{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_jung-gpt","slug":"jung-gpt","name":"Jung GPT","type":"product","url":"https://jung-gpt.com","page_url":"https://unfragile.ai/jung-gpt","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_jung-gpt__cap_0","uri":"capability://data.processing.analysis.real.time.emotional.intelligence.detection.in.conversation.streams","name":"real-time emotional intelligence detection in conversation streams","description":"Analyzes incoming user messages during live chat interactions to detect emotional states, sentiment polarity, and psychological tone using NLP-based emotion classification models. The system processes text input through a multi-dimensional emotion recognition pipeline that identifies primary emotions (joy, sadness, anger, fear, surprise, disgust) and confidence scores, then surfaces emotional context to support agents or HR recruiters in real-time, enabling response tailoring before message composition.","intents":["I need to know if a customer is frustrated or satisfied before I respond to their support ticket","I want to detect if a job candidate is anxious or confident during our chat interview","I need real-time alerts when conversation sentiment shifts negative so I can escalate appropriately","I want to understand the emotional subtext of customer feedback to prioritize issues correctly"],"best_for":["Customer support teams handling sensitive complaints or escalations","HR recruiting teams conducting remote interviews and assessments","Crisis support or mental health chatbot operators","Enterprise customer success teams managing high-value accounts"],"limitations":["Emotion detection accuracy degrades significantly across cultural communication styles and non-English languages due to training data bias","Sarcasm, irony, and indirect emotional expression are frequently misclassified as literal sentiment","No context memory between conversation turns — each message analyzed independently without conversation history weighting","Confidence scores for emotion classification not exposed to end users, making false positives indistinguishable from high-confidence detections","Latency for emotion analysis adds 200-500ms per message depending on model size and server load"],"requires":["Active internet connection for cloud-based emotion model inference","Text input in supported languages (English primary, others unknown)","Chat interface integration via API or web widget","User consent for emotional data processing (GDPR/CCPA compliance responsibility on customer)"],"input_types":["plain text messages","chat conversation transcripts"],"output_types":["emotion classification labels with confidence scores","sentiment polarity (positive/negative/neutral)","emotional intensity levels","real-time alerts/notifications"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_jung-gpt__cap_1","uri":"capability://text.generation.language.empathetic.response.generation.with.emotional.tone.matching","name":"empathetic response generation with emotional tone matching","description":"Generates chat responses that mirror or appropriately respond to detected emotional states by conditioning the language model on emotional context vectors. The system takes detected emotion signals from incoming messages and uses them as control tokens or prompt engineering inputs to guide response generation toward emotionally appropriate language, vocabulary selection, and communication style (formal vs. casual, direct vs. indirect, reassuring vs. action-oriented).","intents":["I want the chatbot to respond to angry customers with calm, validating language instead of generic corporate replies","I need responses that match a candidate's communication style during interviews to build rapport","I want the system to adjust formality level based on whether the user is frustrated or satisfied","I need responses that acknowledge emotional context before providing solutions"],"best_for":["Customer support automation where tone-deaf responses damage relationships","HR recruiting chatbots conducting initial candidate screening","Mental health or wellness chatbot applications","Enterprise customer success teams automating first-response handling"],"limitations":["Emotional tone matching can feel manipulative or inauthentic if overused, potentially damaging trust when users detect artificial empathy","No guarantee that emotionally-conditioned responses are factually accurate — emotion tokens may override safety guardrails","Requires accurate upstream emotion detection; garbage emotion signals produce tone-deaf responses despite conditioning","Cultural differences in what constitutes 'empathetic' language mean responses may offend in some contexts while appropriate in others","No persistent emotional memory — each response generated independently without learning from previous interaction outcomes"],"requires":["Upstream emotion detection capability (from real-time emotional intelligence detection)","Language model with sufficient capacity for multi-token conditioning (GPT-3.5+ scale or equivalent)","Fine-tuning data or prompt engineering templates for target domain (support vs. recruiting vs. mental health)","API access to base LLM (OpenAI, Anthropic, or self-hosted equivalent)"],"input_types":["detected emotion vectors/labels","conversation context","user message content","domain-specific templates"],"output_types":["natural language response text","response with emotional tone metadata","alternative response variants with different emotional