{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_xiaobenyang-com-prediction-examples","slug":"xiaobenyang-com-prediction-examples","name":"prediction-examples","type":"repo","url":"https://github.com/xiaobenyang-com/1804079084099594","page_url":"https://unfragile.ai/xiaobenyang-com-prediction-examples","categories":["data-analysis"],"tags":["mcp","model-context-protocol","smithery:xiaobenyang-com/prediction-examples"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_xiaobenyang-com-prediction-examples__cap_0","uri":"capability://data.processing.analysis.annual.gmv.prediction.modeling","name":"annual gmv prediction modeling","description":"This capability utilizes historical sales data and advanced regression techniques to predict annual Gross Merchandise Value (GMV). It integrates time series analysis with machine learning algorithms to identify trends and seasonality, allowing businesses to forecast growth efficiently. The model is designed to adapt to new data inputs, improving accuracy over time through continuous learning.","intents":["How can I forecast my company's annual sales growth based on past performance?","What tools can help me analyze my sales data for better predictions?","I need to assess the future revenue potential of my business."],"best_for":["business analysts looking to enhance revenue forecasting accuracy"],"limitations":["Requires a substantial amount of historical sales data for effective modeling","Sensitivity to outliers can skew predictions"],"requires":["Python 3.8+","Pandas and Scikit-learn libraries"],"input_types":["structured data (CSV, JSON)"],"output_types":["structured data (predicted GMV values)"],"categories":["data-processing-analysis","business-intelligence"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_xiaobenyang-com-prediction-examples__cap_1","uri":"capability://data.processing.analysis.sentiment.analysis.of.customer.reviews","name":"sentiment analysis of customer reviews","description":"This capability analyzes customer reviews using natural language processing (NLP) techniques to classify sentiments as positive, negative, or neutral. It leverages pre-trained language models to extract contextual meanings and emotional tones from text, providing businesses with actionable insights into customer feedback. The system can be fine-tuned with domain-specific data to improve sentiment classification accuracy.","intents":["How can I determine the overall sentiment of customer feedback on my products?","What insights can I gain from analyzing customer reviews?","I want to identify negative sentiments in customer feedback to address issues."],"best_for":["marketing teams seeking to improve customer satisfaction"],"limitations":["Performance may degrade on highly specialized or niche language without fine-tuning","Requires a significant amount of labeled training data for optimal results"],"requires":["Python 3.7+","NLTK or SpaCy libraries"],"input_types":["text (customer reviews)"],"output_types":["structured data (sentiment scores and classifications)"],"categories":["data-processing-analysis","customer-insights"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_xiaobenyang-com-prediction-examples__cap_2","uri":"capability://data.processing.analysis.key.insights.integration.for.decision.making","name":"key insights integration for decision-making","description":"This capability synthesizes data from various sources to provide key insights that inform marketing and product decisions. It employs data aggregation techniques and visualization tools to present findings in an easily digestible format. By integrating insights from sales data, customer feedback, and market trends, businesses can make informed strategic decisions quickly.","intents":["How can I consolidate data from different sources to make better business decisions?","What tools can help me visualize key insights from my data?","I need to integrate customer feedback with sales data for strategic planning."],"best_for":["product managers and marketers looking to enhance decision-making processes"],"limitations":["Dependent on the quality and availability of data from integrated sources","Visualization capabilities may be limited to specific formats"],"requires":["Python 3.8+","Matplotlib or Seaborn for visualization"],"input_types":["structured data (CSV, JSON)"],"output_types":["visual reports, structured data (insight summaries)"],"categories":["data-processing-analysis","business-intelligence"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","Pandas and Scikit-learn libraries","Python 3.7+","NLTK or SpaCy libraries","Matplotlib or Seaborn for visualization"],"failure_modes":["Requires a substantial amount of historical sales data for effective modeling","Sensitivity to outliers can skew predictions","Performance may degrade on highly specialized or niche language without fine-tuning","Requires a significant amount of labeled training data for optimal results","Dependent on the quality and availability of data from integrated sources","Visualization capabilities may be limited to specific formats","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.16,"ecosystem":0.48999999999999994,"match_graph":0.25,"freshness":0.9,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:28.694Z","last_scraped_at":"2026-05-03T15:19:37.910Z","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=xiaobenyang-com-prediction-examples","compare_url":"https://unfragile.ai/compare?artifact=xiaobenyang-com-prediction-examples"}},"signature":"KTVWCs+Bp5N5rU+DikReY0RXzMb9GI5MI8U7p8tF4+opujAoPIXmCAk3CLwQLawZ9oBeWi8oJnMv65s0x4JqAg==","signedAt":"2026-06-16T02:26:15.195Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/xiaobenyang-com-prediction-examples","artifact":"https://unfragile.ai/xiaobenyang-com-prediction-examples","verify":"https://unfragile.ai/api/v1/verify?slug=xiaobenyang-com-prediction-examples","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"}}