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AI Citability Scoring Skill
Core Insight
AI language models cite passages that meet specific structural criteria. Research from Princeton, Georgia Tech, and IIT Delhi (2024) found that GEO-optimized content achieves 30-115% higher visibility in AI-generated responses. The key finding: AI systems preferentially extract and cite passages that are 134-167 words long , self-contained (understandable without surrounding context), fact-rich (containing specific statistics, dates, or named entities), and directly answer a question in the first 1-2 sentences.
This is fundamentally different from traditional SEO copywriting, which optimizes for keyword density and user engagement metrics. GEO citability optimizes for extractability -- the ease with which an AI system can pull a passage from your content and present it as a direct answer.
Citability Scoring Rubric (0-100)
Category 1: Answer Block Quality (30% of total score)
This measures whether content contains clear, quotable answer passages that AI systems can extract verbatim.
Scoring Criteria:
| Score | Criteria |
|---|
| 90-100 | Every major section opens with a 1-2 sentence direct answer. Uses "X is..." or "X refers to..." patterns. First 40-60 words of each section can stand alone as a complete answer. |
| 70-89 | Most sections have clear answer openings. Some definition patterns present. Answers are identifiable but may need minor context. |
| 50-69 | Some sections have answer-like openings but many bury the answer in the middle or end of paragraphs. Few explicit definition patterns. |
| 30-49 | Answers are generally buried in long paragraphs. No consistent definition patterns. Content is narrative-driven rather than answer-driven. |
| 0-29 | No identifiable answer blocks. Content is entirely narrative, conversational, or fragmented. AI would struggle to extract any quotable passage. |
What to look for:
- Definition patterns: "X is [definition]." / "X refers to [explanation]." / "X means [meaning]."
- Answer-first structure: The answer appears in the first sentence, followed by supporting detail.
- Quantified answers: "The average cost of X is $Y" rather than "Many factors affect the cost of X."
- Comparison answers: "X differs from Y in three ways: [list]" rather than "X and Y are often confused."
High-citability example:
Content delivery networks (CDNs) are distributed server systems that cache and serve
web content from locations geographically close to end users. A CDN reduces latency
by 50-70% on average by serving assets from edge servers rather than a single origin
server. The three largest CDN providers as of 2025 are Cloudflare (serving approximately
20% of all websites), Amazon CloudFront, and Akamai Technologies.
Word count: 58. Self-contained: Yes. Facts: 3 specific data points. Definition pattern: Yes.
Low-citability example:
If you've ever wondered why some websites load faster than others, the answer might
surprise you. There's this amazing technology that has been around for a while now.
It's changed the way we think about web performance. Let me explain how it works and
why you should care about it for your business.
Word count: 52. Self-contained: No (no topic identified). Facts: 0. Definition pattern: No.
Category 2: Passage Self-Containment (25% of total score)
This measures whether individual passages can be extracted and understood without needing the surrounding content.
Scoring Criteria:
| Score | Criteria |
|---|
| 90-100 | 80%+ of content blocks are fully self-contained. Each passage names its subject explicitly. No reliance on pronouns referencing earlier content. Contains specific facts within the passage. |
| 70-89 | 60-79% of content blocks are self-contained. Most passages name their subject. Occasional pronoun references that require context. |
| 50-69 | 40-59% of content blocks are self-contained. Mixed use of explicit subjects and pronouns. Some passages require reading prior sections. |
| 30-49 | 20-39% of content blocks are self-contained. Heavy reliance on pronouns and contextual references. Most passages need surrounding text. |
| 0-29 | Under 20% self-contained. Content reads as a continuous narrative where extracting any paragraph loses meaning. |
Self-containment checklist for each passage:
- Does the passage explicitly name the subject (not "it," "this," "they")?
- Can someone understand the main point reading ONLY this passage?
- Does the passage contain at least one specific fact, statistic, or named entity?
- Is the passage between 50-200 words (the optimal extraction length)?
- Does the passage avoid starting with conjunctions ("But," "However," "And") that imply prior context?
Category 3: Structural Readability (20% of total score)
This measures the structural formatting that helps AI systems parse and segment content.
