EvidenceGraph | Research Graph
Evidence-backed research graphs from sources
Description
The more materials you have, the real difficulty is not summarization but seeing the relationships between concepts, people, events, and evidence. EvidenceGraph reads papers, PDFs, articles, meeting minutes, or multiple links, automatically extracts entities and relationships, merges duplicate concepts, flags conflicting information, and generates an interactive research graph that can be dragged, zoomed, searched, and filtered. Unlike ordinary mind maps, each key node and relationship retains source numbers, evidence descriptions, and confidence levels. Click a node to see why it appears in the graph, avoiding a web of relationships that is beautiful but unverifiable. One run delivers: - Interactive single-file HTML - High-resolution PNG / SVG static images - Editable Mermaid code - Structured JSON data - Source and conflict list Ideal for researchers, students, teachers, product managers, consultants, investment research, and knowledge workers who handle large volumes of material.
Related Skills
View all
Knowledge Graph Engine
Automatically build five types of diagrams (Knowledge Graph, Mind Map, Concept Map, Flowchart/Architecture Diagram, Relation Diagram) from text, files, or topics, and output an interactive web page, static image, and visualization code all in one — a triple-threat solution built to the highest standards. Combines best practices from knowledge graph engineering and information visualization, supporting dynamic granularity control, disambiguation and deduplication, and multi-source cross-validation. Turn your words, files, or even a thought into a draggable, zoomable, searchable interactive knowledge graph in seconds, and export high-definition images and ready-to-use code with one click — this is the knowledge visualization "triple-threat" engine you've never experienced before. 🧠 What is the Knowledge Graph Engine? It's not the simple drawing tool you've seen before. It's a knowledge engineer + visualization expert hidden in your browser. Give it a textbook passage, a thesis, a PDF, or just a keyword, and it will automatically: 🔍 Extract core entities and clarify deep relationships 🧱 Build structured graph JSON (Knowledge Graph / Mind Map / Concept Map / Flowchart / Relation Diagram) 🎨 Output three top-tier forms: interactive web page + high-definition static image + Markdown/Mermaid/Graphviz code From now on, information organization doesn't rely on manual box drawing, and knowledge presentation is no longer just a static picture. ⚡ Why is it "top-tier"? 1. Fully automatic "text-to-graph" pipeline: No need to learn any modeling language or manually define nodes and connections. Just input content, and the engine automatically determines the graph type: Subject knowledge system? → Generates a semantically rich knowledge graph Reading notes deconstruction? → Generates a clear hierarchical mind map Process and decision? → Generates a flowchart/architecture diagram with branches Character relationship network? → Generates a multi-dimensional relation diagram Even if you just throw a topic word, it can independently gather information, fill in the content, and then generate the graph. 2. Triple-threat output covering all use cases: 🖱️ Interactive D3.js web page: Drag nodes, scroll to zoom, click for details, highlight related paths, keyword search... like operating a living map. Single-file HTML, no backend, can be embedded directly into any page or sent to anyone. 🖼️ High-definition static graph: Force-directed layout, color-coded categories, directly usable for thesis illustrations, PPT presentations, teaching materials — every label is sharp and readable. 📜 Visualization code: Generates both Mermaid and Graphviz source code simultaneously. Developers can directly insert into documentation, wiki, Notion, with unlimited expandability for secondary editing. 3. Ultimate user experience design: 🎨 Colors automatically mapped by entity type, hierarchy expressed intuitively by node size 🔗 Relationship labels displayed directly on curves — instantly see 'contains', 'causes', 'supports' 💡 Click any node, non-related parts auto-fade to focus on the thought path 🔄 Reset layout, search positioning, zoom and pan... all operations smooth as silk 4. Engineering wisdom balancing 'breadth' and 'depth': From entity disambiguation and deduplication to multi-source cross-validation; from hierarchical granularity control to dotted-line connections supporting cross-domain relationships — behind this are the best practices of knowledge graph engineering, not a toy but a productivity tool. 👥 Who needs it most? Teachers & Educational Content Creators: Turn entire textbook chapters into an interactive knowledge map — students click to understand concept relationships. Researchers & Students: Literature reviews no longer rely on text walls — one diagram clarifies the theoretical threads of dozens of papers. Product Managers & Enterprise Architects: System architecture, business processes, feature breakdown — instantly generate architecture diagrams, doubling communication efficiency. Readers & Lifelong Learners: Notes are no longer just outlines but explorable thought networks, letting knowledge truly 'grow' together. 🚀 Now, let your knowledge 'come alive' You give content, it gives insights. You give a topic, it gives a system. You give a requirement, it gives a complete deliverable of web pages, images, and code. This is not a feature; it's a workflow that elevates information into cognition. Let the Knowledge Graph Engine become an extension of your thinking, visualize your expertise, and reach every audience's 'aha moment.' — From today, don't 'draw' graphs, 'generate' graphs.
