[ZSX:HX]Localized GEO Detection Protocol v1.0

A developer has released a localized GEO detection protocol v1.0, a manually or AI-executable checklist that assesses an article's AI citation friendliness without external tools. The protocol comprises 11 modular detection modules, each scoring 0-100, and integrates design principles from existing frameworks to provide actionable optimization recommendations.

This protocol is a manually/AI-executable GEO detection checklist that does not rely on any external tools . It is applicable for detecting the "AI citation friendliness" of a single article, outputting a structured detection report, and providing actionable optimization recommendations. | Principle | Description | |---|---| Locally Executable | All detection items can be completed without calling external APIs, manually verifiable | Modular Configuration | 11 detection modules can be dynamically toggled on/off as needed | Quantifiable Scoring | Each module outputs a score of 0-100, generating a comprehensive GEO score | Actionable Optimization | Each low-scoring item is accompanied by specific optimization guidance | Aligned with Existing Frameworks | Integrates design thinking from the 13-module writing template, engagement depth lens, and metaphor-quote formula | This protocol is designed based on the following core GEO principles: | Principle | Data Source | |---|---| Content with high trustworthiness is 3.7 times more likely to be cited by AI | — | Adding FAQ Schema increases AI citation rate by approximately 47% | — | | The same information cross-verified across multiple trusted sources reduces single-point pollution risk | — | Core GEO metrics: Visibility, Source Citation Rate, Information Correction Rate, Expression Richness, Information Balance, Priority Recommendation Rate | — | Four core metrics: Semantic Embedding Depth, Authority Accumulation, Generative Contribution, Transformation Drive | — | | E-E-A-T Experience, Expertise, Authoritativeness, Trustworthiness is the gold standard for content quality in the AI era | — | Detection Goal : Confirm whether AI crawlers can normally access the article content | Item ID | Check Item | Method | Weight | |---|---|---|---| | T1 | Is the article URL publicly accessible? | Open the article link in an incognito/private browser window | High | | T2 | Is the article content readable as plain text not images/PDF ? | Check if the page is HTML text | High | | T3 | Does the article have a clear <title tag? | Check the browser tab title | Medium | | T4 | Does the article have a <meta description ? | View page source or browser description | Medium | | T5 | Does robots.txt allow major AI crawlers GPTBot/ClaudeBot/Bytespider/etc. ? | Check the /robots.txt file | High | Scoring Criteria : 20 points per item, maximum 100 points. Detection Goal : Confirm whether the article uses structured markup understandable by AI | Item ID | Check Item | Method | Weight | |---|---|---|---| | S1 | Is JSON-LD structured data used? | Look for <script type="application/ld+json" in page source | High | | S2 | Does it contain Article Schema markup? | Look for @type: "Article" in JSON-LD | High | | S3 | Does it contain FAQPage Schema markup if applicable ? | Look for @type: "FAQPage" in JSON-LD | Medium | | S4 | Does it contain Author Schema markup? | Look for author information in JSON-LD | Medium | | S5 | Does the Schema contain entity relationship markup like sameAs ? | Look for sameAs , knowsAbout , etc. in JSON-LD | Low | Scoring Criteria : 20 points per item, maximum 100 points. Theoretical Basis: Adding FAQ Schema can increase AI citation rate by approximately 47%. Schema markup helps AI understand the type and relationships of content. Detection Goal : Confirm whether the article structure facilitates AI information extraction | Item ID | Check Item | Method | Weight | |---|---|---|---| | C1 | Is there a clear H1 heading? | Check if the main title uses or H1 | High | | C2 | Are there hierarchical H2/H3 subheadings? | Check if the article has 2-3 levels of heading structure | High | | C3 | Is there an abstract/introduction within 200 words ? | Check if there is a summary paragraph at the beginning | High | | C4 | Are lists/tables used to organize information? | Check for <ul , <ol , or <table in the article | Medium | | C5 | Does each H2 section have at least 300 words of supporting content? | Estimate