{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_qonqur","slug":"qonqur","name":"Qonqur","type":"webapp","url":"https://qonqur.xyz","page_url":"https://unfragile.ai/qonqur","categories":["research-search"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_qonqur__cap_0","uri":"capability://data.processing.analysis.citation.graph.based.article.organization","name":"citation-graph-based article organization","description":"Automatically parses research articles to extract citations and builds a directed knowledge graph where nodes represent articles and edges represent citation relationships. The system clusters articles by citation density and topological proximity to surface knowledge dependencies, enabling users to visualize how research papers relate to and build upon each other. This approach differs from keyword-based organization by preserving the semantic structure of academic discourse through explicit citation links rather than term frequency.","intents":["I need to organize 50+ research papers for a literature review without manually categorizing them","I want to understand which foundational papers are most cited and how recent work builds on them","I need to identify gaps in my understanding by seeing which papers I haven't read that are heavily cited by papers I have read"],"best_for":["researchers conducting systematic literature reviews in controlled domains","graduate students building comprehensive knowledge maps for thesis research","academic teams organizing shared research collections"],"limitations":["Citation parsing fails on non-standard citation formats, malformed references, or papers without explicit citations (e.g., position papers, opinion pieces)","Requires articles in formats with extractable text (PDF, plain text); scanned images or OCR-dependent documents will fail","Citation graph becomes computationally expensive above ~500 articles; no documented performance metrics for larger collections","Cannot disambiguate author names or resolve citation aliases (e.g., 'Smith et al. 2020' vs 'S. Smith, J. Doe 2020'), leading to fragmented graphs","No support for cross-domain citation linking; treats each uploaded collection as isolated"],"requires":["Research articles in PDF or plain text format with parseable citation metadata","Minimum 3-5 articles to generate meaningful citation relationships","Stable internet connection for cloud-based processing"],"input_types":["PDF documents","plain text files","article metadata (format unspecified)"],"output_types":["interactive knowledge graph visualization (format unspecified)","structured citation relationships (JSON or proprietary format unknown)"],"categories":["data-processing-analysis","knowledge-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_qonqur__cap_1","uri":"capability://image.visual.webcam.based.gesture.recognition.for.interface.control","name":"webcam-based gesture recognition for interface control","description":"Captures video from the user's webcam and applies computer vision pose detection (likely using MediaPipe or TensorFlow.js) to recognize hand and body gestures in real-time, mapping detected poses to interface actions (navigation, selection, etc.). The system runs gesture inference locally in the browser or on-device to minimize latency, though accuracy degrades significantly in low-light conditions, cluttered backgrounds, or when the user is partially occluded. Gesture recognition appears to be pre-trained on common presentation gestures rather than user-calibrated.","intents":["I want to control a presentation or navigate content without holding a physical remote or clicker","I need hands-free interaction to move freely around a room while presenting","I want to enable gesture-based navigation for interactive research demonstrations or STEM education"],"best_for":["educators and researchers in controlled environments (lecture halls, labs) with consistent lighting","presenters who want to eliminate physical remotes and move naturally on stage","interactive STEM education scenarios where gesture-based navigation enhances learning"],"limitations":["Gesture recognition accuracy drops sharply in low-light conditions, backlighting, or complex backgrounds; no documented accuracy metrics or threshold specifications","Supported gestures are not documented; users must discover through trial or documentation (likely steep learning curve)","No user-specific calibration or personalization; system assumes one-size-fits-all gesture mappings, which may not match individual body proportions or movement styles","Latency between gesture and action is not specified; likely 100-500ms depending on processing pipeline, which may feel unresponsive for rapid navigation","Requires webcam hardware and browser permissions; incompatible with privacy-focused environments or venues without camera access","No fallback to traditional input methods documented; if gesture recognition fails, user has no alternative control mechanism"],"requires":["Webcam hardware (USB or built-in)","Browser with WebRTC and MediaStream API support (Chrome 50+, Firefox 55+, Safari 11+)","Adequate lighting (minimum ~200 lux; exact threshold unknown)","Clear line of sight to camera (no occlusion)","Microphone permissions enabled in browser"],"input_types":["video stream from webcam","real-time pose data (hand, arm, body position)"],"output_types":["interface control events (navigation, selection, play/pause)","gesture confidence scores (likely internal only)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_qonqur__cap_10","uri":"capability://search.retrieval.masterwork.knowledge.store.curation","name":"masterwork knowledge store curation","description":"Provides a curated collection of high-quality research articles and knowledge resources organized by topic or domain. The Masterwork Knowledge