Descubre Habilidades de Claude para data science & ml. Explora 61 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Optimizes and orchestrates advanced prompt engineering workflows for Claude 4.5 using industry-best patterns, guardrails, and context management.
Implements semantic search and AI-driven discovery for Obsidian vaults using vector embeddings and advanced chunking strategies.
Builds reactive Python notebooks and interactive data applications using marimo's directed acyclic graph (DAG) execution model.
Implement comprehensive evaluation frameworks for LLM applications using automated metrics, human feedback, and statistical benchmarking.
Optimizes AI context windows through strategic compression, masking, and partitioning to handle larger tasks and reduce operational costs.
Generates concise, 24-hour tactical financial market briefs across multiple asset classes using real-time global news.
Architects and manages the end-to-end lifecycle of LLM-powered applications using agentic development methodologies.
Builds and orchestrates end-to-end MLOps pipelines from data preparation and model training to production deployment.
Converts literary works into high-quality supervised fine-tuning (SFT) datasets to train AI models in specific authorial voices.
Implement advanced LLM prompting techniques like few-shot learning and chain-of-thought to enhance production AI reliability and output quality.
Builds high-performance Retrieval-Augmented Generation systems using vector databases and semantic search to ground AI responses in external knowledge.
Optimizes embedding models and chunking strategies to enhance semantic search and RAG application performance.
Generates static bootstrap packages to initialize MOVA AI models and environments without requiring external LLM calls.
Designs and implements sophisticated multi-agent architectures to overcome context limitations and handle complex task decomposition.
Streamlines code reviews for the Llama Stack repository by focusing on distributed system patterns, API compatibility, and automated testing fixtures.
Diagnoses and resolves openai_harmony.HarmonyError and tool calling failures when using GPT-OSS models with vLLM.
Implements comprehensive meta-analysis workflows in R, including effect size calculation, heterogeneity assessment, and publication bias detection.
Streamlines pharmacokinetic and pharmacodynamic modeling in R using industry-standard packages and best practices.
Performs comprehensive genomics and bioinformatics statistical analysis using R and Bioconductor packages.
Performs advanced causal mediation analysis in R to decompose total effects into direct and indirect pathways across various statistical models.
Provides specialized machine learning algorithms for time series tasks including forecasting, classification, and anomaly detection using scikit-learn compatible APIs.
Guides users through the end-to-end Large Language Model fine-tuning lifecycle using a coach-driven workflow.
Scales Python workflows using parallel and distributed computing for datasets that exceed available memory.
Builds, fits, and validates sophisticated Bayesian models using PyMC's probabilistic programming interface.
Streamlines deep learning development by organizing PyTorch code into scalable, boilerplate-free Lightning modules and automated training workflows.
Detects hardware resources and provides strategic architectural recommendations for computationally intensive scientific tasks.
Generates publication-quality scientific diagrams and architectural schematics using AI-driven iterative refinement.
Evaluates scholarly work using the ScholarEval framework to provide structured assessments, quantitative scoring, and actionable feedback across research dimensions.
Performs advanced geospatial vector data analysis, coordinate transformations, and spatial mapping within Python environments.
Streamlines cryptocurrency asset selection by bypassing irrelevant equity filters and handling data gaps in financial datasets.
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