{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_belong-ai","slug":"belong-ai","name":"Belong AI","type":"product","url":"https://belong.life","page_url":"https://unfragile.ai/belong-ai","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_belong-ai__cap_0","uri":"capability://text.generation.language.disease.specific.conversational.mentorship.with.clinical.context.awareness","name":"disease-specific conversational mentorship with clinical context awareness","description":"Delivers personalized AI-driven mentorship conversations tailored to cancer or MS patient journeys by embedding disease-specific knowledge graphs, treatment protocols, and symptom progression patterns into the conversational model. The system maintains contextual awareness of individual patient disease stage, treatment type (chemotherapy, radiation, immunotherapy, DMTs), and psychosocial challenges through multi-turn dialogue state management, enabling responses that reference relevant clinical milestones and evidence-based coping strategies without requiring explicit medical diagnosis input per conversation.","intents":["Get emotional support and practical coping advice specific to my cancer treatment stage without waiting for a therapist appointment","Understand what to expect during MS disease progression and how other patients have managed similar symptoms","Receive validation and mentorship from an AI that understands the specific isolation and identity challenges of my diagnosis","Access 24/7 support during acute treatment phases when anxiety spikes outside clinical hours"],"best_for":["Cancer patients in active treatment seeking supplementary emotional support between oncology appointments","MS patients managing disease progression and medication side effects with limited access to specialized mental health providers","Patients in remote or underserved areas without proximity to cancer support groups or MS clinics","Caregivers seeking to understand patient emotional needs and communication strategies"],"limitations":["Cannot provide medical diagnosis, treatment recommendations, or medication adjustments — relies on user-provided clinical context which may be incomplete or inaccurate","Effectiveness depends entirely on quality and recency of disease-specific training data; outdated protocols or emerging treatments may not be represented","No clinical validation published showing measurable improvements in patient mental health outcomes, anxiety reduction, or quality-of-life metrics","Lacks integration with electronic health records (EHR) or oncology/neurology clinical systems, requiring manual context entry by patients","Cannot detect acute medical crises (sepsis, MS relapse) requiring emergency intervention; no escalation pathway to human clinicians"],"requires":["User self-identification of disease type (cancer subtype or MS type) and current treatment phase","Internet connectivity for real-time conversation","Willingness to share personal health information with third-party AI system (privacy/HIPAA considerations)","Ability to articulate emotional or practical concerns in conversational format"],"input_types":["natural language text describing symptoms, treatment experiences, emotional states, or practical questions"],"output_types":["conversational text responses with disease-specific guidance, validation, and coping strategies"],"categories":["text-generation-language","memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_belong-ai__cap_1","uri":"capability://memory.knowledge.persistent.patient.context.and.conversation.history.management","name":"persistent patient context and conversation history management","description":"Maintains a patient-specific conversational memory system that tracks treatment history, emotional patterns, previously discussed coping strategies, and personal goals across multiple sessions. The system uses session-based state management to recall prior conversations, recognize recurring concerns (e.g., chemotherapy anxiety, fatigue management), and build longitudinal understanding of patient progress without requiring users to re-explain their situation. Context is stored server-side with encryption and user-controlled retention policies.","intents":["Have the AI remember my cancer treatment timeline and not ask me to re-explain my diagnosis every conversation","Track which coping strategies I've already tried so the mentor can suggest new approaches","See patterns in my emotional responses across weeks or months to identify what triggers anxiety or depression","Build a continuous mentoring relationship where the AI knows my personal goals and celebrates progress"],"best_for":["Patients seeking longitudinal support over months of treatment who want continuity without repetition","Users wanting to track emotional patterns and coping strategy effectiveness over time","Patients with complex treatment histories (multiple surgeries, chemotherapy regimens, medication changes) requiring contextual recall"],"limitations":["Conversation history retention depends on user subscription tier; free tier may have limited history access","No integration with clinical EHR systems means treatment updates must be manually entered by patient, risking stale or inaccurate context","Privacy model unclear — unclear whether conversation data is used for model training, sold to third parties, or retained after account deletion","Context window