Data Science & ML Claude 스킬

Polars High-Performance Data Processing

Accelerates data manipulation and analysis using the blazingly fast Polars DataFrame library for Python and Rust.

Seaborn Statistical Visualization

Generates sophisticated, publication-quality statistical graphics and exploratory data visualizations using the Python Seaborn library.

Precompute Relationships Pattern

Optimizes algorithmic performance by calculating static graph, grid, and constraint relationships during module load for constant-time lookups.

Text Data Extraction Patterns

Extracts structured data, numbers, and identifiers from unstructured text using optimized regex patterns and Norvig-inspired utilities.

Reliable Autonomous Agents

Architects reliable, self-correcting AI agent systems using proven patterns like ReAct and Plan-Execute to minimize error compounding in production.

Lifelines Survival Analysis

Performs advanced survival analysis and time-to-event modeling using the lifelines library for medical, clinical, and epidemiological research.

Prompt Engineering Expert

Optimizes LLM performance and reliability using advanced prompting patterns, systematic refinement, and architectural best practices.

Scikit-Learn Machine Learning

Implements industry-standard machine learning workflows in Python for predictive data analysis including classification, regression, and clustering.

Excel Analysis

Analyzes Excel spreadsheets, generates pivot tables, and automates complex data visualization workflows.

Norvig Sparse Set Pattern

Implements memory-efficient sparse data structures using Python sets for infinite grids and large coordinate spaces.

Local Search Optimizer

Solves NP-hard optimization problems using greedy construction and iterative local improvement patterns.

Propagate-Then-Search Logic Pattern

Optimizes constraint satisfaction problem-solving by eliminating impossibilities through inference before initiating recursive search operations.

tqdm Progress Monitoring

Implements intelligent, low-overhead progress bars for Python loops, data processing, and machine learning workflows.

Atomic Simulation Environment (ASE)

Builds, manipulates, and analyzes atomistic simulations using a universal Python interface for molecular dynamics and quantum chemistry codes.

Side-by-Side Comparison Patterns

Visualizes code changes, algorithm results, and data states by displaying multiple outputs in parallel columns.

Priority Queue Implementation Patterns

Implements high-performance priority queues for pathfinding, scheduling, and stream processing using efficient heap-based structures.

Scikit-Learn Model Explainability

Provides specialized guidance and code patterns for interpreting machine learning models using scikit-learn, SHAP, and advanced diagnostic tools.

SunPy Solar Physics Analysis

Enables advanced solar data processing, coordinate transformations, and multi-instrument analysis using the SunPy ecosystem.

Numba JIT Performance

Accelerates Python and NumPy code using Just-In-Time (JIT) compilation for machine-speed execution.

Python Operator Overloading DSL

Enables the creation of expressive domain-specific languages in Python by overloading arithmetic and logical operators.

Unsloth LLM Fine-Tuning

Accelerates LLM fine-tuning by 2x while reducing memory consumption by 80% for models like Llama, Mistral, and Phi.

Sociology Interview Analyst

Facilitates rigorous qualitative analysis of interview data through systematic coding, theoretical synthesis, and evidence-based interpretation.

Dagster Development & Orchestration

Provides expert guidance and standardized patterns for building scalable data pipelines using the Dagster asset-based orchestration framework.

Dagster Prototyper

Generates production-ready Dagster data assets and pipelines using natural language requirements and industry best practices.

Dagster Workspace Creator

Initializes and scaffolds organized multi-project environments for Dagster data orchestration using natural language.

Dagster Project Creator

Initializes new Dagster projects with a recommended structure using natural language commands.

RAG Implementation Specialist

Implements advanced Retrieval-Augmented Generation patterns, optimizing document chunking, embeddings, and search strategies for high-performance AI systems.

Autonomous Agent Architect

Builds reliable, production-ready autonomous AI systems using proven agentic patterns and strict reliability guardrails.

Dagster Integrations Index

Provides a comprehensive, categorized guide to over 82 Dagster integrations for data orchestration and engineering.

Autonomous Agent Architect

Builds and manages reliable AI agents using robust patterns like ReAct and Plan-Execute to ensure production-grade performance.