New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

AI Citability Scorer

by zubair-trabzada in zubair-trabzada/geo-seo-claude

Scores how likely AI assistants are to quote passages from a web page and suggests rewrites that make those passages easier to extract.

MITAuto-check: notesMarketing & SEO

Install AI Citability Scorer

skills CLI
$ npx skills add zubair-trabzada/geo-seo-claude --skill geo-citability -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install zubair-trabzada/geo-seo-claude geo-citability --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/zubair-trabzada/geo-seo-claude.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/geo-citability .claude/skills/geo-citability && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
geo-citability
GitHub stars
11k
Used in
2 other repos
Token cost
~3.7k tokens
SKILL.md length
1,548 words
Files
1
Skills in repo
16
Repo updated
First seen
Licence
MIT

At a glance

Scores how likely AI assistants are to quote passages from a web page and suggests rewrites that make those passages easier to extract.

  • Works in 5 steps: Fetch and Parse Page Content → Segment Content into Blocks → Score Each Block → …
  • Auditing how quotable a landing page or article is for AI answers
  • SKILL.md covers Core Insight, Citability Scoring Rubric…, Analysis Procedure and Output Format, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

The skill measures citability: how readily AI systems such as ChatGPT, Claude, Perplexity and Gemini can lift a passage from a page and present it as an answer. The output is a score from 0 to 100 with specific rewrite suggestions. Its rubric favors passages that are self-contained, rich in facts such as statistics, dates or named entities, and that answer a question in their first sentences, with a preferred length of about 134 to 167 words.

Scoring is split into weighted categories, beginning with answer block quality at 30% of the total. That category looks for definition patterns like 'X is', answer-first structure, quantified answers and direct comparisons, and its bands run from 0-29 for pages with no identifiable answer blocks to 90-100 for pages where every major section opens with a direct answer. The skill frames this as different from classic SEO copywriting, which targets keyword density and engagement.

When your agent uses it

  • Auditing how quotable a landing page or article is for AI answers
  • Rewriting a section so the first sentences answer the question directly
  • Comparing the citability of several pages on one site

Example prompts

  • “Score the citability of https://example.com/pricing and suggest rewrites.”
  • “Check which sections of our FAQ page open with a direct answer and which bury it.”
  • “Rewrite the intro of this blog post so an AI assistant could quote it as a definition.”

Requirements

  • A page URL or file to analyze
  • Pre-approved tools (allowed-tools): Read, Grep, Glob, Bash, WebFetch, Write

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Fetch and Parse Page Content
  2. Segment Content into Blocks
  3. Score Each Block
  4. Calculate Page-Level Score
  5. Generate Rewrite Suggestions

What it can do on your machine

Read from SKILL.md and the folder at commit 989cae0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Grep
    • Glob
    • Bash
    • WebFetch
    • Write

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

AI Citability Scorer loads about 3.7k tokens when it runs. Until then it costs about 67 tokens; SKILL.md has 1,548 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~67
When it runs · the whole SKILL.md, loaded when a task matches
~3.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Grep, Glob, Bash, WebFetch, Write

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from zubair-trabzada/geo-seo-claude at commit 989cae0, republished under its MIT licence (© zubair-trabzada). 1,548 words, ~3,701 tokens.

Download SKILL.mdSave it as .claude/skills/geo-citability/SKILL.md (or your agent's skills folder).
name
geo-citability
description
AI citability scoring and optimization. Analyzes web page content to determine how likely AI systems (ChatGPT, Claude, Perplexity, Gemini) are to cite or quote passages from the page. Provides a citability score (0-100) with specific rewrite suggestions.
allowed-tools
Read, Grep, Glob, Bash, WebFetch, Write

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:

ScoreCriteria
90-100Every 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.
70-89Most sections have clear answer openings. Some definition patterns present. Answers are identifiable but may need minor context.
50-69Some sections have answer-like openings but many bury the answer in the middle or end of paragraphs. Few explicit definition patterns.
30-49Answers are generally buried in long paragraphs. No consistent definition patterns. Content is narrative-driven rather than answer-driven.
0-29No 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:

ScoreCriteria
90-10080%+ 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.
70-8960-79% of content blocks are self-contained. Most passages name their subject. Occasional pronoun references that require context.
50-6940-59% of content blocks are self-contained. Mixed use of explicit subjects and pronouns. Some passages require reading prior sections.
30-4920-39% of content blocks are self-contained. Heavy reliance on pronouns and contextual references. Most passages need surrounding text.
0-29Under 20% self-contained. Content reads as a continuous narrative where extracting any paragraph loses meaning.

