Descubre Habilidades de Claude para data science & ml. Explora 61 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Implements comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking.
Manages biological datasets with automated lineage tracking, ontology-based validation, and FAIR data principles through a unified Python API.
Manages and automates complex text transformation pipelines via the TextCleaner REPL interface.
Analyzes mass spectrometry data for proteomics and metabolomics using the OpenMS Python interface.
Provides a specialized laboratory environment for experimenting with and implementing advanced Claude capabilities.
Simulates complex fluid dynamics using Python-based high-performance solvers for Navier-Stokes, shallow water, and stratified flows.
Implements robust hybrid search systems by combining vector similarity and keyword-based retrieval for enhanced RAG performance.
Accesses the UniProt REST API to retrieve protein sequences, functional annotations, and map identifiers across biological databases.
Implements high-performance similarity search and vector database patterns for AI-driven applications.
Integrates Hugging Face Transformers for advanced natural language processing, computer vision, and audio tasks.
Calculates comprehensive portfolio risk metrics and performance indicators for quantitative trading strategies.
Facilitates high-throughput sequencing data analysis, visualization, and quality control for genomics workflows.
Designs and implements sophisticated LLM applications using LangChain 1.x and LangGraph for advanced agent orchestration and state management.
Performs rigorous statistical modeling, econometric analysis, and hypothesis testing using the Python statsmodels library.
Builds robust, production-grade backtesting systems to validate trading strategies while eliminating common statistical biases.
Builds and automates end-to-end MLOps pipelines from data preparation and model training to production deployment and monitoring.
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking.
Automates the end-to-end process of conducting systematic literature reviews and generating academic-grade reports with verified citations.
Builds robust Retrieval-Augmented Generation systems using vector databases, semantic search, and advanced retrieval patterns for LLM applications.
Queries the Open Targets Platform to identify and prioritize therapeutic drug targets using genetic and clinical data.
Builds and deploys serverless bioinformatics workflows using the Latch SDK and cloud-native infrastructure.
Detects hardware capabilities and provides optimized computational strategies for resource-intensive scientific tasks.
Optimizes vector database performance by tuning HNSW parameters, quantization strategies, and memory usage for efficient AI applications.
Evaluates research rigor and methodology to assess the validity of scientific claims and experimental designs.
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production environments.
Generates professional, evidence-based clinical decision support documents and pharmaceutical cohort analyses in publication-ready LaTeX format.
Optimizes embedding model selection and chunking strategies to improve semantic search and RAG application performance.
Queries the Reactome database to perform biological pathway analysis, gene enrichment, and molecular interaction mapping for systems biology research.
Evaluates the rigor of scientific research by assessing methodology, statistical validity, and potential biases using established frameworks like GRADE and Cochrane.
Designs, analyzes, and generates protein sequences and structures using Evolutionary Scale Modeling (ESM3 and ESM C).
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