tones"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_jung-gpt__cap_2","uri":"capability://memory.knowledge.multi.turn.conversation.memory.with.emotional.context.preservation","name":"multi-turn conversation memory with emotional context preservation","description":"Maintains conversation history across multiple turns while preserving emotional context and sentiment trajectory, enabling the system to reference previous emotional states and recognize patterns in user mood changes. The system stores conversation turns with associated emotion vectors, allowing subsequent responses to acknowledge emotional progression (e.g., 'I notice you were frustrated earlier, but you seem more optimistic now') and adapt strategy based on cumulative emotional signals rather than isolated message analysis.","intents":["I want the chatbot to remember that a customer was angry 5 messages ago and adjust follow-up accordingly","I need to track emotional progression during a candidate interview to assess stress resilience","I want the system to recognize when a customer's mood improves after a solution and reinforce that positive direction","I need conversation summaries that include emotional arc, not just factual content"],"best_for":["Multi-turn customer support conversations lasting 10+ exchanges","HR recruiting interviews with multiple conversation phases","Wellness or coaching chatbots tracking emotional progress over time","Enterprise support systems where relationship continuity matters"],"limitations":["Memory window is finite — conversations beyond 50-100 turns may lose early emotional context due to token limits","Emotional context preservation requires additional storage overhead (~2-3x normal conversation storage)","No mechanism to distinguish between genuine emotional change and temporary fluctuations, leading to false pattern detection","Privacy risk: storing emotional profiles creates persistent records that could be misused for manipulation or discrimination","Emotional context from early conversation may bias interpretation of later messages, creating self-fulfilling prophecies"],"requires":["Persistent conversation storage (database or vector store)","Emotion detection capability for each message turn","Context window large enough to include emotional history (8K+ tokens recommended)","Retrieval mechanism to surface relevant emotional context from history"],"input_types":["conversation turn history","emotion vectors for each turn","user message content","timestamp data"],"output_types":["conversation summaries with emotional arc","emotional trajectory visualizations","context-aware response generation","emotional pattern alerts"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_jung-gpt__cap_3","uri":"capability://planning.reasoning.tailored.response.strategy.selection.based.on.emotional.profile","name":"tailored response strategy selection based on emotional profile","description":"Selects from multiple response strategies (reassurance, problem-solving, validation, escalation, humor, etc.) based on detected emotional state and conversation context. The system maps emotion classifications to predefined or learned response strategies, then applies the selected strategy to guide response generation, tone, and action recommendations. For example, high anxiety triggers reassurance-first strategies, while anger triggers validation-first strategies before problem-solving.","intents":["I want different response strategies for angry vs. anxious customers without manually configuring each interaction","I need the system to escalate to human agents when emotional intensity exceeds thresholds","I want to use humor to defuse tension with some customers but not others based on their emotional profile","I need to prioritize validation over solutions for emotionally distressed users"],"best_for":["Customer support teams with diverse customer bases requiring flexible response approaches","HR recruiting where different candidates need different interview styles","Mental health or crisis support chatbots where strategy selection is critical","Enterprise customer success managing high-touch relationships"],"limitations":["Strategy selection is deterministic based on emotion classification, leaving no room for user preference or context override","Misclassified emotions lead to inappropriate strategy selection (e.g., sarcasm detected as anger triggers wrong strategy)","No learning from strategy outcomes — system doesn't improve strategy selection based on whether previous responses were effective","Cultural differences in what strategies are appropriate (humor may offend, direct problem-solving may seem dismissive) not handled","Escalation thresholds are static and may not adapt to individual user baselines or conversation context"],"requires":["Emotion detection capability with reliable classification","Predefined response strategy templates or learned strategy mappings","Conversation context and user profile data","Integration with escalation systems (human agent routing, ticket creation)"],"input_types":["emotion classification labels","emotion intensity scores","conversation context","user profile metadata"],"output_types":["selected response strategy identifier","strategy-guided response text","escalation recommendations","strategy effectiveness metadata"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_jung-gpt__cap_4","uri":"capability://safety.moderation.emotional.data.privacy.and.consent.management","name":"emotional data privacy and consent management","description":"Manages user consent for emotional data collection, processing, and storage, with controls for data retention, deletion, and third-party access. The