limitations may prevent recall of very early conversations in long-term patient relationships (>100+ sessions)"],"requires":["User account creation and authentication","Persistent storage backend (database) with encryption at rest","User consent for data retention and privacy policy acceptance"],"input_types":["natural language conversation text","user-provided treatment dates, medication names, symptom descriptions"],"output_types":["contextually-aware conversational responses referencing prior sessions","optional: conversation summaries, emotional pattern reports, progress tracking visualizations"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_belong-ai__cap_2","uri":"capability://text.generation.language.empathetic.response.generation.with.clinical.sensitivity","name":"empathetic response generation with clinical sensitivity","description":"Generates conversational responses using fine-tuned language models trained on patient testimonials, clinical psychology principles, and disease-specific communication patterns to produce emotionally validating, non-judgmental mentorship. The system applies safety filters to avoid harmful medical advice while maintaining empathetic tone, using techniques like sentiment-aware response ranking and clinical guideline constraints to ensure responses acknowledge patient suffering without overstepping into medical decision-making or false reassurance.","intents":["Receive validation and emotional support that acknowledges the real difficulty of my diagnosis without toxic positivity or minimization","Get responses that feel human and understanding rather than robotic or dismissive of my concerns","Discuss fears about treatment side effects, mortality, or identity loss without judgment or alarm","Receive practical coping strategies grounded in what other patients have found helpful"],"best_for":["Patients seeking emotional validation and psychological support between therapy sessions","Users in crisis or acute distress who need immediate empathetic response (though not replacement for crisis hotlines)","Patients uncomfortable disclosing sensitive concerns (sexuality, body image, mortality) to human providers initially"],"limitations":["Empathy is simulated through language patterns, not genuine understanding; responses may feel validating but lack authentic human connection","Safety filters may over-constrain responses, producing generic or evasive answers to legitimate medical concerns","No ability to detect suicidal ideation or acute mental health crises requiring human intervention; no escalation to crisis services","Training data bias: if training corpus underrepresents certain patient demographics (e.g., BIPOC cancer patients, LGBTQ+ MS patients), responses may be less culturally sensitive","Cannot provide crisis intervention or emergency mental health support despite patient perception of 24/7 availability"],"requires":["Fine-tuned language model trained on patient testimonials and clinical psychology datasets","Safety filter implementation (content moderation, medical guideline constraints)","Sentiment analysis or response ranking system to select empathetic variants","Clear disclaimers that AI is not a substitute for human mental health care"],"input_types":["natural language text expressing emotions, concerns, or experiences"],"output_types":["empathetic conversational text with validation, coping strategies, and normalization of patient experiences"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_belong-ai__cap_3","uri":"capability://memory.knowledge.disease.specific.knowledge.base.retrieval.and.recommendation","name":"disease-specific knowledge base retrieval and recommendation","description":"Implements a retrieval-augmented generation (RAG) system that grounds conversational responses in a curated knowledge base of disease-specific information including treatment protocols, symptom management strategies, patient testimonials, and clinical guidelines. The system uses semantic search to retrieve relevant knowledge snippets based on user query intent, then synthesizes retrieved information into conversational responses with source attribution. Knowledge base is updated periodically with new clinical evidence and patient-contributed content.","intents":["Get evidence-based information about my specific cancer type or MS disease-modifying therapy without having to search medical literature","Learn what symptoms or side effects are normal for my treatment stage based on clinical guidelines and patient experiences","Discover coping strategies that other patients with my specific diagnosis have found effective","Understand emerging treatments or clinical trials relevant to my disease subtype"],"best_for":["Patients seeking reliable, disease-specific health information without medical training to evaluate source credibility","Users wanting to understand treatment options and side effects before clinical appointments","Patients in rare cancer subtypes or MS variants with limited community support resources"],"limitations":["Knowledge base currency depends on update frequency; emerging treatments or clinical trial data may lag 6-12 months behind publication","No integration with ClinicalTrials.gov or real-time clinical evidence databases; trial recommendations may be outdated","Retrieval quality depends on knowledge base curation; if sources are biased toward certain treatment modalities or