Descubre Habilidades de Claude para data science & ml. Explora 61 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Optimizes LLM performance and reliability through automated model selection, cost estimation, and intelligent fallback strategies.
Integrates Google Gemini's multimodal capabilities to process audio, video, images, and documents directly within the Claude Code environment.
Manage GrepAI semantic search workspaces including creation, project management, and indexing status monitoring.
Manages persistent AI agent memory and reasoning patterns using high-performance vector storage and learning algorithms.
Performs high-performance, vectorized string manipulation and text cleaning using NumPy's specialized string modules.
Configures and troubleshoots embedding providers and models for the GrepAI semantic search tool.
Integrates Google Gemini's multimodal analysis, massive context processing, and image generation capabilities directly into Claude Code.
Automates the creation, editing, and analysis of professional Excel spreadsheets with advanced formula support and financial modeling standards.
Facilitates secure retrieval and rigorous validation of LSEG and Refinitiv financial data using the Python Data Library.
Delegates complex tasks and analysis to Google's Gemini models directly within your Claude Code environment.
Guides researchers through complex mixed methods study designs using systematic qualitative and quantitative integration patterns.
Architects optimized quantitative research designs, experimental methodologies, and sampling strategies using an enhanced three-phase validation process.
Automates large-scale asynchronous document processing and bulk LLM tasks using the Google Gemini Batch API.
Automates and guides systematic qualitative data coding, theme development, and rigorous research methodology implementation.
Analyzes single-cell omics data using deep probabilistic models for integration, batch correction, and differential expression.
Builds cost-free RAG systems using parallel document processing and local vector embeddings.
Manages, analyzes, and visualizes multi-modal spatial omics data using the SpatialData Python ecosystem and OME-NGFF standards.
Automates and manages the end-to-end 7-stage PRISMA 2020 systematic literature review pipeline from research question to RAG system.
Orchestrates complex social science research and systematic reviews using 24 specialized agents and integrated academic database tools.
Validates data analysis workflows by reviewing methodology, data quality, and statistical integrity with a high-confidence scoring system.
Provides comprehensive guidance and command references for the Market Research plugin to streamline competitive analysis and trend reporting.
Generates professional data visualizations and plots using industry-standard Python libraries like Matplotlib, Seaborn, and Plotly.
Provides validated, high-integrity PostgreSQL query patterns and connection logic for Wharton Research Data Services (WRDS) datasets.
Integrates qualitative and quantitative research data to generate rigorous meta-inferences and professional joint displays.
Optimizes Large Language Model performance through advanced prompting techniques like few-shot learning and chain-of-thought reasoning.
Manages complex academic research workflows through 24 specialized agents with mandatory human-in-the-loop checkpoints.
Synthesizes established metrics into structured driver assessments and integrated analytical reports using the HEAD framework.
Builds and manages complex LangGraph agent systems using a structured, 7-layer architectural pattern for scalable AI development.
Generates cinematic video transitions and morphing animations between two keyframe images using Google’s Veo 3.1 via fal.ai.
Architects and implements sophisticated LLM applications using LangChain for agents, memory management, and complex workflows.
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