data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Streamlines the creation of AI-powered features through expert prompt engineering, RAG patterns, and multi-model API integrations.
Performs ultra-fast portfolio backtesting and trading strategy analysis using the Polars library.
Builds sophisticated LLM applications using the LangChain framework with advanced agent, memory, and tool integration patterns.
Implements decentralized prediction markets for pattern discovery based on curiosity-driven compression progress metrics.
Analyzes and implements map projections using category theory and distortion metrics for precise geospatial transformations.
Facilitates high-performance multi-agent coordination through environmental stigmergy and trace-based state modification instead of message passing.
Facilitates computational category theory and operadic composition for advanced AI modeling and string diagram manipulation.
Implements comprehensive evaluation frameworks for LLM applications using automated metrics, human feedback, and benchmarking strategies.
Implements Darwin Gödel Machine patterns to create self-improving AI agents capable of open-ended evolution and lifelong learning.
Automates video metadata extraction, thumbnail generation, web-optimized transcoding, and audio extraction with integrated DuckDB tracking.
Facilitates building distributed cognitive agents and portable WebAssembly applications using the GNU Scheme ecosystem including Guile, Goblins, Hoot, and Fibers.
Reduces 3-SAT problems to colored subgraph isomorphism using local geodesic constraints and GF(3) conservation.
Creates advanced, interactive, and declarative data visualizations using HoloViews and the HoloViz ecosystem for complex data exploration.
Orchestrates distributed LLM inference across Apple Silicon clusters using RDMA and MLX sharding.
Analyzes social network dynamics to trace idea adoption, influence flow, and interperspectival relationships.
Implements interventional and counterfactual reasoning patterns for deliberate System 2 deep learning and causal world modeling.
Calculates the time average of observables along trajectories to analyze the long-term qualitative behavior of dynamical systems.
Facilitates chaotic context injection by performing interleaved random walks across multiple DuckDB database clusters using coupled pendulum dynamics.
Optimizes multi-turn AI conversations by reducing token usage through advanced summarization and context management techniques.
Composes complex dynamical systems and resource sharers using categorical colimits and operad algebra.
Facilitates the development, simulation, and control of 3D-printed humanoid robots for reinforcement learning research.
Bridges Scholze-Clausen condensed mathematics and analytic stacks with sheaf neural networks for advanced topological computation.
Designs and generates high-performance pipelines for synthesizing high-quality LLM training datasets, conversations, and structured data.
Composes complex 3D environments and terrains for robotic simulation and reinforcement learning training.
Implements a recursive, autopoietic loop for state management that synchronizes memory storage, pattern-matching recall, and generative world-building.
Implements a compositional AI framework based on category theory and GF(3) triadic balance for deterministic, self-modifying agent architectures.
Automates safe, structure-preserving self-modification for AI agents using covariant transport and Darwin Gödel Machine evolution loops.
Provides hardware specifications, MuJoCo MJCF models, and deployment workflows for the K-Scale flagship humanoid robot platform.
Generates visual phase portraits and vector fields for 2D dynamical systems to analyze state space behavior.
Formalizes Martin Buber's relational philosophy using category theory and homotopy type theory to enhance AI social intelligence.
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