Store appears to be a pre-built, editorially curated collection that users can browse, add to their personal knowledge maps, or use as a reference. The curation criteria, update frequency, and editorial process are not documented. This feature is available on both Beginner and Advanced tiers.","intents":["I want to discover high-quality papers in a research area without wading through low-quality or irrelevant work","I need a starting point for a literature review in a new domain","I want to see how experts have organized knowledge in my field"],"best_for":["researchers new to a domain who need curated starting points","students learning research methodology and domain structure","educators building reading lists for courses"],"limitations":["Curation criteria are not documented; unclear what makes a paper 'masterwork' or how papers are selected","Coverage is likely limited to popular or well-established domains; niche fields may be underrepresented","Update frequency is not specified; knowledge store may lag behind recent developments","No documented way to contribute or suggest papers for inclusion","Curation bias is unknown; unclear if curators have conflicts of interest or biases toward certain authors/institutions","No documented quality metrics or peer review process for curated collections"],"requires":["Qonqur account (free or paid tier)","Internet access to browse knowledge store"],"input_types":["topic or domain query","optional: filters (date range, author, institution)"],"output_types":["curated list of papers (with metadata)","integrated into knowledge map visualization"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_qonqur__cap_2","uri":"capability://image.visual.interactive.knowledge.map.visualization.and.navigation","name":"interactive knowledge map visualization and navigation","description":"Renders the citation graph and article metadata as an interactive visual map (likely a node-link diagram, force-directed graph, or hierarchical layout) that users can explore by clicking, dragging, or gesturing to zoom, pan, and select articles. The visualization appears to encode article relationships spatially, with proximity or edge weight indicating citation strength. Navigation likely includes filtering by topic, author, or date, though specific filtering mechanisms are not documented. The system may highlight unread articles or articles critical to understanding selected papers.","intents":["I want to visually explore how papers relate to each other without reading abstracts","I need to find the most important foundational papers in a research area by seeing which ones are most cited","I want to navigate from a paper I'm reading to related work and see the full context of a research area"],"best_for":["visual learners who prefer spatial exploration over linear reading","researchers building comprehensive mental models of a research domain","educators using interactive maps to teach research methodology or domain structure"],"limitations":["Visualization layout algorithm is not specified; likely uses force-directed graphs which can produce cluttered, hard-to-read layouts for >100 nodes","No documented zoom/pan performance; interactive responsiveness likely degrades with large graphs (>500 articles)","Filtering and search capabilities are not documented; users may struggle to find specific articles in large maps","No export of visualizations (PNG, SVG, PDF); maps are locked into the Qonqur platform","No collaborative annotation or shared highlighting; each user sees a private view","Mobile responsiveness unknown; gesture control may conflict with map navigation on touch devices"],"requires":["Modern browser with WebGL or Canvas support (Chrome 30+, Firefox 25+, Safari 9+)","Populated citation graph (minimum 5-10 articles for meaningful visualization)","Sufficient screen real estate (likely 1024x768 minimum; optimal at 1920x1080+)"],"input_types":["citation graph data structure","article metadata (title, authors, year, abstract)"],"output_types":["interactive visual map (HTML5 Canvas or WebGL)","selected article details (metadata, abstract, citation count)"],"categories":["image-visual","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_qonqur__cap_3","uri":"capability://memory.knowledge.progress.tracking.along.self.study.and.research.paths","name":"progress tracking along self-study and research paths","description":"Tracks which articles a user has read, marked as important, or annotated within the knowledge map, and aggregates this into a progress metric or learning path visualization. The system likely maintains a per-user reading history and may suggest next articles to read based on citation relationships and user progress. Progress is visualized as a path through the knowledge graph, highlighting completed vs. unread articles. The mechanism for defining 'progress' (e.g., articles read, time spent, comprehension assessment) is not documented.","intents":["I want to see how much of a research area I've covered and identify gaps in my knowledge","I need to track which papers I've read and which I still need to review","I want the system to suggest the next paper to read based on my current progress and understanding"],"best_for":["self-directed learners building domain expertise over weeks or months","students tracking progress through a structured literature review","researchers managing multiple concurrent research projects with different reading lists"],"limitations":["Progress definition is opaque; unclear whether 'progress' means articles opened, articles fully read, time spent, or comprehension assessment","No integration with external reading tools (Readwise, Hypothesis, Kindle); progress tracking is siloed within Qonqur","No spaced repetition or active recall mechanisms documented; system does not appear to