data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Performs comprehensive single-cell RNA-seq analysis workflows including quality control, clustering, and trajectory inference.
Infers gene regulatory networks from transcriptomics data using scalable algorithms like GRNBoost2 and GENIE3.
Automates computational molecular biology tasks including sequence manipulation, NCBI database queries, and structural analysis.
Build, fit, and validate complex Bayesian probabilistic models using the PyMC Python library and modern MCMC sampling techniques.
Integrates Google Gemini's native vision capabilities to analyze, summarize, and extract structured data from complex PDF documents.
Automates the generation and testing of scientific hypotheses by synthesizing empirical data and existing research literature.
Ensures deterministic results in Python research code by centralizing configurations, controlling randomness, and recording run metadata.
Accelerates reinforcement learning workflows with high-performance training, optimized environment vectorization, and seamless multi-agent support.
Analyzes machine learning training logs to visualize loss curves, detect training issues, and provide diagnostic insights.
Predicts 3D protein-ligand binding poses using state-of-the-art diffusion-based deep learning for drug discovery.
Accesses the ZINC22 database to search, filter, and retrieve over 230 million purchasable chemical compounds for drug discovery.
Performs advanced astronomical data analysis, coordinate transformations, and cosmological calculations using the core Astropy Python library.
Integrates real-time financial market data including stock quotes, forex rates, crypto prices, and company fundamentals via the FinnHub API.
Retrieve and analyze over 200 million AI-predicted protein structures from the AlphaFold DB for structural biology and drug discovery.
Processes and analyzes mass spectrometry data through spectral similarity, metadata harmonization, and automated workflows.
Builds and trains sophisticated Graph Neural Networks (GNNs) using the PyTorch Geometric library for irregular data structures.
Automates electronic lab notebook workflows including data uploads, notebook backups, and programmatic entry management via the LabArchives REST API.
Accesses and manages somatic mutation data from the COSMIC database for cancer research and precision oncology.
Streamlines molecular machine learning and drug discovery by providing specialized featurizers, graph neural networks, and chemical benchmark datasets.
Queries and analyzes large-scale single-cell genomics data from the CZ CELLxGENE Census repository.
Implement deep generative models for single-cell omics analysis, enabling batch correction, multimodal integration, and probabilistic differential expression.
Analyzes market data using Hurst exponent, GARCH models, and Markov regime detection to identify optimal trading symbols for quantitative strategies.
Performs comprehensive single-cell RNA-seq analysis workflows including quality control, clustering, and visualization.
Creates publication-quality statistical graphics and complex multi-panel data visualizations with minimal Python code.
Standardizes the implementation of Claude-powered AI assistants across the Insight Business Suite with built-in persona systems and BYOK management.
Simplifies gene data retrieval and annotation by querying the NCBI Gene database via E-utilities and Datasets APIs.
Accelerates reinforcement learning workflows with high-performance environment vectorization and optimized PPO training.
Provides a unified interface for rapid bioinformatics queries, genomic sequence analysis, and protein structure prediction across 20+ scientific databases.
Integrates persistent episodic and short-term memory into Amazon Bedrock agents to maintain user context and learn from interactions across sessions.
Integrates sophisticated large language model chat completions into backend applications using the z-ai-web-dev-sdk.
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