Descubre Habilidades de Claude para data science & ml. Explora 61 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Builds robust AI applications using OpenAI's Agents SDK with multi-agent orchestration, voice capabilities, and advanced error prevention.
Optimizes long-running AI agent sessions by implementing structured context compression to maintain technical accuracy and memory efficiency.
Builds and packages portable AI agents that operate across multiple LLM frameworks and deployment targets without vendor lock-in.
Implements advanced memory architectures for AI agents to maintain session continuity and manage structured entity relationships.
Integrates Claude with the FinnHub API to retrieve real-time stock quotes, fundamental data, crypto prices, and market news.
Implements production-grade prompt engineering patterns, RAG optimization, and agentic system architectures for advanced AI products.
Reconstructs multiplex microscopy images by correctly ordering tiles acquired via snake or serpentine stage patterns.
Architects sophisticated LLM applications using LangChain patterns for agents, memory management, and complex workflow orchestration.
Deploys and optimizes fine-tuned LLMs using native Unsloth kernels, vLLM, or SGLang for high-performance production serving.
Creates, edits, and analyzes sophisticated Excel spreadsheets with advanced formula support and industry-standard financial formatting.
Calculates implied gold prices and sovereign backing ratios to stress-test currency stability in scenarios where the USD loses its reserve status.
Implements sophisticated, multi-layered memory architectures including knowledge graphs and temporal persistence for autonomous AI agents.
Builds, configures, and deploys native Streamlit data applications directly within the Snowflake Data Cloud.
Empowers autonomous AI agents with real-time X (Twitter) search, web search, and sandboxed Python code execution capabilities.
Implements production-grade LLM-as-a-judge patterns to evaluate model outputs with high reliability and bias mitigation.
Implements sophisticated LLM-as-judge methodologies to evaluate and compare AI model outputs with high reliability and bias mitigation.
Processes and analyzes billion-row tabular datasets using lazy, out-of-core DataFrame operations without exceeding available RAM.
Builds type-safe, composable LLM applications in Ruby using the DSPy framework to program AI behavior instead of manual prompting.
Builds production-ready RAG systems and semantic search using optimized Gemini embedding-001 models and vector storage patterns.
Generates high-quality images from text prompts using Google Gemini 3 Pro via the fal.ai API.
Standardizes Python experiment layouts, stage entrypoints, and asset handling for consistent data science workflows.
Builds and manages semantic knowledge graphs to enhance autonomous coding and project understanding.
Automates IGV snapshot generation for visualizing genomic alignments and variant calls in BAM files.
Queries and annotates genomic data using the COSMIC Cancer Gene Census to identify known cancer genes and their clinical properties.
Integrates Google's Gemini 3 Pro API into Python and Node.js applications with advanced reasoning and streaming capabilities.
Analyzes, filters, and exports genomic variant data from VCF and BCF files for bioinformatics and sequencing workflows.
Analyzes genomic alignment files to extract reads, identify insertions and deletions, and calculate coverage statistics for WGS and WES data.
Builds high-performance Retrieval-Augmented Generation (RAG) systems using vector databases, semantic search, and advanced retrieval patterns.
Performs high-speed local DNA sequence alignment against hg38 and CHM13 genomic references without external API dependencies.
Performs NCBI BLAST sequence similarity searches using BioPython to identify homologous DNA or protein sequences.
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