Scoring Criteria:
| Score | Criteria |
|---|
| 90-100 | Clean H1 > H2 > H3 hierarchy. Question-based headings for informational content. Short paragraphs (2-4 sentences). Tables for comparisons. Ordered lists for processes. Unordered lists for features/options. |
| 70-89 | Good heading hierarchy with minor skips. Some question-based headings. Mostly short paragraphs. Some use of tables and lists. |
| 50-69 | Heading hierarchy present but inconsistent. Few question-based headings. Mix of short and long paragraphs. Limited tables/lists. |
| 30-49 | Minimal heading structure. No question-based headings. Long paragraphs dominate. Rare use of tables/lists. |
| 0-29 | No heading structure or severely broken hierarchy. Wall-of-text paragraphs. No tables or lists. |
Structural best practices for AI citability:
- Heading hierarchy: H1 (page title) > H2 (major sections) > H3 (subsections). Never skip levels.
- Question-based headings: "What is [topic]?" and "How does [topic] work?" are directly matchable to AI queries.
- Paragraph length: 2-4 sentences per paragraph. AI systems parse short paragraphs more reliably.
- Tables: Use for any comparison of 3+ items. AI systems extract table data with high accuracy.
- Lists: Use ordered lists for sequential processes, unordered lists for non-sequential items.
- Bold key terms: Bold the first use of important terms. This aids AI entity recognition.
Category 4: Statistical Density (15% of total score)
This measures the presence of specific, verifiable data points that AI systems prioritize when selecting citation sources.
Scoring Criteria:
| Score | Criteria |
|---|
| 90-100 | 5+ specific statistics per 500 words. All claims backed by named sources or dates. Uses exact numbers (not "many" or "several"). Includes percentages, dollar amounts, timeframes, and named studies. |
| 70-89 | 3-4 statistics per 500 words. Most claims have sources. Mostly specific numbers with occasional vague quantifiers. |
| 50-69 | 1-2 statistics per 500 words. Some claims sourced. Mix of specific and vague numbers. |
| 30-49 | Less than 1 statistic per 500 words. Few sourced claims. Predominantly vague quantifiers. |
| 0-29 | No statistics. No sourced claims. All quantifiers are vague ("many," "most," "a lot"). |
What counts as a statistic:
- Specific percentages: "73% of marketers report..."
- Dollar amounts: "The average cost is $4,500 per month"
- Timeframes: "Implementation takes 6-8 weeks on average"
- Named studies: "According to the 2025 HubSpot State of Marketing Report..."
- Specific counts: "The platform integrates with 340+ tools"
- Comparison data: "40% faster than the industry average"
What does NOT count:
- "Many companies use..." (vague)
- "A significant percentage..." (vague)
- "Studies show that..." (no named source)
- "Experts agree..." (no named experts)
Category 5: Uniqueness & Original Data (10% of total score)
This measures whether the content provides information that AI systems cannot find elsewhere, making it a necessary citation source.
Scoring Criteria:
| Score | Criteria |
|---|
| 90-100 | Contains first-party research, proprietary data, original surveys, or unique datasets. Presents analysis or insights not found on any other page. Clear methodological descriptions. |
| 70-89 | Contains some original insights or unique analysis of existing data. Offers a distinct perspective with original examples. |
| 50-69 | Mostly synthesizes existing information but adds some unique commentary or examples. |
| 30-49 | Largely derivative content that restates common knowledge with minimal original contribution. |
| 0-29 | Entirely derivative. All information is available (often verbatim) on higher-authority sources. |
Signals of unique content:
- "Our analysis of [X] data found..."
- "We surveyed [N] [professionals] and found..."
- "Based on our experience with [N] clients..."
- Custom charts, graphs, or data visualizations
- Case studies with specific named outcomes
- Original frameworks, methodologies, or taxonomies
Analysis Procedure
Step 1: Fetch and Parse Page Content
- Use WebFetch to retrieve the target URL.
- Extract the main content area (exclude navigation, footer, sidebar, ads).
- Preserve heading structure (H1-H6 tags).
- Preserve paragraph boundaries, lists, and tables.
- Calculate total word count of main content.
Step 2: Segment Content into Blocks
- Split content at each heading (H2 or H3) to create content blocks.
- For each block, record:
- The heading text
- The full text content under that heading
- Word count of the block
- Number of paragraphs
- Number of lists and tables
- Number of statistics/data points
- Whether the block contains a definition pattern
- Whether the first 60 words form a standalone answer
Step 3: Score Each Block
For each content block, calculate:
- Answer Block Quality sub-score (0-100)
- Self-Containment sub-score (0-100)
- Structural Readability sub-score (0-100)
- Statistical Density sub-score (0-100)
- Uniqueness sub-score (0-100)
Block Citability Score = (Answer * 0.30) + (SelfContain * 0.25) + (Structure * 0.20) + (Stats * 0.15) + (Unique * 0.10)
Step 4: Calculate Page-Level Score
- Calculate the average of all block scores for the page-level citability score.
