发现data science & ml类别的 Claude 技能。浏览 61 个技能,找到适合您 AI 工作流程的完美功能。
Builds, optimizes, and executes quantum circuits and algorithms across various hardware providers and simulators.
Standardizes the creation of R modeling packages by providing consistent preprocessing interfaces and output formatting patterns.
Facilitates the end-to-end development of sophisticated AI agents and stateful workflows using the LangGraph framework.
Performs differential gene expression analysis on bulk RNA-seq data using the PyDESeq2 statistical framework.
Enables parallel and distributed computing for Python data science workflows to process datasets larger than available memory.
Generates professional, publication-quality statistical graphics and complex multi-panel data visualizations using Python's Seaborn library.
Facilitates advanced probabilistic modeling and analysis of single-cell omics data using deep generative models.
Automates the analysis, processing, and visualization of Excel spreadsheets using Python-based data science libraries.
Analyzes mass spectrometry data for proteomics and metabolomics workflows using the PyOpenMS library.
Streamlines machine learning workflows in Python by providing expert guidance on scikit-learn algorithms, data preprocessing, and production-ready pipelines.
Streamlines machine learning workflows in R using the consistent and modular tidymodels ecosystem.
Manipulates R expressions and builds dynamic code using rlang's defuse and inject mechanics.
Processes and prepares whole slide pathology images for deep learning and digital pathology workflows.
Orchestrates advanced R data pipelines using complex branching patterns, custom target factories, and efficient iteration strategies.
Automates the creation, editing, and analysis of professional-grade Excel spreadsheets with precise formula management and financial modeling standards.
Designs and reviews R function APIs to ensure they are predictable, pipe-friendly, and follow Tidy Design Principles.
Streamlines R data visualization workflows with expert guidance on ggplot2 4.0 features and grammar of graphics implementation.
Converts chemical structures into numerical representations for machine learning using over 100 specialized featurizers and pretrained models.
Develops high-performance reinforcement learning systems with optimized PPO training, vectorized simulations, and multi-agent support.
Creates, modifies, and analyzes Excel spreadsheets with production-grade formulas, professional formatting, and financial modeling standards.
Performs hydrological modeling and streamflow forecasting using Julia-based classical and machine learning models.
Simplifies molecular machine learning workflows for drug discovery, property prediction, and materials science using DeepChem's specialized toolsets.
Optimizes AI agent performance through Anthropic-based context engineering and prompt structure standards.
Develops and optimizes quantum machine learning models and circuits across multiple hardware platforms using automatic differentiation.
Processes and visualizes high-throughput sequencing data to generate publication-quality genomic insights and quality control reports.
Provides specialized functions for hydrological modeling and climate data processing within the Julia environment.
Performs comprehensive statistical hypothesis testing, regression analysis, and Bayesian modeling with automated assumption checking and APA-style reporting.
Implements and optimizes reinforcement learning workflows using Stable Baselines3 to train reliable RL agents and design custom Gym environments.
Automates laboratory workflows and controls liquid handling robots, plate readers, and analytical equipment through a unified Python interface.
Streamlines the design and architecture of domain-specific AI agents using Claude Agent SDK patterns.
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