{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_linnk","slug":"linnk","name":"Linnk","type":"product","url":"https://www.linnk.ai","page_url":"https://unfragile.ai/linnk","categories":["app-builders"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_linnk__cap_0","uri":"capability://planning.reasoning.real.time.adaptive.learning.path.generation","name":"real-time adaptive learning path generation","description":"Dynamically adjusts educational content sequencing and difficulty levels based on continuous student performance monitoring. The system likely uses a Bayesian or reinforcement learning approach to model student competency states, comparing predicted vs. actual performance to identify knowledge gaps and recommend optimal next steps. Content difficulty and type (video, quiz, interactive exercise) are selected from a curriculum graph to match the student's current zone of proximal development.","intents":["I want the system to automatically adjust lesson difficulty when a student is struggling or breezing through content","I need to identify which specific concepts a student hasn't mastered and recommend targeted remediation","I want to prevent student frustration by avoiding content that's too hard or boredom from content that's too easy"],"best_for":["Individual students using self-directed learning platforms","Educators managing heterogeneous classrooms without manual differentiation","EdTech platforms seeking to reduce student dropout due to pacing mismatches"],"limitations":["Requires sufficient performance data (typically 5-10 interactions per concept) before adaptation becomes effective; cold-start problem for new students","Adaptation quality depends on curriculum graph completeness; sparse or poorly-structured content libraries limit effectiveness","No transparency on how competency models are trained or validated; unclear if models account for learning styles, motivation, or metacognitive factors"],"requires":["Student interaction history (quiz scores, time-on-task, attempt counts)","Structured curriculum graph mapping prerequisites and learning objectives","Real-time performance data collection infrastructure"],"input_types":["student performance metrics (scores, response times, error patterns)","curriculum metadata (learning objectives, difficulty ratings, prerequisites)"],"output_types":["recommended next learning activity","difficulty adjustment parameters","personalized learning path (sequence of activities)"],"categories":["planning-reasoning","adaptive-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_linnk__cap_1","uri":"capability://data.processing.analysis.knowledge.gap.detection.and.diagnostic.assessment","name":"knowledge gap detection and diagnostic assessment","description":"Analyzes student responses across multiple interactions to identify specific misconceptions, missing prerequisites, or weak conceptual understanding using pattern matching on error types and response latency. The system likely employs item response theory (IRT) or Bayesian knowledge tracing to infer unobserved competency levels from observed responses, then compares inferred competencies against curriculum standards to flag gaps. Diagnostic results are surfaced as actionable insights (e.g., 'student struggles with fraction multiplication but understands division').","intents":["I want to pinpoint exactly which concepts a student hasn't mastered, not just that they failed a test","I need to distinguish between careless errors and genuine conceptual misunderstandings","I want to know which prerequisites are missing so I can recommend targeted review before advancing"],"best_for":["Teachers seeking diagnostic data to inform intervention planning","Students wanting detailed feedback on what they actually understand vs. what they need to review","Curriculum designers validating whether learning objectives are being met"],"limitations":["Diagnostic accuracy depends on assessment item quality; poorly-designed questions yield unreliable competency inferences","Requires multiple attempts per concept to build statistical confidence; single-attempt assessments provide weak signals","No indication of how system handles guessing, test anxiety, or other non-ability factors that confound competency inference"],"requires":["Assessment item bank with tagged learning objectives and difficulty metadata","Student response history (answers, response times, attempt counts)","Competency model or learning objective taxonomy"],"input_types":["student assessment responses (multiple choice, short answer, constructed response)","item metadata (correct answer, difficulty, learning objective tags)"],"output_types":["competency profile (mastered/developing/not-yet-mastered per objective)","knowledge gap report (specific missing concepts)","diagnostic recommendations (remediation suggestions)"],"categories":["data-processing-analysis","assessment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_linnk__cap_2","uri":"capability://text.generation.language.ai.powered.supplementary.content.generation","name":"ai-powered supplementary content generation","description":"Generates tailored educational materials (explanations, practice problems, worked examples, summaries) on-demand using large language models, conditioned on student learning objectives, current competency level, and identified knowledge gaps. The system likely uses prompt engineering or fine-tuned models to ensure generated content aligns with curriculum standards and pedagogical best practices (e.g., scaffolding, concrete-to-abstract progression). Content is generated in multiple modalities (text, potentially images or interactive elements) to support diverse learning preferences.","intents":["I want to quickly create supplementary explanations or practice problems for concepts students are struggling with","I need alternative explanations of the same concept at different difficulty levels or using different examples","I want to generate personalized practice problems that target a student's specific knowledge gaps"],"best_for":["Educators with limited time to create custom materials for differentiated instruction","Students seeking