patient demographics, recommendations will reflect that bias","Cannot provide personalized medical advice based on individual patient genetics, comorbidities, or treatment history; recommendations are population-level only","Source attribution may be unclear or missing, making it difficult for users to verify information or discuss with their oncologist"],"requires":["Curated knowledge base of disease-specific clinical guidelines, treatment protocols, and patient testimonials","Semantic search engine (vector embeddings, similarity matching) to retrieve relevant knowledge snippets","RAG pipeline to synthesize retrieved information into conversational responses","Regular knowledge base updates (quarterly or semi-annual) with new clinical evidence"],"input_types":["natural language queries about symptoms, treatments, side effects, or coping strategies"],"output_types":["conversational text synthesizing retrieved knowledge with source attribution","optional: links to clinical guidelines, patient testimonials, or educational resources"],"categories":["memory-knowledge","search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_belong-ai__cap_4","uri":"capability://planning.reasoning.personalized.coping.strategy.recommendation.and.tracking","name":"personalized coping strategy recommendation and tracking","description":"Generates and tracks personalized coping strategy recommendations based on patient-reported symptoms, emotional patterns, and prior strategy effectiveness. The system uses behavioral pattern analysis to identify which coping approaches (mindfulness, journaling, social connection, physical activity) have worked for the individual patient in past sessions, then recommends new strategies aligned with patient preferences and disease-specific challenges. Tracks strategy adoption and perceived effectiveness through follow-up conversations to refine recommendations over time.","intents":["Get personalized suggestions for managing my anxiety or depression based on what has worked for me before, not generic advice","Discover new coping strategies tailored to my specific symptoms and lifestyle constraints","Track which coping strategies I've tried and how effective they were to identify patterns","Build a personalized toolkit of evidence-based and patient-tested approaches for managing my disease"],"best_for":["Patients seeking behavioral support for anxiety, depression, or adjustment challenges during cancer treatment or MS management","Users wanting to experiment with coping strategies in a low-stakes environment before committing to therapy or medication","Patients with limited access to mental health providers who need practical self-management tools"],"limitations":["Recommendations are based on patient self-report of effectiveness, not objective outcome measures; placebo effect or recall bias may inflate perceived benefit","Cannot diagnose depression, anxiety, or other mental health conditions; relies on user self-identification of emotional challenges","Coping strategy recommendations are general behavioral suggestions, not clinical interventions; cannot replace therapy or psychiatric medication","No integration with wearable devices or objective health metrics (sleep, activity, heart rate) to validate strategy effectiveness","Tracking is conversational only; no structured assessment tools (PHQ-9, GAD-7) to measure clinical outcomes"],"requires":["Patient willingness to report on coping strategy attempts and perceived effectiveness","Behavioral pattern analysis system to identify strategy effectiveness trends","Database of evidence-based and patient-tested coping strategies indexed by symptom type and patient preference","Follow-up conversation prompts to track strategy adoption and refine recommendations"],"input_types":["natural language descriptions of symptoms, emotional states, and coping strategy attempts","user ratings or feedback on strategy effectiveness"],"output_types":["personalized coping strategy recommendations with rationale","optional: coping strategy tracking dashboard, effectiveness trends, personalized toolkit summary"],"categories":["planning-reasoning","data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_belong-ai__cap_5","uri":"capability://memory.knowledge.patient.community.connection.and.peer.experience.sharing","name":"patient community connection and peer experience sharing","description":"Facilitates access to anonymized patient testimonials, shared experiences, and peer-validated coping strategies from a community of cancer and MS patients. The system retrieves relevant peer experiences based on disease type, treatment stage, and symptom similarity, presenting them as contextual examples of how other patients have navigated similar challenges. Optionally enables patients to contribute their own experiences (with anonymization and moderation) to build a growing repository of peer wisdom.","intents":["Learn how other patients with my specific cancer type or MS variant have managed similar symptoms or treatment side effects","Feel less alone by reading authentic patient stories and realizing my experiences are shared by others","Discover practical tips and coping strategies that real patients have found helpful, not just clinical recommendations","Contribute my own experience to help other patients facing similar challenges"],"best_for":["Patients seeking peer support and community connection