{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_stimuler","slug":"stimuler","name":"Stimuler","type":"product","url":"https://stimuler.tech","page_url":"https://unfragile.ai/stimuler","categories":["text-writing"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_stimuler__cap_0","uri":"capability://planning.reasoning.adaptive.difficulty.adjustment.based.on.performance","name":"adaptive-difficulty-adjustment-based-on-performance","description":"Dynamically adjusts English lesson difficulty and content complexity in real-time by analyzing learner performance metrics (accuracy rates, response times, error patterns) against proficiency benchmarks. The system uses performance thresholds to trigger curriculum branching—escalating to harder material when learners exceed 80% accuracy or retreating to foundational content when performance drops below 60%. This closed-loop feedback mechanism personalizes pacing without manual instructor intervention.","intents":["I want lessons that automatically get harder as I improve, not waste time on content I already know","I need the system to slow down and reinforce weak areas without me having to manually select difficulty levels","I want to avoid the frustration of being stuck at one difficulty level for too long"],"best_for":["Self-directed intermediate learners (B1-B2) who prefer algorithmic pacing over static curricula","Learners with inconsistent study patterns who need the system to adapt to their engagement frequency","Non-native speakers frustrated with one-size-fits-all course structures"],"limitations":["Difficulty adjustment relies on performance data quality—noisy or gaming-prone metrics (e.g., random guessing) degrade accuracy","No human instructor oversight means edge cases (e.g., learner struggling with specific phoneme) may not trigger appropriate interventions","Adaptation lag: system may take 5-10 interactions to detect performance shift and adjust, creating temporary mismatch","Cannot distinguish between 'learner doesn't understand concept' vs 'learner had a bad day'—no contextual awareness"],"requires":["Consistent engagement (minimum 3-5 sessions/week for algorithm to build reliable performance baseline)","Internet connectivity for real-time performance tracking and model inference","User account with learning history (cold-start problem for new users without baseline data)"],"input_types":["learner responses (text, audio, multiple-choice selections)","interaction metadata (response time, retry count, confidence signals)"],"output_types":["curriculum branching decisions (next lesson difficulty level, content topic)","performance analytics (proficiency score, weak area identification)"],"categories":["planning-reasoning","personalization-engine"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stimuler__cap_1","uri":"capability://text.generation.language.conversational.ai.practice.with.real.time.feedback","name":"conversational-ai-practice-with-real-time-feedback","description":"Enables synchronous dialogue between learner and AI tutor using speech-to-text input and LLM-based response generation, with real-time feedback on pronunciation, grammar, and fluency delivered after each learner utterance. The system likely uses automatic speech recognition (ASR) to convert audio to text, feeds that text to a language model fine-tuned for English teaching (with grammar/fluency evaluation prompts), and returns corrective feedback with example corrections. Feedback is delivered within 2-3 seconds to maintain conversational flow.","intents":["I want to practice speaking English with an AI that won't judge me like a human would","I need immediate feedback on my pronunciation and grammar mistakes during conversation, not after","I want to simulate real conversation scenarios (ordering food, job interviews, casual chat) without scheduling a human tutor"],"best_for":["Anxious learners who fear judgment from human tutors or peers","Learners in time zones or regions with limited access to qualified English tutors","Intermediate learners (B1-B2) seeking high-frequency conversational exposure without cost of 1-on-1 tutoring"],"limitations":["ASR errors (especially with non-native accents) can propagate into feedback—learner may be corrected for pronunciation they executed correctly but ASR misheard","LLM-based feedback may miss nuanced cultural context or idiomatic usage that native speakers would catch; feedback can be technically correct but pedagogically suboptimal","No human verification of feedback quality—hallucinations or incorrect grammar corrections could reinforce bad habits","Conversational coherence limited by context window; multi-turn dialogues may lose earlier context, breaking narrative flow","Cannot assess non-verbal communication (body language, tone appropriateness) that matters in real conversations"],"requires":["Microphone and audio input capability (mobile device, laptop, or headset)","Stable internet connection (minimum 2 Mbps for real-time ASR and LLM inference)","Language model API access (likely