Descubre Habilidades de Claude para data science & ml. Explora 61 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Accesses and analyzes ES/NQ futures market data using Databento's high-fidelity platform with a cost-optimized workflow.
Simplifies High Energy Physics analysis by integrating Python vector libraries with Awkward Arrays for efficient kinematic calculations.
Detects narrative manipulation and propaganda patterns in text and URLs using the Narrative Credibility Index (NCI) Protocol.
Generates auditable demand forecasts and time-series data artifacts from CSV files using automated discovery and classification.
Generates implementation code for high-speed LLM inference using Cerebras through LiteLLM and OpenRouter.
Automates the creation, editing, and analysis of professional Excel spreadsheets with advanced formula support and industry-standard financial modeling.
Empowers AI agents with adaptive learning and pattern recognition to optimize workflows and decision-making strategies through experience.
Implements comprehensive evaluation frameworks for LLM applications using automated metrics, human feedback, and LLM-as-judge patterns.
Analyzes legacy Thai DBF accounting databases by converting them to Parquet for high-performance DuckDB querying.
Trains and deploys complex neural networks across distributed E2B sandbox environments using custom architectures or pre-built templates.
Builds sophisticated LLM applications using LangChain's agents, memory management, and complex chain patterns.
Orchestrates end-to-end machine learning pipelines from data preparation and training to production deployment and monitoring.
Implements advanced LLM prompt engineering techniques to maximize model performance, reliability, and controllability in production applications.
Builds production-ready Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search to ground AI responses in external data.
Implement comprehensive evaluation frameworks for LLM applications using automated metrics, human feedback, and benchmarking.
Orchestrates end-to-end MLOps pipelines from data preparation and training to validation and production deployment.
Builds robust Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search to ground AI responses in external data.
Implements comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and comparative benchmarking.
Orchestrates end-to-end MLOps pipelines from data preparation and model training to production deployment and monitoring.
Provides strategies, implementation patterns, and workflows for genomics and transcriptomics data analysis.
Implements queue-based GPU allocation and memory cleanup patterns to prevent OOM crashes and ensure reliable progress tracking in parallel workflows.
Streamlines scientific development on HPC environments using multi-root workspaces and automated test data extraction.
Analyzes protein and molecular structures through AlphaFold interpretation, quality validation metrics, and comparative structural techniques.
Manages and routes requests across multiple AI providers including Anthropic, Ollama, and HuggingFace.
Analyzes global investments through the lens of power structures, ethical constraints, and geopolitical alignments.
Streamlines the development of AI-powered features within the Moodle LMS using the official AI Subsystem for version 4.5 and above.
Prevents Jupyter notebook hangs by explicitly managing CuPy GPU memory pools and Python garbage collection.
Implements comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and comparative benchmarking.
Architects sophisticated LLM applications using LangChain patterns for agents, memory management, and complex workflow orchestration.
Reconstructs multiplex microscopy images by correctly ordering tiles acquired via snake or serpentine stage patterns.
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