SEO+GEO Visibility Engine
Spot search and AI gaps; get copy and fixes
Description
Optimize the search performance and AI citation readiness of your articles, product pages, and landing pages. Quickly identify the issues most worth fixing, or get ready-to-use titles, rewrites, content briefs, technical fix lists, or performance retest plans. Four modes route tasks appropriately: send only a link or body copy for a quick diagnosis by default; request writing to get usable text first; use a full audit to break findings down by target engine and page; or use a performance retest with clearly defined data sources, numerators, denominators, and comparison conditions. Simple tasks do not require a full-length report. Supports platform-specific checks for Google AI Overviews / AI Mode, Bing / Copilot, ChatGPT Search, Perplexity, and Claude. Search, training, and user-triggered visits are assessed separately. For Chinese platforms, checks are adapted to actually available sources and direct observations rather than applying rules from other engines. v8 standardizes scoring and evidence criteria, limits the scope of local blocks, distinguishes answer trigger rate, conditional citation rate, and overall citation rate, and updates Google site-level AI control checks. Use it for your own websites, articles on third-party platforms, and unpublished drafts. Recommendations distinguish what you can change from items that require platform-side validation. Each key finding includes evidence or clearly stated limitations. Scores are provided when needed; data, authors, or citations are never fabricated, and rankings, indexing, or AI citations are not guaranteed. Actual run example (2026-09-07; test material, not business results) Title mode: Given test copy for “How to write a team weekly report” with a request to return only 3 titles, the actual output was: 1. How to Write a Team Weekly Report: Blank Template Included 2. Team Weekly Report Writing Guide: Progress, Issues, and Next Week’s Plan (Template Included) 3. Team Weekly Report Template: This Week’s Progress, Issues, and Next Week’s Plan Calculation mode: Simulated 12 attempts, with 10 successes and 2 timeouts; 2 successful runs generated AI answers, and 1 cited the target website. Actual output: AI answer trigger rate 2/10 = 20%, conditional citation rate 1/2 = 50%, and overall observed citation rate 1/10 = 10%. Normal no-answer cases remain in the overall denominator, while timeouts are listed separately. These figures validate the calculation criteria only and do not represent actual citation performance.
Related Skills
View all
Grant Proposal Review PRO V2.0
🎯 Core Functionality Overview This is an intelligent review and optimization system specially designed for national social science, education ministry, and provincial grant applications. It simulates the thinking mode of a senior review expert with 15 years of experience, ensuring academic rigor and competitiveness through three core mechanisms. 🔧 Three Core Mechanisms 1️⃣ 12-Step Structured Methodology Covers the full lifecycle of grant proposal review: Phase 1-3: Basic Diagnosis - In-depth analysis of announcement (funding priorities, review criteria, application requirements) - Cross-disciplinary type judgment (precise identification of 8 types) - Research GAP five-dimension identification (theory/methodology/empirical/policy/technology) Phase 4-7: Core Element Review - Research question TMAQ model analysis (theory/methodology/approach/question four dimensions) - Research objective SMART principle test - Research content framework completeness assessment - Research approach type matching (6 types) Phase 8-10: Deep Quality Enhancement - Precise extraction of key difficulties (distinguish criteria + breakthrough paths) - Innovation point seven-dimension mining - Feasibility seven-dimension argumentation Phase 11-12: Overall Optimization - Nine-dimension quality check (academic rigor, innovativeness, feasibility, etc.) - Comprehensive optimization suggestions and final report 2️⃣ Dual-Core Adversarial Mechanism (Builder vs Supervisor) Working Principle: - Builder (academic writer): Generates optimization plans based on user materials - Supervisor (top journal reviewer): Challenges Builder's plans with the strictest standards - Adversarial iteration: 3 rounds of confrontation to ensure plans are robust Application Scenarios: - Innovation point mining: Builder proposes innovation points → Supervisor questions novelty → iterative optimization - Feasibility argumentation: Builder designs plan → Supervisor challenges feasibility → supplementary argumentation - Literature citation: Builder cites literature → Supervisor verifies authenticity → ensure academic standards 3️⃣ Literature Authenticity Verification Mechanism Two working modes: Mode A: Placeholder Mode (Default) - Use markers like [Literature Placeholder-001] in place of specific references - Output a Literature Requirement List specifying search requirements for each placeholder - User searches and fills in real references Mode B: Real-Time Verification Mode - Call Google Scholar to verify literature authenticity in real time - Generate Literature Verification Report (authenticity/relevance/authority