OpenAI, Anthropic, or proprietary fine-tuned model)","ASR service (Google Speech-to-Text, Azure Speech Services, or proprietary)"],"input_types":["audio (learner speech, typically 16kHz mono WAV or MP3)","text (optional: learner can type instead of speak)"],"output_types":["text (AI tutor response, conversational continuation)","structured feedback (pronunciation score 0-100, grammar errors with corrections, fluency rating)","audio (optional: TTS-generated tutor response for immersion)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stimuler__cap_10","uri":"capability://planning.reasoning.goal.setting.and.milestone.tracking","name":"goal-setting-and-milestone-tracking","description":"Enables learners to set specific, measurable English learning goals (e.g., 'achieve B2 proficiency in 3 months', 'learn 500 new words', 'pass IELTS with 7.0 band score') and tracks progress toward these goals with milestone celebrations and reminders. The system likely breaks down long-term goals into sub-goals and lessons, estimates time-to-goal based on learner engagement rate, and sends reminders if learner falls behind. Milestones trigger notifications and rewards (badges, streak bonuses) to maintain motivation.","intents":["I want to set a clear English learning goal and track my progress toward it","I want the system to tell me if I'm on track to reach my goal or if I need to study more","I want to celebrate milestones and stay motivated throughout my learning journey"],"best_for":["Goal-oriented learners who are motivated by clear targets and progress tracking","Learners preparing for specific exams (IELTS, TOEFL, Cambridge) with defined proficiency targets","Learners who benefit from external accountability and milestone celebrations"],"limitations":["Goal-setting is only effective if goals are realistic; unrealistic goals (e.g., 'reach C1 in 1 month') lead to frustration and abandonment","Time-to-goal estimates are based on historical data and may not account for individual variation; some learners progress faster/slower than average","Learners may set goals and then abandon them if progress is slow; no mechanism to adjust goals or provide encouragement when motivation wanes","Milestone celebrations (badges, streaks) can feel superficial or patronizing to some learners","Goals are static; learners may want to change goals mid-journey but the system may not support this"],"requires":["Goal-setting UI with templates for common goals (exam preparation, proficiency level, vocabulary size)","Goal tracking system with progress calculation (e.g., 'you're 40% toward your goal')","Time-to-goal estimation model based on learner engagement rate and historical data","Notification system for reminders and milestone celebrations"],"input_types":["learner goal (target proficiency level, exam score, vocabulary size, timeline)","learner engagement data (lessons completed, study frequency)"],"output_types":["goal progress dashboard (percentage complete, estimated time-to-goal, milestone list)","notifications (milestone celebrations, behind-schedule alerts, encouragement messages)","goal recommendations (suggested sub-goals to break down long-term goal)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stimuler__cap_11","uri":"capability://memory.knowledge.content.library.with.proficiency.level.tagging","name":"content-library-with-proficiency-level-tagging","description":"Maintains a curated library of English learning content (lessons, exercises, videos, articles) tagged by proficiency level (A1-C2 CEFR), grammar topic, vocabulary theme, and real-world context. The system uses these tags to recommend content matching the learner's current level and goals. Content is organized hierarchically (e.g., 'Grammar > Tenses > Present Perfect') enabling learners to browse or search for specific topics. The library likely includes thousands of exercises and lessons covering comprehensive English curriculum.","intents":["I want to find lessons and exercises on specific topics (e.g., 'phrasal verbs', 'job interview vocabulary') without browsing the entire curriculum","I want content that matches my proficiency level, not too easy or too hard","I want a comprehensive curriculum that covers all aspects of English (grammar, vocabulary, pronunciation, fluency)"],"best_for":["Learners who want structured, comprehensive curriculum coverage","Learners who prefer browsing/searching for specific topics over following a linear path","Intermediate learners (B1-B2) with specific learning goals (e.g., business English, IELTS preparation)"],"limitations":["Content quality varies; not all lessons/exercises are equally effective or well-designed","Tagging is manual and subjective; proficiency level tags may not accurately reflect content difficulty","Content library requires ongoing maintenance and updates; outdated content (e.g., references to old technology) reduces relevance","Learners may get lost in a large library without clear guidance on what to study next","Content may not cover niche topics or regional variations (e.g., Australian English, Indian English)"],"requires":["Content management system (CMS) for storing and organizing lessons/exercises","Tagging system with standardized tags (CEFR levels, grammar topics, vocabulary themes, contexts)","Search and recommendation engine to help learners find relevant content","Content review process to ensure quality and accuracy"],"input_types":["learner proficiency level, learning goals, topic interests","search queries (e.g., 'phrasal verbs', 'job interview')"],"output_types":["recommended content (lessons, exercises, videos matching learner profile)","content library browse/search results (filtered by proficiency level, topic, context)","content metadata (difficulty rating, estimated time, learning objectives)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stimuler__cap_2","uri":"capability://data.processing.analysis.personalized.weakness.identification.and.targeting","name":"personalized-weakness-identification-and-targeting","description":"Analyzes learner interaction history (responses, errors, retry patterns, time-on-task) using diagnostic algorithms to identify specific weak areas (e.g., 'present perfect tense', 'th-sound pronunciation', 'phrasal verbs') and automatically prioritizes these in subsequent lessons. The system likely maintains a learner profile with skill tags and confidence scores, then uses content-tagging to surface exercises targeting low-confidence skills. This creates a personalized curriculum that focuses study time on areas with highest learning ROI.","intents":["I want the system to tell me exactly what grammar or pronunciation issues I have, not make me guess","I want to spend study time fixing my actual weak points, not reviewing things I already know","I want a clear roadmap of what to focus on next based on my specific mistakes"],"best_for":["Learners with limited study time who need to maximize learning efficiency","Self-aware learners who want data-driven insight into their progress","Intermediate learners (B1-B2) with specific, identifiable weak areas (e.g., 'I'm good at reading but bad at listening')"],"limitations":["Weakness identification requires sufficient historical data (typically 20+ interactions per skill area); new users get generic recommendations until enough data accumulates","Algorithmic categorization of errors may be coarse-grained (e.g., 'grammar errors' vs. specific tense confusion); misclassification leads to irrelevant targeting","Learner may avoid practicing weak areas if they're too difficult, creating a 'stuck in the middle' scenario where the system identifies the problem but can't motivate remediation","No distinction between 'learner doesn't know this' vs. 'learner knows this but made a careless mistake'—both get flagged as weaknesses"],"requires":["Minimum 10-20 completed lessons to build reliable weakness profile","Structured error tagging in the content library (each exercise must be tagged with skill/grammar concept)","User account with persistent learning history"],"input_types":["learner response data (correct/incorrect answers, error types)","interaction metadata (time spent, retry count, confidence ratings)"],"output_types":["weakness report (list of low-confidence skills with proficiency scores)","prioritized lesson recommendations (next 5-10 lessons focused on top 3 weaknesses)","progress visualization (skill proficiency heatmap or radar chart)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stimuler__cap_3","uri":"capability://data.processing.analysis.pronunciation.assessment.with.phonetic.scoring","name":"pronunciation-assessment-with-phonetic-scoring","description":"Evaluates learner pronunciation by comparing audio input against reference native-speaker recordings using phonetic analysis (likely mel-frequency cepstral coefficients, MFCC, or deep learning-based acoustic models). The system generates a pronunciation score (0-100) and highlights specific phonemes or stress patterns that deviate from the native reference, providing corrective feedback like 'your /θ/ sound is too close to /s/—try positioning your tongue between your teeth'. This enables learners to self-correct pronunciation without human intervention.","intents":["I want to know if my pronunciation is correct without waiting for a tutor to tell me","I want specific feedback on which sounds I'm mispronouncing, not just a general 'good job' or 'try again'","I want to practice pronunciation repeatedly until I get it right, with immediate feedback each time"],"best_for":["Learners with strong intrinsic motivation to improve pronunciation (self-correcting learners)","Learners in regions without access to pronunciation tutors","Intermediate learners (B1-B2) focused on accent reduction or clarity"],"limitations":["Acoustic models trained on native-speaker audio may penalize non-native accents unfairly; a learner with a 'good' accent for their native language background might score low","Phoneme-level feedback requires high-quality ASR and phonetic alignment; background noise, microphone quality, or speech