without geographic barriers or scheduling constraints of in-person support groups","Users in rare cancer subtypes or MS variants with limited local support communities","Patients wanting to learn from diverse peer experiences before making treatment decisions or lifestyle changes"],"limitations":["Peer experiences are anecdotal and not clinically validated; individual patient outcomes may not generalize to other patients with same diagnosis","Anonymization may reduce trust or relatability compared to identified peer support (support groups, online forums with user profiles)","Moderation quality and bias unknown; unclear whether platform filters out harmful advice, misinformation, or experiences from patients with poor outcomes","No mechanism to verify that shared experiences are authentic or from actual patients (vs. fabricated testimonials)","Community size and diversity unknown; if community is small or skewed toward certain demographics, recommendations may not represent full spectrum of patient experiences"],"requires":["Database of anonymized patient testimonials and experiences indexed by disease type, treatment, and symptom","Semantic matching system to retrieve relevant peer experiences based on user query","Moderation system to filter harmful, misinformation, or inappropriate content","Optional: user contribution system with anonymization, consent, and quality review"],"input_types":["natural language queries about symptoms, treatments, or experiences","optional: user-submitted testimonials or experiences"],"output_types":["anonymized peer testimonials and experiences relevant to user query","conversational synthesis of peer experiences with common themes or patterns"],"categories":["memory-knowledge","search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_belong-ai__cap_6","uri":"capability://text.generation.language.treatment.side.effect.education.and.normalization","name":"treatment side effect education and normalization","description":"Provides disease and treatment-specific education about expected side effects, their typical timeline, severity ranges, and management strategies. The system uses clinical guidelines and patient testimonials to normalize common side effects (hair loss, neuropathy, fatigue, cognitive changes) and distinguish between expected effects and warning signs requiring medical attention. Delivers this information in empathetic, non-alarming language while clearly delineating what requires immediate clinical escalation.","intents":["Understand what side effects are normal for my specific chemotherapy regimen or MS medication and when to expect them","Learn practical strategies for managing common side effects like neuropathy, fatigue, or cognitive changes","Distinguish between expected side effects and warning signs that require calling my oncologist or neurologist","Feel less anxious about side effects by learning they are common and manageable for other patients"],"best_for":["Patients starting new cancer treatments or MS disease-modifying therapies who want to know what to expect","Users experiencing side effects and wanting to understand if they are normal or require medical attention","Patients seeking practical management strategies for expected side effects before clinical appointments"],"limitations":["Side effect profiles vary significantly by individual patient factors (age, comorbidities, genetics, prior treatments); population-level information may not apply to individual patient","Cannot provide personalized medical advice on whether a specific side effect requires emergency care; relies on user judgment and clear escalation guidelines","Information accuracy depends on knowledge base currency; new side effects or long-term effects of newer treatments may not be documented","May inadvertently normalize serious side effects or create false reassurance that delays appropriate medical care","Cannot replace oncologist or neurologist assessment of whether side effects warrant treatment modification"],"requires":["Disease and treatment-specific side effect information indexed by cancer type, chemotherapy regimen, or MS medication","Clinical guidelines on side effect severity, timeline, and warning signs requiring medical attention","Patient testimonials describing side effect experiences and management strategies","Clear escalation guidelines distinguishing expected effects from medical emergencies"],"input_types":["natural language queries about specific side effects or treatment concerns"],"output_types":["conversational education about side effect timeline, severity, management strategies, and escalation guidelines","optional: side effect management guides, symptom tracking tools, escalation decision trees"],"categories":["text-generation-language","memory-knowledge","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_belong-ai__cap_7","uri":"capability://text.generation.language.psychosocial.challenge.navigation.and.identity.support","name":"psychosocial challenge navigation and identity support","description":"Addresses disease-specific psychosocial challenges including identity disruption (hair loss, body image changes, disability identity), relationship strain, sexuality and fertility concerns, return-to-work challenges, and existential questions about mortality and meaning. The system uses empathetic, non-judgmental language to validate these challenges while offering practical