additional practice or alternative explanations beyond textbook content","Small schools or under-resourced districts lacking curriculum development capacity"],"limitations":["Generated content quality is unpredictable; LLMs can produce mathematically incorrect solutions, pedagogically unsound explanations, or content misaligned with curriculum standards","No built-in fact-checking or validation; educators must manually review generated content before assigning to students","Content generation latency (typically 5-30 seconds) may not support real-time in-class use cases","No indication of how system ensures generated content is age-appropriate, culturally responsive, or free from bias"],"requires":["Access to LLM API (likely OpenAI, Anthropic, or proprietary model)","Learning objective and curriculum metadata to condition generation","Student competency profile to tailor difficulty and scaffolding"],"input_types":["learning objective or concept name","target difficulty level","student competency profile","content type request (explanation, practice problem, summary, worked example)"],"output_types":["text-based explanation or tutorial","practice problem with answer key","worked example with step-by-step solution","summary or study guide"],"categories":["text-generation-language","content-creation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_linnk__cap_3","uri":"capability://data.processing.analysis.student.performance.analytics.and.progress.tracking","name":"student performance analytics and progress tracking","description":"Aggregates and visualizes student learning data across multiple interactions, assessments, and activities to surface trends, patterns, and progress toward learning objectives. The system likely computes metrics such as mastery progression over time, time-to-mastery, attempt counts, and engagement indicators, then presents these via dashboards or reports. Analytics may include comparative views (student vs. cohort, current vs. historical) to contextualize individual performance.","intents":["I want to see how much progress a student has made toward mastering a specific learning objective over time","I need to identify students who are falling behind or at risk of disengagement","I want to compare my class's performance on a topic to see if my instruction was effective"],"best_for":["Teachers monitoring student progress and identifying intervention targets","Students tracking their own learning progress and identifying areas for improvement","School administrators evaluating program effectiveness and student outcomes"],"limitations":["Analytics are only as good as the underlying data; incomplete interaction logging or missing assessment data yields incomplete progress pictures","No indication of how system handles data privacy or FERPA compliance; student performance data may be sensitive","Visualizations may obscure important nuances (e.g., a student with high average score but high variance in performance)","No built-in predictive modeling; system shows historical trends but not forward-looking risk indicators"],"requires":["Complete student interaction and assessment history","Learning objective taxonomy and mastery thresholds","Time-series data collection infrastructure"],"input_types":["student interaction logs (timestamps, activity type, performance metrics)","assessment results (scores, response patterns, time-on-task)"],"output_types":["progress dashboard (mastery status per objective, trend charts)","performance report (summary statistics, comparative metrics)","engagement metrics (time-on-task, attempt frequency, session patterns)"],"categories":["data-processing-analysis","analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_linnk__cap_4","uri":"capability://planning.reasoning.personalized.learning.recommendation.engine","name":"personalized learning recommendation engine","description":"Recommends specific learning activities, resources, or interventions tailored to individual student needs using collaborative filtering, content-based filtering, or hybrid approaches. The system likely combines student competency profiles, learning preferences, performance history, and curriculum structure to rank candidate activities by predicted utility (e.g., likelihood of closing a knowledge gap, engagement potential). Recommendations may include suggested study sequences, peer resources, or external content.","intents":["I want the system to suggest what I should study next based on my current progress and learning goals","I need to recommend specific interventions for students struggling with particular concepts","I want to surface high-quality peer resources or external content relevant to a student's current learning needs"],"best_for":["Students seeking guidance on what to study next without teacher direction","Educators managing large classes and needing automated recommendation to scale personalization","Platforms integrating external content (YouTube, Khan Academy, textbooks) and needing to surface relevant resources"],"limitations":["Recommendation quality depends on feature engineering; poor feature selection or weighting yields irrelevant suggestions","Cold-start problem for new students with minimal interaction history; recommendations improve over time but are weak initially","No indication of how system handles student preferences, learning styles, or motivation; recommendations may be statistically optimal but pedagogically misaligned","Potential for filter bubbles or reinforcement of existing biases if recommendation algorithm is not carefully designed"],"requires":["Student competency profile and interaction history","Content library with metadata (learning objectives, difficulty, format, quality ratings)","Curriculum graph or learning pathway definitions","Recommendation algorithm (collaborative filtering, content-based, or hybrid)"],"input_types":["student competency state","student learning history and preferences","content metadata and quality signals"],"output_types":["ranked list of recommended activities or resources","personalized learning path or study sequence","intervention recommendations for struggling