Build recommendation systems in Python using collaborative filtering, matrix factorization with SVD, and hybrid methods. Overcome cold start and sparsity challenges while evaluating performance via precision@K and recall@K metrics.
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npx claudepluginhub secondsky/claude-skills --plugin recommendation-engineThis skill provides comprehensive guidance for SAP BTP Job Scheduling Service development, configuration, and operations. It should be used when creating, managing, or troubleshooting scheduled jobs on SAP Business Technology Platform. The skill covers service setup, REST API usage, schedule types and formats, OAuth 2.0 authentication, multitenancy, Cloud Foundry tasks, Kyma runtime integration, and monitoring with SAP Cloud ALM and Alert Notification Service. Keywords: SAP BTP, Job Scheduling, jobscheduler, cron, schedule, recurring jobs, one-time jobs, Cloud Foundry tasks, CF tasks, Kyma, OAuth 2.0, XSUAA, @sap/jobs-client, REST API, asynchronous jobs, action endpoint, run logs, SAP Cloud ALM, Alert Notification Service, multitenancy, tenant-aware, BC-CP-CF-JBS
secondskyProduction-ready SAP BTP best practices for enterprise architecture, account management, security, and operations. Use when planning BTP implementations, setting up account hierarchies, configuring environments, implementing authentication, designing CI/CD pipelines, establishing governance, building Platform Engineering teams, implementing failover strategies, or managing application lifecycle on SAP BTP. Keywords: SAP BTP, account hierarchy, global account, directory, subaccount, Cloud Foundry, Kyma, ABAP, SAP Identity Authentication, CI/CD, governance, Platform Engineering, failover, multi-region, SAP BTP best practices
secondskyGuides development with SAP AI Core and SAP AI Launchpad for enterprise AI/ML workloads on SAP BTP. Use when: deploying generative AI models (GPT, Llama, Gemini, Mistral), building orchestration workflows with templating/filtering/grounding, implementing RAG with vector databases, managing ML training pipelines with Argo Workflows, configuring content filtering and data masking for PII protection, using the Generative AI Hub for prompt experimentation, or integrating AI capabilities into SAP applications. Covers service plans (Free/Standard/Extended), model providers (Azure OpenAI, AWS Bedrock, GCP Vertex AI, Mistral, IBM), orchestration modules, embeddings, tool calling, and structured outputs.
secondskySAP BTP Cloud Integration Automation Service (CIAS) skill for guided integration workflows. Use when: setting up CIAS subscriptions, configuring destinations, assigning roles (CIASIntegrationAdministrator, CIASIntegrationExpert, CIASIntegrationMonitor), planning integration scenarios, working with My Inbox tasks, monitoring scenario execution, troubleshooting CIAS errors, creating OAuth2 instances, configuring identity providers for CIAS, understanding CIAS security architecture, or integrating SAP products (S/4HANA, SuccessFactors, BTP services, SAP Build, IBP).
secondskySAP HANA Machine Learning Python Client (hana-ml) development skill. Use when: Building ML solutions with SAP HANA's in-database machine learning using Python hana-ml library for PAL/APL algorithms, DataFrame operations, AutoML, model persistence, and visualization. Keywords: hana-ml, SAP HANA, machine learning, PAL, APL, predictive analytics, HANA DataFrame, ConnectionContext, classification, regression, clustering, time series, ARIMA, gradient boosting, AutoML, SHAP, model storage
secondskyBuild recommendation systems and engines
jeremylongshoreDeploy production recommendation systems with feature stores, caching, A/B testing. Use for personalization APIs, low latency serving, or encountering cache invalidation, experiment tracking, quality monitoring issues.
secondskyUse this agent when implementing AI/ML features, integrating language models, building recommendation systems, or adding intelligent automation to applications. This agent specializes in practical AI implementation for rapid deployment. Examples:\n\n<example>\nContext: Adding AI features to an app\nuser: "We need AI-powered content recommendations"\nassistant: "I'll implement a smart recommendation engine. Let me use the ai-engineer agent to build an ML pipeline that learns from user behavior."\n<commentary>\nRecommendation systems require careful ML implementation and continuous learning capabilities.\n</commentary>\n</example>\n\n<example>\nContext: Integrating language models\nuser: "Add an AI chatbot to help users navigate our app"\nassistant: "I'll integrate a conversational AI assistant. Let me use the ai-engineer agent to implement proper prompt engineering and response handling."\n<commentary>\nLLM integration requires expertise in prompt design, token management, and response streaming.\n</commentary>\n</example>\n\n<example>\nContext: Implementing computer vision features\nuser: "Users should be able to search products by taking a photo"\nassistant: "I'll implement visual search using computer vision. Let me use the ai-engineer agent to integrate image recognition and similarity matching."\n<commentary>\nComputer vision features require efficient processing and accurate model selection.\n</commentary>\n</example>
ananddtyagiUse this agent when evaluating new development tools, frameworks, or services for the studio. This agent specializes in rapid tool assessment, comparative analysis, and making recommendations that align with the 6-day development cycle philosophy. Examples:\n\n<example>\nContext: Considering a new framework or library
ccpluginsBusiness analysis with data storytelling and KPI dashboard design
EricGrillUse this agent when implementing AI/ML features, integrating language models, building recommendation systems, or adding intelligent automation to applications. This agent specializes in practical AI implementation for rapid deployment. Examples:\n\n<example>\nContext: Adding AI features to an app\nuser: "We need AI-powered content recommendations"\nassistant: "I'll implement a smart recommendation engine. Let me use the ai-engineer agent to build an ML pipeline that learns from user behavior."\n<commentary>\nRecommendation systems require careful ML implementation and continuous learning capabilities.\n</commentary>\n</example>\n\n<example>\nContext: Integrating language models\nuser: "Add an AI chatbot to help users navigate our app"\nassistant: "I'll integrate a conversational AI assistant. Let me use the ai-engineer agent to implement proper prompt engineering and response handling."\n<commentary>\nLLM integration requires expertise in prompt design, token management, and response streaming.\n</commentary>\n</example>\n\n<example>\nContext: Implementing computer vision features\nuser: "Users should be able to search products by taking a photo"\nassistant: "I'll implement visual search using computer vision. Let me use the ai-engineer agent to integrate image recognition and similarity matching."\n<commentary>\nComputer vision features require efficient processing and accurate model selection.\n</commentary>\n</example>
devsforge