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GEO Content Quality & E-E-A-T Assessment

GEO内容质量与E-E-A-T评估

Purpose

目的

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:
  1. E-E-A-T signals — does the content demonstrate real expertise and trust?
  2. AI citability — is the content structured so AI platforms can extract and cite specific claims?
AI搜索平台不仅是查找内容——它们还会评估内容是否值得被引用。这种评估的核心框架是E-E-A-T（Experience经验、Expertise专业能力、Authoritativeness权威性、Trustworthiness可信度），根据Google 2025年12月更新的质量评估指南，该框架现在适用于所有竞争性查询，而不仅仅是YMYL（Your Money Your Life，涉及金钱与生命的主题）内容。在E-E-A-T上得分高的内容被AI平台引用的可能性要高得多。
本技能从两个维度评估内容：
  1. E-E-A-T信号——内容是否展现出真实的专业能力与可信度？
  2. AI可引用性——内容的结构是否便于AI平台提取并引用特定主张？

How to Use This Skill

如何使用本技能

  1. Fetch the target page(s) — homepage, key blog posts, service/product pages
  2. Evaluate E-E-A-T across the 4 dimensions (25% each)
  3. Assess content quality metrics (structure, readability, depth)
  4. Check for AI content quality signals
  5. Evaluate topical authority across the site
  6. Score and generate GEO-CONTENT-ANALYSIS.md
  1. 获取目标页面——首页、关键博客文章、服务/产品页面
  2. 从4个维度评估E-E-A-T（各占25%）
  3. 评估内容质量指标（结构、可读性、深度）
  4. 检查AI内容质量信号
  5. 评估网站的主题权威性
  6. 打分并生成GEO-CONTENT-ANALYSIS.md

E-E-A-T Framework (100 points total)

E-E-A-T框架（总分100分）

Experience — 25 points

经验——25分

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:
SignalPointsHow to Score
First-person accounts ("I tested...", "We implemented...")55 if present and specific, 3 if generic, 0 if absent
Original research or data not available elsewhere55 if original data, 3 if references original work, 0 if none
Case studies with specific results44 if detailed with numbers, 2 if general, 0 if none
Screenshots, photos, or evidence of direct use33 if authentic evidence, 1 if stock/generic, 0 if none
Specific examples from personal experience44 if specific and unique, 2 if somewhat specific, 0 if generic
Demonstrations of process (not just outcome)44 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")
对主题的第一手知识和直接参与经验。AI平台越来越能区分“报道某主题”的内容和“亲身实践过该主题”的内容。
评估信号：
信号分值评分方式
第一人称叙述（“我测试过……”、“我们实施了……”）5存在且具体得5分，泛泛而谈得3分，完全没有得0分
原创研究或独家数据5有原创数据得5分，引用原创研究得3分，完全没有得0分
带具体结果的案例研究4详细且带数据得4分，一般性描述得2分，完全没有得0分
直接使用的截图、照片或证据3真实证据得3分，通用/库存素材得1分，完全没有得0分
来自个人经验的具体示例4具体且独特得4分，略有具体性得2分，泛泛而谈得0分
流程演示（而非仅展示结果）4基于经验的分步演示得4分，部分演示得2分，完全没有得0分
经验薄弱的警示信号：
  • 仅总结其他来源内容，未添加新视角
  • 适用于任何场景的通用建议（“这取决于你的需求”）
  • 未提及实际使用、测试或直接参与经历
  • 暗示缺乏直接知识的模糊表述（“据报道”、“据说”、“有人认为”）