word count per section | Medium | | C6 | Is there a 40-60 word section summary? | Check if each section ends with a summary paragraph | Low | Scoring Criteria : C1-C3 = 20 points each, C4-C6 = 13.3 points each, maximum 100 points. Detection Goal : Confirm whether the article's core concepts are clearly recognized by AI | Item ID | Check Item | Method | Weight | |---|---|---|---| | E1 | Are core concepts clearly defined in the text? | Check if key terms are explained at first appearance | High | | E2 | Are core concepts used consistently throughout? | Check if the same concept uses consistent terminology | High | | E3 | Does it contain specific data, cases, or verifiable claims? | Check for numbers, cases, citations | High | | E4 | Are entity relationships clearly stated e.g., "A is a superset of B" ? | Check if logical relationships between concepts are clear | Medium | | E5 | Is there a structured question-answer pair format? | Check if the article covers questions readers might ask | Medium | Scoring Criteria : 20 points per item, maximum 100 points. Detection Goal : Confirm whether the article has signals that make it trustworthy to AI | Item ID | Check Item | Method | Weight | |---|---|---|---| | A1 | Is there a clear author byline? | Check if the article has the author's name or pen name | High | | A2 | Is there an author bio or background description? | Check if there is an author introduction | Medium | | A3 | Are authoritative sources cited academic papers/official docs, etc. ? | Check references for authoritative sources | High | | A4 | Are data points accompanied by verifiable source links? | Check if data includes source URLs | High | | A5 | Are publication and update dates clearly marked? | Check if dates are displayed on the article | Medium | | A6 | Is there a link to an "About/Contact" page? | Check if there is a link to author/institution introduction | Low | Scoring Criteria : A1-A5 = 16.7 points each, A6 = 16.5 points, maximum 100 points. Theoretical Basis: Content with high trustworthiness is 3.7 times more likely to be cited by AI. Detection Goal : Confirm whether the article is easy for AI to cite | Item ID | Check Item | Method | Weight | |---|---|---|---| | R1 | Is there a quotable "golden line" or core judgment statement? | Check for sentences that can be cited independently | High | | R2 | Is the core argument summarized within the first 40-60 words? | Check if there is a conclusive statement at the beginning | High | | R3 | Are there "citable statistics" available? | Check for hard data with sources | High | | R4 | Does the article title contain technical keywords? | Check if the title has searchable technical terms | Medium | | R5 | Is there a clear question-answer structure? | Check if the article answers a clear question | Medium | Scoring Criteria : 20 points per item, maximum 100 points. Detection Goal : Confirm whether information in the article is consistent across sources | Item ID | Check Item | Method | Weight | |---|---|---|---| | X1 | Is the core concept self-consistent within the article? | Check if the same concept is expressed consistently across sections | High | | X2 | Are data points consistent within the article? | Check for contradictions in data | High | | X3 | Are core viewpoints consistent with the author's other articles? | Compare with the author's other articles | Medium | | X4 | Do cited sources corroborate each other? | Check if multiple sources point to the same conclusion | Medium | Scoring Criteria : 25 points per item, maximum 100 points. Theoretical Basis: Cross-source consistency is one of the core dimensions of GEO effectiveness measurement from an RAG perspective. Detection Goal : Confirm whether the article has mechanisms to trigger reader interaction | Item ID | Check Item | Method | Weight | |---|---|---|---| | D1 | Does the title contain engagement-triggering words you/how/invite/seek/together ? | Manual check of the title | High | | D2 | Does the opening contain counterintuitive judgments or cognitive conflict? | Check first 300 words for "counterintuitive" statements | High | | D3 | Is there a specific, answerable engagement question at the end? | Check the end for open-ended questions | High | | D4 | Is there a collaboration invitation or participation entry point? | Check for "invite readers to participate" content | Medium | | D5 | Does the article