rate variations degrade accuracy","Cannot assess suprasegmental features (intonation, stress patterns) as reliably as segmental features (individual sounds); feedback on 'you're stressing the wrong syllable' is less reliable","Scoring is deterministic based on acoustic similarity, not pedagogical value; a pronunciation that's 'wrong' acoustically but intelligible to native speakers might still be marked as incorrect","Requires reference recordings for every word/phrase in the curriculum; scaling to large content libraries is resource-intensive"],"requires":["High-quality microphone (noise floor < -40dB recommended)","Stable internet connection for real-time audio processing","Pre-recorded native-speaker reference audio for each word/phrase","Phonetic alignment model (e.g., forced alignment using Kaldi or similar)"],"input_types":["audio (learner speech, typically 16kHz mono WAV or MP3)"],"output_types":["pronunciation score (0-100)","phoneme-level feedback (list of mispronounced sounds with corrective guidance)","visual feedback (waveform comparison, spectrogram overlay showing learner vs. reference)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stimuler__cap_4","uri":"capability://text.generation.language.contextual.grammar.and.fluency.feedback","name":"contextual-grammar-and-fluency-feedback","description":"Analyzes learner text or speech output for grammar errors, awkward phrasing, and fluency issues using an LLM fine-tuned for English teaching. The system generates corrective feedback that explains the error (e.g., 'You used past tense, but the context requires present perfect because the action started in the past and continues now'), provides a corrected version, and optionally suggests similar example sentences. Feedback is contextualized to the lesson topic and learner proficiency level, avoiding overly technical terminology for beginners.","intents":["I want to understand WHY my sentence is wrong, not just be told it's wrong","I want feedback that explains grammar rules in a way I can understand and apply to future sentences","I want to see corrected versions of my sentences so I can learn the right way to say things"],"best_for":["Learners who benefit from explicit grammar instruction (vs. implicit learning through exposure)","Intermediate learners (B1-B2) with foundational grammar knowledge seeking refinement","Learners who want to understand the 'why' behind corrections, not just the 'what'"],"limitations":["LLM-based feedback can hallucinate or provide technically correct but pedagogically confusing explanations; no human review ensures quality","Feedback quality depends on LLM fine-tuning; generic LLMs (GPT-3.5) produce less pedagogically-sound explanations than specialized models","Context window limitations mean the system may miss errors that require understanding of multi-sentence discourse (e.g., pronoun reference errors across paragraphs)","Cannot distinguish between 'learner doesn't know this grammar rule' vs. 'learner knows it but made a typo'; feedback may be condescending or irrelevant","Explanations are text-based; visual or interactive explanations (e.g., animated grammar diagrams) are not provided"],"requires":["LLM API access (OpenAI, Anthropic, or proprietary fine-tuned model)","Grammar tagging system to categorize errors (e.g., 'tense error', 'subject-verb agreement', 'word order')","Learner proficiency level metadata to calibrate explanation complexity"],"input_types":["text (learner sentence or paragraph)","metadata (lesson topic, learner proficiency level, grammar focus area)"],"output_types":["structured feedback (error type, explanation, corrected sentence, example sentences)","text (natural language explanation)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stimuler__cap_5","uri":"capability://text.generation.language.scenario.based.conversational.role.play","name":"scenario-based-conversational-role-play","description":"Generates contextual conversation scenarios (e.g., 'You're at a restaurant ordering food', 'You're in a job interview') and guides learners through role-play dialogue with an AI tutor who plays the other role. The system uses prompt engineering to instruct the LLM to stay in character, respond naturally to learner input, and provide corrective feedback at appropriate moments without breaking immersion. Scenarios are tagged by proficiency level and real-world context (business, travel, social), enabling learners to practice language in realistic situations.","intents":["I want to practice English in realistic situations (job interviews, travel, casual conversation) before I encounter them in real life","I want to learn vocabulary and phrases specific to contexts I care about (business meetings, dating, customer service)","I want to build confidence by practicing high-stakes conversations (like job interviews) in a low-stakes environment"],"best_for":["Intermediate learners (B1-B2) with specific situational needs (e.g., preparing for a job interview in English)","Learners who learn better through immersive, contextual