scores) - Ensure every citation is traceable Preventing AI Hallucination: - Prohibits fabricating authors, journals, DOIs - All references must be verified or marked as placeholders - Guarantees academic integrity bottom line 💡 Core Value and Applicable Scenarios ✅ Key Pain Points Addressed 1. Academic sloppiness: AI-generated content often includes fake references, logical gaps 2. Insufficient innovation: Difficulty uncovering true academic innovation points 3. Weak feasibility: Research plans lack systematic argumentation 4. Cross-disciplinary difficulty: Interdisciplinary topics often fall between two stools 🎓 Target Users - University faculty (social sciences, education, humanities) - Researchers (applying for national and provincial grants) - Academic teams (needing systematic review processes) 📋 Typical Workflow 1. Input: Upload announcement + proposal draft 2. Review: System executes 12-step structured analysis 3. Adversarial: Dual-core mechanism iteratively optimizes key sections 4. Verification: Literature authenticity check 5. Output: Complete review report + optimization suggestions + literature list 🔍 Differences from Traditional Review | Dimension | Traditional Human Review | Expert Review System | |-----------|------------------------|----------------------| | Review depth | Depends on personal experience | 12-step structured + 9D QC | | Academic rigor | Hard to fully audit | Literature verification + dual-core adversarial | | Innovation mining | Subjective judgment | 7-dimension systematic analysis | | Feasibility argumentation | Experience-driven | 7-dimension item-by-item argumentation | | Consistency | Varies by individual | Standardized process | | Efficiency | Days to weeks | 1-2 hours for initial review | The core advantage of this system is: it makes the tacit knowledge of a 15-year senior review expert explicit, structured, and replicable, enabling every user to receive top-level expert review services.
WriteAIGC Reduction & Rewrite v7.0
📚 Academic Paper AIGC Reduction and Quality-Preserving Rewriting Expert v7.0 Designed for graduate students, researchers, and paper authors, this academic text optimization skill operates on a core closed loop of 'source control → process correction → result verification → reverse self-check → iterative re-check'. It systematically diagnoses and optimizes issues such as templated expressions, mechanical logic, vague content, inaccurate terminology, and style inconsistencies while preserving original meaning, technical terms, data, and core conclusions. 🔍📝 🌟 Core Capabilities 🔬 Multi-layer Risk Diagnosis Covers 10 types of universal text fingerprints and assists in identifying common expression patterns of models such as ChatGPT, Claude, DeepSeek, and Wenxin Yiyan. 🧠 Deep Semantic Restructuring Goes beyond synonym replacement to rebuild more natural and in-depth academic reasoning by adjusting proposition expression, information order, argument approach, and evidence organization. ✍️ Quality-Preserving Rewriting Comprehensively applies 13 sentence transformation strategies and 20 methods for cleaning high-frequency templated expressions, improving mechanical sentence structures, repetitive connectors, and overly rigid formatting. 📊 Full-text Structure Diagnosis Through macro-cycle and five key triangles, checks whether research problems, theory, literature review, methods, results, conclusions, and innovation form a complete closed loop. 🧩 Fine-grained Section Adaptation Develops differentiated diagnostic and rewriting strategies for abstract, introduction, literature review, research methods, results, discussion, and conclusion respectively. 🌐 Cross-language Risk Scanning Assists in identifying translationese, passive voice stacking, long sentence nesting, and mixed Chinese-English formatting abnormalities to make Chinese academic expression more natural and accurate. 🔄 Reverse Self-check Loop After rewriting, re-verifies from three aspects: technique distribution, new text fingerprints, and information integrity, to avoid 'becoming more templated' or losing key content. 🛡️ Academic Integrity Protection Does not fabricate literature, data, cases, or policy evidence; separately marks information requiring author verification and reminds users to honestly disclose AI usage. 🎯 Use Cases ✅ Single paragraph or partial section optimization ✅ Targeted modification of marked paragraphs from inspection reports ✅ Polishing of abstract, introduction, literature review, discussion, and conclusion ✅ Full-text AIGC risk feature diagnosis ✅ Language and structure adaptation for target journals ✅ Pre-submission quality review and consistency check 📦 Final Deliverables 📄 Quality-preserving rewritten text 🔎 Risk and issue diagnosis report 🛠️ Rewriting strategy and technique description ✅ Reverse self-check and information integrity report 💡 Items requiring author verification and subsequent revision suggestions 🎓 Original meaning preserved · Logic intact · No fabricated data · Academic quality maintained ⚠️ This skill aims to improve academic expression quality and reduce text risk features. It does not guarantee passage through any specific detection platform or achieving a particular detection score.
