{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_clare-me","slug":"clare-me","name":"Clare & Me","type":"product","url":"https://www.clareandme.com","page_url":"https://unfragile.ai/clare-me","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_clare-me__cap_0","uri":"capability://text.generation.language.multi.channel.conversational.mental.health.support.via.phone.whatsapp.sms","name":"multi-channel conversational mental health support via phone/whatsapp/sms","description":"Delivers AI-powered mental health conversations across three distinct communication channels (phone with voice-to-text transcription, WhatsApp messaging, SMS text) using a unified conversation state backend that maintains context across channel switches. The system routes incoming messages through a natural language understanding pipeline that classifies user intent (emotional support, coping strategy request, crisis signal detection) and generates contextually appropriate responses using a fine-tuned language model trained on mental health conversation patterns. Channel abstraction layer handles protocol-specific formatting (SMS character limits, WhatsApp media support, phone call duration constraints) while preserving conversation continuity.","intents":["I need emotional support at 2am when my therapist isn't available","I want to reach out for help via the messaging app I already use daily","I need to talk through anxiety but don't want to commit to scheduling an appointment","I'm in crisis and need immediate human-like conversation without navigating a phone tree"],"best_for":["individuals in underserved regions with limited therapy access","people experiencing acute emotional distress outside therapy hours","users who prefer asynchronous text-based communication over synchronous calls","patients seeking low-friction emotional support between professional sessions"],"limitations":["No real-time crisis detection or emergency protocol escalation — cannot distinguish between venting and imminent self-harm risk","Voice-to-text transcription accuracy degrades with accents, background noise, or speech disorders; phone channel may misinterpret critical emotional cues","SMS channel truncates context to 160-character messages, forcing fragmented conversations that lose emotional nuance","No persistent conversation history across channel switches if backend session expires; users may repeat context","Latency varies by channel: SMS has 5-30 second delivery delays; WhatsApp 1-3 seconds; phone requires real-time processing adding 500-1500ms response time"],"requires":["Active phone number with SMS/voice capability (Twilio or equivalent carrier integration)","WhatsApp Business API account with webhook configuration","Speech-to-text API (Google Cloud Speech-to-Text or AWS Transcribe) for phone channel","Backend conversation state store (Redis or DynamoDB) with <500ms latency","User consent and HIPAA-compliant data handling infrastructure if operating in regulated jurisdictions"],"input_types":["voice (phone calls with automatic transcription)","text (WhatsApp messages, SMS)","structured metadata (phone number, timestamp, channel identifier)"],"output_types":["natural language text responses","voice synthesis for phone channel (text-to-speech)","formatted messages respecting channel constraints (SMS length limits, WhatsApp formatting)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clare-me__cap_1","uri":"capability://text.generation.language.emotional.state.classification.and.coping.strategy.recommendation","name":"emotional state classification and coping strategy recommendation","description":"Analyzes user messages using a multi-label text classification model trained on mental health conversation datasets to identify emotional states (anxiety, depression, loneliness, anger, grief, etc.) and situational context (work stress, relationship conflict, health anxiety). Based on detected emotional state, the system retrieves and recommends evidence-based coping strategies from a curated knowledge base (cognitive reframing techniques, grounding exercises, breathing patterns, behavioral activation suggestions) matched to the specific emotion and user context. Classification confidence scores determine whether to offer direct strategy recommendations or ask clarifying questions to improve accuracy.","intents":["I'm feeling overwhelmed but can't articulate exactly what's wrong — help me name this emotion","I know I'm anxious but I don't know what to do about it right now","I want coping strategies tailored to my specific situation, not generic advice","I need to understand if what I'm experiencing is normal or requires professional help"],"best_for":["users seeking immediate emotional validation and practical coping tools","individuals building self-awareness about their emotional patterns","people in mild-to-moderate distress who benefit from structured coping techniques","users unable to access therapy and needing interim emotional regulation support"],"limitations":["Classification model trained on English-language mental health conversations; accuracy drops significantly for non-English inputs or culturally-specific emotional expressions","Cannot diagnose clinical conditions (depression, anxiety disorder, PTSD) — only identifies emotional states; risk of false negatives where serious conditions go undetected","Coping strategy recommendations