data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Analyzes machine learning training logs to visualize loss curves, detect training issues, and provide diagnostic insights.
Automates numerical computing, matrix operations, and scientific visualization using MATLAB and GNU Octave.
Standardizes the implementation of Claude-powered AI assistants across the Insight Business Suite with built-in persona systems and BYOK management.
Implements adaptive learning and meta-cognitive capabilities to help AI agents optimize strategies through experience and pattern recognition.
Automatically fixes malformed or incomplete JSON strings generated by AI models to ensure reliable data parsing.
Performs professional financial stock analysis using technical indicators, automated chart generation, and news sentiment tracking.
Instantiates sophisticated multi-agent architectures to handle complex reasoning, research, and implementation tasks.
Implements high-performance semantic vector search and intelligent document retrieval for RAG-based Claude Code workflows.
Automates complex data science workflows using a multi-agent architecture and optimized model routing for efficient, iterative data analysis.
Decomposes mining stock-to-metal price ratios into fundamental drivers like AISC, leverage, and valuation multiples using automated financial data extraction.
Automates the discovery and summarization of relevant arXiv research papers based on specific interests in physics and machine learning.
Orchestrates end-to-end stock trading operations by performing automated screening, multi-model AI analysis, and order execution via Alpaca.
Provides R-based statistical methods and best practices for clinical trial design, analysis, and regulatory reporting.
Implements efficient similarity search and vector database patterns for semantic retrieval and RAG systems.
Implements high-performance hybrid search by combining vector similarity with keyword-based retrieval for RAG and search applications.
Queries the Dimensions database to find and analyze academic publications, grants, patents, and clinical trials for evidence-based research.
Implement industry-standard prompt engineering techniques to improve LLM accuracy, reliability, and structured output handling.
Manages persistent AI agent memory and reasoning patterns using high-performance vector storage and learning algorithms.
Transforms raw datasets into persuasive narratives and executive-ready presentations using structured storytelling frameworks and visualization techniques.
Implements high-performance embedding pipelines and vector search strategies for RAG applications.
Calculates comprehensive portfolio risk metrics including VaR, CVaR, and risk-adjusted return ratios for quantitative trading strategies.
Builds robust, production-grade backtesting systems for trading strategies with specialized handling for look-ahead bias and transaction costs.
Optimizes Apache Spark jobs using advanced partitioning, memory management, and shuffle tuning patterns.
Optimizes vector database performance by tuning HNSW parameters, quantization strategies, and scaling configurations for high-efficiency similarity search.
Implement and automate comprehensive evaluation strategies for Large Language Model applications using metrics, human feedback, and LLM-as-judge patterns.
Designs and implements sophisticated LLM applications using LangChain 1.x and LangGraph for advanced agentic workflows and state management.
Builds and orchestrates end-to-end MLOps pipelines from data preparation through production model deployment.
Automates the creation, editing, and analysis of complex Excel spreadsheets with support for dynamic formulas and financial modeling standards.
Automates professional spreadsheet creation, financial modeling, and data analysis with dynamic formulas and industry-standard formatting.
Performs advanced survival analysis and time-to-event modeling using the scikit-survival Python library.
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