HE Document Write&Review v3.0
🎯 Got your application rejected? Can't find the highlight for your proposal? Don't know where to start with review comments? Three real scenarios: 🔸 Scenario 1: Application season anxiety—Reviewer feedback: "Insufficient theoretical support, vague policy basis." Unsure which documents to cite or which theoretical framework to use. 🔸 Scenario 2: Proposal writing dilemma—In charge of course construction plan: objectives, tasks, pathways, evaluation… each part needs writing, but you feel the logic is not rigorous enough and worry about being questioned on "feasibility" during review. 🔸 Scenario 3: Review dilemma—Need to write peer review comments: must point out issues while maintaining professionalism, be well-founded but not too harsh. How to strike the balance? 💡 What can this system do for you? Not just give advice—it writes, revises, and reviews for you directly. 📝 Writing Mode: From topic to final document Enter your topic and existing materials. The system automatically identifies the document type (application/proposal/report/review). Automatically matches authoritative policy documents and theoretical support. Generates content chapter by chapter, each with evidence, logic, and facts. Key promise: Never fabricates data; clearly tells you what's missing. 🔍 Review Mode: Expert-level diagnosis Upload your text. Professional scoring across 7 dimensions (value, alignment, completeness, innovation, feasibility, support, expression quality). Precisely identifies problem areas. Provides specific revision suggestions + example rewrites. Not general advice, but paragraph-level specific guidance. ✏️ Revision Optimization Mode: Precision enhancement Strengthens arguments based on existing text. Optimizes expression, eliminates empty talk and clichés. Standardizes terminology and logic. Improves overall competitiveness. ⚡ Three Core Mechanisms (Unique) 🛡️ Firewall Mechanism Built-in "fact boundary": User-provided real data is never fabricated; policy basis must have sources; theoretical support cannot be misapplied. Every sentence you see can be traced back to its source. 🔄 Multi-core Adversarial Engine One core writes, another specifically checks for errors. Like having a strict auditor watching, ensuring no "unsubstantiated facts," "logic gaps," or "policy mismatches" occur. 📊 Stepwise Guidance Doesn't ask you 20 questions at once—identifies the most critical gaps and asks only the 3–5 most necessary questions. After each stage, clearly tells you "what's done," "what's missing," and "what to do next." 🎯 Scope of Application (All Higher Education Scenarios) ✅ Teaching achievement award applications (institutional/provincial/national) ✅ Quality engineering project applications (top courses/teaching teams/textbooks, etc.) ✅ Course construction plans, major construction plans ✅ Major self-assessment reports, course acceptance reports ✅ Expert review comments, peer reviews ✅ Education reform project applications, closing reports 🚀 User Experience Writing an application from scratch: Provide the topic and basic materials → System identifies document type, takes inventory, matches policies and theories → Generates outline → Writes chapter by chapter → Consolidates → Get a draft in 1 hour. Reviewing existing text: Upload document → System automatically scores → Lists main issues → Provides revision suggestions and example rewrites → Get review report in 20 minutes. Optimizing existing plan: Provide existing text and optimization direction → System diagnoses weaknesses → Strengthens arguments, optimizes expression → Get optimized version in 30 minutes. 👉 Try it now—make higher education document writing no longer a burden. This is not just a writing assistant; it's an intelligent engine that understands higher education rules, review standards, and professional expression.