Expertise — 25 points

专业能力——25分

Demonstrated knowledge depth and professional competence in the subject matter.
Signals to evaluate:
SignalPointsHow to Score
Author credentials visible (bio, degrees, certifications)55 if full credentials, 3 if basic bio, 0 if no author
Technical depth appropriate to topic55 if thorough technical treatment, 3 if adequate, 0 if superficial
Methodology explanation (how conclusions were reached)44 if clear methodology, 2 if some explanation, 0 if none
Data-backed claims (statistics, research citations)44 if well-sourced, 2 if some data, 0 if unsupported claims
Industry-specific terminology used correctly33 if accurate specialized language, 1 if basic, 0 if errors
Author page with detailed professional background44 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
在主题领域展现出的知识深度和专业能力。
评估信号：
信号分值评分方式
作者资质可见（简介、学位、证书）5完整资质得5分，基础简介得3分，无作者信息得0分
符合主题的技术深度5全面的技术解读得5分，内容充足得3分，流于表面得0分
方法论说明（结论如何得出）4清晰说明方法论得4分，部分说明得2分，完全没有得0分
有数据支持的主张（统计数据、研究引用）4来源可靠得4分，有部分数据得2分，无支持性主张得0分
正确使用行业特定术语3准确使用专业术语得3分，基础使用得1分，术语误用得0分
带详细专业背景的作者页面4有专门作者页面得4分，简短简介得2分，完全没有得0分
专业能力薄弱的警示信号：
  • 主张无证据或来源支持
  • 复杂主题仅做表面覆盖
  • 技术术语使用错误
  • 无可见作者或作者无相关资质
  • 内容宽泛通用而非深入具体

Authoritativeness — 25 points

权威性——25分

Recognition by others as a credible source on the topic.
Signals to evaluate:
SignalPointsHow to Score
Inbound citations from authoritative sources55 if cited by major sources, 3 if some citations, 0 if none
Author quoted or cited in press/media44 if media mentions, 2 if industry mentions, 0 if none
Industry awards or recognition mentioned33 if relevant awards, 1 if tangential, 0 if none
Speaker credentials (conferences, events)33 if listed, 0 if none
Published in peer-reviewed or respected outlets44 if tier-1 publications, 2 if industry outlets, 0 if none
Comprehensive topic coverage (topical authority)33 if site covers topic thoroughly, 1 if some coverage, 0 if isolated
Brand mentioned on Wikipedia or authoritative references33 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")
被他人认可为该主题的可信来源。
评估信号：
信号分值评分方式
来自权威来源的 inbound 引用5被主流来源引用得5分，有部分引用得3分，完全没有得0分
作者被媒体/报刊引用或报道4有媒体提及得4分，行业内提及得2分，完全没有得0分
提及行业奖项或认可3相关奖项得3分，间接奖项得1分，完全没有得0分
演讲者资质（会议、活动）3列出相关资质得3分，完全没有得0分
在同行评审或知名刊物上发表内容4一级刊物发表得4分，行业刊物发表得2分，完全没有得0分
全面的主题覆盖（主题权威性）3网站全面覆盖主题得3分，有部分覆盖得1分，仅孤立内容得0分
品牌被维基百科或权威参考资料提及3被维基百科提及得3分，被其他百科类资料提及得2分，完全没有得0分
权威性薄弱的警示信号：
  • 仅单主题且无深度覆盖的网站
  • 专业能力主张无外部验证
  • 无权威来源的反向链接
  • 无证据的权威性主张（自称“专家”）

Trustworthiness — 25 points

可信度——25分

Signals that the content and its publisher are reliable and transparent.
Signals to evaluate:
SignalPointsHow to Score
Contact information visible (address, phone, email)44 if full contact info, 2 if email only, 0 if none
Privacy policy present and linked22 if present, 0 if absent
Terms of service present11 if present, 0 if absent
HTTPS with valid certificate22 if valid HTTPS, 0 if not
Editorial standards or corrections policy33 if documented, 1 if implicit, 0 if none
Transparent about business model and conflicts33 if clear disclosures, 1 if some, 0 if none
Reviews and testimonials from real customers33 if verified reviews, 1 if testimonials, 0 if none
Accurate claims (no misinformation detected)44 if all claims accurate, 2 if mostly accurate, 0 if errors found
Clear affiliate/sponsorship disclosures33 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
表明内容及其发布者可靠、透明的信号。
评估信号：
信号分值评分方式
可见的联系信息（地址、电话、邮箱）4完整联系信息得4分，仅邮箱得2分，完全没有得0分
存在并链接隐私政策2存在得2分，不存在得0分
存在服务条款1存在得1分，不存在得0分
带有效证书的HTTPS2有效HTTPS得2分，无得0分
编辑标准或纠错政策3有文档记录得3分，隐含标准得1分，完全没有得0分
商业模式和利益冲突透明化3披露清晰得3分，部分披露得1分，完全没有得0分
真实客户的评价和推荐3已验证评价得3分，普通推荐得1分，完全没有得0分
主张准确（未检测到错误信息）4所有主张准确得4分，大部分准确得2分，存在错误得0分
清晰的附属/赞助披露3适当披露得3分，未披露或无得0分
可信度薄弱的警示信号：
  • 无联系信息或物理地址
  • 缺少隐私政策或服务条款
  • 未披露的附属链接或赞助内容
  • 可验证的虚假或误导性主张
  • 无法联系发布者进行纠错