optimize for long-term retention","Recommendations are likely based solely on citation topology, not on user comprehension or learning objectives","No collaborative progress tracking; each user's path is private and cannot be compared or shared with peers","Progress data is not exportable; locked into Qonqur platform"],"requires":["User account with reading history enabled","At least 5-10 articles marked as read to generate meaningful progress metrics","Consistent engagement over time (progress tracking is longitudinal)"],"input_types":["user reading history (article IDs, timestamps)","article metadata and citation graph","user annotations or bookmarks (if supported)"],"output_types":["progress percentage or completion metrics","visual path through knowledge graph","recommended next articles (ranked list)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_qonqur__cap_4","uri":"capability://tool.use.integration.model.context.protocol.mcp.server.integration.for.ai.connection","name":"model context protocol (mcp) server integration for ai connection","description":"Exposes a Model Context Protocol server that allows external AI agents or LLMs to query the user's knowledge graph, retrieve article metadata, and potentially trigger actions within Qonqur. The MCP server likely implements standard endpoints for listing articles, retrieving article details, querying citation relationships, and possibly updating reading status. This enables AI assistants (e.g., Claude, GPT-4) to access the user's research collection and provide context-aware recommendations or summaries without requiring manual copy-paste of article data.","intents":["I want my AI assistant to have access to my research collection so it can answer questions about my papers","I need to ask an LLM to summarize a set of papers and understand their relationships without manually uploading them","I want to use an AI agent to automatically generate a literature review outline based on my knowledge map"],"best_for":["researchers using AI assistants (Claude, GPT-4) for literature analysis and synthesis","teams building custom AI agents that need access to research collections","advanced users comfortable with API integration and MCP protocol"],"limitations":["MCP server API endpoints are not documented; users must reverse-engineer or contact support to understand available operations","Authentication mechanism is not specified; unclear whether API keys, OAuth, or other auth methods are supported","Rate limits and quota restrictions are not documented; unclear if there are throttling policies","No documentation of which AI models/platforms are supported; MCP compatibility is claimed but not verified","Data privacy implications are not addressed; unclear whether article content is sent to external AI services or only metadata","No webhook or event streaming support documented; integration is likely request-response only"],"requires":["MCP-compatible AI client (Claude API, custom agent framework)","API key or authentication credentials for Qonqur MCP server","Knowledge of MCP protocol and endpoint specifications (not provided by Qonqur)","Network access to Qonqur servers from AI client environment"],"input_types":["MCP protocol requests (list articles, get article details, query citations)","optional: article content or metadata filters"],"output_types":["article metadata (JSON or structured format)","citation relationships (graph or list format)","reading status or progress data"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_qonqur__cap_5","uri":"capability://text.generation.language.game.like.tutorial.and.onboarding.for.beginners","name":"game-like tutorial and onboarding for beginners","description":"Provides an interactive, gamified onboarding experience that guides new users through core features (uploading articles, exploring the knowledge map, using gesture controls) via a series of guided tasks or challenges. The tutorial likely uses progress bars, achievement badges, or level-based progression to maintain engagement and reduce cognitive load. Specific game mechanics (e.g., points, leaderboards, time limits) are not documented, but the framing suggests a lighter, more approachable onboarding than traditional documentation.","intents":["I'm new to Qonqur and need a guided introduction without reading dense documentation","I want to understand gesture controls through interactive practice before using them in a real presentation","I need to learn how to organize my research collection without feeling overwhelmed by options"],"best_for":["first-time users with limited technical background","students learning research methodology for the first time","educators introducing Qonqur to a class or research group"],"limitations":["Tutorial content and structure are not documented; unclear how long it takes to complete or what topics are covered","No option to skip tutorial; users may be forced through onboarding even if they have prior knowledge","Tutorial is likely linear and non-adaptive; does not adjust difficulty or pacing based on user performance","No documented way to restart or review tutorial after initial completion","Gamification mechanics may feel patronizing to advanced users or researchers"],"requires":["New user account (tutorial likely triggered on first login)","Webcam access for gesture control tutorial section","Sample articles or demo data provided by Qonqur"],"input_types":["user interactions (clicks, gestures, form submissions)","tutorial progress state"],"output_types":["tutorial UI (guided overlays, tooltips, progress indicators)","achievement badges or completion certificates"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_qonqur__cap_6","uri":"capability://image.visual.multi.screen.gesture.control.and.presentation.mode","name":"multi-screen gesture control and