Descubre Habilidades de Claude para data science & ml. Explora 61 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Integrates Google's Gemini models into your workflow for advanced reasoning and multi-perspective code analysis.
Performs probabilistic modeling and deep generative analysis for single-cell omics data using the scvi-tools framework.
Infers gene regulatory networks from transcriptomics data using scalable algorithms like GRNBoost2 and GENIE3.
Processes gigapixel whole slide images to automate tissue detection and tile extraction for digital pathology.
Generates structured, data-driven scientific research reports by analyzing repository data and verifying metrics against technical standards.
Orchestrates end-to-end machine learning pipelines from automated data preparation through model training, validation, and production deployment.
Develops, trains, and deploys specialized machine learning models for healthcare and clinical data analysis using standardized EHR datasets and medical coding systems.
Performs high-performance computational fluid dynamics simulations and spectral analysis using Python.
Implements high-performance similarity search and vector database patterns for AI-driven applications.
Performs state-of-the-art diffusion-based molecular docking to predict 3D binding poses of small molecule ligands to protein targets.
Performs constraint-based metabolic modeling and systems biology simulations using the COBRApy Python library.
Streamlines molecular machine learning workflows for drug discovery, property prediction, and materials science using the DeepChem library.
Decomposes complex computational problems into three balanced, parallel components using GF(3) conservation and sheaf-theoretic gluing.
Facilitates automated protein design, sequence optimization, and wet-lab validation via a cloud-based laboratory platform.
Provides specialized guidance for molecular analysis, structural manipulation, and chemical property calculation using RDKit.
Optimizes LLM context windows to maximize reasoning quality while minimizing token costs and latency.
Simplifies the creation, orchestration, and deployment of production-grade AI agents using Google's Agent Development Kit.
Automates complex spreadsheet creation, financial modeling, and data analysis with formula integrity and industry-standard formatting.
Generates standardized Python scripts for analyzing model performance and creating publication-ready LaTeX tables and figures.
Streamlines machine learning data preprocessing in R using standardized Tidymodels recipes patterns.
Standardizes machine learning model wrappers to provide a uniform interface for training, inference, and automated hyperparameter tuning.
Manages a complete 4-stage pipeline from Python scripts to interactive notebooks and automated markdown documentation.
Manages end-to-end data pipelines across four stages from raw source ingestion to AI-ready datasets.
Facilitates the interactive drafting and refinement of LaTeX research papers through structured incubator documents and canonical data tracking.
Integrates React and Vite applications with the Bodhi JS SDK to enable local LLM chat capabilities and seamless authentication.
Optimizes LLM performance and reliability through advanced prompting techniques like few-shot learning and chain-of-thought reasoning.
Analyzes German U19 badminton ranking data from the DBV directly within Claude Code.
Streamlines machine learning model endpoint deployment through unified packaging, inference function design, and multi-target deployment.
Develops production-grade AI applications using the OpenAI Agents SDK with multi-agent handoffs, voice integration, and robust error prevention.
Manages and orchestrates end-to-end neural network pipelines across algorithmic, tuning, and deployment layers.
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