Content Quality Metrics

内容质量指标

Word Count Benchmarks

字数基准

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 TypeMinimum WordsIdeal RangeNotes
Homepage500500-1,500Clear value proposition, not a wall of text
Blog post1,5001,500-3,000Thorough but focused
Pillar content / Ultimate guide2,0002,500-5,000Comprehensive topic coverage
Product page300500-1,500Descriptions, specs, use cases
Service page500800-2,000What, how, why, for whom
About page300500-1,000Company/person story and credentials
FAQ page5001,000-2,500Thorough answers, not one-liners
这些是最低要求，而非目标。字数多不代表内容好。该基准是为满足AI可引用性而充分覆盖主题的最低长度。
页面类型最低字数理想范围说明
首页500500-1,500清晰的价值主张，不要堆砌文字
博客文章1,5001,500-3,000全面但聚焦主题
支柱内容 / 终极指南2,0002,500-5,000全面覆盖主题
产品页面300500-1,500描述、规格、使用场景
服务页面500800-2,000服务内容、方式、原因、受众
关于页面300500-1,000公司/个人故事及资质
FAQ页面5001,000-2,500详尽的回答，而非一句话概括

Readability Assessment

可读性评估

  • 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
  • **目标Flesch可读性得分：**60-70（对应8-9年级阅读水平）
  • 这不是直接排名因素，但会影响可引用性——AI平台偏好清晰明确的内容
  • 过于学术化的写作（得分<30）会降低通用查询的可引用性
  • 过于简单的写作（得分>80）可能缺乏专业能力信号所需的深度
无需工具的估算方法：
  • 平均句子长度：15-20词为理想
  • 平均段落长度：2-4句
  • 术语：首次使用时应定义
  • 被动语态：占比<15%

Paragraph Structure for AI Parsing

便于AI解析的段落结构

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
AI平台会按段落提取内容。每个段落应是独立的意义单元。
最优段落结构：
  • 每段2-4句（1句段落较弱；5句以上难以提取）
  • 一段一个核心观点——不要在段落中混合多个主题
  • 核心观点前置——第一句应包含主要论点
  • 用证据支撑——剩余句子提供数据、示例或背景
  • 可独立引用——段落单独提取时仍有意义

Heading Structure

标题结构

  • 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
  • 每页一个H1——代表核心主题/标题
  • H2用于主要章节——应对应不同子主题
  • H3用于子章节——嵌套在相关H2下
  • 不要跳过层级——不要从H1直接到H3而没有H2
  • 描述性标题——比如“如何针对AI搜索优化”而非“第2节”
  • 适当使用问题式标题——可直接匹配AI查询

Internal Linking

内部链接

  • 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
  • 每个内容页面应链接到同站点的3-5个相关页面
  • 链接使用描述性锚文本（而非“点击这里”）
  • 创建主题集群结构：支柱页面与所有相关子主题页面互链
  • 孤立页面（无内部链接指向）很少被AI引用

AI Content Assessment

AI内容评估

AI-Generated Content Policy

AI生成内容政策

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.
根据Google 2024年3月的说明，只要AI生成内容展现真实的E-E-A-T信号且有人工监督，就是可接受的。关注点不在于内容“如何生成”，而在于内容“是否提供价值”。

Signs of Low-Quality AI Content (flag these)

低质量AI内容的信号（需标记）

SignalDescription
Generic phrasing"In today's fast-paced world...", "It's important to note that...", "At the end of the day..."
No original insightContent that only rephrases widely available information
Lack of first-hand experienceNo personal anecdotes, case studies, or specific examples
Perfect but empty structureWell-formatted headings with shallow content beneath them
No specific examplesUses abstract explanations without concrete instances
Repetitive conclusionsEach 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 voiceNo opinions, preferences, or professional judgment expressed
Filler contentParagraphs that could be deleted without losing information
No data or sourcesClaims presented as facts without attribution or evidence
信号描述
通用表述“在当今快节奏的世界……”、“需要注意的是……”、“归根结底……”
无原创见解仅改写广泛可得的信息
缺乏第一手经验无个人轶事、案例研究或具体示例
完美但空洞的结构标题格式规范，但内容肤浅
无具体示例仅用抽象解释，无具体实例
结论重复每个章节结尾都是类似观点的变体
过度模糊“一般来说”、“在大多数情况下”、“取决于多种因素”却不说明具体因素
缺乏人类声音无观点、偏好或专业判断表达
填充内容删除后不影响信息完整性的段落
无数据或来源主张作为事实呈现，但无归因或证据

