data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Automates the creation of robust data ingestion providers using standardized patterns for registry mapping, storage abstraction, and metadata tracking.
Simulates high-performance computational fluid dynamics using Python-based pseudospectral methods and MPI support.
Simulates complex fluid dynamics using high-performance Python pseudospectral solvers for Navier-Stokes and geophysical flows.
Applies advanced machine learning to chemistry, biology, and materials science for drug discovery and molecular property prediction.
Applies machine learning to chemistry, biology, and materials science for drug discovery and molecular property prediction.
Performs constraint-based reconstruction and analysis of metabolic models for systems biology and metabolic engineering.
Builds process-based discrete-event simulations in Python to model complex systems, resource contention, and queue behaviors.
Builds complex discrete-event simulations in Python to model systems with processes, queues, and shared resources.
Generates standardized Jupyter notebooks for fantasy football data analysis, integrating DuckDB connections, dbt mart queries, and professional visualization patterns.
Streamlines bioinformatics workflows by providing a unified CLI and Python interface to over 20 genomic and proteomic databases.
Models and simulates complex discrete-event systems using Python generator functions and shared resource management.
Executes structured, atomic tasks for fantasy football analytics, FASA optimization, and trade intelligence within a standardized sprint framework.
Infers large-scale gene regulatory networks from transcriptomics data using scalable GRNBoost2 and GENIE3 algorithms.
Generates interactive, publication-quality scientific and statistical charts using the Python Plotly library.
Predicts high-accuracy 3D protein-ligand binding poses using diffusion-based deep learning for structure-based drug discovery.
Generates interactive, publication-quality scientific and statistical visualizations using the Plotly Python library.
Builds, simulates, and optimizes quantum circuits for Google Quantum AI and other leading hardware providers.
Performs constraint-based reconstruction and analysis of metabolic models for systems biology and metabolic engineering.
Provides comprehensive tools for protein sequence generation, structure prediction, and representation learning using ESM3 and ESM C models.
Generates publication-quality scientific plots and charts using Matplotlib and Seaborn for local execution.
Simplifies molecular cheminformatics and drug discovery workflows using a Pythonic interface for RDKit.
Facilitates advanced protein design, structure prediction, and representation learning using ESM3 and ESM C models.
Simplifies molecular cheminformatics workflows with a Pythonic interface for RDKit, handling molecular standardization, descriptors, and 3D conformers.
Integrates ChromaDB to store embeddings and perform high-performance semantic search for AI applications.
Architects, simulates, and optimizes quantum circuits for execution on high-performance simulators and real quantum hardware using Google's open-source framework.
Cleans, reshapes, and preprocesses datasets locally using pandas, numpy, and sklearn for any LLM provider.
Access and download NCBI Gene Expression Omnibus (GEO) datasets for transcriptomics and genomics research.
Processes and generates multimedia content including audio, video, images, and documents using the Google Gemini API.
Builds and manages discrete-event simulations in Python for modeling complex systems like logistics, manufacturing, and network traffic.
Integrates Google's Gemini models into your workflow for advanced reasoning and multi-perspective code analysis.
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