data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Implements advanced Retrieval-Augmented Generation (RAG) architectures using vector databases and semantic search to ground LLM responses in external knowledge.
Integrates real-time xAI Grok sentiment with major financial APIs to provide a holistic view of market trends, price action, and fundamental health.
Architects high-performance LLM prompts and multi-agent systems using advanced reasoning patterns and optimization techniques.
Refines and enhances AI prompts to improve model performance and output accuracy using a structured analysis framework.
Builds custom plugins for the Semantik search engine to handle document ingestion, embeddings, and AI-powered reranking.
Implements a persistent knowledge graph memory system for Auto-Claude to retain context and learn patterns across development sessions.
Persists project knowledge and error-solution patterns across autonomous coding sessions to prevent repetitive mistakes.
Transforms raw prompts into optimized, production-ready instructions using advanced prompt engineering techniques and model-specific optimizations.
Simplifies the selection and optimization of xAI Grok models for development tasks through detailed capability and cost analysis.
Builds and manages fully managed RAG solutions using Amazon Bedrock for semantic search and document-based AI applications.
Design and implement multi-layered memory architectures for AI agents to ensure long-term state persistence and entity consistency.
Persists learned solutions and error patterns across autonomous coding sessions to create a self-healing development environment.
Orchestrates complex multi-agent systems using Microsoft Agent Framework patterns like AutoGen and Semantic Kernel.
Generates and refines structured physics analysis specifications using standardized templates and domain-specific best practices.
Streamlines the creation, sampling, and diagnostics of Bayesian statistical models using PyMC and ArviZ.
Integrates comprehensive financial market data for stocks, forex, crypto, and technical indicators into the Claude environment.
Integrates Twelve Data for real-time and historical financial market data across stocks, forex, crypto, and technical indicators.
Implements self-learning agent workflows by tracking operational trajectories and recognizing successful automation patterns.
Provides comprehensive guidance and technical specifications for selecting and optimizing xAI Grok models within developer workflows.
Provides comprehensive guidance on leveraging Claude Opus 4.5, including reasoning control via the effort parameter and performance optimization.
Customizes and optimizes Amazon Bedrock foundation models through fine-tuning, continued pre-training, reinforcement learning, and model distillation.
Implements a multi-layered persistent memory architecture that enables AI agents to learn from experience and retain knowledge across sessions.
Implements production-grade LLM-as-a-Judge techniques for evaluating AI outputs through rigorous scoring, pairwise comparisons, and bias mitigation.
Integrates Alpha Vantage APIs to fetch real-time and historical market data, technical indicators, and financial fundamentals.
Streamlines the development of Python workflows using Awkward Array for jagged, nested, and record-based data structures.
Implement efficient vector-based similarity search, semantic retrieval, and RAG patterns across major vector databases.
Proposes structured, data-driven experiment plans by analyzing historical training reports, logs, and research goals.
Optimizes long-running AI agent sessions by implementing structured context compression to maintain technical accuracy and memory efficiency.
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.
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