data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Accesses over 600 scientific tools and databases for bioinformatics, drug discovery, and computational biology research.
Implement high-performance semantic vector search and intelligent document retrieval using AgentDB optimized HNSW indexing and quantization.
Automates laboratory data management and life sciences R&D workflows by integrating with the Benchling platform via Python SDK and REST API.
Accesses over 40 bioinformatics web services and databases through a unified Python interface for biological data retrieval and analysis.
Searches the arXiv repository to retrieve and summarize the latest scholarly articles in STEM fields.
Infers gene regulatory networks from transcriptomics data using scalable GRNBoost2 and GENIE3 algorithms.
Simplifies complex molecular informatics workflows by providing a Pythonic interface for RDKit with sensible defaults and built-in parallelization.
Simplifies molecular cheminformatics and drug discovery workflows with a Pythonic abstraction layer over RDKit.
Integrates high-performance semantic vector search and HNSW indexing for intelligent document retrieval and RAG systems.
Simplifies molecular cheminformatics and drug discovery workflows using a Pythonic interface for RDKit.
Builds discrete-event simulation models in Python to analyze complex systems involving queues, resources, and time-based processes.
Develops, tests, and deploys machine learning models for clinical healthcare data using standardized pipelines and specialized medical architectures.
Integrates managed vector databases into AI applications for production-grade RAG, semantic search, and recommendation systems.
Analyzes high-throughput sequencing data to perform quality control, normalization, and publication-quality visualization for NGS experiments.
Implements ultra-high-performance semantic vector search and document retrieval for Claude-powered RAG systems and intelligent knowledge bases.
Implement high-performance semantic search and vector storage for intelligent document retrieval and RAG systems.
Simulates and analyzes genome-scale metabolic models using constraint-based reconstruction and analysis (COBRA) techniques.
Builds process-based discrete-event simulations in Python for modeling complex systems like logistics, manufacturing, and networks.
Executes complex autonomous research tasks across genomics, drug discovery, and clinical analysis using integrated biomedical databases.
Accesses the European Nucleotide Archive (ENA) to retrieve genomic sequences, raw reads, and metadata for bioinformatics pipelines.
Analyzes Excel spreadsheets, generates pivot tables, and creates data visualizations using Python libraries like pandas and openpyxl.
Builds, simulates, and executes quantum circuits using Google’s open-source framework for NISQ-era quantum computers.
Accesses the ZINC22 database to search, filter, and retrieve 230M+ purchasable chemical compounds for virtual screening and drug discovery.
Automates protein sequence optimization and experimental validation through cloud-based laboratory testing.
Implements high-performance persistent memory and context management for AI agents using AgentDB.
Streamlines astronomical data analysis and astrophysical calculations using the core Astropy Python library.
Queries and analyzes clinical trial data from the official ClinicalTrials.gov API v2 for research and patient matching.
Executes complex autonomous biomedical research tasks including genomics, drug discovery, and clinical data analysis.
Performs constraint-based reconstruction and analysis of metabolic models for systems biology and metabolic engineering.
Searches the arXiv preprint repository for scholarly articles across various scientific and technical domains.
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