are generic and not personalized to individual trauma history, medication interactions, or contraindicated techniques (e.g., exposure exercises for untreated PTSD)","No feedback loop to measure whether recommended strategies actually helped the user; system cannot learn from failed recommendations","Emotional state classification has ~15-20% error rate on ambiguous inputs where users express mixed emotions or sarcasm"],"requires":["Pre-trained text classification model (fine-tuned BERT or RoBERTa on mental health datasets like CLPsych or DAIC-WOZ)","Curated knowledge base of 50+ evidence-based coping strategies with metadata (emotion category, difficulty level, time-to-benefit)","Semantic similarity search (embeddings-based retrieval) to match user context to relevant strategies","User consent to process emotional content; data retention policy for training data"],"input_types":["natural language text (user messages describing emotional state)","implicit context (conversation history, time of day, channel used)"],"output_types":["emotion labels with confidence scores","recommended coping strategies with brief explanations","follow-up clarifying questions if classification confidence is low"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clare-me__cap_2","uri":"capability://safety.moderation.crisis.signal.detection.and.escalation.routing","name":"crisis signal detection and escalation routing","description":"Monitors incoming messages for linguistic markers of acute crisis (explicit suicidal ideation, self-harm intent, severe substance use, psychotic symptoms, acute trauma response) using a rule-based pattern matcher combined with a trained anomaly detection model that identifies unusual conversation patterns (rapid message escalation, emotional intensity spikes, topic shifts to harm). When crisis signals are detected above a confidence threshold, the system triggers an escalation workflow: generating a crisis-aware response, offering immediate resources (crisis hotline numbers, emergency contact options), and optionally routing to human review or emergency services depending on jurisdiction and user consent settings. The system maintains an audit log of all crisis detections for compliance and safety review.","intents":["I'm having thoughts of harming myself and need immediate help","I'm experiencing a panic attack and need grounding techniques right now","I'm in an unsafe situation and need to know how to get help","The AI should recognize I'm in crisis and not just give me generic coping advice"],"best_for":["emergency response teams needing to identify high-risk users for escalation","platforms required to comply with mental health crisis reporting regulations","users in acute distress who need immediate resource access and human intervention","organizations building safety infrastructure into conversational AI systems"],"limitations":["Rule-based patterns miss indirect or coded language (e.g., 'I won't be here tomorrow' vs explicit 'I want to kill myself'); false negative rate estimated at 10-25% for ambiguous crisis signals","False positive rate of 5-15% on emotionally intense but non-crisis messages (e.g., 'I hate my job' misclassified as self-harm ideation), leading to unnecessary escalations and user distrust","No real-time integration with emergency services (911, police) — escalation limited to hotline referrals and human review; cannot dispatch emergency responders","Jurisdiction-specific legal obligations vary (duty to warn, mandatory reporting thresholds); system cannot adapt escalation rules to local laws without manual reconfiguration","Latency in crisis detection (2-5 seconds for model inference) may be unacceptable during rapidly escalating situations"],"requires":["Labeled training dataset of crisis vs non-crisis mental health conversations (CLPsych, DAIC-WOZ, or proprietary annotated data)","Rule-based pattern library for explicit crisis keywords and phrases","Anomaly detection model (Isolation Forest or LSTM-based) trained on conversation flow patterns","Integration with crisis resource database (SAMHSA, Crisis Text Line, local hotlines) with real-time number validation","Legal review and compliance audit for jurisdiction-specific crisis reporting obligations","Human review queue and escalation SLA (e.g., crisis flags reviewed by trained staff within 5 minutes)"],"input_types":["natural language text (user messages)","conversation history and metadata (message timestamps, escalation velocity, user profile flags)"],"output_types":["crisis risk score (0-100)","escalation action (none, hotline referral, human review, emergency services contact)","crisis-aware response text","audit log entry with timestamp and reasoning"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clare-me__cap_3","uri":"capability://memory.knowledge.conversation.context.persistence.and.session.management","name":"conversation context persistence and session management","description":"Maintains conversation state across multiple messages and channel switches using a session store (Redis or DynamoDB) that persists user context, emotional history, and previous coping strategies discussed. The system implements a sliding context window that retains the last 10-20 messages (or ~2000 tokens) to provide coherent multi-turn conversation while managing memory constraints. When users switch channels (e.g., SMS to WhatsApp), the