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。
Deploys high-performance reinforcement learning policies to real-world robots using a specialized Rust-based ONNX inference engine.
Facilitates creative scientific ideation by generating hypotheses, exploring interdisciplinary connections, and challenging research assumptions.
Trains humanoid robot locomotion and manipulation policies using a JAX-native Reinforcement Learning framework built on MuJoCo.
Creates journal-ready, publication-quality scientific figures with standardized styling, multi-panel layouts, and colorblind-safe palettes.
Implements weighted limits and colimits for J-indexed diagrams within enriched category theory frameworks.
Optimizes training epochs for machine learning models using Walk-Forward Efficiency and efficient frontier analysis.
Implements Darwin Gödel Machine patterns to enable AI agents to autonomously improve their code and capabilities through open-ended evolution.
Evaluates financial models using state-of-the-art metrics specifically designed for range bar (price-based sampling) data.
Implements topological sheaf theory to manage distributed coordination and data consistency across multi-agent systems.
Implements and applies the formal axioms of monoidal categories to define structural relationships and coherent transformations in complex systems.
Calculates Möbius functions and alternating sums on posets to solve complex combinatorial, graph theory, and network centrality problems.
Accesses the ClinicalTrials.gov API v2 to search, filter, and export global clinical study data for medical research and patient matching.
Automates the creation, editing, and analysis of professional Excel spreadsheets with advanced formula support and financial modeling standards.
Quantifies and extracts financial value from system inefficiencies by modeling transitions between selfish equilibria and optimal states.
Generates syntactically guaranteed Hy s-expressions and JAX-compatible functions using constrained LLM decoding.
Transforms complex datasets into immersive auditory experiences using the Erie sonification grammar and multi-agent collaboration.
Optimizes Mojo tensor and array operations using SIMD vectorization to maximize computational throughput on modern hardware.
Guides R developers in choosing and implementing the optimal OOP system, including S7, S3, S4, and vctrs.
Formalizes and analyzes bidirectional observation relationships between agents using sheaf-theoretic consistency and structured decompositions.
Models complex interactions and compositional systems using the categorical framework of polynomial functors and dialectica categories.
Automates the end-to-end scientific research pipeline from initial hypothesis generation to publication-ready LaTeX manuscripts.
Guides the planning, parameter selection, and documentation of LLM fine-tuning and evaluation experiments.
Bridges the gap between robotic simulations and real-world deployment using active inference and predictive coding frameworks.
Optimizes the tradeoff between representation compression and computational response time using spectral graph theory and convergence analysis.
Generates high-performance, structured prompts for LLMs using research-backed techniques like XML tagging, few-shot examples, and chain-of-thought reasoning.
Leverages a multi-model ensemble to provide diverse, parallel insights from GPT, Gemini, and Claude for complex problem-solving.
Unifies topos-theoretic resources, categorical databases, and infinity-topoi for advanced mathematical modeling and cognitive agent development.
Implements adaptive learning and high-performance trajectory tracking for autonomous agents using the AgentDB vector backend.
Builds, fits, and validates sophisticated Bayesian statistical models using PyMC's probabilistic programming framework.
Processes large-scale document leaks using investigative journalism methodologies to extract entities and build searchable databases.
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