geo-citability
geo-citability is an code AI skill with a core value of AI citability scoring and optimization. It
helps developers solve real-world problems in the code domain, boosting
efficiency, automating repetitive tasks, and optimizing workflows.
AI citability scoring and optimization.
Quick Facts
mkdir -p ./skills/geo-citability && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/geo-citability/SKILL.md -o ./skills/geo-citability/SKILL.md Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# 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-contain
🎯 Best For
- Claude users
- Software engineers
- Development teams
- Tech leads
💡 Use Cases
- Code quality improvement
- Best practice enforcement
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply geo-citability to Your Work
Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.
- 4
Review and Refine
Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.
❓ Frequently Asked Questions
Is geo-citability compatible with Cursor and VS Code?
Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.
Do I need specific dependencies for geo-citability?
Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.
How do I install geo-citability?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/geo-citability/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Skipping validation
Always test AI-generated code changes, even for simple refactors.
Missing dependency updates
Check if the skill requires updated dependencies or new packages.