data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Enables natural language data exploration and conversational analytics through AI-powered agents and automated SQL generation.
Simplifies astronomical data analysis and processing using the Astropy ecosystem for coordinates, units, and FITS files.
Analyzes and processes labeled multidimensional scientific datasets using Xarray, Dask, and geospatial extensions.
Streamlines scientific Python development by managing complex conda and PyPI dependencies with fast, reproducible Pixi environments.
Enables high-performance rasterization and visualization for massive datasets exceeding 100 million points using Datashader and HoloViews.
Creates complex, interactive, and multi-dimensional data visualizations using the HoloViz ecosystem.
Creates interactive, high-performance geographic visualizations and spatial data analyses using GeoViews and GeoPandas.
Generates interactive, publication-quality visualizations and dashboards using the HoloViz ecosystem.
Standardizes the creation and distribution of Python packages using modern Hatchling and PEP 621 conventions for scientific computing.
Master color management and accessible visual styling using perceptually uniform Colorcet palettes for scientific data visualizations.
Implements declarative, type-safe parameter systems with automatic validation and reactive UI generation for Python applications.
Orchestrates complex data pipelines and automated workflows using Apache Airflow's DAG-based architecture and extensive operator ecosystem.
Architects production-grade LLM applications using advanced LangChain orchestration patterns, agents, and complex memory systems.
Architects and optimizes scalable, distributed data pipelines and analytics systems using the Apache Spark framework.
Implements production-grade machine learning lifecycles using MLflow for experiment tracking, model registration, and multi-cloud deployment patterns.
Searches the arXiv preprint repository for scholarly articles across scientific fields like machine learning, physics, and mathematics.
Automates Benchling R&D platform operations including sequence registry management, laboratory inventory tracking, and electronic lab notebook (ELN) documentation.
Orchestrates complex multi-agent workflows and AI-powered programs using a specialized language where the LLM acts as the runtime.
Automates professional-grade spreadsheet creation, financial modeling, and data analysis with dynamic formula preservation and error-free validation.
Recommends optimal document chunking strategies to improve retrieval quality and accuracy in RAG pipelines.
Audits and optimizes Retrieval-Augmented Generation (RAG) implementations for performance, accuracy, and production readiness.
Generates production-ready Retrieval-Augmented Generation (RAG) pipeline boilerplate code with integrated best practices.
Evaluates RAG system performance through automated metrics, LLM-as-judge scoring, and competitive benchmarking.
Automates the identification and resolution of performance discrepancies between Python and AILANG benchmark success rates.
Analyzes molecular structures, chemical reactions, and material properties through a rigorous scientific and analytical lens.
Builds production-ready AI agents using Anthropic's official framework for tool orchestration and autonomous workflows.
Analyzes complex events and social systems through the lens of structural-functionalism, conflict theory, and symbolic interactionism.
Analyzes living systems and life sciences phenomena through evolutionary, molecular, and ecological frameworks.
Analyzes complex events through a rigorous chemistry lens, applying principles of molecular structure, reaction mechanisms, and thermodynamics.
Analyzes social events and structures using rigorous sociological frameworks and theoretical perspectives.
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