Descubre Habilidades de Claude para data science & ml. Explora 61 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Encapsulates complex state management into robust class structures to handle transitions, backtracking, and algorithmic branching.
Implements high-performance priority queues for pathfinding, scheduling, and stream processing using efficient heap-based structures.
Solves NP-hard optimization problems using greedy construction and iterative local improvement patterns.
Optimizes constraint satisfaction problem-solving by eliminating impossibilities through inference before initiating recursive search operations.
Implements comprehensive evaluation frameworks for LLM applications using automated metrics, human feedback, and LLM-as-judge patterns.
Manages local Ollama LLM models for development, testing, and VRAM optimization within Claude Code workflows.
Implements the Gale-Shapley algorithm to solve stable matching problems for two-sided markets like residency and admissions.
Implements efficient counting and frequency analysis patterns using Python's collections.Counter for data processing and distribution analysis.
Refines and compresses LLM prompts to minimize token usage and maximize response quality.
Enables the creation of expressive domain-specific languages in Python by overloading arithmetic and logical operators.
Optimizes Python functions by implementing memoization and dynamic programming patterns to eliminate redundant recursive computations.
Analyzes and processes genomic data sequences to extract biological insights and automate bio-informatics workflows.
Automates spreadsheet creation, complex financial modeling, and data analysis with professional formatting and formula integrity.
Implements immutable, memory-efficient data structures using Python's namedtuple for cleaner and more readable code.
Implements memory-efficient combinatorial iteration patterns in Python using the itertools library.
Implements symbolic computation patterns and Abstract Syntax Trees for mathematical modeling and code transformation.
Analyzes CSV files to provide immediate statistical summaries and automated data visualizations using Python and pandas.
Implements memory-efficient data processing using Python generators and lazy evaluation patterns.
Builds production-grade AI agents using the Pydantic AI framework with robust type safety and structured outputs.
Implements efficient computational geometry algorithms for calculating convex hulls, point enclosures, and polygon operations.
Generates human-readable, aligned tables and statistical summaries for reporting results and data comparisons.
Implements elegant, idiomatic data transformations using Pythonic list, dictionary, and set comprehensions inspired by Peter Norvig.
Automates the analysis, manipulation, and visualization of Excel spreadsheets using Python-based data science libraries.
Implements exact probability calculations and combinatorial counting patterns using a functional, Norvig-inspired approach.
Analyzes logistics exports to provide structured rate preparation intelligence and shipping profile discovery.
Optimizes algorithmic performance by calculating static graph, grid, and constraint relationships during module load for constant-time lookups.
Automates the creation, editing, and analysis of professional-grade Excel spreadsheets with precise formula management and financial modeling standards.
Automates the analysis, processing, and visualization of Excel spreadsheets using Python-based data science libraries.
Extracts structured data, numbers, and identifiers from unstructured text using optimized regex patterns and Norvig-inspired utilities.
Implements memory-efficient sparse data structures using Python sets for infinite grids and large coordinate spaces.
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