data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Performs constraint-based reconstruction and analysis of metabolic models for systems biology and metabolic engineering.
Implements managed Retrieval-Augmented Generation (RAG) using Google File Search and Gemini models for high-accuracy document retrieval and grounding.
Automates laboratory workflows and controls liquid handling robots, plate readers, and other lab equipment using a hardware-agnostic Python SDK.
Provides rapid, unified access to over 20 genomic and proteomic databases for sequence analysis and protein structure prediction.
Simplifies molecular cheminformatics workflows by providing a Pythonic wrapper around RDKit with sensible defaults and parallel processing.
Queries the NCBI Gene database to retrieve comprehensive genetic information, sequences, and functional annotations via E-utilities and Datasets APIs.
Optimizes embedding model selection, configuration, and cost estimation for RAG pipelines.
Integrates ChromaDB capabilities for building AI applications with persistent memory and semantic search.
Provides comprehensive access to the Human Metabolome Database (HMDB) for metabolite identification, chemical analysis, and clinical research.
Accesses and retrieves genomic data, nucleotide sequences, and metadata from the European Nucleotide Archive (ENA) via REST APIs and FTP.
Builds, simulates, and optimizes quantum circuits for execution on leading quantum hardware and simulators.
Streamlines machine learning model development with production-ready templates for classification, text generation, and parameter-efficient fine-tuning.
Provides a comprehensive toolkit for protein language models to design, predict, and analyze protein sequences and structures.
Provides programmatic access to over 40 bioinformatics web services and databases for integrated biological data analysis.
Generates interactive, publication-quality scientific and statistical visualizations for Python data analysis.
Accesses and analyzes real-time SEC filings and financial statements with token-efficient data retrieval.
Optimizes Mem0 performance through advanced query tuning, multi-layer caching strategies, and cost-reduction patterns.
Provides specialized technical auditing and compliance reviews for Physics-Guided GNN research papers targeting IEEE Power & Energy Society submissions.
Architects and implements production-grade LLM applications, RAG pipelines, and intelligent agent orchestrations.
Manages, analyzes, and visualizes multi-modal spatial omics data using the SpatialData Python ecosystem and OME-NGFF standards.
Queries 20+ genomic databases for gene information, protein structures, and sequence analysis directly within your development environment.
Processes and analyzes physiological biosignals including cardiac, neural, and autonomic data for psychophysiology and clinical research.
Automates laboratory liquid handling and hardware control using a hardware-agnostic Python interface.
Implements robust multi-agent systems using Pydantic tool schemas, state management, and advanced orchestration patterns.
Facilitates creative scientific ideation by generating hypotheses, exploring interdisciplinary connections, and identifying research gaps through collaborative dialogue.
Builds and trains self-improving AI agents using nine reinforcement learning algorithms and WASM-accelerated inference.
Simplifies molecular cheminformatics and drug discovery workflows through a Pythonic interface for RDKit.
Optimizes Text-to-Speech model training and voice cloning using the Unsloth library for faster performance and lower VRAM usage.
Facilitates astronomical research and data analysis by providing tools for celestial coordinates, physical units, and FITS file manipulation.
Accesses and analyzes comprehensive drug data including properties, interactions, targets, and chemical structures from the DrugBank database.
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