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Classifies feedback as positive, negative, or neutral with contextual understanding.","intents":["I need to prioritize which customer issues are causing the most frustration","I want to identify my most satisfied customers from their feedback","I need to track sentiment trends over time to measure satisfaction changes"],"best_for":["customer support managers","customer success teams","product teams"],"limitations":["May struggle with sarcasm, irony, or culturally specific expressions","Requires context to distinguish between constructive criticism and complaints","Less accurate with very short feedback snippets"],"requires":["Text-based customer feedback","Sufficient context in feedback for accurate tone detection","Understanding that sentiment is probabilistic, not absolute"],"input_types":["text"],"output_types":["sentiment scores","emotional classifications","tone labels"],"categories":["customer-support","data-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_viable__cap_3","uri":"capability://customer.support.multi.source.feedback.aggregation.and.normalization","name":"multi-source feedback aggregation and normalization","description":"Consolidates customer feedback from multiple sources (Zendesk, Intercom, Slack, surveys, etc.) into a single unified dataset for analysis. 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Creates trend visualizations and highlights what's changing over time.","intents":["I need a weekly report of what customers are saying without manually reviewing everything","I want to show my leadership team the top customer issues in a clear format","I need to track how customer sentiment is changing month-over-month"],"best_for":["product managers","customer support managers","executives"],"limitations":["Reports are only as good as the underlying data quality","May oversimplify complex customer issues into high-level summaries","Requires interpretation—automated insights still need human validation"],"requires":["Sufficient historical data for trend analysis","Regular feedback collection to enable meaningful comparisons","Time to review and validate automated insights"],"input_types":["tagged feedback","categorized data","sentiment scores"],"output_types":["reports","trend visualizations","summary documents"],"categories":["customer-support","reporting","analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_viable__cap_5","uri":"capability://customer.support.custom.category.and.taxonomy.creation","name":"custom category and taxonomy creation","description":"Allows users to define custom tagging schemes, category hierarchies, and domain-specific taxonomies tailored to their business needs. Enables AI to learn and apply these custom categories to new feedback.","intents":["I need to tag feedback using my company's specific product categories, not generic ones","I want to create a custom taxonomy that matches how my team thinks about customer issues","I need to train the AI to understand my industry-specific terminology"],"best_for":["product teams","customer support managers","researchers"],"limitations":["Requires upfront effort to define meaningful categories","AI performance depends on quality of category definitions and examples","Steeper learning curve than using default categories"],"requires":["Clear understanding of desired categorization scheme","Time to define categories and provide training examples","Willingness to iterate and refine categories based on results"],"input_types":["category definitions","training examples","taxonomy structures"],"output_types":["custom tagging rules","category hierarchies"],"categories":["customer-support","configuration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_viable__cap_6","uri":"capability://customer.support.feedback.search.and.filtering","name":"feedback search and filtering","description":"Enables users to search, filter, and query the aggregated feedback database using natural language or structured filters. Quickly locate specific feedback by theme, sentiment, source, or custom criteria.","intents":["I need to find all feedback about a specific feature or bug","I want to see only negative feedback from the past month","I need to locate customer feedback mentioning a competitor"],"best_for":["customer support teams","product managers","researchers"],"limitations":["Search quality depends on how well feedback was tagged and categorized","Natural language search may miss relevant feedback with different wording","Large datasets may return too many results without precise filters"],"requires":["Indexed feedback database","Clear tagging and categorization for effective filtering","Understanding of available filter options"],"input_types":["search queries","filter criteria"],"output_types":["filtered feedback results","matching records"],"categories":["customer-support","search"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_viable__cap_7","uri":"capability://customer.support.actionable.insight.generation.for.product.teams","name":"actionable insight generation for product teams","description":"Transforms raw feedback patterns into specific, prioritized recommendations for product improvements and feature development. 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Highlights what customers say about how your product compares to alternatives.","intents":["I want to know what competitors customers mention in their feedback","I need to understand why customers choose us over alternatives","I want to track competitive positioning based on customer perception"],"best_for":["product managers","marketing teams","competitive intelligence teams"],"limitations":["Only captures competitive mentions that customers explicitly state","May miss implied comparisons or indirect references","Requires context to understand sentiment toward competitors"],"requires":["Feedback that includes customer comparisons or alternative mentions","Sufficient volume to identify patterns","Understanding that this reflects customer perception, not market reality"],"input_types":["text feedback"],"output_types":["competitor mentions","comparison summaries","positioning insights"],"categories":["customer-support","competitive-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_viable__cap_9","uri":"capability://customer.support.feedback.quality.assessment.and.data.validation","name":"feedback quality assessment and data validation","description":"Evaluates the quality and relevance of feedback entries, identifying spam, duplicates, off-topic comments, or low-quality data. 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