practice than abstract grammar drills","Anxious learners who want to rehearse high-stakes conversations before real-world encounters"],"limitations":["LLM role-play can break character or respond unrealistically if the learner's input is unexpected or out-of-domain; the AI may not handle edge cases well","Scenario realism is limited by LLM's training data; the AI may not capture authentic cultural nuances or regional variations (e.g., how job interviews differ between US and UK)","No assessment of non-verbal communication (eye contact, body language, tone) that matters in real scenarios","Learner may game the system by using simple, safe language rather than pushing themselves; no mechanism to force complexity escalation","Feedback delivery during role-play can break immersion; pausing to explain grammar errors interrupts conversational flow"],"requires":["LLM API access with sufficient context window (minimum 4K tokens to maintain multi-turn dialogue)","Scenario library with detailed prompts and context (e.g., 'You're interviewing for a software engineer role at a startup; the interviewer is asking about your experience with cloud infrastructure')","Proficiency-level tagging for scenarios to match learner ability"],"input_types":["learner text or speech (conversational input)","scenario selection (learner chooses context and difficulty)"],"output_types":["AI tutor response (natural language, in-character)","optional feedback (grammar/vocabulary corrections, cultural notes)","scenario transcript (full dialogue history for review)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stimuler__cap_6","uri":"capability://data.processing.analysis.learner.progress.tracking.and.analytics","name":"learner-progress-tracking-and-analytics","description":"Maintains a persistent learner profile tracking performance metrics across lessons (accuracy, speed, pronunciation score, fluency rating) and generates analytics dashboards showing progress over time. The system likely stores learner data in a database (user ID, lesson history, skill proficiency scores) and computes aggregate metrics (e.g., 'average accuracy over last 7 days', 'skills improved this week'). Visualizations include progress charts, skill heatmaps, and milestone celebrations to maintain motivation.","intents":["I want to see how much I've improved over time so I stay motivated","I want to understand which skills I'm strong in and which need work","I want to track my consistency and identify patterns in my learning (e.g., I do better in the morning)"],"best_for":["Self-directed learners who are motivated by data and progress visualization","Learners who benefit from gamification and milestone rewards","Learners who want to track long-term progress (weeks/months) to maintain motivation"],"limitations":["Analytics are only as good as the underlying metrics; if pronunciation scoring is inaccurate, progress tracking will be misleading","Learners may optimize for metrics rather than actual learning (e.g., rushing through lessons to maximize 'lessons completed' count)","Progress plateaus are common in language learning but may be demoralizing if the dashboard shows stagnation; no built-in explanation for plateaus","Privacy concerns: learner data must be securely stored and encrypted; data breaches expose sensitive learning history","Comparison features (e.g., 'leaderboards') can demotivate learners who are slower to progress"],"requires":["User account with persistent data storage (database with learner ID, lesson history, performance metrics)","Consistent metric collection across all lessons (each lesson must record accuracy, time, etc.)","Data retention policy (how long to keep historical data)"],"input_types":["lesson performance data (accuracy, response time, pronunciation score, fluency rating)","learner metadata (proficiency level, study frequency, goals)"],"output_types":["progress dashboard (charts, heatmaps, summary statistics)","analytics reports (weekly/monthly progress summaries, skill proficiency breakdown)","notifications (milestone celebrations, streak reminders, weak skill alerts)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stimuler__cap_7","uri":"capability://planning.reasoning.ai.tutor.personalization.based.on.learning.style","name":"ai-tutor-personalization-based-on-learning-style","description":"Adapts the AI tutor's teaching approach based on inferred learner preferences (e.g., visual learner vs. auditory, prefers explanations vs. implicit learning, likes gamification vs. serious tone). The system likely uses early interactions to infer learning style (e.g., if learner frequently asks 'why', they prefer explicit explanations) and adjusts subsequent feedback and lesson structure accordingly. This might include changing the ratio of explanation-to-practice, adding visual aids for visual learners, or emphasizing audio for auditory learners.","intents":["I want the AI to teach me in a way that matches how I learn best, not force me into a one-size-fits-all approach","I want more explanations if I'm