session lookup retrieves prior context and seamlessly continues the conversation. Session metadata includes user preferences (preferred coping strategies, communication style, crisis contact info), conversation tags (topics discussed, emotional themes), and timestamps for conversation analytics.","intents":["I started a conversation on WhatsApp yesterday and want to continue it today without repeating myself","I want the AI to remember that I mentioned my anxiety about work last week","I switched from SMS to phone call and don't want to re-explain my situation","I want my conversation history to inform better recommendations over time"],"best_for":["users seeking continuity across multiple conversations and channels","platforms building long-term user relationships and personalization","systems needing to track emotional patterns and treatment progress over weeks/months","organizations complying with data retention and user privacy regulations"],"limitations":["Session expiration (typically 30-90 days) causes context loss for users with gaps in conversation; no automatic context recovery","Sliding context window of 2000 tokens loses detailed information from earlier conversations; users may need to re-explain complex situations","No explicit consent mechanism for long-term data retention; users may not realize conversations are persisted beyond current session","Cross-device session management is fragile — if user accesses from different phone numbers or devices, session lookup fails and context is lost","Storage costs scale with user base; persistent sessions for 100k+ users require significant database infrastructure"],"requires":["Session store with sub-100ms latency (Redis with replication, or DynamoDB with on-demand scaling)","User authentication mechanism (phone number verification, optional account creation) to link sessions","Data encryption at rest and in transit (TLS 1.3, AES-256)","Privacy policy and data retention schedule compliant with GDPR, CCPA, or local regulations","Backup and disaster recovery for session store (daily snapshots, cross-region replication)"],"input_types":["user identifier (phone number, WhatsApp ID, or account ID)","conversation messages and metadata (timestamp, channel, emotion tags)"],"output_types":["session context (prior messages, user preferences, conversation summary)","session metadata (creation date, last activity, conversation tags)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clare-me__cap_4","uri":"capability://automation.workflow.freemium.access.control.and.usage.metering","name":"freemium access control and usage metering","description":"Implements a freemium model with tiered access using a usage metering system that tracks conversations per user (free tier: 5 conversations/month, paid: unlimited) and enforces rate limits via a token bucket algorithm. Free users receive full feature access (emotional support, coping strategies, crisis detection) but with conversation quotas; paid users unlock unlimited conversations and optional premium features (conversation export, progress tracking, therapist integration). The system uses phone number or WhatsApp ID as the user identifier for quota enforcement; quota resets occur on a monthly calendar basis. Upgrade prompts are triggered when users approach quota limits (e.g., 'You have 1 conversation remaining this month').","intents":["I want to try this service without paying to see if it helps me","I need unlimited access because I'm in crisis and can't ration my conversations","I want to upgrade to premium features like conversation history export","I need to understand my usage and when my quota resets"],"best_for":["early-stage mental health platforms seeking user acquisition without upfront cost barriers","organizations building sustainable business models around AI mental health support","users with limited financial resources who need low-barrier access to emotional support","platforms testing product-market fit before investing in clinical validation"],"limitations":["Quota-based access creates perverse incentives: users may ration conversations during crisis to preserve quota, delaying help-seeking","Phone number-based user identification is fragile — users with new phones or shared numbers may lose quota tracking or be incorrectly attributed","No grace period or emergency override for quota-exhausted users in crisis; system enforces hard limits that could harm vulnerable users","Upgrade friction may deter users from seeking help; payment processing adds latency and failure modes (declined cards, payment gateway downtime)","Free tier limitations may select for less severe cases, leaving highest-risk users unable to afford unlimited access"],"requires":["Payment processor integration (Stripe, PayPal) for subscription management","Usage metering backend (Stripe Billing, custom quota service) with atomic counter updates","User authentication and phone number verification (SMS OTP or WhatsApp verification)","Subscription state store (PostgreSQL or DynamoDB) tracking tier, billing cycle, and quota reset dates","Billing compliance (PCI DSS for payment processing, tax calculation for jurisdictions)"],"input_types":["user identifier (phone number, WhatsApp ID)","conversation initiation request with timestamp"],"output_types":["quota check result (allowed/denied with remaining conversations)","upgrade prompt or quota