Descubre Habilidades de Claude para data science & ml. Explora 61 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Configures Google ADK bidirectional streaming to build low-latency, multimodal AI agents with real-time voice and video capabilities.
Automates complex time-series forecasting pipelines including trend analysis, seasonality detection, and multi-model predictions.
Manages complex Excel workbooks with automated formula creation, financial modeling standards, and data analysis.
Automates hyperparameter tuning and model selection using intelligent search strategies like Bayesian optimization.
Manages persistent AI agent memory and reasoning patterns using high-performance vector storage and learning algorithms.
Provides expert methodological guidance and implementation patterns for conducting rigorous Simulated Treatment Comparisons (STC) in clinical trial analysis.
Implements robust evaluation frameworks for Large Language Model applications using automated metrics, human feedback, and statistical testing.
Generates text-based visualizations and comprehensive reports to analyze the relationship between typographic errors and semantic drift in experimental data.
Provides expert guidance for conducting and reviewing Matching-Adjusted Indirect Comparisons (MAIC) in clinical data analysis.
Implements high-performance semantic vector search and intelligent document retrieval for RAG-based Claude Code workflows.
Instantiates sophisticated multi-agent architectures to handle complex reasoning, research, and implementation tasks.
Optimizes GPU VRAM usage through OOM retry logic, idle auto-unloading, and inter-service memory coordination protocols.
Analyzes datasets, calculates statistical metrics, and generates visual insights to drive data-informed decisions.
Implements adaptive learning and meta-cognitive capabilities to help AI agents optimize strategies through experience and pattern recognition.
Master the core principles and mechanics of context management to build more efficient and accurate AI agent architectures.
Builds and analyzes phylogenetic trees using distance-based, maximum likelihood, and Bayesian inference methods.
Orchestrates end-to-end MLOps pipelines from data preparation and model training to production deployment and monitoring.
Integrates multiple academic databases including PubMed, Semantic Scholar, and OpenAlex for comprehensive literature reviews and full-text retrieval.
Implements professional-grade trend-following strategies, volatility targeting, and multi-scale momentum indicators for financial data analysis.
Automates the creation, analysis, and formatting of Excel spreadsheets using programmatic data manipulation and visualization techniques.
Refactors Scikit-learn and machine learning code into production-ready pipelines that ensure reproducibility and prevent data leakage.
Systematically diagnoses and resolves Scikit-learn errors, data integrity issues, and model convergence failures.
Streamlines the creation, manipulation, and visualization of multidimensional histograms using the scikit-hep Python ecosystem.
Provides a rapid diagnostic summary of Sparse Autoencoder (SAE) features to generate research hypotheses and identify model behaviors.
Provides a comprehensive toolkit for building, analyzing, and visualizing complex network structures and graph algorithms in Python.
Provides a structured framework for comprehensive data processing, multi-step analysis patterns, and standardized output generation.
Empowers autonomous AI agents with real-time X (Twitter) search, web search, and sandboxed Python code execution capabilities.
Searches the arXiv open-access repository for the latest research papers across various scientific and technical disciplines.
Searches the arXiv preprint repository for scholarly articles across science, mathematics, and technology fields.
Builds type-safe, composable, and optimized LLM applications using the programmatic DSPy paradigm in Ruby.
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