a visual/analytical learner, or more practice if I'm a kinesthetic learner","I want the tutor to remember my preferences and apply them consistently across all lessons"],"best_for":["Learners with strong self-awareness about their learning style","Learners who have struggled with traditional teaching methods and want a different approach","Intermediate learners (B1-B2) with established learning preferences"],"limitations":["Learning style theory is controversial in education research; the assumption that matching teaching style to learning style improves outcomes is not strongly supported by evidence","Inferring learning style from limited interaction data is unreliable; early inferences may be wrong and persist throughout the learner's journey","Personalization adds complexity and latency; generating customized feedback for each learner takes longer than generic feedback","Learners may not know their learning style or may have multiple preferences; forcing a single style can be limiting","No mechanism to update learning style inferences if learner preferences change over time"],"requires":["Learning style inference model (trained on interaction data to predict learner preferences)","Tutor configuration system that allows adjusting teaching approach (explanation ratio, visual aids, gamification level)","Sufficient interaction history (10-20 lessons) to reliably infer learning style"],"input_types":["learner interaction patterns (questions asked, time spent on explanations vs. practice, engagement with visual aids)","explicit learner preferences (optional: learner can specify preferred learning style)"],"output_types":["tutor configuration (explanation ratio, visual aid frequency, gamification level)","adapted feedback (customized to learner's inferred style)"],"categories":["planning-reasoning","personalization-engine"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stimuler__cap_8","uri":"capability://automation.workflow.spaced.repetition.scheduling.for.vocabulary.retention","name":"spaced-repetition-scheduling-for-vocabulary-retention","description":"Implements spaced repetition algorithm (likely Leitner system or SM-2) to schedule vocabulary review at optimal intervals based on learner performance. When a learner encounters a new word, the system tracks whether they recall it correctly and adjusts the review interval accordingly: correct recalls increase the interval (e.g., review in 3 days, then 1 week, then 1 month), while incorrect recalls reset the interval to 1 day. This maximizes long-term retention by reviewing words just before they're forgotten.","intents":["I want to remember new vocabulary long-term, not just for the current lesson","I want the system to automatically schedule vocabulary review at the right time, not make me manually review flashcards","I want to focus review time on words I struggle with, not waste time on words I already know"],"best_for":["Learners with vocabulary gaps who want systematic, long-term vocabulary building","Learners who prefer algorithmic scheduling over manual flashcard review","Intermediate learners (B1-B2) expanding their active vocabulary"],"limitations":["Spaced repetition is optimized for recognition/recall, not productive use; a learner may recognize a word in context but struggle to use it in speech","Algorithm assumes consistent engagement; if learner stops using the app for weeks, the review schedule becomes stale and loses effectiveness","Vocabulary difficulty is not uniform; some words are inherently harder to remember (e.g., abstract nouns vs. concrete nouns); algorithm doesn't account for this","No distinction between 'learner forgot the word' vs. 'learner knew it but made a typo'; both trigger interval reset","Scheduling is deterministic; learner cannot override the schedule if they want to review a word sooner"],"requires":["Persistent learner profile with vocabulary history (word ID, review dates, performance history)","Spaced repetition algorithm implementation (Leitner, SM-2, or similar)","Vocabulary database with word difficulty ratings (to calibrate initial review intervals)"],"input_types":["vocabulary recall performance (correct/incorrect, confidence rating)","learner engagement data (days since last review, cumulative reviews)"],"output_types":["review schedule (next review date for each word)","vocabulary queue (words due for review today, prioritized by urgency)","retention statistics (percentage of vocabulary retained, average review interval)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_stimuler__cap_9","uri":"capability://text.generation.language.multi.modal.content.delivery.text.audio.video","name":"multi-modal-content-delivery-text-audio-video","description":"Delivers lesson content in multiple formats (text explanations, audio recordings, video demonstrations) allowing learners to choose their preferred modality or consume content in multiple formats for reinforcement. The system likely stores content in multiple formats (e.g., grammar explanation as text + audio narration + video