AGENT SKILLBY ZUBAIR-TRABZADA
GEO content quality & e-e-a-t assessment
AT A GLANCE
GEO content quality & e-e-a-t assessment is an SEO agent skill from zubair-trabzada. Content quality and E-E-A-T assessment for AI citability — evaluate experience, expertise, authoritativeness, trustworthiness, and content structure. The zubair-trabzada/geo-seo-claude repository on GitHub has 9.5K stars and the MIT license. To add it, run npx skills add zubair-trabzada/geo-seo-claude --skill geo-content
- SOURCE REPOSITORY
- zubair-trabzada/geo-seo-claude (opens GitHub in a new tab)
- REPO STARS
- SOURCE REPOSITORY
- LICENSE
- MIT
- SKILL.MD SIZE
- 15.2 KB337 LINES
QUICK ANSWER
- WHAT IT IS01
- Content quality and E-E-A-T assessment for AI citability, evaluate experience, expertise, authoritativeness, trustworthiness, and content structure. Its edge is a particular angle on search and SEO workflows, giving the agent tighter constraints than a plain GEO content quality & e-e-a-t assessment request.
- INPUTS02
- Context for search and SEO workflows: your goals, audience, constraints, and any source material the skill asks for.
- OUTPUT03
- A ready-to-use result for search and SEO workflows: the analysis, copy, or recommendations the agent produces.
INSTALL
One command adds this skill from its source repository.
npx skills add zubair-trabzada/geo-seo-claude --skill geo-contentREAD THE SKILL.MD BELOW BEFORE YOU RUN IT. SKILLS INSTRUCT YOUR AGENT.
SKILL.MD
The full file, as published in the source repository. Copy it, download it or open it on GitHub.
ON THIS PAGE · 29
GEO Content Quality & E-E-A-T Assessment#
Purpose#
AI search platforms do not just find content — they evaluate whether content deserves to be cited. The primary framework for this evaluation is E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness), which per Google's December 2025 Quality Rater Guidelines update now applies to ALL competitive queries, not just YMYL (Your Money Your Life) topics. Content that scores high on E-E-A-T is dramatically more likely to be cited by AI platforms.
This skill evaluates content through two lenses:
- E-E-A-T signals — does the content demonstrate real expertise and trust?
- AI citability — is the content structured so AI platforms can extract and cite specific claims?
How to Use This Skill#
- Fetch the target page(s) — homepage, key blog posts, service/product pages
- Evaluate E-E-A-T across the 4 dimensions (25% each)
- Assess content quality metrics (structure, readability, depth)
- Check for AI content quality signals
- Evaluate topical authority across the site
- Score and generate GEO-CONTENT-ANALYSIS.md
E-E-A-T Framework (100 points total)#
Experience — 25 points#
First-hand knowledge and direct involvement with the topic. AI platforms increasingly distinguish between content that reports on a topic and content from someone who has DONE it.
Signals to evaluate:
| Signal | Points | How to Score |
|---|---|---|
| First-person accounts ("I tested...", "We implemented...") | 5 | 5 if present and specific, 3 if generic, 0 if absent |
| Original research or data not available elsewhere | 5 | 5 if original data, 3 if references original work, 0 if none |
| Case studies with specific results | 4 | 4 if detailed with numbers, 2 if general, 0 if none |
| Screenshots, photos, or evidence of direct use | 3 | 3 if authentic evidence, 1 if stock/generic, 0 if none |
| Specific examples from personal experience | 4 | 4 if specific and unique, 2 if somewhat specific, 0 if generic |
| Demonstrations of process (not just outcome) | 4 | 4 if step-by-step from experience, 2 if partial, 0 if none |
What to flag as weak Experience:
- Content that only summarizes what other sources say without adding new perspective
- Generic advice that could apply to any situation ("It depends on your needs")
- No mention of actual usage, testing, or direct involvement
- Hedging language that suggests lack of direct knowledge ("reportedly", "supposedly", "some say")
Expertise — 25 points#
Demonstrated knowledge depth and professional competence in the subject matter.
Signals to evaluate:
| Signal | Points | How to Score |
|---|---|---|
| Author credentials visible (bio, degrees, certifications) | 5 | 5 if full credentials, 3 if basic bio, 0 if no author |
| Technical depth appropriate to topic | 5 | 5 if thorough technical treatment, 3 if adequate, 0 if superficial |
| Methodology explanation (how conclusions were reached) | 4 | 4 if clear methodology, 2 if some explanation, 0 if none |
| Data-backed claims (statistics, research citations) | 4 | 4 if well-sourced, 2 if some data, 0 if unsupported claims |
| Industry-specific terminology used correctly | 3 | 3 if accurate specialized language, 1 if basic, 0 if errors |
| Author page with detailed professional background | 4 | 4 if dedicated author page, 2 if brief bio, 0 if none |
What to flag as weak Expertise:
- Claims without supporting evidence or sources
- Surface-level coverage of complex topics
- Misuse of technical terminology
- No visible author or author without relevant credentials
- Content that is broad and generic rather than deep and specific
Authoritativeness — 25 points#
Recognition by others as a credible source on the topic.