High-Quality Content Signals (regardless of production method)

高质量内容信号（无论生成方式）

SignalDescription
Original dataSurveys, experiments, benchmarks, proprietary analysis
Specific examplesNamed products, companies, dates, numbers
Contrarian or nuanced viewsDisagreement with conventional wisdom, backed by reasoning
First-person experience"When I tested this..." or "Our team found..."
Updated informationReferences to recent events, current data
Expert opinionClear professional judgment, not just facts
Practical recommendationsSpecific, actionable advice, not vague guidance
Trade-offs acknowledged"This approach works well for X but not for Y because..."
信号描述
原创数据调查、实验、基准测试、专有分析
具体示例具名产品、公司、日期、数字
逆向或细致观点有理由地反对传统认知
第一人称经验“我测试这个时……”或“我们团队发现……”
最新信息提及近期事件、当前数据
专家意见明确的专业判断，而非仅事实陈述
实用建议具体、可操作的建议，而非模糊指导
承认权衡“这种方法适用于X但不适用于Y，因为……”

Content Freshness Assessment

内容新鲜度评估

Publication Dates

发布日期

  • Check for visible 
    datePublished
    and 
    dateModified
    in 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")
  • 检查内容和结构化数据中是否同时显示
    datePublished
    和
    dateModified
  • 无日期的内容会被AI平台视为可信度较低
  • 日期应具体（2026年1月15日）而非模糊（“最近”）

Freshness Scoring

新鲜度评分

CriterionScore
Updated within 3 monthsExcellent — current and relevant
Updated within 6 monthsGood — still reasonably current
Updated within 12 monthsAcceptable — may need refresh
Updated 12-24 months agoWarning — review for accuracy
No date or 24+ months oldCritical — AI platforms may deprioritize
标准评分
3个月内更新优秀——当前且相关
6个月内更新良好——仍具合理性
12个月内更新可接受——可能需要刷新
12-24个月前更新警示——需审查准确性
无日期或24个月以上未更新严重——AI平台可能降低优先级

Evergreen Indicators

常青内容指标

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")
部分内容无论年龄大小都保持相关性。若符合以下条件，标记为常青内容：
  • 涵盖不随时间变化的基础概念（物理、基础数学、法律定义）
  • 明确标记为针对持久概念的参考/指南
  • 无时间依赖性主张（如“最新”、“当前”、“2024年”）

Topical Authority Assessment

主题权威性评估

What It Is

定义

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.
主题权威性衡量网站是否全面覆盖某主题，而非仅做表面涉及。AI平台更倾向于引用被公认为该主题权威的网站。

How to Assess

评估方法

  1. Content breadth: Does the site have multiple pages covering different aspects of its core topic?
  2. Content depth: Do individual pages go deep into subtopics?
  3. Topic clustering: Are pages organized into logical groups with internal linking?
  4. Content gaps: Are there obvious subtopics that the site should cover but does not?
  5. Competitor comparison: Do competitors cover subtopics that this site misses?
  1. 内容广度：网站是否有多个页面覆盖核心主题的不同方面？
  2. 内容深度：单个页面是否深入覆盖子主题？
  3. 主题集群：页面是否通过内部链接组织成逻辑组？
  4. 内容缺口：网站是否明显缺少应覆盖的子主题？
  5. 竞品对比：竞品覆盖的子主题是否本网站未涉及？

Scoring

评分

LevelDescriptionScore Impact
Authority20+ pages covering topic comprehensively, strong clustering+10 bonus
Developing10-20 pages with some clustering+5 bonus
Emerging5-10 pages on topic, limited clustering+0
Thin< 5 pages, no clustering-5 penalty
等级描述分数影响
权威20+页面全面覆盖主题，集群结构完善+10分加分
发展中10-20页面，有部分集群结构+5分加分
新兴5-10页面，集群结构有限+0分
薄弱<5页面，无集群结构-5分扣分

