data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Performs constraint-based metabolic modeling and systems biology simulations using the COBRApy framework.
Engineers production-ready Agent Development Kit (ADK) applications with automated testing, multi-agent orchestration, and GCP deployment.
Automates the fine-tuning and adaptation of pre-trained machine learning models for new tasks and datasets.
Scales Python data processing and scientific computing across multiple cores or clusters for datasets that exceed available memory.
Architects production-ready autonomous AI agents and multi-agent systems using Google's Agent Development Kit and Claude.
Automates the configuration and integration of MLflow and Weights & Biases to streamline machine learning experiment management.
Analyzes textual data using natural language processing to extract sentiment, keywords, and core topics.
Provides programmatic access to global statistical datasets including demographic, economic, and environmental indicators via the Data Commons API.
Automates the complete scientific research lifecycle from initial data analysis to publication-ready LaTeX manuscripts.
Queries the Ensembl REST API to retrieve gene data, sequences, and variant analysis for over 250 species.
Builds professional investment banking-standard discounted cash flow (DCF) models in Excel with automated financial projections and sensitivity analysis.
Identifies outliers and unusual patterns in datasets using advanced machine learning algorithms.
Optimizes machine learning model performance by automatically searching for ideal hyperparameter configurations using advanced search strategies.
Integrates Codex CLI command patterns into Claude Code to enable advanced multi-agent planning and high-reasoning execution.
Constructs and configures custom neural network architectures including CNNs, RNNs, and Transformers directly within the Claude Code environment.
Provides comprehensive cheminformatics capabilities for molecular analysis, structural manipulation, and computational chemistry workflows.
Facilitates biological pathway analysis and gene-mapping by querying the Reactome open-source database and REST API.
Develops and trains sophisticated Graph Neural Networks (GNNs) for irregular data structures using the PyTorch Geometric library.
Manages academic citations and BibTeX entries by searching scholarly databases and validating metadata for research papers.
Processes and analyzes complex physiological signals including ECG, EEG, EDA, and more using the NeuroKit2 Python library.
Accesses and processes gene expression and functional genomics data from the NCBI Gene Expression Omnibus repository.
Automates the creation, editing, and analysis of professional spreadsheets with support for complex formulas and financial modeling standards.
Provides programmatic access to over 40 bioinformatics web services and databases for integrated biological data analysis and workflow automation.
Performs differential gene expression analysis for bulk RNA-seq data using the Python implementation of DESeq2.
Implements probabilistic deep learning models for comprehensive single-cell omics data analysis and multimodal integration.
Generates professional, publication-quality plots and charts using Python's foundational visualization library.
Implements rigorous evaluation frameworks for Large Language Model applications using automated metrics, LLM-as-judge patterns, and human feedback loops.
Configures and manages Mozilla Llamafile to run high-performance GGUF models locally with an OpenAI-compatible API.
Provides a specialized toolkit for applying machine learning to molecular property prediction, drug discovery, and materials science.
Exports FiftyOne computer vision datasets to industry-standard formats like COCO, YOLO, and VOC for machine learning training.
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