Descubre Habilidades de Claude para data science & ml. Explora 53 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Automates complex biomedical research tasks including genomics, drug discovery, and clinical analysis through autonomous reasoning and code execution.
Processes and analyzes high-performance genomic interval data using Rust-powered algorithms and Python bindings.
Streamlines astronomical data analysis and astrophysical calculations using the core Astropy Python library.
Processes and analyzes comprehensive physiological signals including ECG, EEG, and EDA for psychophysiology and clinical research.
Analyzes single-cell omics data using the scvi-tools framework for probabilistic modeling and batch correction.
Enables advanced protein engineering, sequence generation, and structure prediction using Evolutionary Scale Modeling.
Performs constraint-based reconstruction and analysis of metabolic models for systems biology and metabolic engineering.
Enables advanced protein engineering through generative design, structure prediction, and high-performance embeddings using ESM3 and ESM C models.
Analyzes Excel spreadsheets, generates pivot tables, and creates data visualizations using Python libraries like pandas and openpyxl.
Automates scientific hypothesis generation and testing by combining observational data with literature-based insights using large language models.
Simplifies the creation, manipulation, and analysis of complex networks and graph data structures in Python.
Simplifies high-performance computational fluid dynamics (CFD) simulations and analysis using Python and pseudospectral methods.
Simplifies PDF manipulation, data extraction, and document generation using industry-standard Python libraries and CLI tools.
Simplifies molecular cheminformatics and drug discovery workflows using a Pythonic wrapper for RDKit.
Converts diverse file formats including PDF, Office docs, and media into clean, LLM-optimized Markdown.
Automates complex biomedical research tasks including genomic analysis, drug discovery, and clinical interpretation using an autonomous agent framework.
Builds, simulates, and executes quantum circuits using Google’s open-source framework for NISQ-era quantum computers.
Retrieves genomic, transcriptomic, and proteomic data from 20+ bioinformatics databases using a unified interface.
Transforms, cleans, and reshapes complex datasets locally using industry-standard Python libraries like pandas and numpy.
Predicts 3D protein-ligand binding poses using state-of-the-art diffusion models for structure-based drug discovery.
Streamlines computational molecular biology tasks including sequence analysis, NCBI database queries, and structural bioinformatics using the Biopython toolkit.
Integrates and manages Pinecone vector databases for production-grade AI applications and low-latency semantic search.
Integrates Google's Gemini models into your terminal workflow for advanced code analysis and complex multi-model reasoning.
Generates interactive, publication-quality scientific and statistical visualizations using the Plotly Python library.
Processes and prepares gigapixel whole slide images for digital pathology and machine learning workflows.
Builds complex discrete-event simulations in Python for modeling processes, resource contention, and time-based systems.
Accesses and integrates data from over 40 bioinformatics web services and databases using a unified Python API.
Automates laboratory workflows by controlling liquid handling robots, plate readers, and analytical equipment through a hardware-agnostic Python interface.
Processes and analyzes high-throughput sequencing data to generate publication-quality visualizations and quality control metrics for genomics research.
Automates complex scientific research workflows and tool discovery across bioinformatics, genomics, and drug discovery domains.
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