Descubre Habilidades de Claude para data science & ml. Explora 61 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Builds production-grade Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search for LLM applications.
Plans data science and hardware optimization experiments by analyzing historical project logs and domain-specific configurations.
Builds and automates end-to-end MLOps pipelines from data ingestion and preparation through model training, validation, and production deployment.
Optimizes embedding model selection and chunking strategies to improve semantic search and RAG application accuracy.
Analyzes global investments through the lens of power structures, ethical constraints, and geopolitical alignments.
Transforms complex datasets into persuasive narratives and executive-ready presentations using proven storytelling frameworks.
Implements comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking.
Builds production-ready Apache Airflow DAGs using industry-standard patterns for orchestration and data engineering.
Generates complex AI-driven video compositions and media pipelines using the Renku CLI.
Manages and routes requests across multiple AI providers including Anthropic, Ollama, and HuggingFace.
Analyzes and extracts deep insights from images, videos, and audio files using advanced AI models.
Streamlines building high-performance OLAP applications using DuckDB, MotherDuck, and Parquet in Node.js and TypeScript.
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.
Analyzes protein and molecular structures through AlphaFold interpretation, quality validation metrics, and comparative structural techniques.
Builds sophisticated LLM applications and autonomous agents using the LangChain framework's core patterns and integrations.
Implements a multi-layered memory architecture based on Mem0 research to boost AI accuracy and persistence across sessions.
Streamlines scientific development on HPC environments using multi-root workspaces and automated test data extraction.
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.
Enables persistent semantic search and long-term memory capabilities using Qdrant vector database for advanced RAG workflows.
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.
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.
Implements queue-based GPU allocation and memory cleanup patterns to prevent OOM crashes and ensure reliable progress tracking in parallel workflows.
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.
Scroll for more results...