SEO+GEO Visibility Engine
Spot search and AI gaps; get copy and fixes
Description
Optimize the search performance and AI citation readiness of your articles, product pages, and landing pages. Quickly identify the issues most worth fixing, or get ready-to-use titles, rewrites, content briefs, technical fix lists, or performance retest plans. Four modes route tasks appropriately: send only a link or body copy for a quick diagnosis by default; request writing to get usable text first; use a full audit to break findings down by target engine and page; or use a performance retest with clearly defined data sources, numerators, denominators, and comparison conditions. Simple tasks do not require a full-length report. Supports platform-specific checks for Google AI Overviews / AI Mode, Bing / Copilot, ChatGPT Search, Perplexity, and Claude. Search, training, and user-triggered visits are assessed separately. For Chinese platforms, checks are adapted to actually available sources and direct observations rather than applying rules from other engines. v8 standardizes scoring and evidence criteria, limits the scope of local blocks, distinguishes answer trigger rate, conditional citation rate, and overall citation rate, and updates Google site-level AI control checks. Use it for your own websites, articles on third-party platforms, and unpublished drafts. Recommendations distinguish what you can change from items that require platform-side validation. Each key finding includes evidence or clearly stated limitations. Scores are provided when needed; data, authors, or citations are never fabricated, and rankings, indexing, or AI citations are not guaranteed. Actual run example (2026-09-07; test material, not business results) Title mode: Given test copy for “How to write a team weekly report” with a request to return only 3 titles, the actual output was: 1. How to Write a Team Weekly Report: Blank Template Included 2. Team Weekly Report Writing Guide: Progress, Issues, and Next Week’s Plan (Template Included) 3. Team Weekly Report Template: This Week’s Progress, Issues, and Next Week’s Plan Calculation mode: Simulated 12 attempts, with 10 successes and 2 timeouts; 2 successful runs generated AI answers, and 1 cited the target website. Actual output: AI answer trigger rate 2/10 = 20%, conditional citation rate 1/2 = 50%, and overall observed citation rate 1/10 = 10%. Normal no-answer cases remain in the overall denominator, while timeouts are listed separately. These figures validate the calculation criteria only and do not represent actual citation performance.
Related Skills
View allGrant Proposal Review PRO V2.0
🎯 Core Functionality Overview This is an intelligent review and optimization system specially designed for national social science, education ministry, and provincial grant applications. It simulates the thinking mode of a senior review expert with 15 years of experience, ensuring academic rigor and competitiveness through three core mechanisms. 🔧 Three Core Mechanisms 1️⃣ 12-Step Structured Methodology Covers the full lifecycle of grant proposal review: Phase 1-3: Basic Diagnosis - In-depth analysis of announcement (funding priorities, review criteria, application requirements) - Cross-disciplinary type judgment (precise identification of 8 types) - Research GAP five-dimension identification (theory/methodology/empirical/policy/technology) Phase 4-7: Core Element Review - Research question TMAQ model analysis (theory/methodology/approach/question four dimensions) - Research objective SMART principle test - Research content framework completeness assessment - Research approach type matching (6 types) Phase 8-10: Deep Quality Enhancement - Precise extraction of key difficulties (distinguish criteria + breakthrough paths) - Innovation point seven-dimension mining - Feasibility seven-dimension argumentation Phase 11-12: Overall Optimization - Nine-dimension quality check (academic rigor, innovativeness, feasibility, etc.) - Comprehensive optimization suggestions and final report 2️⃣ Dual-Core Adversarial Mechanism (Builder vs Supervisor) Working Principle: - Builder (academic writer): Generates optimization plans based on user materials - Supervisor (top journal reviewer): Challenges Builder's plans with the strictest standards - Adversarial iteration: 3 rounds of confrontation to ensure plans are robust Application Scenarios: - Innovation point mining: Builder proposes innovation points → Supervisor questions novelty → iterative optimization - Feasibility argumentation: Builder designs plan → Supervisor challenges feasibility → supplementary argumentation - Literature citation: Builder cites literature → Supervisor verifies authenticity → ensure academic standards 3️⃣ Literature Authenticity Verification Mechanism Two working modes: Mode A: Placeholder Mode (Default) - Use markers like [Literature Placeholder-001] in place of specific references - Output a Literature Requirement List specifying search requirements for each placeholder - User searches and fills in real references Mode B: Real-Time Verification Mode - Call Google Scholar to verify literature authenticity in real time - Generate Literature Verification Report (authenticity/relevance/authority