data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Analyzes time-series data using scaling theory to identify turbulence regimes, persistence, and long-range dependencies.
Analyzes symmetric bifurcations and symmetry-breaking patterns in complex dynamical systems and differential equations.
Analyzes and identifies sets within dynamical systems that are preserved by the flow of differential equations and manifolds.
Implements and analyzes agreement protocols within multi-agent dynamical systems using algebraic dynamics and topological principles.
Enables Claude to process, analyze, and generate audio, image, video, and document content using Google Gemini APIs.
Facilitates Galois adjunctions between local agent operations and global cognitive category theory using Mazzola’s mathematical music structures.
Optimizes AI outputs using research-backed prompting techniques to increase response quality and accuracy by up to 115%.
Integrates deterministic color generation with bisimulation game semantics for verifiable GF(3) conservation logic.
Automates professional spreadsheet creation, data analysis, and financial modeling with a focus on formula-driven logic.
Enhances AI response quality by 45-115% using research-backed techniques like monetary framing, expert personas, and step-by-step reasoning.
Navigates complex conceptual possibility spaces using type-theoretic bridge transitions and ordered locale structures.
Analyzes conversation threads and concept networks using advanced relational thinking and ACSet modeling.
Manages ergodic local updates using Blume-Capel dynamics and GF(3) conservation for topological graph rewriting.
Implements self-indexing automata and metabolic computational patterns at the edge of chaos using GF(3) logic.
Implements coordinate changes that preserve system dynamics within complex dynamical systems and manifolds.
Orchestrates triadic parallel agent dispatch using ordered locale theory and deterministic seeding for balanced AI workflows.
Leverages SAP's open-source tabular foundation model to perform predictive analytics on structured business data without model training.
Masters the art of prompting by treating LLMs like senior engineers through goal-oriented instructions and clear role definition.
Manages multi-model access, fallbacks, and cost-based routing using OpenRouter and LiteLLM.
Standardizes the integration and optimization of Large Language Models through live research protocols and production-grade engineering patterns.
Integrates Ollama into projects for local AI inference, model management, and streaming API implementation.
Optimizes LLM performance and reliability through advanced prompt engineering techniques like few-shot learning and chain-of-thought reasoning.
Ensures alignment between philosophical stances, analytical language, and research practices in qualitative studies.
Orchestrates end-to-end MLOps pipelines from data preparation and model training to production deployment and monitoring.
Implements optimized document splitting and processing workflows for Retrieval-Augmented Generation (RAG) systems.
Implements Renormalization Group (RG) flows using categorical ACSet semantics and topological XY model defect analysis.
Builds robust Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search to ground AI responses in proprietary data.
Automates the intelligent ingestion of Excel and Word documents into MXCP servers for structured querying and semantic search.
Automates the translation of MetaTrader 5 (MQL5) indicators into validated Python implementations for algorithmic trading.
Conducts rigorous statistical subgroup analyses to identify effect moderation and data heterogeneity across diverse research populations.
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