- Identify the top 3 highest-scoring blocks (highlight as strengths).
- Identify the bottom 3 lowest-scoring blocks (flag for rewriting).
- Calculate the percentage of blocks scoring above 70 (the "citability coverage" metric).
Step 5: Generate Rewrite Suggestions
For each block scoring below 60, generate a specific rewrite suggestion:
- Identify the primary weakness (buried answer, lack of facts, poor structure, etc.).
- Propose a rewritten opening sentence using a definition or answer-first pattern.
- Suggest specific statistics or facts that could be added.
- Recommend structural improvements (add list, add table, split paragraph).
Output Format
Generate a file called GEO-CITABILITY-SCORE.md:
# AI Citability Analysis: [Page Title]
**URL:** [URL]
**Analysis Date:** [Date]
**Overall Citability Score: [X]/100**
**Citability Coverage:** [X]% of content blocks score above 70
---
## Score Summary
| Category | Score | Weight | Weighted |
|---|---|---|---|
| Answer Block Quality | [X]/100 | 30% | [X] |
| Passage Self-Containment | [X]/100 | 25% | [X] |
| Structural Readability | [X]/100 | 20% | [X] |
| Statistical Density | [X]/100 | 15% | [X] |
| Uniqueness & Original Data | [X]/100 | 10% | [X] |
| **Overall** | | | **[X]/100** |
---
## Strongest Content Blocks
### 1. "[Heading]" -- Score: [X]/100
> [First 2 sentences of the block]
**Why it works:** [Explanation]
### 2. "[Heading]" -- Score: [X]/100
> [First 2 sentences of the block]
**Why it works:** [Explanation]
---
## Weakest Content Blocks (Rewrite Priority)
### 1. "[Heading]" -- Score: [X]/100
**Current opening:**
> [First 2 sentences as they exist]
**Problem:** [Specific issue -- buried answer, no facts, etc.]
**Suggested rewrite:**
> [Rewritten opening 2-3 sentences with answer-first pattern and facts]
**Additional improvements:**
- [Add table comparing X, Y, Z]
- [Include statistic about ...]
- [Split long paragraph into 2-3 shorter ones]
---
## Quick Win Reformatting Recommendations
1. **[Specific recommendation]** -- Expected citability lift: +[X] points
2. **[Specific recommendation]** -- Expected citability lift: +[X] points
3. **[Specific recommendation]** -- Expected citability lift: +[X] points
4. **[Specific recommendation]** -- Expected citability lift: +[X] points
5. **[Specific recommendation]** -- Expected citability lift: +[X] points
---
## Per-Section Scores
| Section Heading | Words | Answer Quality | Self-Contained | Structure | Stats | Unique | Overall |
|---|---|---|---|---|---|---|---|
| [H2 heading] | [N] | [X] | [X] | [X] | [X] | [X] | [X] |
Reference Data
Optimal Passage Characteristics (from GEO Research)
- Optimal length for AI citation: 134-167 words (Bortolato 2025 analysis of AI Overview passages)
- Definition patterns increase citation rate by: 2.1x (Georgia Tech 2024)
- Adding statistics to passages increases citation by: 40% (Princeton GEO study 2024)
- Adding quotations from authorities increases citation by: 115% in certain categories (IIT Delhi 2024)
- Fluency optimization increases visibility by: 30% on average across all query types
- Content with source citations is cited: 20-25% more often by Perplexity and ChatGPT search
AI System Citation Preferences
| AI System | Citation Preference |
|---|
| ChatGPT (Search) | Prefers passages with explicit definitions, named sources, and recent dates. Tends to cite 2-4 sources per response. |
| Perplexity | Heavily favors fact-dense passages with statistics. Cites 4-8 sources per response. Values recency highly. |
| Claude | Prefers well-structured, comprehensive passages. Values nuance and accuracy over brevity. |
| Gemini (AI Overviews) | Prefers concise answer blocks (40-60 words). Values content already ranking in top 10 organic results. |
| Copilot (Bing) | Similar to Gemini. Prefers passages from high-authority domains with clear factual claims. |
Weekly Installs
55
Repository
zubair-trabzada…o-claude
GitHub Stars
3.7K
First Seen
Feb 27, 2026
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