scores) - Ensure every citation is traceable Preventing AI Hallucination: - Prohibits fabricating authors, journals, DOIs - All references must be verified or marked as placeholders - Guarantees academic integrity bottom line 💡 Core Value and Applicable Scenarios ✅ Key Pain Points Addressed 1. Academic sloppiness: AI-generated content often includes fake references, logical gaps 2. Insufficient innovation: Difficulty uncovering true academic innovation points 3. Weak feasibility: Research plans lack systematic argumentation 4. Cross-disciplinary difficulty: Interdisciplinary topics often fall between two stools 🎓 Target Users - University faculty (social sciences, education, humanities) - Researchers (applying for national and provincial grants) - Academic teams (needing systematic review processes) 📋 Typical Workflow 1. Input: Upload announcement + proposal draft 2. Review: System executes 12-step structured analysis 3. Adversarial: Dual-core mechanism iteratively optimizes key sections 4. Verification: Literature authenticity check 5. Output: Complete review report + optimization suggestions + literature list 🔍 Differences from Traditional Review | Dimension | Traditional Human Review | Expert Review System | |-----------|------------------------|----------------------| | Review depth | Depends on personal experience | 12-step structured + 9D QC | | Academic rigor | Hard to fully audit | Literature verification + dual-core adversarial | | Innovation mining | Subjective judgment | 7-dimension systematic analysis | | Feasibility argumentation | Experience-driven | 7-dimension item-by-item argumentation | | Consistency | Varies by individual | Standardized process | | Efficiency | Days to weeks | 1-2 hours for initial review | The core advantage of this system is: it makes the tacit knowledge of a 15-year senior review expert explicit, structured, and replicable, enabling every user to receive top-level expert review services.
WriteAIGC Reduction & Rewrite v7.0
📚 Academic Paper AIGC Reduction and Quality-Preserving Rewriting Expert v7.0 Designed for graduate students, researchers, and paper authors, this academic text optimization skill operates on a core closed loop of 'source control → process correction → result verification → reverse self-check → iterative re-check'. It systematically diagnoses and optimizes issues such as templated expressions, mechanical logic, vague content, inaccurate terminology, and style inconsistencies while preserving original meaning, technical terms, data, and core conclusions. 🔍📝 🌟 Core Capabilities 🔬 Multi-layer Risk Diagnosis Covers 10 types of universal text fingerprints and assists in identifying common expression patterns of models such as ChatGPT, Claude, DeepSeek, and Wenxin Yiyan. 🧠 Deep Semantic Restructuring Goes beyond synonym replacement to rebuild more natural and in-depth academic reasoning by adjusting proposition expression, information order, argument approach, and evidence organization. ✍️ Quality-Preserving Rewriting Comprehensively applies 13 sentence transformation strategies and 20 methods for cleaning high-frequency templated expressions, improving mechanical sentence structures, repetitive connectors, and overly rigid formatting. 📊 Full-text Structure Diagnosis Through macro-cycle and five key triangles, checks whether research problems, theory, literature review, methods, results, conclusions, and innovation form a complete closed loop. 🧩 Fine-grained Section Adaptation Develops differentiated diagnostic and rewriting strategies for abstract, introduction, literature review, research methods, results, discussion, and conclusion respectively. 🌐 Cross-language Risk Scanning Assists in identifying translationese, passive voice stacking, long sentence nesting, and mixed Chinese-English formatting abnormalities to make Chinese academic expression more natural and accurate. 🔄 Reverse Self-check Loop After rewriting, re-verifies from three aspects: technique distribution, new text fingerprints, and information integrity, to avoid 'becoming more templated' or losing key content. 🛡️ Academic Integrity Protection Does not fabricate literature, data, cases, or policy evidence; separately marks information requiring author verification and reminds users to honestly disclose AI usage. 🎯 Use Cases ✅ Single paragraph or partial section optimization ✅ Targeted modification of marked paragraphs from inspection reports ✅ Polishing of abstract, introduction, literature review, discussion, and conclusion ✅ Full-text AIGC risk feature diagnosis ✅ Language and structure adaptation for target journals ✅ Pre-submission quality review and consistency check 📦 Final Deliverables 📄 Quality-preserving rewritten text 🔎 Risk and issue diagnosis report 🛠️ Rewriting strategy and technique description ✅ Reverse self-check and information integrity report 💡 Items requiring author verification and subsequent revision suggestions 🎓 Original meaning preserved · Logic intact · No fabricated data · Academic quality maintained ⚠️ This skill aims to improve academic expression quality and reduce text risk features. It does not guarantee passage through any specific detection platform or achieving a particular detection score.
