data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
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.
Reads, writes, and manipulates biological sequence data formats like FASTA, FASTQ, and GenBank.
Manages, versions, and tests Amazon Bedrock prompt templates to streamline enterprise-grade prompt engineering workflows.
Builds and manages autonomous AI agents on Amazon Bedrock using foundation models, action groups, and knowledge bases.
Generates and validates recursive string diagrams using category theory primitives for complex system modeling.
Integrates Google's Gemini 3 Pro API and SDK into applications for advanced reasoning, streaming chat, and large-context processing.
Creates, optimizes, and debugs high-performing, production-ready prompts for Claude 4, GLM 4.7, and Gemini 3 using evidence-based techniques.
Streamlines the creation, versioning, and lifecycle management of Amazon Bedrock prompt templates with variable substitution and A/B testing.
Implements comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and comparative benchmarking.
Queries and analyzes personal book libraries from Goodreads CSV exports to provide reading insights and statistics.
Combines vector similarity and keyword-based search to optimize retrieval accuracy in RAG systems and search engines.
Implements high-performance similarity search and vector database patterns for semantic retrieval and RAG systems.
Optimizes Large Language Model performance through advanced reasoning patterns, few-shot learning, and structured prompt templates.
Calculates comprehensive portfolio risk metrics and performance indicators for quantitative trading strategies and investment management.
Implements modern AI patterns including streaming responses, tool calling, and structured outputs using the Vercel AI SDK.
Builds production-grade backtesting systems for trading strategies while mitigating common biases and accounting for realistic market costs.
Builds sophisticated LLM applications using the LangChain framework to implement autonomous agents, complex workflows, and persistent memory.
Optimizes vector database indexes for production performance by balancing search latency, recall accuracy, and memory consumption.
Builds production-grade Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search for LLM applications.
Plans data science and hardware optimization experiments by analyzing historical project logs and domain-specific configurations.
Prevents Jupyter notebook hangs by explicitly managing CuPy GPU memory pools and Python garbage collection.
Streamlines the development of AI-powered features within the Moodle LMS using the official AI Subsystem for version 4.5 and above.
Builds and automates end-to-end MLOps pipelines from data ingestion and preparation through model training, validation, and production deployment.
Optimizes embedding model selection and chunking strategies to improve semantic search and RAG application accuracy.
Transforms complex datasets into persuasive narratives and executive-ready presentations using proven storytelling frameworks.
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