reset notification","subscription status and billing information"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clare-me__cap_5","uri":"capability://text.generation.language.conversation.summarization.and.progress.tracking","name":"conversation summarization and progress tracking","description":"Generates automatic summaries of multi-turn conversations using extractive and abstractive summarization techniques (BART or T5 models fine-tuned on mental health conversations) to identify key emotional themes, discussed coping strategies, and user-reported outcomes. Summaries are stored in the session context and can be retrieved by users (in paid tier) to review conversation history without scrolling through full message logs. The system also tracks progress metrics over time (frequency of emotional states, coping strategy effectiveness ratings, user-reported mood trends) by aggregating summaries across multiple conversations, enabling users to visualize emotional patterns and treatment progress.","intents":["I want to review what we talked about last week without scrolling through 50 messages","I want to see if my anxiety has improved over the past month","I want to export my conversation history to share with my therapist","I want to understand which coping strategies actually helped me"],"best_for":["users seeking self-awareness and progress tracking in their mental health journey","therapists integrating AI support into treatment (via conversation export feature)","platforms building long-term engagement through progress visualization","users with limited memory or cognitive capacity who benefit from conversation summaries"],"limitations":["Abstractive summarization models hallucinate or omit critical details; summaries may misrepresent user's emotional state or important context","Progress metrics are self-reported and biased; users may rate coping strategies as 'effective' due to placebo effect or social desirability bias","No clinical validation that tracking progress via AI summaries improves outcomes; may create false sense of progress without actual symptom improvement","Conversation export feature requires explicit user consent and HIPAA-compliant handling; exporting to third-party therapists introduces data sharing risks","Summarization adds 2-5 second latency per conversation; not suitable for real-time crisis response"],"requires":["Pre-trained abstractive summarization model (BART, T5, or Pegasus fine-tuned on mental health conversations)","Summary storage in session context or document database (MongoDB, Firestore)","Progress tracking database with time-series data (InfluxDB, TimescaleDB) for aggregating metrics across conversations","User consent and data export functionality (CSV, PDF generation) for therapist sharing","Privacy controls to allow users to opt-out of progress tracking or delete historical summaries"],"input_types":["multi-turn conversation messages (10-50 messages per conversation)","user ratings or feedback on coping strategy effectiveness (optional)"],"output_types":["conversation summary (200-500 words)","progress metrics (mood trend, coping strategy effectiveness scores, emotional theme frequency)","exportable conversation history (CSV, PDF, or therapist-shareable format)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clare-me__cap_6","uri":"capability://planning.reasoning.personalized.coping.strategy.learning.and.recommendation.refinement","name":"personalized coping strategy learning and recommendation refinement","description":"Tracks user interactions with recommended coping strategies (which strategies were tried, user feedback on effectiveness, follow-up emotional state) and uses this feedback to refine future recommendations via collaborative filtering and contextual bandit algorithms. The system maintains a user-strategy interaction matrix where each user has implicit and explicit ratings for strategies (tried and reported helpful, tried but unhelpful, not tried). When recommending strategies, the system balances exploitation (recommending strategies with high historical effectiveness for this user) with exploration (suggesting new strategies to expand the user's toolkit). Recommendations are contextualized by emotional state, time of day, and previous conversation patterns.","intents":["I've tried breathing exercises before and they helped — recommend similar techniques","I want to try new coping strategies I haven't used yet","Recommend strategies that work for my specific type of anxiety, not generic advice","I want to understand why you're recommending this particular strategy for me"],"best_for":["users seeking personalized mental health support that adapts to their preferences","platforms building long-term engagement through increasingly relevant recommendations","users with diverse coping strategy preferences who benefit from exploration","systems optimizing for user satisfaction and strategy adoption rates"],"limitations":["Cold-start problem: new users have no interaction history, forcing generic recommendations until sufficient data is collected (typically 5-10 conversations)","Feedback bias: users may rate strategies as effective due to placebo effect or temporal coincidence (mood improved after strategy, but unrelated to strategy)","No causal inference: system cannot determine whether strategy actually caused mood improvement or was correlated with