Signals to evaluate:
| Signal | Points | How to Score |
|---|---|---|
| Inbound citations from authoritative sources | 5 | 5 if cited by major sources, 3 if some citations, 0 if none |
| Author quoted or cited in press/media | 4 | 4 if media mentions, 2 if industry mentions, 0 if none |
| Industry awards or recognition mentioned | 3 | 3 if relevant awards, 1 if tangential, 0 if none |
| Speaker credentials (conferences, events) | 3 | 3 if listed, 0 if none |
| Published in peer-reviewed or respected outlets | 4 | 4 if tier-1 publications, 2 if industry outlets, 0 if none |
| Comprehensive topic coverage (topical authority) | 3 | 3 if site covers topic thoroughly, 1 if some coverage, 0 if isolated |
| Brand mentioned on Wikipedia or authoritative references | 3 | 3 if Wikipedia, 2 if other encyclopedic refs, 0 if none |
What to flag as weak Authoritativeness:
- Single-topic site with no depth of coverage
- No external validation of expertise claims
- No backlinks from authoritative sources
- Claims of authority without evidence (self-proclaimed "expert")
Trustworthiness — 25 points#
Signals that the content and its publisher are reliable and transparent.
Signals to evaluate:
| Signal | Points | How to Score |
|---|---|---|
| Contact information visible (address, phone, email) | 4 | 4 if full contact info, 2 if email only, 0 if none |
| Privacy policy present and linked | 2 | 2 if present, 0 if absent |
| Terms of service present | 1 | 1 if present, 0 if absent |
| HTTPS with valid certificate | 2 | 2 if valid HTTPS, 0 if not |
| Editorial standards or corrections policy | 3 | 3 if documented, 1 if implicit, 0 if none |
| Transparent about business model and conflicts | 3 | 3 if clear disclosures, 1 if some, 0 if none |
| Reviews and testimonials from real customers | 3 | 3 if verified reviews, 1 if testimonials, 0 if none |
| Accurate claims (no misinformation detected) | 4 | 4 if all claims accurate, 2 if mostly accurate, 0 if errors found |
| Clear affiliate/sponsorship disclosures | 3 | 3 if properly disclosed, 0 if undisclosed or absent |
What to flag as weak Trustworthiness:
- No contact information or physical address
- Missing privacy policy or terms
- Undisclosed affiliate links or sponsored content
- Claims that are verifiably false or misleading
- No way to contact the publisher for corrections
Content Quality Metrics#
Word Count Benchmarks#
These are floors, not targets. More words does not mean better content. The benchmark is the minimum length to adequately cover a topic for AI citability.
| Page Type | Minimum Words | Ideal Range | Notes |
|---|---|---|---|
| Homepage | 500 | 500-1,500 | Clear value proposition, not a wall of text |
| Blog post | 1,500 | 1,500-3,000 | Thorough but focused |
| Pillar content / Ultimate guide | 2,000 | 2,500-5,000 | Comprehensive topic coverage |
| Product page | 300 | 500-1,500 | Descriptions, specs, use cases |
| Service page | 500 | 800-2,000 | What, how, why, for whom |
| About page | 300 | 500-1,000 | Company/person story and credentials |
| FAQ page | 500 | 1,000-2,500 | Thorough answers, not one-liners |
Readability Assessment#
- Target Flesch Reading Ease: 60-70 (8th-9th grade level)
- This is NOT a direct ranking factor but affects citability — AI platforms prefer content that is clear and unambiguous
- Overly academic writing (score < 30) reduces citability for general queries
- Overly simple writing (score > 80) may lack the depth needed for expertise signals
How to estimate without a tool:
- Average sentence length: 15-20 words is ideal
- Average paragraph length: 2-4 sentences
- Presence of jargon: should be defined when first used
- Passive voice: < 15% of sentences
Paragraph Structure for AI Parsing#
AI platforms extract content at the paragraph level. Each paragraph should be a self-contained unit of meaning.
