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。
Provides specialized guidance for implementing differentiable physics simulations and solving partial differential equations using JAX.
Accelerates data manipulation and analysis using the blazingly fast Polars DataFrame library for Python and Rust.
Optimizes and executes quantum circuits on physical IBM Quantum hardware using advanced error mitigation and pulse-level control.
Provides specialized tools for reading, modifying, and writing DICOM medical imaging data within Python environments.
Accelerates LLM fine-tuning by 2x while reducing memory consumption by 80% for models like Llama, Mistral, and Phi.
Builds, manipulates, and analyzes atomistic simulations using a universal Python interface for molecular dynamics and quantum chemistry codes.
Implements intelligent, low-overhead progress bars for Python loops, data processing, and machine learning workflows.
Implements industry-standard machine learning workflows in Python for predictive data analysis including classification, regression, and clustering.
Provides expert guidance and standardized patterns for building scalable data pipelines using the Dagster asset-based orchestration framework.
Generates production-ready Dagster data assets and pipelines using natural language requirements and industry best practices.
Initializes and scaffolds organized multi-project environments for Dagster data orchestration using natural language.
Simplifies scientific image processing and analysis using Python-based algorithms and NumPy-compatible workflows.
Performs advanced survival analysis and time-to-event modeling using the lifelines library for medical, clinical, and epidemiological research.
Generates high-performance, structured prompts using official Anthropic conventions and 2025 best practices.
Optimizes algorithmic performance by calculating static graph, grid, and constraint relationships during module load for constant-time lookups.
Extracts structured data, numbers, and identifiers from unstructured text using optimized regex patterns and Norvig-inspired utilities.
Initializes new Dagster projects with a recommended structure using natural language commands.
Implements memory-efficient data processing using Python generators and lazy evaluation patterns.
Implements immutable, memory-efficient data structures using Python's namedtuple for cleaner and more readable code.
Enables the creation of expressive domain-specific languages in Python by overloading arithmetic and logical operators.
Implements advanced Retrieval-Augmented Generation patterns, optimizing document chunking, embeddings, and search strategies for high-performance AI systems.
Builds reliable, production-ready autonomous AI systems using proven agentic patterns and strict reliability guardrails.
Provides specialized data structures and statistical methods for rigorous bioinformatics, microbiome research, and community ecology analysis.
Optimizes constraint satisfaction problem-solving by eliminating impossibilities through inference before initiating recursive search operations.
Provides a comprehensive, categorized guide to over 82 Dagster integrations for data orchestration and engineering.
Optimizes NumPy performance through advanced memory management, stride manipulation, and zero-copy operations.
Performs high-precision cartographic projections and coordinate transformations using the PROJ library.
Builds and manages reliable AI agents using robust patterns like ReAct and Plan-Execute to ensure production-grade performance.
Facilitates rigorous qualitative analysis of interview data through systematic coding, theoretical synthesis, and evidence-based interpretation.
Guides artists and developers through the end-to-end process of training custom AI art models with a focus on dataset quality and resource optimization.
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