GEO Detector — AI Recommendation Integrity Checker

Detects whether AI-recommended products or websites are genuinely high-quality or artificially boosted through GEO (Generative Engine Optimization) techniques. Reverse-engineers known GEO methods to produce a GEO Manipulation Score.

What This Skill Does

When AI engines (ChatGPT, Perplexity, Google AI Overview, Claude, Copilot) recommend products or websites, their recommendations may be influenced by GEO-optimized content rather than genuine product quality. This skill analyzes the recommended content and scores the likelihood of GEO manipulation across 6 detection dimensions.

This skill does NOT:

• Determine product quality directly (it detects manipulation signals, not quality)
• Replace human judgment (it provides data for informed decisions)
• Access proprietary ranking algorithms (it analyzes publicly visible signals)

Quick Start

AI推荐了这个洗衣液，帮我验一下：[product name or URL]
Verify this AI recommendation: [product URL]
这个网站为什么总被AI推荐？https://example.com
Check if this product is GEO-manipulated: [brand name]
ChatGPT推荐了这些产品，帮我看看哪个是真的好：[product list]

Architecture: Engine + Knowledge Separation

This skill separates detection logic (this file) from detection knowledge (references/):

SKILL.md                          ← Engine (stable logic)
references/
├── knowledge-manifest.md         ← Knowledge version control
├── knowledge-sources.md          ← 📡 Multi-tier source framework (update intelligence)
├── geo-fingerprints.md           ← 🔄 GEO signal fingerprint library
├── detection-rubric.md           ← 🔄 6-dimension scoring rubric
├── scoring-weights.md            ← 🔄 Product/Website weight profiles
├── platform-signals.md           ← 🔄 AI platform citation preferences
├── cross-validation.md           ← Cross-validation suggestion templates
└── update-protocol.md            ← Knowledge update SOP (multi-source)

Files marked 🔄 are hot-swappable knowledge modules — they can be updated independently without changing the engine logic. Each module carries its own version in the YAML frontmatter.

Knowledge independence: This SKILL does not depend on any upstream GEO/SEO project for its maintenance. All knowledge updates are sourced autonomously via a 4-tier public source hierarchy (academic → industry → community → empirical) with mandatory cross-validation. See references/knowledge-sources.md for details.

Detection Workflow

Step 0: Identify Target Type

Determine whether the user is asking about a product or a website:

• Product mode: AI recommended a specific product (e.g., a laundry detergent, a robot vacuum, a snack brand). Focus on product page content, seller authority signals, and review manipulation.
• Website mode: AI recommended or frequently cites a specific website. Focus on technical GEO signals, schema over-engineering, and crawler friendliness.

If ambiguous, ask the user:

> "Would you like me to analyze this as a product recommendation or a website/platform recommendation?"

Step 1: Gather Target Content

If URL provided:

1. Fetch the page content using WebFetch
2. Extract: page text, meta tags, schema markup (JSON-LD), heading structure, robots.txt (website mode)

If product name/brand provided (no URL):

1. Search for the product's primary sales/marketing page
2. Search for the product on major platforms (Amazon, JD, official site)
3. Use the most GEO-relevant page (usually official product page or top-ranking content page)

If multiple products:

1. Process each product sequentially
2. Output a comparison table at the end

Step 2: Execute 6-Dimension Scan

Load the detection knowledge from references/:

Load: references/geo-fingerprints.md    → Signal definitions & thresholds
Load: references/detection-rubric.md    → Scoring criteria per dimension
Load: references/scoring-weights.md     → Weight profile (product vs website)
Load: references/platform-signals.md    → Platform-specific signal patterns

Run each dimension scan in order:

Dimension 1: Citation & Statistics Density Anomaly

Detect abnormal density of citations and statistical data — the top 2 GEO methods (Citation +40%, Statistics +37%).

Scan for:

• Citation count per 500 words vs. natural baseline
• Relevance of cited sources to the actual product/topic
• Statistical data density and whether stats support claims or are decorative
• Self-citation or circular citation patterns

Dimension 2: Schema & Structure Over-engineering

Detect excessive structured markup designed for AI extraction rather than user experience.

Scan for:

• FAQPage Schema: count, quality, and specificity of Q&A pairs
• JSON-LD density and type variety beyond what content warrants
• Artificial content chunking (forced 2-3 sentence paragraphs)
• Excessive heading nesting without proportional content depth

Dimension 3: AI-Bait Content Patterns

Detect content structured specifically to be extracted by AI engines.

Scan for:

• "Answer-first" positioning (core answer in first 100-150 words)
• Definition-style opening sentences ("X is...", "X is defined as...")
• TL;DR / Key Takeaways boxes that duplicate the main content
• Content matching known AI response formats (lists, comparisons, direct Q&A)

Dimension 4: Authority Signal Stuffing

Detect inflated authority claims without substantive backing.

Scan for:

• Frequency of authority markers ("industry-leading", "award-winning", "#1") vs. evidence
• Credential listing irrelevant to the product category
• Social proof density (user counts, testimonials) without verifiable sources
• Expert quote stuffing (GEO method: Quotation +30%)

Dimension 5: AI Crawler Over-Optimization (website mode weighted higher)

Detect technical signals of deliberate AI crawler courting.