presentation mode","description":"Extends gesture recognition to support multi-screen setups (e.g., presenter view on laptop, slides on projector) and provides a dedicated presentation mode that optimizes the interface for hands-free control. In presentation mode, the system likely hides non-essential UI elements, enlarges gesture targets, and maps gestures to presentation-specific actions (next slide, previous slide, show notes). Multi-screen support requires detecting which screen the user is facing and routing gesture commands to the appropriate display.","intents":["I want to present research findings using gesture control while seeing speaker notes on my laptop","I need to navigate between slides and knowledge map visualizations without touching a keyboard or mouse","I want to enable interactive demonstrations where the audience can see the knowledge map on a projector while I control it with gestures"],"best_for":["presenters using dual-monitor setups (laptop + projector)","researchers conducting interactive demonstrations in lecture halls or conferences","educators using gesture-controlled presentations to engage students"],"limitations":["Multi-screen support is only available on Advanced tier ($17.77/mo); not available to Beginner tier users","Screen detection mechanism is not documented; unclear how system determines which screen to control","Gesture mapping for presentation mode is not specified; users must learn custom gesture vocabulary","No documented support for more than 2 screens; unclear if system scales to 3+ displays","Latency between gesture and action on remote screen (projector) is not specified; may be noticeable if network-dependent","No fallback to traditional input if gesture recognition fails during presentation"],"requires":["Advanced tier subscription ($17.77/mo)","Multiple displays connected to the same computer","Adequate lighting and camera positioning to capture user gestures while facing projector","Browser support for multi-screen APIs (likely Electron or native app required)"],"input_types":["video stream from webcam","screen configuration data (display count, resolution, position)","presentation state (current slide, speaker notes)"],"output_types":["control commands routed to appropriate screen","presentation UI on primary display","speaker notes or metadata on secondary display"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_qonqur__cap_7","uri":"capability://search.retrieval.free.and.open.access.knowledge.base.integration","name":"free and open access knowledge base integration","description":"Integrates pre-curated collections of freely available research articles and open access papers into the knowledge map, allowing users to bootstrap their research without uploading articles. The system likely indexes sources such as arXiv, PubMed Central, SSRN, or institutional repositories and makes these articles discoverable within Qonqur. Users can add open access papers to their personal knowledge map and explore relationships with their own uploaded articles. The curation mechanism and update frequency are not documented.","intents":["I want to explore a research area without manually finding and uploading papers","I need to find open access papers related to my research topic","I want to see how my papers relate to the broader open access literature"],"best_for":["researchers in fields with strong open access cultures (computer science, physics, biology)","students with limited access to paywalled journals","exploratory researchers browsing a new domain without a specific reading list"],"limitations":["Open access knowledge base is not documented; unclear which sources are indexed or how comprehensive coverage is","Curation mechanism is unknown; unclear if papers are manually curated or automatically indexed","Update frequency is not specified; knowledge base may be stale or lag behind new publications","No documented way to contribute or suggest papers for inclusion in the open access base","Coverage is likely biased toward well-indexed fields (CS, physics, biology) and may be sparse in niche domains","No documented filtering by quality, impact, or relevance; users may be overwhelmed by low-quality or tangential papers"],"requires":["Internet access to query open access sources","Qonqur account (free or paid tier)","Familiarity with research topic to navigate large open access collections"],"input_types":["search query or topic (user-provided)","optional: filters (date range, author, institution)"],"output_types":["list of open access papers (with metadata)","integrated into knowledge map visualization"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_qonqur__cap_8","uri":"capability://image.visual.advanced.ai.powered.gesture.recognition.and.control","name":"advanced ai-powered gesture recognition and control","description":"Provides enhanced gesture recognition capabilities beyond basic hand/arm detection, likely including multi-hand gestures, body posture recognition, or context-aware gesture interpretation. Advanced mode may use larger or more sophisticated ML models, enable user-specific gesture calibration, or support custom gesture definition. The system may also apply gesture smoothing, prediction, or confidence thresholding to reduce false positives and improve responsiveness. Advanced gesture control is only available on the Advanced tier ($17.77/mo).","intents":["I want more accurate gesture recognition that works in varied lighting conditions","I need to define custom gestures that match my natural movement patterns","I want to use complex multi-hand gestures for fine-grained control (e.g., pinch to zoom, swipe to navigate)"],"best_for":["power users who present frequently and need reliable gesture control","researchers in high-stakes presentations (conferences, grant reviews) where