Descubre Habilidades de Claude para data science & ml. Explora 61 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Builds production-grade Apache Airflow pipelines using industry-standard best practices for orchestration and reliability.
Analyzes CSV files automatically to generate comprehensive statistical summaries and tailored visualizations using Python and pandas.
Analyzes high-dimensional single-cell gene expression data to identify cell types, states, and developmental trajectories.
Provides a standardized framework for developing specialized scientific research and data analysis capabilities within Claude Code.
Performs advanced manipulation and analysis of 2D planar geometric objects using standardized algorithms.
Models and analyzes large-scale metabolic networks in microorganisms using constraints-based reconstruction and flux balance analysis.
Simplifies physical and analytical chemistry tasks by automating chemical equation balancing, kinetic modeling, and equilibrium calculations.
Analyzes and manipulates complex network structures and graph algorithms using Python's leading network science library.
Analyzes protein dynamics, evolution, and structural flexibility using Elastic Network Models and structural ensemble analysis.
Builds and implements advanced AI-powered features using the latest Vercel AI SDK patterns and documentation.
Models complex real-world systems using process-based discrete-event simulation in Python.
Simulates open and closed quantum system dynamics using the Quantum Toolbox in Python framework.
Implements exact probability calculations and combinatorial counting patterns using a functional, Norvig-inspired approach.
Accelerates Python and NumPy code using Just-In-Time (JIT) compilation for machine-speed execution.
Manages and analyzes multi-dimensional labeled arrays and datasets for scientific computing and physical sciences.
Formulates and solves complex mathematical optimization problems using a natural Pythonic syntax for linear, integer, and non-linear models.
Integrates state-of-the-art machine learning models for natural language processing, computer vision, and scientific data analysis using the Hugging Face ecosystem.
Builds sophisticated, interactive, and data-driven visualizations using the D3.js library for custom charts and complex diagrams.
Queries the ESS-DeepDive fusion database to retrieve field-level metadata and dataset file information for environmental science research.
Automates professional spreadsheet creation, complex financial modeling, and advanced data analysis within Claude Code.
Manipulates and analyzes genomic alignment and variant files using the pysam library for high-throughput sequencing pipelines.
Simplifies scientific video analysis by providing a NumPy-based interface for FFmpeg, motion estimation, and video quality metrics.
Analyzes and visualizes human neurophysiological data including EEG, MEG, and intracranial recordings using Python-based best practices.
Builds, trains, and optimizes high-performance neural networks and scientific computing models using the PyTorch framework.
Searches, retrieves, and parses environmental science datasets from the Department of Energy's ESS-DIVE repository.
Implements a decentralized context-sharing protocol for multi-agent systems using cryptographic sharding and Byzantine fault tolerance.
Facilitates seamless conversion between ESS-DIVE dataset identifiers and DOIs while providing geographic visualization tools.
Performs complex symbolic mathematical operations including calculus, equation solving, and algebraic manipulation using the SymPy library.
Deploys scientific models and data applications using high-performance FastAPI backends and interactive Streamlit frontends.
Accelerates Python and NumPy programs using composable transformations for high-performance machine learning and scientific simulations.
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