data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Generates advanced financial projections, Monte Carlo simulations, and tax-optimized strategies for active trading portfolios.
Conducts comprehensive pre-submission reviews for journal manuscripts, orchestrating statistical validation, bias detection, and compliance checks.
Builds and validates complex Bayesian models for probabilistic inference using PyMC and ArviZ.
Accesses and retrieves gene expression and functional genomics data from the NCBI Gene Expression Omnibus (GEO) repository.
Accelerates data manipulation and analysis using the high-performance Polars DataFrame library and its optimized expression-based API.
Conducts rigorous, systematic evaluations of scientific manuscripts and grant proposals across various research disciplines.
Simplifies molecular cheminformatics and drug discovery workflows using a Pythonic interface for RDKit.
Performs end-to-end single-cell RNA-seq analysis workflows including quality control, clustering, and cell type annotation using the Scanpy toolkit.
Applies medicinal chemistry rules and structural filters to prioritize compound libraries for drug discovery.
Converts over 20 diverse file formats into LLM-optimized Markdown for seamless data processing and RAG integration.
Queries and retrieves comprehensive genomic data from NCBI Gene databases for biological research and bioinformatics.
Identifies and analyzes profitable cryptocurrency arbitrage opportunities across centralized and decentralized exchanges in real-time.
Manages biological datasets with full lineage tracking, ontology-based validation, and FAIR compliance.
Performs constraint-based metabolic modeling and analysis for systems biology and metabolic engineering using the COBRApy library.
Guides the architectural design and refactoring of AI agents into focused, single-purpose specialists to maximize performance and accuracy.
Automates complex biomedical research tasks including genomics analysis, drug discovery, and clinical data interpretation through autonomous AI agents.
Streamlines machine learning workflows for genomic interval data using region embeddings, single-cell analysis, and consensus peak building.
Manages annotated data matrices and metadata for single-cell genomics and large-scale biological datasets in Python.
Conducts advanced machine learning research for Radio Access Networks using reinforcement learning, causal inference, and cognitive frameworks.
Streamlines computational molecular biology tasks and bioinformatics workflows using the Biopython library.
Enables advanced geospatial vector data analysis, geometric operations, and spatial mapping within Python environments.
Automates the design, configuration, and implementation of complex neural network architectures for various machine learning tasks.
Executes high-performance computational fluid dynamics simulations and analysis using pseudospectral methods in Python.
Implements high-performance persistent memory and pattern learning for stateful AI agents using AgentDB.
Optimizes 5G RAN mobility and handover performance using cognitive AI and predictive trajectory modeling.
Accesses and manages AI-ready drug discovery datasets, benchmarks, and molecular oracles for therapeutic machine learning.
Implements adaptive learning and meta-cognitive systems to enable AI agents to recognize patterns and optimize strategies through experience.
Simulates and analyzes quantum mechanical systems using the Quantum Toolbox in Python (QuTiP).
Enables high-performance distributed vector search and multi-agent coordination using QUIC synchronization and hybrid search.
Empowers AI agents to learn and improve through experience using 9 specialized reinforcement learning algorithms and WASM-accelerated inference.
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