data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Implements comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and rigorous benchmarking.
Applies medicinal chemistry rules and structural filters to prioritize and triage molecular compound libraries for drug discovery.
Implements a systematic, hypothesis-driven methodology for the iterative improvement of AI agents through automated evaluations and data-driven feedback loops.
Creates publication-quality statistical graphics and complex multi-panel data visualizations with minimal Python code.
Validates evaluation contract readiness and metadata integrity before initiating agentic optimization loops.
Implements advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production applications.
Builds and orchestrates end-to-end MLOps pipelines from data preparation through production deployment.
Provides a unified interface for rapid bioinformatics queries, genomic sequence analysis, and protein structure prediction across 20+ scientific databases.
Builds robust Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search to ground LLM responses in proprietary data.
Streamlines deep learning development by organizing PyTorch code into scalable, production-ready modules for efficient neural network training.
Architects sophisticated LLM applications using the LangChain framework for agents, memory management, and complex tool integration.
Searches and retrieves life sciences preprints from the bioRxiv database with support for metadata extraction and PDF downloads.
Implements lightweight dataset tracking and reproducibility patterns to ensure data changes are explicit and traceable.
Identifies and removes duplicate or visually similar images in FiftyOne datasets using deep learning embeddings.
Implements high-performance adaptive learning and experience replay for AI agents using the AgentDB vector engine.
Parses, processes, and generates Flow Cytometry Standard (FCS) files for biological data analysis and bioinformatics pipelines.
Orchestrates dynamic AI context, intelligent memory systems, and RAG workflows for enterprise-scale multi-agent applications.
Optimizes LLM performance and reliability through advanced prompting techniques like few-shot learning and chain-of-thought reasoning.
Manages complex Excel workbooks with automated formula recalculation, professional financial modeling standards, and deep data analysis capabilities.
Evaluates scholarly research and academic papers using the structured ScholarEval methodology for rigor and quality.
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production applications.
Implements advanced prompt engineering techniques to optimize LLM performance, reliability, and structured output in production environments.
Monitors and summarizes the Nixtla forecasting ecosystem to provide actionable updates on TimeGPT, StatsForecast, and MLForecast.
Performs constraint-based reconstruction and analysis of metabolic models for systems biology and metabolic engineering.
Combines vector similarity and keyword-based search to improve retrieval accuracy in RAG systems and search engines.
Implement high-performance similarity search and vector retrieval patterns across multiple database providers.
Implements sophisticated autonomous agent architectures and workflow patterns using the Vercel AI SDK.
Accesses and retrieves 3D protein and nucleic acid structures from the RCSB PDB for structural biology and drug discovery research.
Designs framework-agnostic, portable AI agents and multi-agent workflows using Oracle's Open Agent Specification.
Calculates comprehensive financial risk metrics including VaR, CVaR, Sharpe, and Sortino ratios for quantitative portfolio management.
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