ResearchMulti-Source Mind Map 2.0
Turn complex documents, web pages, or long text into clearly structured visual mind maps with one click. Whether it's academic papers, course materials, or technical documents, this tool intelligently extracts core topics and multi-level knowledge points, helping you quickly clarify the logical structure. Through in-depth content analysis of the input source, it automatically identifies section structure and key details, generating a well-organized knowledge system. This tool supports generating mind maps in multiple styles, including professional educational tree diagrams (PlantUML format) and intuitive center-radial diagrams (Mermaid format). You can directly get SVG images viewable in a browser, along with the corresponding source code files. This means you can quickly preview the results and also import the source code into XMind, FreeMind, or a Markdown editor for further editing and in-depth processing. Simply upload a PDF, Word, or text file, or provide a web link, to transform a large amount of information into a visual knowledge map. It's ideal for study notes, structured paper reading, and brainstorming presentations, making knowledge organization and sharing more efficient and professional.
9D Visual Deconstruction
Break down one or more infographics, data visualizations, flowcharts, timelines, maps, knowledge graphs, and organizational charts into reusable visual and information structures. Understand why an image is organized the way it is and rebuild a similar presentation. The skill examines visual factors such as spatial composition, dimensions and materials, color hierarchy, typography and layout, and module relationships. It also identifies chart encodings, reading paths, visual-symbol metaphors, and geographic or network relationships, so the analysis goes beyond simply describing what the style resembles. You will receive a parameterized visual deconstruction covering the canvas and compositional logic, visual hierarchy, text density, module topology, methods for expressing data and relationships, and the narrative flow through the image. For statistical charts, maps, and relationship networks, the output focuses on how data is mapped to visual variables such as color, position, size, lines, and nodes. For complex subject-specific visuals, it further organizes the coordinated relationships among graphics, text, and metaphor systems. Based on this analysis, the skill generates detailed, clearly structured English image-generation prompts for infographic recreation, visual research, design references, brand content exploration, and concept development. Whether you provide an existing image or an idea for a theme and visual direction, you will receive prompts designed for closer structural reproduction rather than broad descriptions of style.
EvidenceGraph | Research Graph
Evidence-backed research graphs from sources
Description
The more materials you have, the real difficulty is not summarization but seeing the relationships between concepts, people, events, and evidence. EvidenceGraph reads papers, PDFs, articles, meeting minutes, or multiple links, automatically extracts entities and relationships, merges duplicate concepts, flags conflicting information, and generates an interactive research graph that can be dragged, zoomed, searched, and filtered. Unlike ordinary mind maps, each key node and relationship retains source numbers, evidence descriptions, and confidence levels. Click a node to see why it appears in the graph, avoiding a web of relationships that is beautiful but unverifiable. One run delivers: - Interactive single-file HTML - High-resolution PNG / SVG static images - Editable Mermaid code - Structured JSON data - Source and conflict list Ideal for researchers, students, teachers, product managers, consultants, investment research, and knowledge workers who handle large volumes of material.
Related Skills
View allKnowledge Graph Engine
Automatically build five types of diagrams (Knowledge Graph, Mind Map, Concept Map, Flowchart/Architecture Diagram, Relation Diagram) from text, files, or topics, and output an interactive web page, static image, and visualization code all in one — a triple-threat solution built to the highest standards. Combines best practices from knowledge graph engineering and information visualization, supporting dynamic granularity control, disambiguation and deduplication, and multi-source cross-validation. Turn your words, files, or even a thought into a draggable, zoomable, searchable interactive knowledge graph in seconds, and export high-definition images and ready-to-use code with one click — this is the knowledge visualization "triple-threat" engine you've never experienced before. 🧠 What is the Knowledge Graph Engine? It's not the simple drawing tool you've seen before. It's a knowledge engineer + visualization expert hidden in your browser. Give it a textbook passage, a thesis, a PDF, or just a keyword, and it will automatically: 🔍 Extract core entities and clarify deep relationships 🧱 Build structured graph JSON (Knowledge Graph / Mind Map / Concept Map / Flowchart / Relation Diagram) 🎨 Output three top-tier forms: interactive web page + high-definition static image + Markdown/Mermaid/Graphviz code From now on, information organization doesn't rely on manual box drawing, and knowledge presentation is no longer just a static picture. ⚡ Why is it "top-tier"? 