students"],"categories":["planning-reasoning","recommendation-system"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_linnk__cap_5","uri":"capability://text.generation.language.multi.modal.learning.content.support","name":"multi-modal learning content support","description":"Supports and adapts educational content across multiple modalities (text, images, video, interactive elements, audio) to accommodate diverse learning preferences and accessibility needs. The system likely detects or infers student learning style preferences from interaction patterns, then prioritizes content delivery in preferred modalities. May include text-to-speech, image captioning, or interactive simulations to support different learner needs.","intents":["I want content presented in my preferred format (video, text, interactive) rather than a one-size-fits-all approach","I need accessible alternatives (captions, transcripts, alt-text) for content I can't access in its original format","I want interactive simulations or visualizations to understand abstract concepts better than text alone"],"best_for":["Students with diverse learning preferences or accessibility needs","Educators seeking to support neurodivergent or differently-abled learners","Platforms aiming to maximize content accessibility and reduce barriers to learning"],"limitations":["Creating high-quality multi-modal content is resource-intensive; platform likely relies on user-generated or third-party content with variable quality","No indication of how system detects or validates learning style preferences; learning style theory is controversial and may not improve outcomes","Accessibility features (captions, alt-text) require manual creation or high-quality automated generation; auto-generated captions may be inaccurate","Multi-modal content delivery increases storage and bandwidth requirements, potentially limiting scalability"],"requires":["Content library with multiple modality variants (text, video, images, interactive)","Accessibility metadata (captions, transcripts, alt-text, audio descriptions)","Student learning preference data or inference mechanism"],"input_types":["learning content in multiple formats (text, video, images, interactive elements)","student learning preference signals (explicit preferences or inferred from interaction patterns)"],"output_types":["content in student's preferred modality","accessible alternatives (captions, transcripts, alt-text)","interactive visualizations or simulations"],"categories":["text-generation-language","image-visual","accessibility"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_linnk__cap_6","uri":"capability://data.processing.analysis.student.engagement.and.motivation.tracking","name":"student engagement and motivation tracking","description":"Monitors behavioral and engagement indicators (session frequency, time-on-task, attempt patterns, interaction consistency) to infer student motivation and engagement levels, then surfaces alerts or interventions when engagement drops. The system likely uses time-series analysis or anomaly detection to identify disengagement patterns (e.g., sudden drop in login frequency, decreased attempt counts) and may trigger automated interventions (reminders, encouragement messages, difficulty adjustments) or alerts to educators.","intents":["I want to know which students are at risk of disengaging or dropping out so I can intervene early","I want the system to automatically adjust difficulty or content type when I'm getting frustrated or bored","I want to understand my own engagement patterns and get feedback on my learning habits"],"best_for":["Educators managing large classes and needing early warning systems for at-risk students","Online learning platforms seeking to reduce dropout rates","Students seeking self-awareness about their learning habits and motivation"],"limitations":["Engagement indicators are proxies for motivation; low login frequency may indicate disengagement or external constraints (illness, family issues) unrelated to the platform","No indication of how system distinguishes between productive struggle and frustration; may incorrectly flag struggling students as disengaged","Automated interventions (reminders, difficulty adjustments) may backfire if perceived as intrusive or patronizing","Privacy concerns: continuous monitoring of engagement patterns may feel invasive to students"],"requires":["Complete interaction logs with timestamps and activity types","Baseline engagement metrics for cohort or individual students","Anomaly detection or time-series analysis algorithms"],"input_types":["student interaction logs (login times, session duration, activity frequency)","performance metrics (attempt counts, response times, accuracy trends)"],"output_types":["engagement score or status (high/medium/low)","disengagement alerts or risk flags","engagement trend reports","automated intervention recommendations"],"categories":["data-processing-analysis","monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_linnk__cap_7","uri":"capability://data.processing.analysis.curriculum.alignment.and.standards.mapping","name":"curriculum alignment and standards mapping","description":"Maps learning content and student competencies to educational standards (Common Core, state standards, IB, etc.) to ensure curriculum coherence and standards alignment. The system likely uses semantic matching or manual curation to link learning objectives to standards, then tracks student progress toward standards mastery. May provide reports on standards coverage and student achievement by standard.","intents":["I want to ensure my curriculum is aligned with state standards and that students are making progress toward standards mastery","I need to report on student achievement by standard for accountability purposes","I want to identify which standards are under-represented in my curriculum or where students are struggling"],"best_for":["Schools and districts required to report on standards-based achievement","Educators designing curriculum and needing to ensure standards alignment","Administrators evaluating curriculum effectiveness and standards coverage"],"limitations":["Standards mapping