Self-containment checklist for each passage:

  1. Does the passage explicitly name the subject (not "it," "this," "they")?
  2. Can someone understand the main point reading ONLY this passage?
  3. Does the passage contain at least one specific fact, statistic, or named entity?
  4. Is the passage between 50-200 words (the optimal extraction length)?
  5. 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:

ScoreCriteria
90-100Clean 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.
70-89Good heading hierarchy with minor skips. Some question-based headings. Mostly short paragraphs. Some use of tables and lists.
50-69Heading hierarchy present but inconsistent. Few question-based headings. Mix of short and long paragraphs. Limited tables/lists.
30-49Minimal heading structure. No question-based headings. Long paragraphs dominate. Rare use of tables/lists.
0-29No 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:

ScoreCriteria
90-1005+ 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.
70-893-4 statistics per 500 words. Most claims have sources. Mostly specific numbers with occasional vague quantifiers.
50-691-2 statistics per 500 words. Some claims sourced. Mix of specific and vague numbers.
30-49Less than 1 statistic per 500 words. Few sourced claims. Predominantly vague quantifiers.
0-29No 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)
Show full SKILL.md (596 more words)Show less
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:

ScoreCriteria
90-100Contains first-party research, proprietary data, original surveys, or unique datasets. Presents analysis or insights not found on any other page. Clear methodological descriptions.
70-89Contains some original insights or unique analysis of existing data. Offers a distinct perspective with original examples.
50-69Mostly synthesizes existing information but adds some unique commentary or examples.
30-49Largely derivative content that restates common knowledge with minimal original contribution.
0-29Entirely 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
  1. Use WebFetch to retrieve the target URL.
  2. Extract the main content area (exclude navigation, footer, sidebar, ads).
  3. Preserve heading structure (H1-H6 tags).
  4. Preserve paragraph boundaries, lists, and tables.
  5. Calculate total word count of main content.
Step 2: Segment Content into Blocks
  1. Split content at each heading (H2 or H3) to create content blocks.
  2. 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
  1. Calculate the average of all block scores for the page-level citability score.
  2. Identify the top 3 highest-scoring blocks (highlight as strengths).
  3. Identify the bottom 3 lowest-scoring blocks (flag for rewriting).
  4. 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:

  1. Identify the primary weakness (buried answer, lack of facts, poor structure, etc.).
  2. Propose a rewritten opening sentence using a definition or answer-first pattern.
  3. Suggest specific statistics or facts that could be added.
  4. Recommend structural improvements (add list, add table, split paragraph).

Output Format

Generate a file called GEO-CITABILITY-SCORE.md:

markdown
# 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 SystemCitation Preference
ChatGPT (Search)Prefers passages with explicit definitions, named sources, and recent dates. Tends to cite 2-4 sources per response.
PerplexityHeavily favors fact-dense passages with statistics. Cites 4-8 sources per response. Values recency highly.
ClaudePrefers 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.

© zubair-trabzada, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/geo-citability of zubair-trabzada/geo-seo-claude.

Open the folder on GitHubat commit 989cae0

Used in 2 other repositories

We found 6 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in zubair-trabzada/geo-seo-claude, which our catalogue first saw on October 7, 2026.

Compare with similar skills

AI Citability Scorer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

AI Citability Scorer compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
AI Citability Scorer this skillzubair-trabzada/geo-seo-claude11k2 repos~3.7kAutomated safety check: NotesMIT
SEO Content Auditseranking/seo-skills161—~3kAutomated safety check: PassMIT
SEO ContentAgriciDaniel/codex-seo7995 repos~2.3kAutomated safety check: PassMIT
Editorial QArampstackco/claude-skills945—~5.6kAutomated safety check: PassMIT
SEO Planseranking/seo-skills161—~4.4kAutomated safety check: PassMIT
SEO Geoericrisco/rsc-harness180—~2.8kAutomated safety check: PassMIT

Similar skills

  • SEO Content Audit

    seranking/seo-skills

    E-E-A-T + CITE quality audit for an EXISTING piece of content.

    161 GitHub stars~3k tokensUpdated 3 mo ago
    Marketing & SEOAuto-check passed

  • SEO Content

    AgriciDaniel/codex-seo

    Content quality and E-E-A-T analysis with AI citation readiness assessment.

    799 GitHub starsUsed in 5 repos~2.3k tokens
    Marketing & SEOAuto-check passed

  • Editorial QA

    rampstackco/claude-skills

    Pre-publish QA framework for content. An agent skill from rampstackco/claude-skills.

    945 GitHub stars~5.6k tokensUpdated 4 days ago
    Marketing & SEOAuto-check passed

  • SEO Plan

    seranking/seo-skills

    Build a phased SEO roadmap for a domain — quarter-by-quarter, tied to the site's competitive position, content gaps, technical debt, and AI Search readiness.

    161 GitHub stars~4.4k tokensUpdated 3 mo ago
    Marketing & SEOAuto-check passed

  • SEO Geo

    ericrisco/rsc-harness

    A skill your agent uses when one existing page needs to rank in Google AND get cited by AI answer engines — auditing a URL for on-page SEO, structured-data JSON-LD, GEO citation levers, Core Web…

    180 GitHub stars~2.8k tokensUpdated yesterday
    Marketing & SEOAuto-check passed

  • AI Content Audit

    mohitagw15856/pm-claude-skills

    Audit a content library, docs site, or blog for AI-generated filler that's eroding trust and search performance — and triage what to fix, rewrite, or delete.