Scan for:

• robots.txt allowing all major AI bots (GPTBot, PerplexityBot, ClaudeBot, Bingbot, anthropic-ai)
• Abnormally frequent content updates (freshness gaming)
• Multi-platform optimization traces (simultaneous Google + Bing + Brave optimization)
• SpeakableSpecification or other AI-specific schema

Dimension 6: Content Naturalness Analysis

Detect whether content reads like "written by humans for humans" or "written for AI to recommend."

Scan for:

• Vocabulary diversity score (artificially high = GEO method: Unique Words +15%)
• Absence of product limitations/downsides (authentic reviews mention cons)
• Overly authoritative tone without personal experience signals
• Keyword stuffing avoidance patterns (paradoxically detectable)

Step 3: Calculate GEO Manipulation Score

Apply the appropriate weight profile from references/scoring-weights.md:

Product weight profile:

Website weight profile:

Score interpretation:

• 0–30: Low manipulation suspicion 🟢 — Content appears naturally high-quality
• 31–60: Moderate GEO signals 🟡 — Some optimization detected; cross-verify recommended
• 61–100: High manipulation suspicion 🔴 — Strongly suggests GEO-driven recommendation

Step 4: Generate Detection Report

Output the report in the user's language. Follow this structure:

╔══════════════════════════════════════════════╗
║   GEO Manipulation Score: [XX]/100 [emoji]   ║
║   [Interpretation text]                       ║
╚══════════════════════════════════════════════╝
📋 Knowledge Base: v[X.Y.Z] ([date])
🎯 Detection Mode: [Product / Website]
🔗 Target: [product name / URL]

📊 Dimension Breakdown:
┌────────────────────────────┬───────┬─────────────────────────────┐
│ Dimension                  │ Score │ Key Finding                  │
├────────────────────────────┼───────┼─────────────────────────────┤
│ Citation & Stats Density   │ XX 🔴 │ [specific finding]           │
│ Schema & Structure         │ XX 🟡 │ [specific finding]           │
│ AI-Bait Patterns           │ XX 🔴 │ [specific finding]           │
│ Authority Stuffing         │ XX 🟢 │ [specific finding]           │
│ AI Crawler Optimization    │ XX 🟡 │ [specific finding]           │
│ Content Naturalness        │ XX 🔴 │ [specific finding]           │
└────────────────────────────┴───────┴─────────────────────────────┘

🔍 Key Evidence:
1. [Most significant GEO signal found with specific quote/data]
2. [Second most significant signal]
3. [Third signal]

⚠️ Recommendation:
[Contextual advice based on score level — always include cross-validation steps]

Step 5: Cross-Validation Suggestions

Always end with actionable suggestions from references/cross-validation.md:

For products (score > 30):

• Search for independent third-party reviews (Consumer Reports, Wirecutter, 什么值得买, 老爸评测)
• Look for long-term user feedback on Reddit, 知乎, or specialized forums
• Compare with competing products' independent ratings
• Check if the product has industry certifications relevant to its category

For websites (score > 30):

• Check the website's age and reputation on web archives (Wayback Machine)
• Search for independent reviews of the website/platform
• Verify claimed credentials and partnerships
• Check if the site appears in non-AI search results organically

Knowledge Update

This SKILL maintains its knowledge base autonomously through a multi-source intelligence framework. It does not depend on any upstream project.

How It Works

The update system uses a 4-tier source hierarchy (defined in references/knowledge-sources.md):

Cross-validation rule: Every knowledge update requires evidence from ≥2 different tiers.

Trigger an Update

Update GEO detector knowledge base
更新GEO检测知识库
geo-detector update

This triggers the update protocol defined in references/update-protocol.md:

1. Search across all 4 tiers for latest GEO research, platform changes, and community findings
2. Cross-validate findings (≥2 tiers required per change)
3. Generate an update proposal with full evidence traceability
4. Apply updates after user confirmation
5. Bump version in references/knowledge-manifest.md

See references/knowledge-sources.md for the complete source list and search query templates.

Tips for Accurate Detection

1. URL is better than name — Direct page analysis is more accurate than searching
2. Check multiple pages — A product may have both organic and GEO-optimized pages
3. Context matters — A high GEO score doesn't mean the product is bad; it means the recommendation may not be purely quality-driven
4. Compare siblings — Checking multiple products in the same category reveals relative manipulation levels
5. Freshness matters — GEO techniques evolve; keep the knowledge base updated

Limitations

• Cannot access private/paywalled content
• Cannot determine actual product quality (only manipulation signals)
• Detection accuracy depends on knowledge base currency
• Some legitimate high-quality content may score moderate due to naturally good structure
• Cannot detect paid placements or private deals with AI platforms

Example

See examples/sample-detection.md for complete worked examples of both product and website detection.

Next Best Action

After detection:

• If score 🟢: Product/website likely recommended on merit. Proceed with confidence.
• If score 🟡: Cross-verify with independent sources before deciding.
• If score 🔴: Strongly recommend independent verification. Consider alternative products/websites with lower GEO scores.