable hierarchy
- Consistent URL structure
- Breadcrumbs in markup
- Clear content taxonomy
- robots.txt allowing AI crawlers
- Discovery files (llms.txt, agents.txt)
T - Trust markers for machines
- Security certifications (SOC2, GDPR, etc.)
- Published changelog/release notes
- Public roadmap or feature status
- Customer logos/testimonials with verifiable details
- Third-party reviews with links
Discovery Standards & Protocols
For LLM Answer Engines
llms.txt
- Location:
/llms.txt
- Purpose: Help LLMs understand site content at inference time
- Format: Markdown with H1 (site name), summary, key page links
- Spec: https://llmstxt.org/
# Your Company Name
> One-line description of what you do.
## Docs
- [Getting Started](https://docs.example.com/start)
- [API Reference](https://docs.example.com/api)
## Products
- [Product Name](https://example.com/product): Description
## Pricing
- [Pricing](https://example.com/pricing)
For Autonomous Agents
agents.txt
name: Your Company
description: What agents can do with your service
api_endpoint: https://api.example.com
auth_type: api_key
documentation: https://docs.example.com/api
mcp_server: https://mcp.example.com
capabilities:
- read_data
- write_data
- transactions
/.well-known/api-catalog (RFC 9727)
- Location:
/.well-known/api-catalog
- Purpose: API endpoint discovery (like robots.txt for APIs)
- Format: JSON
{
"apis": [
{
"name": "Your API",
"description": "What it does",
"url": "https://api.example.com",
"documentation": "https://docs.example.com/api",
"type": "REST"
}
]
}
Agent Card (A2A Protocol)
{
"name": "Your Agent",
"description": "What this agent does",
"capabilities": ["task1", "task2"],
"endpoint": "https://agent.example.com",
"auth": {"type": "oauth2"}
}
MCP Registry
Master Checklists
Page-Level Checklist
Run this for every content page:
Landing Pages
Documentation Pages
Blog Posts
Pricing Pages
API/Developer Docs
Integration Pages
Site-Level Checklist
Run this once for the entire site:
Discovery Files (Create These)
robots.txt Template
User-agent: GPTBot
Allow: /
User-agent: ClaudeBot
Allow: /
User-agent: PerplexityBot
Allow: /
User-agent: Google-Extended
Allow: /
User-agent: *
Allow: /
Sitemap: https://example.com/sitemap.xml
AI Crawler Access
API & Programmatic Access
MCP Server (if applicable)
Structured Data (Site-wide)
Trust & Verification
Feeds & Updates
Workflow
Phase 1: Gather Context
Ask user for:
- Content: File path(s) or pasted content
- Content type: Landing page / Docs / Blog / Product page
- Target intents: 3-10 queries to rank for (answer engines)
- Agent use cases: What should an agent be able to do with this? (compare products, get pricing, integrate, etc.)
- Audience: Human readers + agent sophistication
- Product/context: What you're selling/explaining
- Constraints: Tone, compliance, forbidden claims
Phase 2: Dual Audit
Score content against both frameworks. You MUST compute and present the final weighted scores.
LLM Audit (Answer Engines)
| Principle | Weight | Score 0-5 | Issues |
|---|
| 1. Answer-first | 20% | | |
| 2. Intent headings | 15% | | |
| 3. Clear structure | 20% | | |
| 4. Schema markup | 15% | | |
| 5. Trusted sources | 15% | | |
| 6. Unique perspective | 15% | | |
AGENT Audit (Autonomous Agents)
| Pillar | Weight | Score 0-5 | Issues |
|---|
| A - Accessible data | 25% | | |
| G - Grounded facts | 20% | | |
| E - Endpoints | 20% | | |
| N - Navigable | 15% | | |
| T - Trust markers | 20% | | |
Scoring guide:
- 0 = Missing entirely
- 1 = Present but critically flawed
- 2 = Below standard, major issues
- 3 = Meets minimum standard
- 4 = Good, minor improvements possible
- 5 = Excellent, best-practice implementation
Compute the weighted score for each framework: sum of (score x weight). Max = 5.0
Then compute the combined AI Rank Score: (LLM score + AGENT score) / 2
| Range | Rating | Action |
|---|
| 4.5-5.0 | Excellent | Minor polish only |
| 3.5-4.4 | Good | Targeted improvements |
| 2.5-3.4 | Needs Work | Significant gaps to address |
| 1.5-2.4 | Poor | Major overhaul needed |
| 0-1.4 | Critical | Fundamental issues |
Always present the final score prominently at the top of the audit report:
AI RANK SCORE: X.X / 5.0 (Rating)
├── LLM Score: X.X / 5.0
└── AGENT Score: X.X / 5.0