Optimal paragraph structure:
- 2-4 sentences per paragraph (1-sentence paragraphs are weak; 5+ sentences are hard to extract)
- One idea per paragraph — do not mix topics within a paragraph
- Lead with the key claim — first sentence should contain the main point
- Support with evidence — remaining sentences provide data, examples, or context
- Quotable standalone — each paragraph should make sense if extracted in isolation
Heading Structure#
- One H1 per page — the primary topic/title
- H2 for major sections — should represent distinct subtopics
- H3 for subsections — nested under relevant H2
- No skipped levels — do not go from H1 to H3 without an H2
- Descriptive headings — "How to Optimize for AI Search" not "Section 2"
- Question-based headings where appropriate — these map directly to AI queries
Internal Linking#
- Every content page should link to 3-5 related pages on the same site
- Links should use descriptive anchor text (not "click here")
- Create a topic cluster structure: pillar page linked to/from all related subtopic pages
- Orphan pages (no internal links pointing to them) are rarely cited by AI
AI Content Assessment#
AI-Generated Content Policy#
AI-generated content is acceptable per Google's guidance (March 2024 clarification) as long as it demonstrates genuine E-E-A-T signals and has human oversight. The concern is not HOW content is created but WHETHER it provides value.
Signs of Low-Quality AI Content (flag these)#
| Signal | Description |
|---|---|
| Generic phrasing | "In today's fast-paced world...", "It's important to note that...", "At the end of the day..." |
| No original insight | Content that only rephrases widely available information |
| Lack of first-hand experience | No personal anecdotes, case studies, or specific examples |
| Perfect but empty structure | Well-formatted headings with shallow content beneath them |
| No specific examples | Uses abstract explanations without concrete instances |
| Repetitive conclusions | Each section ends with a variation of the same point |
| Hedging overload | "Generally speaking", "In most cases", "It depends on various factors" without specifying which factors |
| Missing human voice | No opinions, preferences, or professional judgment expressed |
| Filler content | Paragraphs that could be deleted without losing information |
| No data or sources | Claims presented as facts without attribution or evidence |
High-Quality Content Signals (regardless of production method)#
| Signal | Description |
|---|---|
| Original data | Surveys, experiments, benchmarks, proprietary analysis |
| Specific examples | Named products, companies, dates, numbers |
| Contrarian or nuanced views | Disagreement with conventional wisdom, backed by reasoning |
| First-person experience | "When I tested this..." or "Our team found..." |
| Updated information | References to recent events, current data |
| Expert opinion | Clear professional judgment, not just facts |
| Practical recommendations | Specific, actionable advice, not vague guidance |
| Trade-offs acknowledged | "This approach works well for X but not for Y because..." |
Content Freshness Assessment#
Publication Dates#
- Check for visible
datePublishedanddateModifiedin both the content and structured data - Content without dates is treated as less trustworthy by AI platforms
- Dates should be specific (January 15, 2026) not vague ("recently")
Freshness Scoring#
| Criterion | Score |
|---|---|
| Updated within 3 months | Excellent — current and relevant |
| Updated within 6 months | Good — still reasonably current |
| Updated within 12 months | Acceptable — may need refresh |
| Updated 12-24 months ago | Warning — review for accuracy |
| No date or 24+ months old | Critical — AI platforms may deprioritize |
Evergreen Indicators#
Some content remains relevant regardless of age. Flag content as evergreen if:
- It covers fundamental concepts that do not change (physics, basic math, legal definitions)
- It is clearly labeled as a reference/guide for lasting concepts
- It does not contain time-dependent claims ("the latest", "currently", "in 2024")
Topical Authority Assessment#
What It Is#
Topical authority measures whether a site comprehensively covers a topic rather than touching on it superficially. AI platforms prefer citing sites that are recognized authorities on their topics.
How to Assess#
- Content breadth: Does the site have multiple pages covering different aspects of its core topic?
- Content depth: Do individual pages go deep into subtopics?
- Topic clustering: Are pages organized into logical groups with internal linking?
- Content gaps: Are there obvious subtopics that the site should cover but does not?
- Competitor comparison: Do competitors cover subtopics that this site misses?