contain "reflexive design" demonstrating what it advocates ? | Check if the article demonstrates the method it teaches | Medium | | D6 | Is there a "reward-style" question that encourages readers to verify? | Check for phrasing like "If you've also noticed... feel free to share" | Low | Scoring Criteria : D1-D3 = 20 points each, D4-D6 = 13.3 points each, maximum 100 points. Theoretical Basis: Based on historical data analysis of 28 articles — titles with engagement-triggering words have an average engagement depth of 5.42, compared to 3.72 without, a 31.4% increase. Detection Goal : Confirm whether the article meets the requirements of standards such as T/CAPT 026—2026 | Item ID | Check Item | Method | Weight | |---|---|---|---| | G1 | Is the content authentic and traceable? | Check for fabricated data or unverifiable claims | High | | G2 | Are facts and opinions clearly distinguished? | Check if personal opinions are presented as facts | High | | G3 | Are sources clearly cited? | Check if data/citations have sources | High | | G4 | Does it involve prohibited behaviors such as "corpus poisoning" or "answer hegemony"? | Check for intent to manipulate AI output | High | | G5 | Is full-chain traceability supported? | Check for modification records or version notes | Medium | Scoring Criteria : G1-G4 = 20 points each, G5 = 20 points, maximum 100 points. Theoretical Basis: T/CAPT 026—2026 "Generative Engine Optimization GEO — Trustworthy Information Dissemination and Information Ecology Governance Specification" requires content to be authentic, traceable, fully chain-operable, and delivered with white-box transparency. Detection Goal : Confirm whether the article adopts content formats preferred by AI | Item ID | Check Item | Method | Weight | |---|---|---|---| | F1 | Does it have the "one-sentence answer + table + FAQ" structure? | Check if the article contains all three elements | High | | F2 | Is there a 40-60 word conclusion placed at the beginning of each section? | Check if each section starts with a summary sentence | Medium | | F3 | Are visual elements like Mermaid diagrams used? | Check for flowcharts/architecture diagrams | Medium | | F4 | Is there multimodal content text + images + tables ? | Check if content formats are diverse | Medium | | F5 | Does it contain an llms.txt entry if applicable ? | Check if the site root has llms.txt | Low | Scoring Criteria : 20 points per item, maximum 100 points. Detection Goal : Confirm whether the article has "communication units" that can be remembered and cited by AI | Item ID | Check Item | Method | Weight | |---|---|---|---| | M1 | Is there an independently quotable golden line counterintuitive/shareable ? | Check for sentences like "The article with the lowest readership had the highest engagement depth" | High | | M2 | Is there a central metaphor running through the entire article? | Check if there is a concrete metaphor anchoring the whole article | High | | M3 | Is there a reusable formula for generating golden lines? | Check if the "construction method" of the golden line is made explicit | Medium | | M4 | Is there a memorable one-sentence conclusion? | Check the end for a "one-sentence wrap-up" | High | | M5 | Does it have a complete "source metaphor + metaphor chain" structure? | Check if multiple sub-metaphors are derived from a "source metaphor" | Medium | Scoring Criteria : 20 points per item, maximum 100 points. Theoretical Basis: Based on the "Metaphor & Quote Formula" module design from the published article "Skill Doesn't Need to Be Skill-ified." Sentences that can be remembered and shared are the core material for AI citation. | Step | Action | Notes | |---|---|---| | 1 | Open the full page of the article to be detected | Ensure the page is fully loaded | | 2 | Open the page source code Ctrl+U or right-click "View Page Source" | Used for structured data detection | | 3 | Prepare a detection record form | Can use Excel or pen and paper | It is recommended to detect modules in the following order, recording scores after each module: Total Estimated Time : Approximately 80-90 minutes per article. ┌──────────────────────────────────────────────────────────────────┐ │ GEO DETECTION REPORT │ │ Article Title: Title │ │ Detection Date: YYYY-MM-DD │ │ Detected By: Name │ ├──────────────────────────────────────────────────────────────────┤ │ I. Comprehensive GEO Score: X /100 │ │ │ │ II. Module Score Breakdown │ │ Module 1 Technical Crawlability: X /100 ████████░░ Rating │ │ Module 2 Structured Data Markup: X /100 ████████░░ Rating │ │ Module 3 Content Structure: X /100 ████████░░ Rating │ │ Module 4 Semantic Clarity: X /100 ████████░░ Rating │ │ Module 5 Trustworthiness: X /100 ████████░░ Rating │ │ Module 6 Citation Readiness: X /100 ████████░░ Rating │ │ Module 7 Cross-Source Consistency: X /100 ████████░░ Rating │ │ Module 8 Engagement Depth: X /100 ████████░░ Rating │ │ Module 9 GEO Compliance: X /100 ████████░░ Rating │ │ Module 10 AI-Friendly Format: X /100 ████████░░ Rating │ │ Module 11 Metaphor & Quote Readiness: X /100 ████████░░ Rating │ ├──────────────────────────────────────────────────────────────────┤ │ III. Low-Score Diagnostics & Optimization Recommendations │ │ Item ID : Current Status → Recommended Action │ │ Item ID : Current Status → Recommended Action │ │ ... │ ├──────────────────────────────────────────────────────────────────┤ │ IV. Optimization Priority Ranking │ │ 🔴 High Priority highest impact, implement immediately │ │ 🟡 Medium Priority implement in near term │ │ 🟢 Low Priority can optimize opportunistically │ ├──────────────────────────────────────────────────────────────────┤ │ V. Expected Post-Optimization Results │ │ Expected AI Citation Rate Increase: X % │ │ Expected Engagement Depth Increase: X % │ └──────────────────────────────────────────────────────────────────┘ | Score Range | Grade | Status Description | |---|---|---| | 90-100 | A | Excellent — very high AI citation friendliness, maintain current standards | | 70-89 | B | Good — minor optimization opportunities, targeted improvements recommended | | 50-69 | C | Average — clear gaps present, systematic optimization recommended | | 30-49 | D | Below Average — multiple dimensions require improvement | | 0-29 | E | Poor — article structure should be re-evaluated | This protocol supports on-demand module selection. Recommended configurations for different scenarios are as follows: | Scenario | Recommended Modules | Notes | |---|---|---| Quick Screening | Modules 1, 3, 5, 6 | 15 minutes for basic assessment | Deep Optimization | All 11 modules | 90 minutes full assessment | Technical Articles | Modules 1, 2, 3, 4, 5, 10 | Focus on structure and trustworthiness | Operational/Method Articles | Modules 3, 6, 8, 11 | Focus on engagement and citation readiness | GEO Compliance Audit | Modules 1, 5, 9 | Focus on compliance | Pre-Publication Check | Modules 1, 2, 3, 4, 5, 6, 8 | Final check before publishing | | Item | Issue When Score is Low | Recommended Action | |---|---|---| | T1 | Article requires login to view | Set article to "Public" in CSDN settings | | T2 | Content is in image/PDF format | Ensure body text is HTML text format | | T3 | No <title or unclear title | Set article title in CSDN editor | | T4 | No <meta description | Fill in article summary in CSDN editor | | T5 | robots.txt blocks AI crawlers | Modify robots.txt at site root, add: Allow: GPTBot , Allow: ClaudeBot | | Item | Issue When Score is Low | Recommended Action | |---|---|---| | S1-S5 | Missing JSON-LD structured data | Add <script type="application/ld+json" to the page, including Article , Author , FAQPage Schemas | JSON-LD Template Example : { "@context": "https://schema.org", "@type": "Article", "headline": "Article Title", "description": "Article Summary", "author": { "@type": "Person", "name": "Author Name" }, "datePublished": "2026-08-22", "dateModified": "2026-08-22" } | Item | Issue When Score is Low | Recommended Action | |---|---|---| | C1 | No clear H1 heading | Ensure the article has a clear title | | C2 | No H2/H3 hierarchy | Use and for sub-sections | | C3 | No summary or summary too long | Add a 150-200 word summary at the beginning | | C4 | No lists/tables | Insert <ul / <ol or tables where appropriate | | C5 | Section content insufficient | Add at least 300 words under each H2 | | C6 | No section summary | Add 1-2 summary sentences at the end of each section | | Item | Issue When Score is Low | Recommended Action | |---|---|---| | E1 | Core concept not defined | Provide a clear definition at first appearance | | E2 | Terminology inconsistent | Unify terminology throughout the article | | E3 | Missing specific data/cases | Add verifiable data or cases | | E4 | Entity relationships unclear | Use clear phrasing like "A is