    1.4k GitHub stars~1.5k tokensUpdated 2 days ago
    Marketing & SEOAuto-check passed

More from zubair-trabzada/geo-seo-claude

All 16 skills in this repo

  • GEO-First SEO Audit Tool

    zubair-trabzada/geo-seo-claude

    Audits a website for AI search visibility across ChatGPT, Claude, Perplexity and Google AI Overviews while checking traditional SEO, schema and E-E-A-T content quality.

    11k GitHub stars~2.8k tokensUpdated yesterday
    Auto-check: notes

  • GEO Monthly Delta Report

    zubair-trabzada/geo-seo-claude

    Compares a baseline and a current GEO audit for a client, calculates score changes and action item progress, and writes a monthly progress report.

    11k GitHub stars~2.4k tokensUpdated yesterday
    Auto-check: notes

  • GEO Service Proposal Generator

    zubair-trabzada/geo-seo-claude

    Builds a client-ready AI-search-optimization proposal from an existing GEO audit, with pricing tiers, an ROI estimate and a markdown document ready to send.

    11k GitHub stars~3k tokensUpdated yesterday
    Auto-check: notes

  • GEO Content E-E-A-T Scorer

    zubair-trabzada/geo-seo-claude

    Scores a page's content against Google's E-E-A-T framework and AI-citability structure, then writes a scored gap-analysis report.

    11k GitHub starsUsed in 2 repos~4k tokens
    Auto-check: notes

  • GEO Prospect Tracker

    zubair-trabzada/geo-seo-claude

    Tracks GEO agency leads and clients through a sales pipeline in a local JSON file, with notes, audit scores, deal values and a pipeline summary.

    11k GitHub stars~1.7k tokensUpdated yesterday
    Auto-check: notes

  • llms.txt Analyzer and Generator

    zubair-trabzada/geo-seo-claude

    Validates an existing llms.txt file or crawls a site to generate a new one, following the format rules for a root-level Markdown file aimed at AI systems.

    11k GitHub starsUsed in 2 repos~3.9k tokens
    Auto-check: notes

Categories

Questions about AI Citability Scorer

What does AI Citability Scorer do?

Scores how likely AI assistants are to quote passages from a web page and suggests rewrites that make those passages easier to extract. The skill measures citability: how readily AI systems such as ChatGPT, Claude, Perplexity and Gemini can lift a passage from a page and present it as an answer. The output is a score from 0 to 100 with specific rewrite suggestions.

When should I use AI Citability Scorer?

AI Citability Scorer fits situations like: auditing how quotable a landing page or article is for AI answers; rewriting a section so the first sentences answer the question directly; comparing the citability of several pages on one site.

How do I install AI Citability Scorer in Claude Code?

Run `npx skills add zubair-trabzada/geo-seo-claude --skill geo-citability -a claude-code`. Or copy the skill folder (skills/geo-citability in zubair-trabzada/geo-seo-claude) into .claude/skills/geo-citability in your project. Claude Code loads it when a task matches its description.

How do I install AI Citability Scorer in Codex?

Run `npx skills add zubair-trabzada/geo-seo-claude --skill geo-citability -a codex`. Or copy the skill folder (skills/geo-citability in zubair-trabzada/geo-seo-claude) into .agents/skills/geo-citability in your project. Codex loads it when a task matches its description.

Can I use AI Citability Scorer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add zubair-trabzada/geo-seo-claude --skill geo-citability -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/geo-citability, .gemini/skills/geo-citability, .github/skills/geo-citability and .opencode/skills/geo-citability in your project.

What does AI Citability Scorer need to run?

SKILL.md names no scripts, command-line tools or credentials: AI Citability Scorer is instructions for the agent only. Our summary lists: A page URL or file to analyze. Its frontmatter pre-approves these tools: Read, Grep, Glob, Bash, WebFetch, Write.

Does AI Citability Scorer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is AI Citability Scorer safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does AI Citability Scorer use?

AI Citability Scorer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does AI Citability Scorer use?

About 3.7k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to AI Citability Scorer?

Skills that share tags, products or a category with AI Citability Scorer: SEO Content Audit (seranking/seo-skills, 161 stars), SEO Content (AgriciDaniel/codex-seo, 799 stars), Editorial QA (rampstackco/claude-skills, 945 stars) and SEO Plan (seranking/seo-skills, 161 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains AI Citability Scorer?

zubair-trabzada (a GitHub user) maintains it in zubair-trabzada/geo-seo-claude, which has 10,982 GitHub stars. The repository holds 16 skills in this directory. The repository was last updated on October 10, 2026.

Source: zubair-trabzada/geo-seo-claude on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.