gesture recognition must be highly accurate","educators who want to customize gestures for specific classroom scenarios"],"limitations":["Advanced gesture recognition specifics are not documented; unclear what improvements are included vs. basic tier","Custom gesture definition mechanism is not documented; unclear if users can define gestures via UI or require API access","Calibration process is not specified; unclear how long it takes or how many samples are required","Advanced mode is only available on paid tier ($17.77/mo); no free trial or demo documented","No documented accuracy improvements or benchmarks comparing basic vs. advanced mode","Advanced mode likely requires more computational resources; may not work on older devices or slower internet connections"],"requires":["Advanced tier subscription ($17.77/mo)","Webcam with good resolution (1080p+ recommended)","Adequate lighting (likely >300 lux for advanced mode)","Optional: time investment in gesture calibration"],"input_types":["high-resolution video stream from webcam","optional: user-provided gesture samples for calibration","optional: custom gesture definitions"],"output_types":["gesture recognition with higher confidence scores","custom gesture mappings","gesture prediction or smoothing"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_qonqur__cap_9","uri":"capability://automation.workflow.direct.ceo.support.and.priority.assistance","name":"direct ceo support and priority assistance","description":"Provides direct access to the Qonqur CEO for technical support, feature requests, and troubleshooting. This is a premium support tier that likely includes faster response times, personalized onboarding, and influence over product roadmap. Support is likely delivered via email, chat, or scheduled calls. This capability is only available on the Advanced tier ($17.77/mo).","intents":["I need urgent help with gesture recognition issues before a presentation","I want to request custom features or integrations for my research team","I need personalized guidance on optimizing Qonqur for my specific use case"],"best_for":["teams or institutions using Qonqur at scale","researchers with mission-critical presentations or demonstrations","early adopters who want to influence product development"],"limitations":["Support SLA is not documented; unclear what response time to expect","Support channels are not specified; unclear if support is via email, chat, phone, or video call","Support scope is not defined; unclear if CEO support covers feature development or only troubleshooting","No documented escalation path if CEO is unavailable","Support is only available on Advanced tier; not available to free or Beginner tier users","Scalability of CEO support is questionable; unclear how this model scales if user base grows significantly"],"requires":["Advanced tier subscription ($17.77/mo)","Active Qonqur account with documented issue or request"],"input_types":["support request (email, chat, or call)","issue description or feature request"],"output_types":["personalized guidance or troubleshooting","feature implementation or roadmap influence"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Research articles in PDF or plain text format with parseable citation metadata","Minimum 3-5 articles to generate meaningful citation relationships","Stable internet connection for cloud-based processing","Webcam hardware (USB or built-in)","Browser with WebRTC and MediaStream API support (Chrome 50+, Firefox 55+, Safari 11+)","Adequate lighting (minimum ~200 lux; exact threshold unknown)","Clear line of sight to camera (no occlusion)","Microphone permissions enabled in browser","Qonqur account (free or paid tier)","Internet access to browse knowledge store"],"failure_modes":["Citation parsing fails on non-standard citation formats, malformed references, or papers without explicit citations (e.g., position papers, opinion pieces)","Requires articles in formats with extractable text (PDF, plain text); scanned images or OCR-dependent documents will fail","Citation graph becomes computationally expensive above ~500 articles; no documented performance metrics for larger collections","Cannot disambiguate author names or resolve citation aliases (e.g., 'Smith et al. 2020' vs 'S. Smith, J. Doe 2020'), leading to fragmented graphs","No support for cross-domain citation linking; treats each uploaded collection as isolated","Gesture recognition accuracy drops sharply in low-light conditions, backlighting, or complex backgrounds; no documented accuracy metrics or threshold specifications","Supported gestures are not documented; users must discover through trial or documentation (likely steep learning curve)","No user-specific calibration or personalization; system assumes one-size-fits-all gesture mappings, which may not match individual body proportions or movement styles","Latency between gesture and action is not specified; likely 100-500ms depending on processing pipeline, which may feel unresponsive for rapid navigation","Requires webcam hardware and browser permissions; incompatible with privacy-focused environments or venues without camera access","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2833333333333333,"quality":0.6799999999999999,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:32.438Z","last_scraped_at":"2026-04-05T13:23:42.562Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=qonqur","compare_url":"https://unfragile.ai/compare?artifact=qonqur"}},"signature":"a6ZuZHjZ1vmGiLvbX59cTdPbOyDQZOh+eN+fahCR5KuTqgFxS0K73gfHxV/hFOSx82zPrUc5xaBDILyXIAh2AQ==","signedAt":"2026-06-22T02:55:25.910Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qonqur","artifact":"https://unfragile.ai/qonqur","verify":"https://unfragile.ai/api/v1/verify?slug=qonqur","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}