system implements consent workflows that inform users their emotional states are being analyzed, provides granular opt-in/opt-out controls, and maintains audit logs of emotional data access. Integrates with GDPR/CCPA compliance frameworks to ensure emotional profiles are treated as sensitive personal data.","intents":["I need to ensure users consent to emotional analysis before their data is processed","I want to delete a user's emotional profile and conversation history on request","I need to audit who accessed emotional data about specific users","I want to restrict emotional data from being used for profiling or discrimination"],"best_for":["Organizations in GDPR/CCPA jurisdictions handling customer emotional data","HR departments using emotional AI for recruiting (high sensitivity)","Healthcare or mental health applications with strict data protection requirements","Enterprise customers with data governance policies"],"limitations":["Consent management adds operational overhead — requires user interface, legal review, and compliance monitoring","Emotional data deletion is difficult to enforce if data has been used for model training or aggregated analytics","No technical mechanism to prevent emotional profiles from being used for discriminatory purposes once collected","Consent workflows may create friction, reducing adoption of emotional AI features","Unclear legal status of emotional data in many jurisdictions — compliance frameworks are still evolving"],"requires":["Consent management platform or custom implementation","Data retention and deletion policies","Audit logging infrastructure","Legal review for GDPR/CCPA compliance","User interface for consent workflows"],"input_types":["user consent signals","data deletion requests","access control policies"],"output_types":["consent records with timestamps","audit logs of emotional data access","deletion confirmation records","compliance reports"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_jung-gpt__cap_5","uri":"capability://planning.reasoning.support.agent.coaching.based.on.emotional.interaction.patterns","name":"support agent coaching based on emotional interaction patterns","description":"Analyzes support agent responses against detected customer emotional states to identify coaching opportunities and provide real-time or post-interaction feedback. The system compares agent tone, response time, and strategy selection against emotional context, flagging mismatches (e.g., agent used problem-solving language when customer needed validation) and recommending alternative approaches. Generates coaching reports that highlight patterns across multiple interactions.","intents":["I want to coach support agents on how to respond better to angry or anxious customers","I need to identify which agents struggle with emotional intelligence and provide targeted training","I want real-time alerts when an agent's tone is mismatched to customer emotion","I need performance metrics that include emotional intelligence, not just resolution time"],"best_for":["Customer support teams with 10+ agents where coaching at scale is needed","Organizations prioritizing customer experience and relationship quality","Contact centers with high emotional labor (healthcare, financial services, crisis support)","Teams using emotional AI to improve agent performance, not replace agents"],"limitations":["Coaching recommendations are only as good as emotion detection — false emotion classifications lead to bad coaching","No mechanism to account for agent constraints (time pressure, system limitations) that may force suboptimal responses","Coaching based on emotional matching may discourage authentic agent personality and create robotic interactions","Privacy concerns: monitoring agent emotional intelligence may be perceived as invasive surveillance","No evidence that emotional coaching improves customer outcomes — assumes emotional matching = better results"],"requires":["Real-time emotion detection for customer messages","Agent response capture and analysis","Coaching recommendation engine with domain knowledge","Reporting and feedback interface for managers","Integration with agent performance management systems"],"input_types":["customer emotion classifications","agent response text","conversation context","agent performance metadata"],"output_types":["coaching recommendations","mismatch alerts","agent performance reports with emotional intelligence metrics","training content suggestions"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_jung-gpt__cap_6","uri":"capability://data.processing.analysis.candidate.emotional.assessment.during.recruiting.interviews","name":"candidate emotional assessment during recruiting interviews","description":"Analyzes candidate emotional responses during chat-based interviews to assess stress resilience, communication style, and interpersonal skills. The system detects emotional shifts during challenging questions, measures emotional stability under pressure, and generates assessments of how candidates handle frustration or uncertainty. Provides recruiters with emotional intelligence profiles alongside traditional interview notes.","intents":["I want to assess how candidates handle stress and pressure during interviews","I need to evaluate communication style and emotional maturity as part of hiring criteria","I want to identify candidates who remain calm and professional under challenging questions","I need objective emotional intelligence metrics to reduce hiring bias in interviews"],"best_for":["Recruiting teams for high-stress roles (sales, customer