animation) and allows learners to toggle between formats. This accommodates different learning preferences and accessibility needs (e.g., deaf learners prefer video with captions, blind learners prefer audio).","intents":["I want to learn in the format that works best for me (text, audio, or video), not be forced into one format","I want to reinforce my learning by consuming the same content in multiple formats","I want accessibility features (captions, transcripts, audio descriptions) so I can learn regardless of my abilities"],"best_for":["Learners with diverse learning preferences and accessibility needs","Learners with hearing or vision impairments who require alternative formats","Learners in low-bandwidth environments who can choose text over video to save data"],"limitations":["Creating multi-modal content is resource-intensive; each lesson requires text, audio, and video production, increasing development cost","Quality consistency across formats is hard to maintain; audio narration may not match text explanations, or video may be outdated","Accessibility features (captions, transcripts, audio descriptions) require additional production effort and may be incomplete or inaccurate","Learners may choose the easiest format (e.g., video) and avoid challenging formats (e.g., reading), limiting learning depth","Synchronization issues: if text and audio are out of sync, learners become confused"],"requires":["Content production pipeline for multiple formats (text writers, audio engineers, video producers)","Video hosting and streaming infrastructure (CDN for efficient delivery)","Accessibility compliance (WCAG 2.1 AA minimum for captions, transcripts, alt text)","Media player with format-switching capability"],"input_types":["learner format preference (text, audio, or video)","accessibility requirements (captions, transcripts, audio descriptions)"],"output_types":["lesson content in selected format (text, audio, or video)","accessibility features (captions, transcripts, audio descriptions)"],"categories":["text-generation-language","image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Consistent engagement (minimum 3-5 sessions/week for algorithm to build reliable performance baseline)","Internet connectivity for real-time performance tracking and model inference","User account with learning history (cold-start problem for new users without baseline data)","Microphone and audio input capability (mobile device, laptop, or headset)","Stable internet connection (minimum 2 Mbps for real-time ASR and LLM inference)","Language model API access (likely OpenAI, Anthropic, or proprietary fine-tuned model)","ASR service (Google Speech-to-Text, Azure Speech Services, or proprietary)","Goal-setting UI with templates for common goals (exam preparation, proficiency level, vocabulary size)","Goal tracking system with progress calculation (e.g., 'you're 40% toward your goal')","Time-to-goal estimation model based on learner engagement rate and historical data"],"failure_modes":["Difficulty adjustment relies on performance data quality—noisy or gaming-prone metrics (e.g., random guessing) degrade accuracy","No human instructor oversight means edge cases (e.g., learner struggling with specific phoneme) may not trigger appropriate interventions","Adaptation lag: system may take 5-10 interactions to detect performance shift and adjust, creating temporary mismatch","Cannot distinguish between 'learner doesn't understand concept' vs 'learner had a bad day'—no contextual awareness","ASR errors (especially with non-native accents) can propagate into feedback—learner may be corrected for pronunciation they executed correctly but ASR misheard","LLM-based feedback may miss nuanced cultural context or idiomatic usage that native speakers would catch; feedback can be technically correct but pedagogically suboptimal","No human verification of feedback quality—hallucinations or incorrect grammar corrections could reinforce bad habits","Conversational coherence limited by context window; multi-turn dialogues may lose earlier context, breaking narrative flow","Cannot assess non-verbal communication (body language, tone appropriateness) that matters in real conversations","Goal-setting is only effective if goals are realistic; unrealistic goals (e.g., 'reach C1 in 1 month') lead to frustration and abandonment","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3333333333333333,"quality":0.74,"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":"2026-05-24T12:16:33.648Z","last_scraped_at":"2026-04-05T13:23:42.552Z","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=stimuler","compare_url":"https://unfragile.ai/compare?artifact=stimuler"}},"signature":"qVUzQ3d7wD1FXlQUfQXx0LZ/A/jW3qgyDwaa8PCYe1PW//kDjjamuszxQuf1k47QYmdi+sIHUJKYwkCg+v4qCA==","signedAt":"2026-06-20T12:09:59.792Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/stimuler","artifact":"https://unfragile.ai/stimuler","verify":"https://unfragile.ai/api/v1/verify?slug=stimuler","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"}}