other factors (e.g., time passing, external events)","Exploration-exploitation tradeoff creates risk: recommending untested strategies may harm users if strategies are contraindicated for their condition","Requires explicit user feedback on strategy effectiveness; implicit feedback (e.g., whether user tried strategy) is unreliable"],"requires":["User-strategy interaction database (PostgreSQL or MongoDB) tracking tried strategies, effectiveness ratings, and timestamps","Collaborative filtering or contextual bandit algorithm (Thompson Sampling, LinUCB) for recommendation ranking","Coping strategy knowledge base with metadata (emotion category, difficulty level, time-to-benefit, contraindications)","User feedback mechanism (in-conversation rating: 'Did this help?' with 1-5 scale)","A/B testing infrastructure to validate recommendation algorithm improvements"],"input_types":["user emotional state and context (detected emotion, time of day, conversation history)","user feedback on strategy effectiveness (explicit ratings, implicit signals like follow-up messages)"],"output_types":["ranked list of recommended coping strategies with confidence scores","explanation of why each strategy is recommended (e.g., 'You found breathing exercises helpful before')","user interaction tracking for future refinement"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clare-me__cap_7","uri":"capability://text.generation.language.natural.language.response.generation.with.mental.health.fine.tuning","name":"natural language response generation with mental health fine-tuning","description":"Generates contextually appropriate, empathetic responses to user messages using a large language model (likely GPT-3.5 or similar) fine-tuned on mental health conversation datasets to adopt a supportive tone, validate emotions, and avoid harmful language. The generation pipeline includes prompt engineering (system prompt specifying role as supportive AI, constraints on medical advice), response filtering to remove harmful content (suicide methods, medication dosing, diagnostic claims), and tone adjustment to match user communication style (formal vs casual, verbose vs concise). The system uses temperature and top-p sampling to balance response diversity (avoiding repetitive canned responses) with consistency (ensuring responses stay on-topic and emotionally appropriate).","intents":["I want the AI to understand my feelings and respond with empathy, not clinical detachment","I want responses that feel natural and conversational, not robotic","I want the AI to validate my emotions without minimizing my concerns","I want responses tailored to my communication style and preferences"],"best_for":["users seeking emotionally intelligent conversation that feels human-like","platforms prioritizing user engagement and therapeutic alliance","systems aiming to reduce stigma by normalizing mental health conversations","users with diverse communication styles and cultural backgrounds"],"limitations":["Fine-tuning on mental health data introduces bias toward specific emotional expressions and cultural norms; responses may feel inauthentic to users from underrepresented groups","Response generation is non-deterministic; same user input may produce different responses, reducing consistency and trust","No guarantee that generated responses are clinically appropriate or evidence-based; fine-tuning on conversational data does not ensure therapeutic validity","Hallucination risk: model may generate plausible-sounding but false information (e.g., 'Research shows that X coping strategy cures depression'), especially if training data contains misinformation","Response filtering (removing harmful content) is imperfect; some harmful suggestions may slip through, while some benign responses may be incorrectly filtered"],"requires":["Access to large language model API (OpenAI GPT-3.5/4, Anthropic Claude, or open-source model like Llama 2)","Mental health conversation dataset for fine-tuning (CLPsych, DAIC-WOZ, or proprietary annotated conversations)","Prompt engineering and system prompt design to specify role, constraints, and tone","Response filtering pipeline (rule-based or classifier-based) to detect and remove harmful content","Human review process to validate generated responses and identify failure modes"],"input_types":["user message (natural language text)","conversation context (prior messages, detected emotional state, user preferences)"],"output_types":["generated response text (50-500 words)","response confidence score (0-100) indicating whether response passed safety filters"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_clare-me__cap_8","uri":"capability://text.generation.language.user.preference.learning.and.communication.style.adaptation","name":"user preference learning and communication style adaptation","description":"Learns user communication preferences (verbosity, formality, emotional tone, language) from conversation history and adapts response generation to match. The system tracks metadata about user messages (average message length, use of formal language, emotional intensity, emoji usage) and adjusts generation parameters (response length, vocabulary complexity, tone) to mirror the user's style. For example, a user who sends short, casual messages receives concise, informal responses; a user who writes long, detailed messages receives more