Descubre Habilidades de Claude para data science & ml. Explora 61 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Automates dataset analysis and cleaning by detecting data types, identifying quality issues, and generating Python scripts for standardized data preparation.
Optimizes LLM context windows to maximize reasoning quality while minimizing token costs and latency.
Provides specialized guidance for molecular analysis, structural manipulation, and chemical property calculation using RDKit.
Facilitates automated protein design, sequence optimization, and wet-lab validation via a cloud-based laboratory platform.
Streamlines molecular machine learning workflows for drug discovery, property prediction, and materials science using the DeepChem library.
Performs constraint-based metabolic modeling and systems biology simulations using the COBRApy Python library.
Performs state-of-the-art diffusion-based molecular docking to predict 3D binding poses of small molecule ligands to protein targets.
Performs high-performance computational fluid dynamics simulations and spectral analysis using Python.
Develops, trains, and deploys specialized machine learning models for healthcare and clinical data analysis using standardized EHR datasets and medical coding systems.
Processes gigapixel whole slide images to automate tissue detection and tile extraction for digital pathology.
Infers gene regulatory networks from transcriptomics data using scalable algorithms like GRNBoost2 and GENIE3.
Performs probabilistic modeling and deep generative analysis for single-cell omics data using the scvi-tools framework.
Integrates Google's Gemini models into your workflow for advanced reasoning and multi-perspective code analysis.
Builds and manages discrete-event simulations in Python for modeling complex systems like logistics, manufacturing, and network traffic.
Processes and generates multimedia content including audio, video, images, and documents using the Google Gemini API.
Access and download NCBI Gene Expression Omnibus (GEO) datasets for transcriptomics and genomics research.
Cleans, reshapes, and preprocesses datasets locally using pandas, numpy, and sklearn for any LLM provider.
Architects, simulates, and optimizes quantum circuits for execution on high-performance simulators and real quantum hardware using Google's open-source framework.
Analyzes, cleans, and visualizes Excel spreadsheet data using Python libraries like pandas and openpyxl.
Integrates ChromaDB to store embeddings and perform high-performance semantic search for AI applications.
Generates and validates executable Python behavior trees for robotic systems using natural language task descriptions.
Simplifies molecular cheminformatics workflows with a Pythonic interface for RDKit, handling molecular standardization, descriptors, and 3D conformers.
Facilitates advanced protein design, structure prediction, and representation learning using ESM3 and ESM C models.
Simplifies molecular cheminformatics and drug discovery workflows using a Pythonic interface for RDKit.
Generates publication-quality scientific plots and charts using Matplotlib and Seaborn for local execution.
Provides comprehensive tools for protein sequence generation, structure prediction, and representation learning using ESM3 and ESM C models.
Performs constraint-based reconstruction and analysis of metabolic models for systems biology and metabolic engineering.
Builds, simulates, and optimizes quantum circuits for Google Quantum AI and other leading hardware providers.
Generates interactive, publication-quality scientific and statistical visualizations using the Plotly Python library.
Predicts high-accuracy 3D protein-ligand binding poses using diffusion-based deep learning for structure-based drug discovery.
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