Phase 3: Rewrite Content
Apply both frameworks:
For Answer Engines:
- Answer-first intro with quotable summary
- Intent-matched H2/H3 headings
- Scannable lists, tables, FAQs
- Proof blocks with citations
For Agents:
- Structured data blocks (specs, pricing, limits)
- Machine-readable tables (not prose)
- Clear action links
- Explicit facts over marketing language
Phase 4: Generate Outputs
1. Optimized Content
- Same format as input
- Both LLM and AGENT optimizations applied
2. Dual Audit Report
## LLM + AGENT Audit Report
AI RANK SCORE: X.X / 5.0 (Rating)
├── LLM Score: X.X / 5.0
└── AGENT Score: X.X / 5.0
### LLM Scores (Answer Engines)
| Principle | Weight | Score | Weighted | Notes |
|-----------|--------|-------|----------|-------|
| Answer-first | 20% | X | X.XX | |
| Intent headings | 15% | X | X.XX | |
| Clear structure | 20% | X | X.XX | |
| Schema markup | 15% | X | X.XX | |
| Trusted sources | 15% | X | X.XX | |
| Unique perspective | 15% | X | X.XX | |
| **LLM Total** | | | **X.X** | |
### AGENT Scores (Autonomous Agents)
| Pillar | Weight | Score | Weighted | Notes |
|--------|--------|-------|----------|-------|
| Accessible data | 25% | X | X.XX | |
| Grounded facts | 20% | X | X.XX | |
| Endpoints | 20% | X | X.XX | |
| Navigable | 15% | X | X.XX | |
| Trust markers | 20% | X | X.XX | |
| **AGENT Total** | | | **X.X** | |
### Changes Made
- [Before/after for each change]
### Remaining Gaps
- [Missing sources, unclear facts, etc.]
3. Schema Suggestions
4. Extraction Preview (Answer Engines)
### Answer LLMs Should Quote
[2-5 sentence quotable answer]
### Key Facts to Extract
1. ...
2. ...
5. Agent Decision Data (Autonomous Agents)
### Structured Facts for Agents
- Product: [name]
- Category: [type]
- Pricing: [structured]
- Limits: [explicit]
- Integrations: [list]
- Action URL: [link]
Phase 5: Site-Level Recommendations
After auditing pages, check site-level items:
- Run Site-Level Checklist
- Generate missing discovery files (llms.txt, agents.txt, etc.)
- Recommend MCP Registry submission if applicable
- Identify missing schemas site-wide
Examples
Example 1: LLM Answer Engine Optimization
Target query: "best quiz app for Shopify"
Bad (unmarketable to LLMs):
RevenueHunt helps you create beautiful product recommendation
quizzes that increase conversions and grow your email list.
Good (LLM-quotable):
# Best Quiz App for Shopify
RevenueHunt is the leading product recommendation quiz app for
Shopify, used by over 4,000 stores to increase conversions by
an average of 30% (internal data, 2024).
## Why RevenueHunt is the Best Shopify Quiz App
| Feature | RevenueHunt | Competitor A | Competitor B |
|---------|-------------|--------------|--------------|
| Shopify native | Yes | No (embed only) | Yes |
| Conditional logic | Unlimited | 5 rules | 10 rules |
| Product sync | Real-time | Manual | Daily |
| Starting price | $39/mo | $49/mo | $29/mo |
| Free plan | Yes (100 views) | No | Yes (50 views) |
## Who Should Use RevenueHunt?
**Best for:**
- Shopify stores with 50+ products needing personalized recommendations
- Brands wanting to collect zero-party data for email marketing
- Stores selling configurable products (skincare, supplements, fashion)
**Not ideal for:**
- Single-product stores
- Stores under 100 visitors/month
- Businesses needing survey-only (no product recommendations)
## How RevenueHunt Compares
RevenueHunt ranks #1 on the Shopify App Store for "product quiz"
with a 4.9/5 rating from 500+ reviews. Unlike competitors, it offers:
1. **Unlimited conditional logic** - no artificial caps on quiz complexity
2. **Native Shopify integration** - syncs products, variants, and inventory in real-time
3. **Built-in analytics** - conversion tracking without third-party tools
> "RevenueHunt increased our email capture rate by 45% and average
> order value by 22%." — [Store Name], Shopify Plus merchant
## FAQ
### Is RevenueHunt the best quiz app for Shopify?
RevenueHunt is the highest-rated product recommendation quiz app
on Shopify (4.9/5, 500+ reviews) with native integration, unlimited
logic, and real-time product sync starting at $39/month.
### How much does RevenueHunt cost?
Plans start at $39/month for 1,000 quiz views. A free plan with
100 views/month is available for testing.