strategies and peer perspectives. Acknowledges that these challenges are normal and significant, distinct from clinical depression or anxiety.","intents":["Discuss how cancer or MS has changed my identity and body image without judgment or toxic positivity","Navigate relationship strain with partner, family, or friends due to diagnosis and treatment demands","Explore sexuality and intimacy concerns related to treatment side effects or disability","Understand return-to-work challenges and how to communicate with employers about limitations","Grapple with existential questions about mortality, meaning, and life priorities without clinical pathologization"],"best_for":["Patients seeking to process identity and psychosocial impacts of diagnosis in a judgment-free space","Users uncomfortable discussing sensitive topics (sexuality, mortality, body image) with human providers initially","Patients wanting peer perspectives on navigating relationships, work, and identity during treatment"],"limitations":["Cannot provide therapy or clinical mental health treatment for depression, anxiety, or trauma; these require human providers","Responses are empathetic but simulated; lack authentic human understanding and lived experience of illness","May inadvertently minimize serious psychosocial challenges or suggest coping strategies insufficient for clinical-level distress","Cannot address complex relationship dynamics or family systems issues requiring couples or family therapy","Existential and spiritual questions may be inadequately addressed by AI; patients may need chaplaincy, spiritual direction, or existential therapy"],"requires":["Training data including patient testimonials about identity, relationships, sexuality, work, and existential challenges","Empathetic response generation with validation of psychosocial challenges as normal and significant","Knowledge base of practical strategies for navigating relationships, work accommodations, and identity integration","Clear boundaries and escalation pathways for clinical mental health concerns"],"input_types":["natural language sharing of identity concerns, relationship challenges, sexuality questions, work concerns, or existential reflections"],"output_types":["empathetic conversational responses validating challenges and offering practical strategies or peer perspectives"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_belong-ai__cap_8","uri":"capability://text.generation.language.multi.turn.conversational.context.management.with.disease.progression.awareness","name":"multi-turn conversational context management with disease progression awareness","description":"Manages complex multi-turn conversations that track disease progression, treatment changes, and evolving patient needs across extended dialogue. The system maintains awareness of conversation flow, recognizes when users are revisiting prior topics or introducing new concerns, and adapts response depth and focus based on conversation history. Detects transitions in disease stage (e.g., from active treatment to survivorship, or MS relapse to remission) and adjusts mentorship focus accordingly.","intents":["Have natural, flowing conversations where the AI understands context from earlier in our discussion without me repeating information","Transition from discussing active treatment challenges to survivorship concerns as my disease stage changes","Explore how my needs and concerns have evolved over months of treatment without starting from scratch each conversation","Receive mentorship that adapts to my current disease stage and treatment phase"],"best_for":["Patients in long-term treatment relationships wanting natural, contextually-aware conversations","Users transitioning between disease stages (active treatment to survivorship, relapse to remission) needing mentorship adaptation","Patients with complex treatment histories requiring conversation management across multiple topics and sessions"],"limitations":["Context window limitations may prevent recall of very early conversations or detailed context from >100+ sessions","Disease stage detection relies on user-provided information; system cannot access clinical records to verify treatment changes","Conversation flow management may fail with non-linear or tangential discussions; system may lose context if user jumps between topics","No explicit disease progression modeling; system relies on implicit pattern recognition rather than structured disease timeline"],"requires":["Multi-turn dialogue management system with context tracking and state management","Disease stage classification model to detect transitions (active treatment, survivorship, relapse, remission)","Conversation history retrieval and synthesis to maintain context across extended dialogues","Response generation that adapts depth, focus, and mentorship approach based on detected disease stage"],"input_types":["natural language conversation text across multiple turns and sessions"],"output_types":["contextually-aware conversational responses that reference prior discussion and adapt to disease stage"],"categories":["text-generation-language","memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_belong-ai__cap_9","uri":"capability://safety.moderation.safety.guardrails.and.medical.escalation.pathways","name":"safety guardrails and medical escalation pathways","description":"Implements content safety filters and escalation