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。
Optimizes text embedding selection and chunking strategies to enhance semantic search and RAG application performance.
Predicts age, gender, and ethnicity from person data and images to enrich datasets and customer profiles.
Optimizes vector database performance by tuning HNSW parameters and implementing advanced quantization strategies for AI applications.
Empowers Claude to perform advanced time series machine learning, including classification, forecasting, and anomaly detection using the specialized aeon toolkit.
Manages large-scale N-dimensional arrays with chunked storage, compression, and seamless cloud integration for scientific computing pipelines.
Implement comprehensive evaluation frameworks for AI applications using automated metrics, human feedback, and LLM-as-judge patterns.
Implements comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking frameworks.
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production environments.
Generates high-quality statistical graphics and data visualizations with seamless Pandas integration.
Deploys and optimizes serverless AI models, embedding generation, and RAG architectures directly on Cloudflare’s edge network.
Manages end-to-end bioinformatics research by orchestrating hypothesis-driven experiment planning, execution, and standardized lab notebook documentation.
Builds, trains, and optimizes hybrid quantum-classical models using automatic differentiation and hardware-agnostic circuit programming.
Optimizes vector search and RAG applications through intelligent embedding model selection and advanced document chunking strategies.
Builds process-based discrete-event simulations in Python to model complex systems with resource contention and time-based events.
Performs precise unit conversions, dimensional analysis, and unit-aware arithmetic using the Pint library.
Transforms monolithic Python research code and notebooks into modular, production-ready package structures.
Transforms chemical structures into machine learning-ready numerical features using a library of over 100 featurizers and pretrained models.
Analyzes, cleans, and visualizes Excel spreadsheet data using Python libraries like pandas and openpyxl.
Evaluates scientific research rigor and evidence quality using standardized frameworks like GRADE and Cochrane.
Manages and analyzes microscopy data through the OMERO Python API for scientific imaging and high-content screening workflows.
Provides comprehensive tools for phylogenetic tree manipulation, evolutionary analysis, and high-quality biological data visualization.
Streamlines deep learning development by organizing PyTorch code into scalable, boilerplate-free Lightning modules and automated training workflows.
Performs advanced data analysis and business intelligence using specialized SQL patterns for statistical and exploratory insights.
Builds production-ready Retrieval-Augmented Generation (RAG) systems for LLM applications using vector databases and semantic search.
Deploys and manages a self-hosted AI infrastructure stack including LLM proxies, inference servers, vector databases, and observability tools.
Implements high-performance, accessible, and perceptually accurate data visualizations using industry-standard algorithms and best practices.
Generates high-quality videos with consistent subject appearance using Google’s Veo 3.1 model and reference images.
Provides direct access to the KEGG REST API for biological pathway analysis, gene mapping, and metabolic research.
Automates scientific data analysis and graphing workflows using GraphPad Prism scripting and XML manipulation.
Develops and trains Graph Neural Networks (GNNs) using the PyTorch Geometric library for irregular data structures and geometric deep learning.
Scroll for more results...