1. Fully automatic "text-to-graph" pipeline: No need to learn any modeling language or manually define nodes and connections. Just input content, and the engine automatically determines the graph type: Subject knowledge system? → Generates a semantically rich knowledge graph Reading notes deconstruction? → Generates a clear hierarchical mind map Process and decision? → Generates a flowchart/architecture diagram with branches Character relationship network? → Generates a multi-dimensional relation diagram Even if you just throw a topic word, it can independently gather information, fill in the content, and then generate the graph. 2. Triple-threat output covering all use cases: 🖱️ Interactive D3.js web page: Drag nodes, scroll to zoom, click for details, highlight related paths, keyword search... like operating a living map. Single-file HTML, no backend, can be embedded directly into any page or sent to anyone. 🖼️ High-definition static graph: Force-directed layout, color-coded categories, directly usable for thesis illustrations, PPT presentations, teaching materials — every label is sharp and readable. 📜 Visualization code: Generates both Mermaid and Graphviz source code simultaneously. Developers can directly insert into documentation, wiki, Notion, with unlimited expandability for secondary editing. 3. Ultimate user experience design: 🎨 Colors automatically mapped by entity type, hierarchy expressed intuitively by node size 🔗 Relationship labels displayed directly on curves — instantly see 'contains', 'causes', 'supports' 💡 Click any node, non-related parts auto-fade to focus on the thought path 🔄 Reset layout, search positioning, zoom and pan... all operations smooth as silk 4. Engineering wisdom balancing 'breadth' and 'depth': From entity disambiguation and deduplication to multi-source cross-validation; from hierarchical granularity control to dotted-line connections supporting cross-domain relationships — behind this are the best practices of knowledge graph engineering, not a toy but a productivity tool. 👥 Who needs it most? Teachers & Educational Content Creators: Turn entire textbook chapters into an interactive knowledge map — students click to understand concept relationships. Researchers & Students: Literature reviews no longer rely on text walls — one diagram clarifies the theoretical threads of dozens of papers. Product Managers & Enterprise Architects: System architecture, business processes, feature breakdown — instantly generate architecture diagrams, doubling communication efficiency. Readers & Lifelong Learners: Notes are no longer just outlines but explorable thought networks, letting knowledge truly 'grow' together. 🚀 Now, let your knowledge 'come alive' You give content, it gives insights. You give a topic, it gives a system. You give a requirement, it gives a complete deliverable of web pages, images, and code. This is not a feature; it's a workflow that elevates information into cognition. Let the Knowledge Graph Engine become an extension of your thinking, visualize your expertise, and reach every audience's 'aha moment.' — From today, don't 'draw' graphs, 'generate' graphs.
ResearchMulti-Source Mind Map 2.0
Turn complex documents, web pages, or long text into clearly structured visual mind maps with one click. Whether it's academic papers, course materials, or technical documents, this tool intelligently extracts core topics and multi-level knowledge points, helping you quickly clarify the logical structure. Through in-depth content analysis of the input source, it automatically identifies section structure and key details, generating a well-organized knowledge system. This tool supports generating mind maps in multiple styles, including professional educational tree diagrams (PlantUML format) and intuitive center-radial diagrams (Mermaid format). You can directly get SVG images viewable in a browser, along with the corresponding source code files. This means you can quickly preview the results and also import the source code into XMind, FreeMind, or a Markdown editor for further editing and in-depth processing. Simply upload a PDF, Word, or text file, or provide a web link, to transform a large amount of information into a visual knowledge map. It's ideal for study notes, structured paper reading, and brainstorming presentations, making knowledge organization and sharing more efficient and professional.
9D Visual Deconstruction
Break down one or more infographics, data visualizations, flowcharts, timelines, maps, knowledge graphs, and organizational charts into reusable visual and information structures. Understand why an image is organized the way it is and rebuild a similar presentation. The skill examines visual factors such as spatial composition, dimensions and materials, color hierarchy, typography and layout, and module relationships. It also identifies chart encodings, reading paths, visual-symbol metaphors, and geographic or network relationships, so the analysis goes beyond simply describing what the style resembles. You will receive a parameterized visual deconstruction covering the canvas and compositional logic, visual hierarchy, text density, module topology, methods for expressing data and relationships, and the narrative flow through the image. For statistical charts, maps, and relationship networks, the output focuses on how data is mapped to visual variables such as color, position, size, lines, and nodes. For complex subject-specific visuals, it further organizes the coordinated relationships among graphics, text, and metaphor systems. Based on this analysis, the skill generates detailed, clearly structured English image-generation prompts for infographic recreation, visual research, design references, brand content exploration, and concept development. Whether you provide an existing image or an idea for a theme and visual direction, you will receive prompts designed for closer structural reproduction rather than broad descriptions of style.
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