Scoring#
| Level | Description | Score Impact |
|---|---|---|
| Authority | 20+ pages covering topic comprehensively, strong clustering | +10 bonus |
| Developing | 10-20 pages with some clustering | +5 bonus |
| Emerging | 5-10 pages on topic, limited clustering | +0 |
| Thin | < 5 pages, no clustering | -5 penalty |
Overall Scoring (0-100)#
Score Composition#
| Component | Weight | Max Points |
|---|---|---|
| Experience | 25% | 25 |
| Expertise | 25% | 25 |
| Authoritativeness | 25% | 25 |
| Trustworthiness | 25% | 25 |
| Subtotal | 100 | |
| Topical Authority Modifier | +10 to -5 | |
| Final Score | Capped at 100 |
Score Interpretation#
- 85-100: Exceptional — strong AI citation candidate across platforms
- 70-84: Good — solid foundation, specific improvements will increase citability
- 55-69: Average — multiple E-E-A-T gaps reducing AI visibility
- 40-54: Below Average — significant content quality and trust issues
- 0-39: Poor — fundamental content strategy overhaul needed
Output Format#
Generate GEO-CONTENT-ANALYSIS.md with:
# GEO Content Quality & E-E-A-T Analysis — [Domain]
Date: [Date]
## Content Score: XX/100
## E-E-A-T Breakdown
| Dimension | Score | Key Finding |
|---|---|---|
| Experience | XX/25 | [One-line summary] |
| Expertise | XX/25 | [One-line summary] |
| Authoritativeness | XX/25 | [One-line summary] |
| Trustworthiness | XX/25 | [One-line summary] |
## Topical Authority Modifier: [+10 to -5]
## Pages Analyzed
| Page | Word Count | Readability | Heading Structure | Citability Rating |
|---|---|---|---|---|
| [URL] | [Count] | [Score] | [Pass/Warn/Fail] | [High/Medium/Low] |
## E-E-A-T Detailed Findings
### Experience
[Specific passages and pages with strong/weak experience signals]
### Expertise
[Author credentials found, technical depth assessment, specific gaps]
### Authoritativeness
[External validation found, topical authority assessment, gaps]
### Trustworthiness
[Trust signals present/missing, accuracy concerns if any]
## Content Quality Issues
[Specific passages flagged with reasons and rewrite suggestions]
## AI Content Concerns
[Any low-quality AI content patterns detected, with specific examples]
## Freshness Assessment
| Page | Published | Last Updated | Status |
|---|---|---|---|
| [URL] | [Date] | [Date] | [Current/Stale/No Date] |
## Citability Assessment
### Most Citable Passages
[Top 5 passages that AI platforms are most likely to cite, with reasons]
### Least Citable Pages
[Pages with lowest citability, with specific improvement recommendations]
## Improvement Recommendations
### Quick Wins
[Specific content changes that can be made immediately]
### Content Gaps
[Topics the site should cover to strengthen topical authority]
### Author/E-E-A-T Improvements
[Specific steps to strengthen E-E-A-T signals]FAQ
07 QUESTIONS
01What is the GEO content quality & e-e-a-t assessment agent skill?
Content quality and E-E-A-T assessment for AI citability, evaluate experience, expertise, authoritativeness, trustworthiness, and content structure. It is listed under SEO and content in the 100xLab agent skills directory.
02How do I install GEO content quality & e-e-a-t assessment in Cursor, Claude Code, or Codex?
Run npx skills add zubair-trabzada/geo-seo-claude --skill geo-content in the project where you want it, then ask your agent for the skill by name. The --skill flag installs only GEO content quality & e-e-a-t assessment, not every skill in the repository.
03What does the GEO content quality & e-e-a-t assessment SKILL.md cover?
The SKILL.md includes sections on Purpose, How to Use This Skill, E-E-A-T Framework (100 points total), Experience — 25 points, Expertise — 25 points and Authoritativeness — 25 points. Read the full text on this page or in plain markdown at /resources/agent-skills/zubair-trabzada-geo-seo-claude--geo-content--r40cg4/raw.
04Which agents can use GEO content quality & e-e-a-t assessment?
GEO content quality & e-e-a-t assessment installs with the cross-agent skills installer (npx skills add), which sets it up for agents such as Claude Code, Cursor and Codex. Each agent loads the SKILL.md when you ask for the skill by name.
05What license is GEO content quality & e-e-a-t assessment under?
The zubair-trabzada/geo-seo-claude repository declares the MIT license. Read the license file in the repository before you copy or redistribute the skill.
06Is GEO content quality & e-e-a-t assessment free to use?
Reading, copying and installing GEO content quality & e-e-a-t assessment costs nothing: it is published in a public GitHub repository under the MIT license. Reuse and redistribution follow that license.
07Where is the source of GEO content quality & e-e-a-t assessment?
GEO content quality & e-e-a-t assessment comes from https://github.com/zubair-trabzada/geo-seo-claude, which has 9.5K GitHub stars. The SKILL.md is at https://github.com/zubair-trabzada/geo-seo-claude/blob/HEAD/skills/geo-content/SKILL.md. The author is zubair-trabzada.
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ATTRIBUTION
GEO content quality & e-e-a-t assessment was written by its original authors and is published in zubair-trabzada/geo-seo-claude (opens GitHub in a new tab) under the MIT license. All rights stay with the original authors.
100xLab did not write, maintain or endorse this skill. This page lists it for discovery. Repo stars belong to the source repository, not to the skill. Review any skill before you install it.