data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Provides foundational knowledge for writing, reviewing, and optimizing high-performance Stan 2.37 Bayesian models.
Implements advanced Bayesian time series analysis using Stan and JAGS for probabilistic forecasting and state-space modeling.
Implements and optimizes hierarchical Bayesian models with support for partial pooling and advanced parameterization techniques.
Evaluates Bayesian model convergence and sampling performance using MCMC diagnostics for Stan and JAGS frameworks.
Enables professional-grade spreadsheet creation, financial modeling, and data analysis with automated formula verification and industry-standard formatting.
Decomposes mining stock-to-metal price ratios into fundamental drivers like AISC, leverage, and valuation multiples using automated financial data extraction.
Analyzes CSV files automatically to provide statistical summaries, domain-specific insights, and relevant visualizations without requiring user intervention.
Orchestrates end-to-end MLOps pipelines from data preparation and model training to production deployment and monitoring.
Builds and optimizes reactive Python notebooks using marimo for interactive data analysis, dashboards, and machine learning workflows.
Builds and analyzes robust Bayesian statistical models using Stan-based R packages like brms and rstanarm.
Synthesizes patient-level data across multiple studies using advanced meta-analysis methods and R statistical frameworks.
Implements rigorous evaluation strategies for AI applications using automated metrics, human feedback, and LLM-as-judge patterns.
Standardizes AI agent development through reusable, composable, and version-controlled prompt templates.
Analyzes and extracts deep insights from video files and YouTube URLs using the Google Gemini API.
Facilitates creative research ideation and exploratory scientific problem-solving through structured conversational partnership.
Builds reactive Python notebooks, interactive dashboards, and data-driven applications using the marimo framework.
Manages large N-dimensional arrays with chunking and compression for high-performance scientific computing and cloud storage.
Builds, optimizes, and executes quantum circuits and algorithms on real hardware and high-performance simulators.
Implements multi-objective and single-objective optimization algorithms to solve complex engineering and mathematical problems.
Converts chemical structures into numerical representations for molecular machine learning and drug discovery workflows.
Optimizes AI agent behavior through specialized prompt engineering patterns and best practices for complex, autonomous workflows.
Manages fast, reproducible scientific Python environments by unifying the conda and PyPI ecosystems.
Implements comprehensive evaluation frameworks to measure LLM application quality using automated metrics, human feedback, and comparative benchmarks.
Builds high-performance Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search to ground LLMs in external data.
Implements advanced prompt engineering techniques to maximize LLM performance, reliability, and reasoning capabilities in production environments.
Accesses the Ensembl REST API to retrieve genomic sequences, gene annotations, and variant analysis data for over 250 species.
Evaluates research rigor by assessing methodology, statistical validity, and bias using established frameworks like GRADE and Cochrane.
Provides comprehensive financial frameworks for modeling, valuation, corporate finance decisions, and advanced statement analysis.
Train, deploy, and manage distributed neural networks within E2B sandboxes using the Flow Nexus ecosystem.
Implements high-performance adaptive learning and memory distillation for AI agents using the ultra-fast AgentDB vector engine.
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