Descubre Habilidades de Claude para data science & ml. Explora 61 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Implements high-performance persistent memory and reinforcement learning patterns for AI agents using AgentDB.
Implements comprehensive evaluation frameworks to measure LLM application quality using automated metrics, human feedback, and comparative benchmarks.
Builds high-performance Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search to ground LLMs in external data.
Implements advanced prompt engineering techniques to maximize LLM performance, reliability, and reasoning capabilities in production environments.
Accelerates scientific discovery by generating hypotheses, identifying research gaps, and facilitating interdisciplinary ideation through structured collaborative dialogue.
Detects and analyzes system hardware to provide strategic architectural recommendations for computationally intensive scientific tasks.
Provides comprehensive financial frameworks for modeling, valuation, corporate finance decisions, and advanced statement analysis.
Train, deploy, and manage distributed neural networks within E2B sandboxes using the Flow Nexus ecosystem.
Implements high-performance adaptive learning and memory distillation for AI agents using the ultra-fast AgentDB vector engine.
Orchestrates multi-agent AI systems for parallel task execution and intelligent workflow coordination using dynamic topologies.
Analyzes phosphoproteomics data using R to identify differential post-translational modification abundance, stoichiometry, and kinase activity.
Integrates nine reinforcement learning algorithms to build, train, and deploy self-learning AI agents that evolve through experience.
Queries, interprets, and processes human genetic variant data from the NCBI ClinVar archive for genomic research.
Streamlines Snakemake pipeline development using modular patterns and compact rule definitions for reproducible data science.
Automates laboratory liquid handling and hardware control for Opentrons Flex and OT-2 robots using Protocol API v2.
Develops and optimizes Python-based liquid handling protocols for Opentrons Flex and OT-2 robots.
Facilitates seamless Python-based interaction with the B-Fabric Laboratory Information Management System for managing research data and workflows.
Generates high-performance interactive visualizations using Plotly Express and Graph Objects patterns.
Implements high-performance, Rust-powered tokenization for training and deploying custom NLP models with speed and precision.
Implements ultra-fast semantic vector search and RAG capabilities using AgentDB's high-performance indexing and quantization.
Streamlines single-cell RNA-seq analysis using standardized AnnData and Scanpy workflows.
Converts existing Snakemake analysis projects into pip-installable CLI tools for automated workflow deployment.
Analyzes system hardware to provide strategic recommendations for high-performance computing and scientific tasks.
Streamlines single-cell transcriptomics workflows using standardized scverse, AnnData, and Scanpy implementation patterns.
Provides expert guidance for building, configuring, and optimizing Retrieval-Augmented Generation (RAG) pipelines.
Generates interactive statistical and scientific visualizations using Plotly Express and Graph Objects APIs.
Accesses the NIH Metabolomics Workbench database to query metabolite structures, experimental study data, and standardized nomenclature.
Provides ultra-fast semantic vector search and intelligent document retrieval using AgentDB's high-performance HNSW indexing.
Generates optimized LlamaFarm configuration files from natural language descriptions for RAG and document processing workflows.
Generates publication-quality statistical graphics and complex data visualizations using the Seaborn Python library.
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