Descubre Habilidades de Claude para data science & ml. Explora 53 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Implement high-performance semantic search and vector storage for intelligent document retrieval and RAG systems.
Implements ultra-high-performance semantic vector search and document retrieval for Claude-powered RAG systems and intelligent knowledge bases.
Queries and analyzes clinical trial data from the official ClinicalTrials.gov API v2 for research and patient matching.
Implement high-performance semantic vector search and intelligent document retrieval using AgentDB optimized HNSW indexing and quantization.
Facilitates creative scientific research ideation through collaborative hypothesis generation and interdisciplinary connection mapping.
Integrates high-performance semantic vector search and HNSW indexing for intelligent document retrieval and RAG systems.
Accesses and queries the NIH Metabolomics Workbench to retrieve study data, standardize nomenclature, and perform mass spectrometry searches.
Empowers drug discovery and molecular analysis by integrating the DeepChem toolkit for property prediction, featurization, and graph neural networks.
Manages AI model configurations, pricing, and capabilities within backend registries for LLM-powered applications.
Implements advanced prompting patterns and psychological persuasion principles to maximize LLM performance, reliability, and controllability.
Provides comprehensive guidance on visual encoding, chart selection, and technical implementation for building accessible, high-performance data representations.
Performs comprehensive data cleaning, statistical analysis, and professional visualization using Python and pandas.
Orchestrates specialized research workflows and methodology skills for academic studies, statistical analysis, and systematic reviews.
Analyzes and summarizes Nextflow pipelines to provide immediate structural insights and dependency maps.
Builds high-performance, scalable data input pipelines using the TensorFlow tf.data API to maximize training efficiency.
Deploy, optimize, and serve TensorFlow and JAX models across production, mobile, and edge environments.
Builds and trains sophisticated neural network architectures using TensorFlow's Keras API and custom low-level implementations.
Builds sophisticated, interactive data visualizations and complex SVG-based diagrams using d3.js across various JavaScript environments.
Crafts high-quality, structured AI prompts through collaborative architectural design and advanced engineering techniques like Chain-of-Thought and Step-Back prompting.
Configures and optimizes LangChain4j vector stores for RAG applications, enabling seamless semantic search and embedding storage in Java environments.
Provides specialized guidance for designing machine learning systems, computer vision pipelines, and production-ready AI architectures.
Builds ultra-performant real-time ETL pipelines for AI indexing, vector search, and incremental data processing.
Creates and optimizes advanced prompts using patterns like few-shot learning, chain-of-thought, and system prompt design to significantly improve LLM performance.
Correlates qualitative customer feedback with quantitative telemetry and revenue data to drive data-backed business decisions.
Builds sophisticated TAM/SAM/SOM models and financial scenarios to forecast market potential and stress-test revenue assumptions.
Facilitates multi-model AI consultations, parallel multi-agent research, and structured peer-reviewed deliberation to provide diverse expert perspectives.
Invokes the Google Gemini CLI to perform advanced reasoning, web-enabled research, and specialized code optimization tasks.
Builds, tests, and deploys interactive data applications using Streamlit with a focus on Snowflake integration and production-grade best practices.
Streamlines the production machine learning lifecycle with automated data ingestion, secure deployment patterns, and real-time drift detection.
Guides the full lifecycle of large language model systems from architecture selection and dataset design to production deployment and monitoring.
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