Overall Scoring (0-100)

总体评分（0-100分）

Score Composition

分数构成

ComponentWeightMax Points
Experience25%25
Expertise25%25
Authoritativeness25%25
Trustworthiness25%25
Subtotal100
Topical Authority Modifier+10 to -5
Final ScoreCapped at 100
组件权重最高分
经验25%25
专业能力25%25
权威性25%25
可信度25%25
小计100
主题权威性修正分+10至-5
最终得分最高100分

Score Interpretation

得分解读

  • 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
  • 85-100：优秀——各平台AI引用的优质候选内容
  • 70-84：良好——基础扎实，针对性改进可提升可引用性
  • 55-69：一般——存在多个E-E-A-T缺口，降低AI可见性
  • 40-54：较差——存在严重的内容质量和可信度问题
  • 0-39：糟糕——需要彻底 overhaul 内容策略

Output Format

输出格式

Generate GEO-CONTENT-ANALYSIS.md with:
markdown
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生成GEO-CONTENT-ANALYSIS.md，格式如下：
markdown
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GEO Content Quality & E-E-A-T Analysis — [Domain]

GEO Content Quality & E-E-A-T Analysis — [Domain]

Date: [Date]
Date: [Date]

Content Score: XX/100

Content Score: XX/100

E-E-A-T Breakdown

E-E-A-T Breakdown

DimensionScoreKey Finding
ExperienceXX/25[One-line summary]
ExpertiseXX/25[One-line summary]
AuthoritativenessXX/25[One-line summary]
TrustworthinessXX/25[One-line summary]
DimensionScoreKey Finding
ExperienceXX/25[One-line summary]
ExpertiseXX/25[One-line summary]
AuthoritativenessXX/25[One-line summary]
TrustworthinessXX/25[One-line summary]

Topical Authority Modifier: [+10 to -5]

Topical Authority Modifier: [+10 to -5]

Pages Analyzed

Pages Analyzed

PageWord CountReadabilityHeading StructureCitability Rating
[URL][Count][Score][Pass/Warn/Fail][High/Medium/Low]
PageWord CountReadabilityHeading StructureCitability Rating
[URL][Count][Score][Pass/Warn/Fail][High/Medium/Low]

E-E-A-T Detailed Findings

E-E-A-T Detailed Findings

Experience

Experience

[Specific passages and pages with strong/weak experience signals]
[Specific passages and pages with strong/weak experience signals]

Expertise

Expertise

[Author credentials found, technical depth assessment, specific gaps]
[Author credentials found, technical depth assessment, specific gaps]

Authoritativeness

Authoritativeness

[External validation found, topical authority assessment, gaps]
[External validation found, topical authority assessment, gaps]

Trustworthiness

Trustworthiness

[Trust signals present/missing, accuracy concerns if any]
[Trust signals present/missing, accuracy concerns if any]

Content Quality Issues

Content Quality Issues

[Specific passages flagged with reasons and rewrite suggestions]
[Specific passages flagged with reasons and rewrite suggestions]

AI Content Concerns

AI Content Concerns

[Any low-quality AI content patterns detected, with specific examples]
[Any low-quality AI content patterns detected, with specific examples]

Freshness Assessment

Freshness Assessment

PagePublishedLast UpdatedStatus
[URL][Date][Date][Current/Stale/No Date]
PagePublishedLast UpdatedStatus
[URL][Date][Date][Current/Stale/No Date]

Citability Assessment

Citability Assessment

Most Citable Passages

Most Citable Passages

[Top 5 passages that AI platforms are most likely to cite, with reasons]
[Top 5 passages that AI platforms are most likely to cite, with reasons]

Least Citable Pages

Least Citable Pages

[Pages with lowest citability, with specific improvement recommendations]
[Pages with lowest citability, with specific improvement recommendations]

Improvement Recommendations

Improvement Recommendations

Quick Wins

Quick Wins

[Specific content changes that can be made immediately]
[Specific content changes that can be made immediately]

Content Gaps

Content Gaps

[Topics the site should cover to strengthen topical authority]
[Topics the site should cover to strengthen topical authority]

Author/E-E-A-T Improvements

Author/E-E-A-T Improvements

[Specific steps to strengthen E-E-A-T signals]
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[Specific steps to strengthen E-E-A-T signals]
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