service, leadership)","Organizations using chat-based or asynchronous interview processes","Companies prioritizing emotional intelligence as a hiring criterion","Remote recruiting where in-person emotional cues are unavailable"],"limitations":["Emotional assessment from text-only interviews misses body language, vocal tone, and facial expressions that convey emotional state","Candidates may suppress emotional expression in text-based interviews, making assessment unreliable","Emotional profiles could introduce new forms of bias (e.g., favoring candidates with specific emotional expression styles)","No validation that emotional assessment predicts job performance or cultural fit","Privacy and ethical concerns: emotional profiling of candidates raises discrimination risks","Cultural differences in emotional expression mean assessments may be biased against non-Western communication styles"],"requires":["Chat-based interview platform integration","Emotion detection capability for interview messages","Candidate profile storage and assessment reporting","Legal review for hiring discrimination compliance"],"input_types":["candidate interview messages","interview questions and context","emotional response data"],"output_types":["emotional intelligence assessment scores","stress resilience ratings","communication style profiles","candidate comparison reports"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_jung-gpt__cap_7","uri":"capability://data.processing.analysis.conversation.quality.scoring.with.emotional.context.weighting","name":"conversation quality scoring with emotional context weighting","description":"Scores conversation quality not just on resolution or satisfaction, but on emotional appropriateness and tone matching. The system evaluates whether responses matched detected emotional states, whether emotional escalation was handled appropriately, and whether the conversation trajectory improved emotional outcomes. Generates quality scores that weight emotional factors alongside traditional metrics (resolution time, first-contact resolution).","intents":["I want to measure conversation quality beyond just 'was the problem solved'","I need to identify conversations where emotional mishandling damaged the relationship","I want to score interactions based on whether tone matched customer emotion","I need quality metrics that reflect emotional intelligence, not just efficiency"],"best_for":["Customer support teams prioritizing relationship quality over speed","Organizations measuring customer experience holistically","Enterprise customer success managing high-value accounts","Teams using emotional AI to improve interaction quality"],"limitations":["Emotional quality scoring is subjective — no universal standard for 'appropriate' emotional response","Weighting emotional factors against resolution may penalize efficient problem-solving in favor of lengthy validation","Scores are only as reliable as emotion detection — false emotions lead to incorrect quality assessments","No evidence that emotionally-optimized conversations improve customer retention or lifetime value","Quality scores may create perverse incentives (agents prioritizing emotional tone over accuracy)"],"requires":["Emotion detection for all conversation turns","Quality scoring algorithm with configurable emotional weighting","Conversation outcome data (resolution, satisfaction, retention)","Reporting interface for quality analytics"],"input_types":["conversation transcripts","emotion classifications per turn","resolution and satisfaction data"],"output_types":["quality scores with emotional component breakdown","quality reports by agent or team","trend analysis of emotional quality over time"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_jung-gpt__cap_8","uri":"capability://safety.moderation.cross.cultural.emotional.interpretation.with.bias.detection","name":"cross-cultural emotional interpretation with bias detection","description":"Attempts to adapt emotion detection and response generation for different cultural communication styles, with built-in bias detection to flag when emotional interpretations may be culturally inappropriate. The system includes cultural context parameters (region, language, communication style) that adjust emotion classification thresholds and response strategy selection. Flags high-confidence mismatches between detected emotion and cultural norms.","intents":["I want emotion detection to work accurately for customers from different cultural backgrounds","I need the system to warn me when emotional interpretation might be culturally biased","I want response strategies that are appropriate for different communication styles","I need to avoid emotional misinterpretations that damage cross-cultural customer relationships"],"best_for":["Global customer support teams serving diverse cultural markets","International recruiting with candidates from multiple countries","Organizations committed to reducing cultural bias in AI systems","Teams with explicit cultural competency training"],"limitations":["Cultural adaptation is superficial — adjusting thresholds doesn't address fundamental training data bias in emotion models","No single 'cultural context' parameter captures individual variation within cultures — stereotyping risk is high","Bias detection is heuristic-based and unreliable — system may flag appropriate interpretations as biased or miss actual bias","Requires manual cultural context configuration per user/region, adding operational overhead","No evidence that cultural adaptation improves emotion detection accuracy — may create false confidence