data science & ml向けのClaudeスキルを発見してください。53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。
Performs advanced health economic evaluations including cost-effectiveness analysis, Markov modeling, and probabilistic sensitivity analysis in R.
Provides R-based statistical methods and best practices for clinical trial design, analysis, and regulatory reporting.
Ranks and filters retrieved documents based on vector similarity metrics to optimize RAG pipeline relevance.
Automates the creation of FiftyOne datasets from local media files and executes machine learning model inference pipelines.
Identifies and removes duplicate or visually similar images in FiftyOne datasets using deep learning embeddings.
Builds robust Retrieval-Augmented Generation (RAG) systems for LLM applications using vector databases and semantic search.
Implements systematic evaluation strategies for LLM applications using automated metrics, human feedback loops, and benchmarking frameworks.
Optimizes LLM performance and reliability through advanced prompting techniques like few-shot learning and chain-of-thought reasoning.
Optimizes LLM performance and reliability through advanced prompting techniques like few-shot learning and chain-of-thought reasoning.
Build, automate, and manage end-to-end MLOps pipelines from data ingestion through production deployment.
Implements end-to-end machine learning pipelines in R using the modern tidymodels ecosystem.
Streamlines R development using modern data analysis patterns, automated testing with testthat, and strict linting standards.
Implements Retrieval-Augmented Generation (RAG) systems with vector databases and semantic search to build grounded, knowledge-aware AI applications.
Detects structural breaks and regime shifts in financial time-series using Gaussian Process models to identify market transitions.
Implements robust Retrieval-Augmented Generation (RAG) pipelines including document ingestion, hybrid search, reranking, and intelligent query routing.
Implements comprehensive evaluation frameworks for LLM applications using automated metrics, human feedback, and LLM-as-judge patterns.
Architects sophisticated LLM applications using LangChain patterns for autonomous agents, conversational memory, and complex workflow orchestration.
Implement comprehensive evaluation frameworks for LLM applications using automated metrics, human feedback, and benchmarking.
Architects and implements sophisticated LLM applications using the LangChain framework for agents, memory management, and complex AI workflows.
Provides a rapid diagnostic summary of Sparse Autoencoder (SAE) features to generate research hypotheses and identify model behaviors.
Provides expert guidance for high-performance data manipulation and ETL pipeline optimization using the Polars library in Python.
Manages Google Gemini File Search stores and implements production-ready RAG systems for documents and codebases.
Calculates optimal portfolio weights and identifies the tangency portfolio to maximize the Sharpe ratio based on user-provided asset data.
Manages fast, reproducible scientific Python environments by unifying the conda and PyPI ecosystems.
Implements modern R programming practices, tidyverse patterns, and performance optimizations for data science workflows.
Orchestrates end-to-end stock trading operations by performing automated screening, multi-model AI analysis, and order execution via Alpaca.
Analyzes CSV files automatically to provide statistical summaries, domain-specific insights, and relevant visualizations without requiring user intervention.
Designs, optimizes, and troubleshoots large language model prompts using research-backed techniques and structured design patterns.
Optimizes LLM performance and reliability through advanced prompting techniques like few-shot learning and chain-of-thought reasoning.
Orchestrates end-to-end MLOps pipelines from data preparation and model training to production deployment and monitoring.
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