data science & ml向けのClaudeスキルを発見してください。61個のスキルを閲覧し、AIワークフローに最適な機能を見つけましょう。
Orchestrates distributed LLM inference across Apple Silicon clusters using RDMA and MLX sharding.
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.
Facilitates building distributed cognitive agents and portable WebAssembly applications using the GNU Scheme ecosystem including Guile, Goblins, Hoot, and Fibers.
Automates video metadata extraction, thumbnail generation, web-optimized transcoding, and audio extraction with integrated DuckDB tracking.
Implements Darwin Gödel Machine patterns to create self-improving AI agents capable of open-ended evolution and lifelong learning.
Facilitates computational category theory and operadic composition for advanced AI modeling and string diagram manipulation.
Facilitates high-performance multi-agent coordination through environmental stigmergy and trace-based state modification instead of message passing.
Analyzes and implements map projections using category theory and distortion metrics for precise geospatial transformations.
Implements decentralized prediction markets for pattern discovery based on curiosity-driven compression progress metrics.
Streamlines the creation of AI-powered features through expert prompt engineering, RAG patterns, and multi-model API integrations.
Computes and visualizes monoidal categories, quantum circuits, and natural language structures using string diagrams.
Standardizes variable summation and entity aggregation within the PolicyEngine microsimulation framework.
Refactors, cleans, and optimizes Jupyter notebooks to improve code readability, maintainability, and reproducibility.
Automates the creation of robust data cleaning and preprocessing pipelines for Python-based data science workflows.
Provides PhD-level guidance on research ethics, IRB compliance, and data privacy analysis for study protocols and algorithmic models.
Models and analyzes dynamical systems by assigning vectors to points in phase space to define complex flows and trajectories.
Applies category theory and sheaves on tree decompositions to solve complex combinatorial problems with Fixed-Parameter Tractable (FPT) algorithms.
Constructs high-fidelity psychological models from interaction patterns to predict intellectual trajectories and maintain authentic voice consistency.
Provides high-performance LLVM-level automatic differentiation for Julia code on CPU and GPU architectures.
Integrates advanced topological graph theory and bicomodule structures into Claude for complex scientific computing and algebraic modeling.
Performs advanced Möbius inversion on partially ordered sets and lattices to solve complex combinatorial, topological, and graph-theoretic problems.
Models and analyzes dynamical systems on graph structures to understand complex flow behaviors and stability.
Implements play/coplay arena theory for autopoietic closure and learning through GF(3) conservation.
Performs vector similarity search and hierarchical clustering for agent skills using P-adic ultrametrics and MLX-powered embeddings.
Coordinates multi-agent systems and state transitions using GF(3) Galois Field conservation principles.
Extracts behavioral patterns and trains cognitive learning agents from interaction sequences.
Generates and analyzes Chemical Reaction Network (CRN) hypergraph structures to predict dynamical behaviors like stability and oscillations.
Implements local univalence and complete Segal space semantics for synthetic infinity-category theory and topological chemistry.
Analyzes and models complex dynamical systems using ergodicity principles where time averages equate to space averages.
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