requires manual curation or high-quality semantic matching; misalignment between content and standards yields inaccurate progress reports","Different standards frameworks (Common Core, state standards, IB) require separate mappings; platform may not support all relevant standards","Standards are often vague or overlapping; multiple learning objectives may map to the same standard, making aggregation ambiguous","No indication of how system handles standards evolution or updates; standards change over time and mappings may become stale"],"requires":["Standards framework definitions (learning objectives, grade levels, competency descriptions)","Content-to-standards mappings (manual or automated)","Student competency data aligned to standards"],"input_types":["learning objectives and content metadata","standards framework definitions","student competency profiles"],"output_types":["standards alignment report (coverage by standard, gaps)","student achievement by standard","standards mastery progress tracking"],"categories":["data-processing-analysis","curriculum-alignment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_linnk__cap_8","uri":"capability://text.generation.language.collaborative.learning.and.peer.interaction.facilitation","name":"collaborative learning and peer interaction facilitation","description":"Enables peer-to-peer learning through discussion forums, peer review mechanisms, or collaborative problem-solving activities, with AI-powered moderation and facilitation. The system likely uses NLP to detect low-quality or off-topic discussions, flag misconceptions in peer explanations, or suggest discussion prompts to deepen engagement. May include peer recommendation (matching students for collaborative work based on complementary skills or learning needs).","intents":["I want to facilitate peer learning and discussion without manually moderating every post","I want to match students with peers who have complementary skills or learning needs for collaborative work","I want to detect when peer explanations contain misconceptions and flag them for teacher review"],"best_for":["Online learning communities seeking to scale peer interaction without manual moderation","Educators wanting to leverage peer learning as a pedagogical strategy","Platforms building social learning features and community engagement"],"limitations":["AI moderation is imperfect; may miss harmful content or flag benign discussions as problematic","Peer recommendation algorithms may reinforce existing social groups or exclude students from collaborative opportunities","No indication of how system handles toxic behavior, bullying, or harassment; moderation policies and enforcement mechanisms are unclear","Peer learning quality depends on peer expertise; incorrect peer explanations may reinforce misconceptions if not caught by moderation"],"requires":["Discussion forum or collaboration platform infrastructure","NLP models for content moderation and misconception detection","Student profile data for peer matching (skills, learning needs, preferences)"],"input_types":["peer discussion posts or explanations","student profile data (competencies, learning needs, preferences)"],"output_types":["moderated discussion threads","peer recommendations for collaboration","flagged misconceptions or low-quality content","discussion engagement metrics"],"categories":["text-generation-language","safety-moderation","community"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"low","permissions":["Student interaction history (quiz scores, time-on-task, attempt counts)","Structured curriculum graph mapping prerequisites and learning objectives","Real-time performance data collection infrastructure","Assessment item bank with tagged learning objectives and difficulty metadata","Student response history (answers, response times, attempt counts)","Competency model or learning objective taxonomy","Access to LLM API (likely OpenAI, Anthropic, or proprietary model)","Learning objective and curriculum metadata to condition generation","Student competency profile to tailor difficulty and scaffolding","Complete student interaction and assessment history"],"failure_modes":["Requires sufficient performance data (typically 5-10 interactions per concept) before adaptation becomes effective; cold-start problem for new students","Adaptation quality depends on curriculum graph completeness; sparse or poorly-structured content libraries limit effectiveness","No transparency on how competency models are trained or validated; unclear if models account for learning styles, motivation, or metacognitive factors","Diagnostic accuracy depends on assessment item quality; poorly-designed questions yield unreliable competency inferences","Requires multiple attempts per concept to build statistical confidence; single-attempt assessments provide weak signals","No indication of how system handles guessing, test anxiety, or other non-ability factors that confound competency inference","Generated content quality is unpredictable; LLMs can produce mathematically incorrect solutions, pedagogically unsound explanations, or content misaligned with curriculum standards","No built-in fact-checking or validation; educators must manually review generated content before assigning to students","Content generation latency (typically 5-30 seconds) may not support real-time in-class use cases","No indication of how system ensures generated content is age-appropriate, culturally responsive, or free from bias","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.15000000000000002,"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":"2026-05-24T12:16:31.447Z","last_scraped_at":"2026-04-05T13:23:42.560Z","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=linnk","compare_url":"https://unfragile.ai/compare?artifact=linnk"}},"signature":"rsyJrHQ/hwWlvMLLognE4soxziDFa00CcTFEfP9rXBU2DGXlAUHcuLBS3MBNA1dq3Q8ywcttKIT0Rf3LWnV9Ag==","signedAt":"2026-06-22T01:10:59.785Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/linnk","artifact":"https://unfragile.ai/linnk","verify":"https://unfragile.ai/api/v1/verify?slug=linnk","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"}}