data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Simulates and analyzes Last Passage Percolation models to study Tracy-Widom fluctuations, geodesics, and KPZ universality.
Orchestrates complex AI skill ecosystems using category theory, topological synthesis, and deterministic GF(3) conservation.
Decomposes complex computational problems into three GF(3)-balanced components for optimized parallel execution and sheaf-theoretic gluing.
Queries the Open Targets Platform to identify and prioritize therapeutic drug targets through human genetics and clinical evidence.
Builds and manages complex signal processing flowgraphs and custom Python blocks for Software Defined Radio (SDR) applications.
Generates publication-ready scientific figures with journal-specific styling, colorblind-safe palettes, and high-resolution export formats.
Implements self-indexing automata and metabolic computation models at the intersection of quantum and classical logic.
Enables structured generation and algebraic composition of n-ary operations using colored operads.
Implements massively parallel functional computation using interaction nets and GPU-accelerated graph reduction.
Guides R developers in choosing and implementing the optimal OOP system, including S7, S3, S4, and vctrs.
Generates and evolves topological code patterns through autopoietic interaction and color-based seeds.
Performs complex algebraic graph rewriting over Attributed C-Sets (ACSets) using DPO, SPO, and SqPO methodologies.
Verifies mathematical conservation laws and manages autopoietic logic within topological computational frameworks.
Implements high-performance LLVM-level automatic differentiation for Julia code on both CPU and GPU architectures.
Implements adaptive learning and high-performance trajectory tracking for autonomous agents using the AgentDB vector backend.
Accesses and analyzes NCBI Gene Expression Omnibus (GEO) data for transcriptomics and functional genomics research.
Facilitates topological diffusion and autopoietic learning patterns within the Model Context Protocol framework.
Implements and optimizes reinforcement learning agents using the Stable Baselines3 library for PyTorch.
Provides unified access to over 400 AI models through a single, standardized API with intelligent routing and automatic fallbacks.
Bridges neural network sparsity research with Unison’s content-addressed ability system to optimize sparse computation and scaling.
Integrates the COSMIC cancer mutation database into bioinformatics workflows for somatic mutation analysis and precision oncology research.
Analyzes planetary intelligence and crypto-geographic data using AI2 OlmoEarth foundation models optimized for Apple Silicon.
Linearizes non-linear dynamical systems using Koopman operator theory to generate predictive models from observables.
Integrates the Reactome pathway database to perform biological enrichment analysis, gene mapping, and molecular interaction queries for systems biology research.
Processes mass spectrometry data, standardizes metadata, and calculates spectral similarities for metabolomics workflows.
Analyzes and evaluates LLM prompts using a structured 10-Layer Architecture to improve clarity, precision, and output performance.
Facilitates self-evolving topological aggregations and pattern recognition within the Gay MCP framework.
Facilitates topological data integration and autopoietic skill evolution within the worldmat framework.
Computes shortest paths on spherical surfaces and Riemannian manifolds for navigation, aviation routing, and spatial analysis.
Facilitates creative scientific ideation by generating hypotheses, exploring interdisciplinary connections, and challenging research assumptions.
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