data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Analyzes historical time-series data to predict future values and identify temporal patterns like seasonality and trends.
Builds and deploys production-ready generative AI agents leveraging Google Cloud's Vertex AI and Gemini models.
Accesses and processes gene expression and functional genomics data from the NCBI Gene Expression Omnibus repository.
Generates high-performance text embeddings via the Google Gemini API for semantic search, RAG, and data classification tasks.
Simplifies Python-based LLM interactions by providing a unified interface for over 100 cloud and local providers using the OpenAI format.
Configures and manages Mozilla Llamafile to run high-performance GGUF models locally with an OpenAI-compatible API.
Implements probabilistic deep learning models for comprehensive single-cell omics data analysis and multimodal integration.
Provides programmatic access to over 40 bioinformatics web services and databases for integrated biological data analysis and workflow automation.
Builds professional investment banking-standard discounted cash flow (DCF) models in Excel with automated financial projections and sensitivity analysis.
Implements systematic evaluation strategies for Large Language Model applications using automated metrics, LLM-as-judge patterns, and statistical testing.
Builds sophisticated recommendation systems using collaborative filtering, content-based algorithms, and hybrid machine learning models.
Integrates Google's Gemini API suite into Claude Code for multimodal analysis, image generation, and real-time search grounding.
Enhances LLM performance and reliability through advanced techniques like Chain-of-Thought, structured outputs, and few-shot learning.
Accesses and queries the PubChem database via PUG-REST API and PubChemPy for comprehensive chemical informatics and compound analysis.
Composes complex 3D environments and terrains for robotic simulation and reinforcement learning training.
Implements a compositional AI framework based on category theory and GF(3) triadic balance for deterministic, self-modifying agent architectures.
Facilitates the development, simulation, and control of 3D-printed humanoid robots for reinforcement learning research.
Implements time-symmetric, information-preserving computation patterns for Janus-style reversible logic and quantum-ready algorithms.
Orchestrates self-learning signal processing and spectral exploration using software-defined radio (SDR) and categorical database integration.
Unifies mathematical topology with computational agency to detect solitons and bootstrap self-aware agentic skills.
Implements directed point-free topology using frames and preorders satisfying the open cone condition.
Calculates molecular complexity using Assembly Theory to identify biosignatures and validate chemical pathways.
Analyzes OpenStreetMap road networks using graph theory and GF(3) topological coloring for robust geographic data processing.
Bridges the gap between robotic simulations and real-world deployment using maximum entropy reinforcement learning and information-theoretic alignment.
Analyzes and solves Ordinary Differential Equations (ODEs) by applying existence and uniqueness theorems within dynamical systems.
Generates deterministic, scientifically-consistent colors and parallel-invariant fingerprints for Julia-based data visualizations.
Implements high-performance persistent memory and reinforcement learning patterns for AI agents using AgentDB and ReasoningBank.
Provides hardware specifications, MuJoCo MJCF models, and deployment workflows for the K-Scale flagship humanoid robot platform.
Analyzes graph topology and connectivity using the Ihara zeta function and non-backtracking spectral analysis.
Analyzes and models dynamical systems that vary with external parameters to understand qualitative shifts and stability.
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