comprehensive responses. Preference learning is implicit (inferred from conversation patterns) rather than explicit (requiring user configuration), reducing friction.","intents":["I want the AI to talk to me in my natural style, not force me to adapt to its style","I prefer brief, direct responses without a lot of explanation","I want detailed, thoughtful responses that match my communication style","I want the AI to understand my cultural communication norms"],"best_for":["users with diverse communication styles and preferences","platforms seeking to increase user engagement through personalization","multilingual or multicultural user bases with different communication norms","users with communication disabilities (e.g., dyslexia) who benefit from adapted response formats"],"limitations":["Preference inference from conversation history is noisy; users may vary their communication style based on emotional state, context, or mood","Adapting to user style may reinforce unhelpful patterns (e.g., if user communicates in avoidant language, AI mirrors avoidance rather than encouraging expression)","No explicit user control over preference learning; users may not realize AI is adapting to their style and may attribute changes to other factors","Preference learning requires sufficient conversation history (typically 10+ messages); new users receive generic responses until preferences are learned","Cultural communication norms are not captured by simple metrics (message length, formality); system may misinterpret cultural differences as individual preferences"],"requires":["Conversation history database with message metadata (length, formality score, emotional intensity, emoji usage)","Preference inference model (rule-based or ML-based) to extract communication style from message patterns","Response generation system with tunable parameters (length, formality, tone) to adapt output","User feedback mechanism to correct preference inferences (optional: 'I prefer longer responses')"],"input_types":["user messages with metadata (length, language features, emotional tone)","conversation history for preference inference"],"output_types":["inferred user preferences (verbosity, formality, tone)","adapted response generation parameters"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Active phone number with SMS/voice capability (Twilio or equivalent carrier integration)","WhatsApp Business API account with webhook configuration","Speech-to-text API (Google Cloud Speech-to-Text or AWS Transcribe) for phone channel","Backend conversation state store (Redis or DynamoDB) with <500ms latency","User consent and HIPAA-compliant data handling infrastructure if operating in regulated jurisdictions","Pre-trained text classification model (fine-tuned BERT or RoBERTa on mental health datasets like CLPsych or DAIC-WOZ)","Curated knowledge base of 50+ evidence-based coping strategies with metadata (emotion category, difficulty level, time-to-benefit)","Semantic similarity search (embeddings-based retrieval) to match user context to relevant strategies","User consent to process emotional content; data retention policy for training data","Labeled training dataset of crisis vs non-crisis mental health conversations (CLPsych, DAIC-WOZ, or proprietary annotated data)"],"failure_modes":["No real-time crisis detection or emergency protocol escalation — cannot distinguish between venting and imminent self-harm risk","Voice-to-text transcription accuracy degrades with accents, background noise, or speech disorders; phone channel may misinterpret critical emotional cues","SMS channel truncates context to 160-character messages, forcing fragmented conversations that lose emotional nuance","No persistent conversation history across channel switches if backend session expires; users may repeat context","Latency varies by channel: SMS has 5-30 second delivery delays; WhatsApp 1-3 seconds; phone requires real-time processing adding 500-1500ms response time","Classification model trained on English-language mental health conversations; accuracy drops significantly for non-English inputs or culturally-specific emotional expressions","Cannot diagnose clinical conditions (depression, anxiety disorder, PTSD) — only identifies emotional states; risk of false negatives where serious conditions go undetected","Coping strategy recommendations are generic and not personalized to individual trauma history, medication interactions, or contraindicated techniques (e.g., exposure exercises for untreated PTSD)","No feedback loop to measure whether recommended strategies actually helped the user; system cannot learn from failed recommendations","Emotional state classification has ~15-20% error rate on ambiguous inputs where users express mixed emotions or sarcasm","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":"2026-05-24T12:16:29.716Z","last_scraped_at":"2026-04-05T13:23:42.561Z","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=clare-me","compare_url":"https://unfragile.ai/compare?artifact=clare-me"}},"signature":"7oMRA9iBhuCO2zi/IG+HpUvf8XWef9p6yaPDAuDjVF9OTBH0+KQjF61wgj+2XslT0dm/TNkLebKTUC6aivypBg==","signedAt":"2026-06-21T12:02:11.561Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/clare-me","artifact":"https://unfragile.ai/clare-me","verify":"https://unfragile.ai/api/v1/verify?slug=clare-me","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"}}