HE Document Write&Review v3.0
🎯 Got your application rejected? Can't find the highlight for your proposal? Don't know where to start with review comments? Three real scenarios: 🔸 Scenario 1: Application season anxiety—Reviewer feedback: "Insufficient theoretical support, vague policy basis." Unsure which documents to cite or which theoretical framework to use. 🔸 Scenario 2: Proposal writing dilemma—In charge of course construction plan: objectives, tasks, pathways, evaluation… each part needs writing, but you feel the logic is not rigorous enough and worry about being questioned on "feasibility" during review. 🔸 Scenario 3: Review dilemma—Need to write peer review comments: must point out issues while maintaining professionalism, be well-founded but not too harsh. How to strike the balance? 💡 What can this system do for you? Not just give advice—it writes, revises, and reviews for you directly. 📝 Writing Mode: From topic to final document Enter your topic and existing materials. The system automatically identifies the document type (application/proposal/report/review). Automatically matches authoritative policy documents and theoretical support. Generates content chapter by chapter, each with evidence, logic, and facts. Key promise: Never fabricates data; clearly tells you what's missing. 🔍 Review Mode: Expert-level diagnosis Upload your text. Professional scoring across 7 dimensions (value, alignment, completeness, innovation, feasibility, support, expression quality). Precisely identifies problem areas. Provides specific revision suggestions + example rewrites. Not general advice, but paragraph-level specific guidance. ✏️ Revision Optimization Mode: Precision enhancement Strengthens arguments based on existing text. Optimizes expression, eliminates empty talk and clichés. Standardizes terminology and logic. Improves overall competitiveness. ⚡ Three Core Mechanisms (Unique) 🛡️ Firewall Mechanism Built-in "fact boundary": User-provided real data is never fabricated; policy basis must have sources; theoretical support cannot be misapplied. Every sentence you see can be traced back to its source. 🔄 Multi-core Adversarial Engine One core writes, another specifically checks for errors. Like having a strict auditor watching, ensuring no "unsubstantiated facts," "logic gaps," or "policy mismatches" occur. 📊 Stepwise Guidance Doesn't ask you 20 questions at once—identifies the most critical gaps and asks only the 3–5 most necessary questions. After each stage, clearly tells you "what's done," "what's missing," and "what to do next." 🎯 Scope of Application (All Higher Education Scenarios) ✅ Teaching achievement award applications (institutional/provincial/national) ✅ Quality engineering project applications (top courses/teaching teams/textbooks, etc.) ✅ Course construction plans, major construction plans ✅ Major self-assessment reports, course acceptance reports ✅ Expert review comments, peer reviews ✅ Education reform project applications, closing reports 🚀 User Experience Writing an application from scratch: Provide the topic and basic materials → System identifies document type, takes inventory, matches policies and theories → Generates outline → Writes chapter by chapter → Consolidates → Get a draft in 1 hour. Reviewing existing text: Upload document → System automatically scores → Lists main issues → Provides revision suggestions and example rewrites → Get review report in 20 minutes. Optimizing existing plan: Provide existing text and optimization direction → System diagnoses weaknesses → Strengthens arguments, optimizes expression → Get optimized version in 30 minutes. 👉 Try it now—make higher education document writing no longer a burden. This is not just a writing assistant; it's an intelligent engine that understands higher education rules, review standards, and professional expression.
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