data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Implements gradient-free optimization and ternary learning using discrete trit-based perturbations for non-differentiable systems.
Implements Renormalization Group (RG) flows using categorical ACSet semantics and topological XY model defect analysis.
Conducts exhaustive, multi-source research across academic databases, semantic search engines, and local knowledge bases.
Implements the directed Yoneda lemma as a principle for directed path induction within synthetic infinity-categories.
Implements frame-invariant self-inverse derivation patterns using GF(3) conservation and triadic color dynamics.
Facilitates model-independent ∞-category theory using the Riehl-Verity ∞-cosmos formalism for formal verification and mathematical modeling.
Formalizes AI agency and belief systems using condensed mathematics and categorical limit constructions.
Optimizes interaction sequences using information theory to maximize learning efficiency and pattern recognition.
Builds high-performance, autonomous AI agents using battle-tested system prompts and production-ready tool implementations.
Implements Cohesive Linear Homotopy Type Theory patterns for formalizing interaction entropy and generating categorical diagrams.
Executes polyglot code blocks across multiple languages directly within Org-mode files to enable seamless literate programming workflows.
Implements production-grade sorting and searching algorithms with comprehensive complexity analysis and unit testing templates.
Implements a triadic framework balancing category theory, cybernetic agency, and phenomenological observation for complex system modeling.
Replaces temporal succession with deterministic seed-chaining to enable verifiable, frame-invariant state transitions.
Builds and validates high-fidelity psychological models to predict behaviors and generate authentic cognitive responses matching a subject's unique patterns.
Implements deterministic seed-chaining and color derivations to replace temporal sequencing with verifiable mathematical logic.
Generates high-performance, deterministic SplitMix64 pseudo-random sequences for MLX and JAX workflows on Apple Silicon.
Optimizes LLM performance on Apple M-series chips using the MLX framework for high-efficiency local inference and fine-tuning.
Models and analyzes time-varying data using sheaves on time categories for compositional temporal reasoning.
Interleaves context-aware Mermaid diagrams into interactions using GF(3) triadic selection logic.
Bridges classical music theory with quantum computing to compose and perform algorithmic music using quantum circuits and ZX-calculus.
Provides a powerful Clojure environment for symbolic mathematics, automatic differentiation, and computational classical mechanics.
Detects phase transitions and classifies dynamical system states using fixed-point distance measurements and self-loop closure verification.
Implements high-performance JAX-based agentic simulations to model emergent behaviors, tool use, and open-ended cultural evolution.
Orchestrates complex AI behaviors using a tripartite framework of hierarchical stratification, compositional fabrication, and mathematical conservation.
Balances algorithmic complexity with exploratory game theory to optimize proof discovery and extract World Extractable Value (WEV).
Orchestrates complex world-state transformations using categorical rewriting, graph grafting, and formal semantic verification.
Bridges Scholze-Clausen condensed mathematics and analytic stacks to sheaf neural networks via 6-functor formalisms.
Generates deterministic behavioral patterns using seed chaining and GF(3) conservation laws as a high-speed alternative to temporal training.
Provides high-performance bidirectional data navigation and transformation for Julia collections, S-expressions, and ACSets.
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