SEO+GEO Visibility Engine
Spot search and AI gaps; get copy and fixes
SEO+GEO Visibility Engine
Spot search and AI gaps; get copy and fixes
Showcase
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 Use it for your own websites, articles on third-party platforms, and unpublished drafts.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
Search Engine
Turn natural-language questions into traceable public web search results for research, fact-checking, finding public-account articles, troubleshooting program errors, and searching for papers, technical documentation, and open-source projects. For topics requiring current information or evidence from multiple sources, it combines Chinese and international search results across different types of public sources, helping reduce omissions and bias from relying on a single search engine. In addition to summarizing relevant pages, it organizes titles, sources, links, and available publication dates, merges duplicate results, and distinguishes independent evidence from different organizations or websites. For key claims, the results present supporting and opposing information, disagreements between sources, and a credibility assessment based on the amount and quality of the evidence. This helps you see which points are supported by cross-checking and which remain isolated claims or cannot be confirmed. You can enter search terms or a claim to verify directly, and add a field, time range, region, language preference, or specific websites. The final output includes a structured search summary, source list, cross-checking results, and items requiring verification. Content that cannot be accessed, requires login, or has scraping restrictions is clearly marked; speculation is never used in place of real sources.
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.
Google Content Quality Checker
Before you publish, check an SEO article for whether readers can truly use it, which claims don’t hold up, and what might make it seem untrustworthy. It compares the article against Google Search Central’s public content quality guidance, ranks the 3–5 highest-impact fixes, points to the exact location in the original text, and gives specific ways to improve each one. If you want, it can also rewrite the article—but it won’t invent experiences or data that aren’t in the original. Paste the full text or send a link. v2.0 update: Removed hard-coded years and algorithm update names. When specific policies are involved, it checks Google’s official documentation in real time. It no longer scores content using mechanical rules such as word count or keyword frequency. The report is now a prioritized revision checklist instead of a large eight-dimension table. During rewrites, it does not fabricate the author’s experiences, examples, or data.
Information
- Version
- v9
- Last updated
- Runtime credits
- Usage-based
- Models
- Auto
