Descubre Habilidades de Claude para data science & ml. Explora 61 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Analyzes global investments through the lens of power structures, ethical constraints, and geopolitical alignments.
Manages and routes requests across multiple AI providers including Anthropic, Ollama, and HuggingFace.
Integrates 300+ AI models into Claude Code for specialized tasks, high-fidelity image generation, and cross-model reasoning.
Transcribes audio files into text using a local whisper.cpp server with GPU acceleration.
Builds sophisticated LLM applications and autonomous agents using the LangChain framework's core patterns and integrations.
Analyzes protein and molecular structures through AlphaFold interpretation, quality validation metrics, and comparative structural techniques.
Implements a multi-layered memory architecture based on Mem0 research to boost AI accuracy and persistence across sessions.
Enables persistent semantic search and long-term memory capabilities using Qdrant vector database for advanced RAG workflows.
Streamlines scientific development on HPC environments using multi-root workspaces and automated test data extraction.
Generates realistic AI avatar lip-sync videos from a single image and audio file using the OmniHuman1 framework.
Transforms raw research data into structured executive reports and technical implementation plans.
Automates Benchling life sciences workflows and manages biological data via the Python SDK and REST API.
Implements queue-based GPU allocation and memory cleanup patterns to prevent OOM crashes and ensure reliable progress tracking in parallel workflows.
Implement advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production environments.
Implements end-to-end machine learning pipelines in R using the modern tidymodels ecosystem.
Quantifies hedge fund capital flows in agricultural commodity markets using CFTC COT data and macro sentiment indicators.
Parses and generates Flow Cytometry Standard (FCS) files to facilitate cytometry data preprocessing and scientific analysis.
Architects and optimizes high-performance Retrieval-Augmented Generation systems using advanced embedding, chunking, and search strategies.
Automates tissue detection and tile extraction from whole slide images for digital pathology and machine learning workflows.
Builds advanced financial models including DCF analysis, Monte Carlo simulations, and scenario planning for data-driven investment decisions.
Architects and implements high-performance full-text search engines and vector retrieval systems for AI-driven applications.
Simplifies molecular cheminformatics and drug discovery workflows with a Pythonic abstraction layer over RDKit.
Queries and interprets genetic variant clinical significance data from the NCBI ClinVar database.
Generates high-quality videos and animations from text or images using the Google GenAI Veo 3.1 model.
Builds and orchestrates sophisticated AI agents and multi-agent workflows using the Microsoft Agent Framework for .NET applications.
Validates speleothem-based paleoseismic research by testing cave geochemical records against modern earthquake catalogs.
Standardizes the development of Python machine learning experiments using specific layout, execution, and asset management patterns.
Indexes and manages multi-format reference documents to enhance RAG-based context retrieval during development tasks.
Streamlines computational molecular biology tasks including sequence analysis, biological file parsing, and programmatic NCBI database access.
Streamlines the analysis of high-density neural recordings from Neuropixels probes through automated preprocessing, spike sorting, and quality curation.
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