data science & ml Claude 스킬을 발견하세요. 53개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Creates, modifies, and analyzes Excel spreadsheets with production-grade formulas, professional formatting, and financial modeling standards.
Performs hydrological modeling and streamflow forecasting using Julia-based classical and machine learning models.
Designs resilient contingency module architectures to manage failure scenarios within AI governance frameworks.
Translates trading strategy documentation into production-ready Python backtesting code and TradingView Pine Script.
Analyzes market trends and technical indicators to provide actionable trading insights and detailed risk assessments.
Designs adaptive user-facing agent experts to create personalized product experiences and dynamic UX.
Streamlines the development, validation, and systematic documentation of trading strategies and market edges.
Synchronizes and caches comprehensive Charles Schwab market data, including account status, real-time quotes, and option chains for trading agents.
Creates and manages professional spreadsheets with dynamic formulas, financial formatting standards, and advanced data analysis capabilities.
Predicts high-accuracy 3D protein-ligand binding poses using diffusion-based deep learning for structure-based drug design.
Performs high-performance nonlinear dimensionality reduction for data visualization, clustering preprocessing, and supervised manifold learning.
Manipulates genomic datasets and processes Next-Generation Sequencing (NGS) files using a Pythonic interface to htslib.
Connects Claude's cognitive orchestration to physical robotics and sensor systems using low-latency control and cryptographic integrity.
Provides comprehensive tools for astronomical data analysis, coordinate transformations, and cosmological calculations within Python environments.
Analyzes biological data including sequences, phylogenetic trees, and microbial diversity metrics using specialized Python tools.
Empowers AI agents to learn and improve through experience using 9 specialized reinforcement learning algorithms and WASM-accelerated inference.
Enables high-performance distributed vector search and multi-agent coordination using QUIC synchronization and hybrid search.
Simulates and analyzes quantum mechanical systems using the Quantum Toolbox in Python (QuTiP).
Implements adaptive learning and meta-cognitive systems to enable AI agents to recognize patterns and optimize strategies through experience.
Accesses and manages AI-ready drug discovery datasets, benchmarks, and molecular oracles for therapeutic machine learning.
Processes and prepares whole slide pathology images for deep learning and digital pathology workflows.
Optimizes 5G RAN mobility and handover performance using cognitive AI and predictive trajectory modeling.
Implements high-performance persistent memory and pattern learning for stateful AI agents using AgentDB.
Executes high-performance computational fluid dynamics simulations and analysis using pseudospectral methods in Python.
Enables advanced geospatial vector data analysis, geometric operations, and spatial mapping within Python environments.
Applies structured self-analysis and governance protocols to evaluate and validate AI decision-making processes.
Streamlines computational molecular biology tasks and bioinformatics workflows using the Biopython library.
Conducts advanced machine learning research for Radio Access Networks using reinforcement learning, causal inference, and cognitive frameworks.
Manages annotated data matrices and metadata for single-cell genomics and large-scale biological datasets in Python.
Guides the architectural design and refactoring of AI agents into focused, single-purpose specialists to maximize performance and accuracy.
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