the parent of B..." or "A consists of B..." | | E5 | No question-answer structure | Embed FAQ or question-driven sections in the article | | Item | Issue When Score is Low | Recommended Action | |---|---|---| | A1 | No author byline | Add author name at the beginning or end of the article | | A2 | No author bio | Add 2-3 sentences of author introduction at the end | | A3 | No authoritative citations | Cite authoritative sources such as academic papers, official documentation | | A4 | Data without source links | Add a verifiable URL for each data point | | A5 | No publication date | Mark publication and update dates at the beginning or end of the article | | A6 | No "About/Contact" link | Add an "About the Author" or contact link in the author bio section | | Item | Issue When Score is Low | Recommended Action | |---|---|---| | R1 | No "golden line" | Distill core judgment statements and place them prominently | | R2 | No core conclusion in the opening | Summarize the core argument within the first 40-60 words | | R3 | No citable statistics | Supplement with hard data and sources | | R4 | Title lacks technical keywords | Add searchable technical terms to the title | | R5 | No question-answer structure | Embed clear Q&A format in the article | | Item | Issue When Score is Low | Recommended Action | |---|---|---| | X1 | Same concept expressed inconsistently | Unify terminology throughout the article | | X2 | Data contradictions | Check and unify all data points | | X3 | Contradicts the author's other articles | Check consistency across series articles | | X4 | Cited sources contradict each other | Verify cited sources and ensure they corroborate | | Item | Issue When Score is Low | Recommended Action | |---|---|---| | D1 | Title lacks engagement-triggering words | Add "you/how/invite/seek/together" to the title | | D2 | Opening lacks cognitive conflict | Add counterintuitive statements within the first 300 words | | D3 | End lacks engagement question | Add a specific, answerable question at the end | | D4 | No collaboration invitation | Add "invite readers to participate" content at the end | | D5 | No reflexive design | Have the article demonstrate the method it advocates | | D6 | No reward-style question | Set questions like "If you've also noticed... feel free to share" | | Item | Issue When Score is Low | Recommended Action | |---|---|---| | G1 | Content cannot be traced | Ensure all claims are verifiable | | G2 | Facts and opinions are confused | Clearly distinguish "facts" from "my opinions" | | G3 | No source citations | Add sources for all citations | | G4 | Involves manipulation of AI output | Avoid prohibited behaviors such as "corpus poisoning" | | G5 | No modification records | Add version number and modification date at the end | | Item | Issue When Score is Low | Recommended Action | |---|---|---| | F1 | Missing "one-sentence answer + table + FAQ" structure | Add these three elements to the article | | F2 | No conclusion at section beginnings | Add a 40-60 word conclusion at the start of each H2/H3 | | F3 | No charts | Add Mermaid flowcharts or architecture diagrams | | F4 | Content format is too uniform | Add images, tables, and other diverse formats | | F5 | No llms.txt | Create llms.txt at the site root | | Item | Issue When Score is Low | Recommended Action | |---|---|---| | M1 | No independently quotable golden line | Distill a counterintuitive core judgment statement | | M2 | No central metaphor | Choose a concrete metaphor to run through the entire article | | M3 | No golden line formula | Make the construction method of the golden line explicit | | M4 | No one-sentence conclusion | Add a shareable concluding sentence at the end | | M5 | No source metaphor chain | Derive multiple sub-metaphors from a "source metaphor" | | Article Status | Detection Frequency | Notes | |---|---|---| | Before publishing new articles | Required for each article | Ensure basic GEO detection is completed before publishing | | Already published articles | Once per quarter | Monitor AI citation rate changes and assess optimization needs | | Engagement depth decline | Immediate detection | Check all dimensions when article "activity" declines | | Series articles | Each article | Ensure consistency across series articles | | Version | Date | Updates | |---|---|---| | v1.0 | 2026-08-22 | Initial version, includes 11 detection modules |