pathways to prevent harmful medical advice while maintaining empathetic support. The system uses rule-based and learned filters to identify high-risk scenarios (suicidal ideation, acute medical crises, medication errors) and provides clear escalation guidance to human providers or crisis services. Maintains explicit disclaimers that AI mentorship is supplementary and cannot replace clinical care, with clear boundaries on what the system can and cannot do.","intents":["Get support for emotional challenges while knowing the system will escalate if I'm in crisis","Receive clear guidance on when to contact my oncologist or neurologist vs. when AI support is sufficient","Understand that this AI is a supplement to, not replacement for, clinical care and human relationships","Know that the system won't give me dangerous medical advice or encourage me to avoid necessary treatment"],"best_for":["Patients seeking emotional support with confidence that safety guardrails prevent harmful advice","Healthcare systems wanting to integrate AI mentorship with clear liability boundaries and escalation pathways","Regulatory bodies requiring transparent safety mechanisms in patient-facing AI systems"],"limitations":["Safety filters may over-constrain responses, producing evasive or unhelpful answers to legitimate concerns","Cannot reliably detect all high-risk scenarios (suicidal ideation, medication errors, acute medical crises) through text analysis alone","Escalation pathways depend on user action (calling crisis line, contacting clinician); system cannot force escalation or contact providers directly","Liability model unclear: unclear whether platform or user bears responsibility if AI advice contributes to harm despite safety filters","Safety filters may have demographic bias, over-flagging concerns from certain patient groups while under-flagging others"],"requires":["Content moderation system with rule-based and learned filters for high-risk scenarios","Clear escalation guidelines and crisis resource links (suicide hotline, emergency services, oncology/neurology contact info)","Explicit disclaimers in user interface and terms of service about AI limitations and supplementary nature","Regular safety audits and adversarial testing to identify filter failures","Legal review of liability model and user responsibility"],"input_types":["natural language conversation text"],"output_types":["filtered conversational responses with safety constraints","escalation guidance and crisis resource links when high-risk scenarios detected"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["User self-identification of disease type (cancer subtype or MS type) and current treatment phase","Internet connectivity for real-time conversation","Willingness to share personal health information with third-party AI system (privacy/HIPAA considerations)","Ability to articulate emotional or practical concerns in conversational format","User account creation and authentication","Persistent storage backend (database) with encryption at rest","User consent for data retention and privacy policy acceptance","Fine-tuned language model trained on patient testimonials and clinical psychology datasets","Safety filter implementation (content moderation, medical guideline constraints)","Sentiment analysis or response ranking system to select empathetic variants"],"failure_modes":["Cannot provide medical diagnosis, treatment recommendations, or medication adjustments — relies on user-provided clinical context which may be incomplete or inaccurate","Effectiveness depends entirely on quality and recency of disease-specific training data; outdated protocols or emerging treatments may not be represented","No clinical validation published showing measurable improvements in patient mental health outcomes, anxiety reduction, or quality-of-life metrics","Lacks integration with electronic health records (EHR) or oncology/neurology clinical systems, requiring manual context entry by patients","Cannot detect acute medical crises (sepsis, MS relapse) requiring emergency intervention; no escalation pathway to human clinicians","Conversation history retention depends on user subscription tier; free tier may have limited history access","No integration with clinical EHR systems means treatment updates must be manually entered by patient, risking stale or inaccurate context","Privacy model unclear — unclear whether conversation data is used for model training, sold to third parties, or retained after account deletion","Context window limitations may prevent recall of very early conversations in long-term patient relationships (>100+ sessions)","Empathy is simulated through language patterns, not genuine understanding; responses may feel validating but lack authentic human connection","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"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":"2026-05-24T12:16:29.714Z","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=belong-ai","compare_url":"https://unfragile.ai/compare?artifact=belong-ai"}},"signature":"e1eAlgN8ZYx3dgbwOstKPcWU6hj2/HrOWc7xX/H3Uqi2Dznud44YVXya54awjA5X92YpnHcL7A+Z70KSCpBuBw==","signedAt":"2026-06-22T13:27:17.668Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/belong-ai","artifact":"https://unfragile.ai/belong-ai","verify":"https://unfragile.ai/api/v1/verify?slug=belong-ai","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"}}