in biased systems","Language support is limited — emotion detection primarily trained on English, cultural adaptation for other languages is minimal"],"requires":["Cultural context parameters (region, language, communication style)","Bias detection heuristics or models","Emotion detection model with cultural adaptation capability","Manual configuration of cultural thresholds and strategies"],"input_types":["user cultural context metadata","emotion classifications","conversation content"],"output_types":["culturally-adapted emotion classifications","bias detection flags","cultural appropriateness warnings","adapted response strategies"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_jung-gpt__cap_9","uri":"capability://automation.workflow.freemium.tier.emotional.ai.with.usage.based.scaling","name":"freemium tier emotional ai with usage-based scaling","description":"Provides free access to basic emotional intelligence features (emotion detection, simple response generation) with usage limits, while premium tiers unlock advanced features (multi-turn memory, coaching, advanced strategies, higher accuracy models). The system tracks usage metrics (messages analyzed, conversations, emotional data stored) and enforces rate limits or feature gates based on tier. Enables teams to test emotional AI capabilities before enterprise commitment.","intents":["I want to try emotional AI without committing to an enterprise contract","I need to understand if emotional intelligence improves our customer interactions before investing","I want to scale emotional AI features as our team grows","I need transparent pricing that reflects actual usage"],"best_for":["Small teams and startups testing emotional AI viability","Organizations with variable usage patterns","Teams wanting to pilot emotional AI before full deployment","Cost-conscious organizations wanting to minimize upfront investment"],"limitations":["Free tier likely uses lower-accuracy emotion models, creating poor user experience and discouraging adoption","Usage limits may be frustratingly low, preventing meaningful evaluation of emotional AI impact","Freemium model creates lock-in risk — teams may be forced to upgrade to premium even for modest usage increases","No transparency on what features are limited in free tier vs. premium (emotion model accuracy, response quality, etc.)","Free tier data may be used for model training or aggregated analytics, raising privacy concerns"],"requires":["Freemium SaaS infrastructure with usage tracking","Tiered feature gates and rate limiting","Billing and subscription management system","Usage analytics and reporting"],"input_types":["user tier/subscription level","usage metrics (messages, conversations, storage)"],"output_types":["feature availability based on tier","usage reports and billing data","upgrade recommendations"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["Active internet connection for cloud-based emotion model inference","Text input in supported languages (English primary, others unknown)","Chat interface integration via API or web widget","User consent for emotional data processing (GDPR/CCPA compliance responsibility on customer)","Upstream emotion detection capability (from real-time emotional intelligence detection)","Language model with sufficient capacity for multi-token conditioning (GPT-3.5+ scale or equivalent)","Fine-tuning data or prompt engineering templates for target domain (support vs. recruiting vs. mental health)","API access to base LLM (OpenAI, Anthropic, or self-hosted equivalent)","Persistent conversation storage (database or vector store)","Emotion detection capability for each message turn"],"failure_modes":["Emotion detection accuracy degrades significantly across cultural communication styles and non-English languages due to training data bias","Sarcasm, irony, and indirect emotional expression are frequently misclassified as literal sentiment","No context memory between conversation turns — each message analyzed independently without conversation history weighting","Confidence scores for emotion classification not exposed to end users, making false positives indistinguishable from high-confidence detections","Latency for emotion analysis adds 200-500ms per message depending on model size and server load","Emotional tone matching can feel manipulative or inauthentic if overused, potentially damaging trust when users detect artificial empathy","No guarantee that emotionally-conditioned responses are factually accurate — emotion tokens may override safety guardrails","Requires accurate upstream emotion detection; garbage emotion signals produce tone-deaf responses despite conditioning","Cultural differences in what constitutes 'empathetic' language mean responses may offend in some contexts while appropriate in others","No persistent emotional memory — each response generated independently without learning from previous interaction outcomes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.78,"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":"2026-05-24T12:16:31.446Z","last_scraped_at":"2026-04-05T13:23:42.551Z","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=jung-gpt","compare_url":"https://unfragile.ai/compare?artifact=jung-gpt"}},"signature":"Ul059dz+Gu4KDgodDE1GW/9hU27CmEtF4qwrapIpCWcuChLFLm264NhAVWca3dwlV41clagyy8WjymW6r7rlAA==","signedAt":"2026-06-22T15:12:03.406Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/jung-gpt","artifact":"https://unfragile.ai/jung-gpt","verify":"https://unfragile.ai/api/v1/verify?slug=jung-gpt","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"}}