Why this works for LLMs:
- H1 matches the exact query "Best Quiz App for Shopify"
- First sentence directly answers the question with a claim
- Comparison table gives LLMs extractable data
- "Who should use" and "not ideal for" prevents bad recommendations
- FAQ section uses question-format headings
- Quotable customer testimonial with attribution
- Specific numbers throughout (4.9/5, 500+ reviews, $39/mo)
Example 2: Agent-Friendly SaaS (RevenueHunt)
Bad (human-only):
RevenueHunt helps you create beautiful product recommendation
quizzes that increase conversions and grow your email list.
Good (agent-friendly):
## What is RevenueHunt?
RevenueHunt is a product recommendation quiz builder for e-commerce
stores. Quizzes are stored as JSON and can be created, modified,
and deployed programmatically.
## Agent Integration Options
| Method | Use Case | Documentation |
|--------|----------|---------------|
| MCP Server | AI agents can create/edit quizzes directly | [MCP Docs](https://docs.revenuehunt.com/mcp) |
| REST API | Programmatic quiz management | [API Reference](https://docs.revenuehunt.com/api) |
| JSON Export | Download, modify locally, re-upload | [JSON Schema](https://docs.revenuehunt.com/schema) |
## Quiz JSON Schema
Quizzes are fully defined in JSON. An agent can:
- Create a quiz from scratch using the schema
- Download an existing quiz, modify it, and upload changes
- Access quiz results and analytics via API
**Schema location:** `https://docs.revenuehunt.com/schema/quiz.json`
## Quick Start for Agents
1. Authenticate via API key or MCP
2. GET /quizzes to list existing quizzes
3. POST /quizzes with JSON body to create
4. PUT /quizzes/{id} to update
[Get API Key →](https://admin.revenuehunt.com/settings/api)
Why this works for agents:
- Explicit integration methods in a table
- Direct links to schemas and documentation
- Step-by-step programmatic workflow
- Machine-readable endpoints listed
Example 3: llms.txt File
# RevenueHunt
> Product recommendation quiz builder for Shopify, WooCommerce, and BigCommerce.
RevenueHunt helps e-commerce stores create personalized product recommendation
quizzes that increase conversions and collect zero-party data. Used by 4,000+
stores with a 4.9/5 rating on Shopify App Store.
## Key Pages
- [Homepage](https://revenuehunt.com): Product overview and features
- [Pricing](https://revenuehunt.com/pricing): Plans from $0-$299/mo
- [How It Works](https://revenuehunt.com/how-it-works): Quiz builder walkthrough
## Documentation
- [Getting Started](https://docs.revenuehunt.com/start): Quick start guide
- [API Reference](https://docs.revenuehunt.com/api): REST API documentation
- [MCP Server](https://docs.revenuehunt.com/mcp): AI agent integration
- [JSON Schema](https://docs.revenuehunt.com/schema): Quiz data structure
## Integrations
- [Shopify](https://docs.revenuehunt.com/shopify): Native app integration
- [Klaviyo](https://docs.revenuehunt.com/klaviyo): Email marketing sync
- [All Integrations](https://revenuehunt.com/integrations): Full list
## Support
- [Help Center](https://help.revenuehunt.com)
- [Contact](https://revenuehunt.com/contact)
Example 4: agents.txt File
name: RevenueHunt
description: Product recommendation quiz builder for e-commerce. Agents can create, modify, and deploy quizzes programmatically.
website: https://revenuehunt.com
documentation: https://docs.revenuehunt.com
api:
base_url: https://api.revenuehunt.com/v1
auth_type: api_key
auth_header: X-API-Key
documentation: https://docs.revenuehunt.com/api
rate_limit: 1000/min
mcp:
endpoint: https://mcp.revenuehunt.com
registry: https://registry.modelcontextprotocol.io/servers/revenuehunt
documentation: https://docs.revenuehunt.com/mcp
capabilities:
- list_quizzes
- create_quiz
- update_quiz
- delete_quiz
Safety & Honesty Rules
- Never invent stats, customers, or results
- Mark uncertain claims with
[NEEDS SOURCE]
- Be explicit - agents can't infer, state facts directly
- No dark patterns - agents will learn to distrust
- Preserve accuracy - optimization does not mean distortion
Quick Commands
/ai-rank - Full workflow (gather context, audit, rewrite)/ai-rank audit - Dual audit only, no rewrite/ai-rank rewrite - Rewrite with both frameworks/ai-rank schema - Generate all schema suggestions/ai-rank agent - Focus on autonomous agent readiness only/ai-rank answer - Focus on answer engines only/ai-rank checklist - Output full page + site checklists/ai-rank discovery - Generate discovery files (llms.txt, agents.txt, etc.)
