data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Connects Claude to the KEGG REST API for advanced biological pathway analysis, gene mapping, and molecular network discovery.
Converts over 20 file formats including PDFs, Office documents, and media into clean, LLM-optimized Markdown.
Read, write, and process medical imaging data in the DICOM standard format using Python.
Generates publication-quality statistical graphics and complex data visualizations using the Seaborn Python library.
Provides ultra-fast semantic vector search and intelligent document retrieval using AgentDB's high-performance HNSW indexing.
Accesses the NIH Metabolomics Workbench database to query metabolite structures, experimental study data, and standardized nomenclature.
Generates interactive statistical and scientific visualizations using Plotly Express and Graph Objects APIs.
Streamlines single-cell transcriptomics workflows using standardized scverse, AnnData, and Scanpy implementation patterns.
Analyzes system hardware to provide strategic recommendations for high-performance computing and scientific tasks.
Converts existing Snakemake analysis projects into pip-installable CLI tools for automated workflow deployment.
Integrates Google's Gemini 3 Pro API and SDK into applications for advanced reasoning, streaming chat, and large-context processing.
Streamlines single-cell RNA-seq analysis using standardized AnnData and Scanpy workflows.
Implements ultra-fast semantic vector search and RAG capabilities using AgentDB's high-performance indexing and quantization.
Generates high-performance interactive visualizations using Plotly Express and Graph Objects patterns.
Facilitates seamless Python-based interaction with the B-Fabric Laboratory Information Management System for managing research data and workflows.
Develops and optimizes Python-based liquid handling protocols for Opentrons Flex and OT-2 robots.
Automates laboratory liquid handling and hardware control for Opentrons Flex and OT-2 robots using Protocol API v2.
Streamlines Snakemake pipeline development using modular patterns and compact rule definitions for reproducible data science.
Queries, interprets, and processes human genetic variant data from the NCBI ClinVar archive for genomic research.
Integrates nine reinforcement learning algorithms to build, train, and deploy self-learning AI agents that evolve through experience.
Analyzes phosphoproteomics data using R to identify differential post-translational modification abundance, stoichiometry, and kinase activity.
Detects and analyzes system hardware to provide strategic architectural recommendations for computationally intensive scientific tasks.
Accelerates scientific discovery by generating hypotheses, identifying research gaps, and facilitating interdisciplinary ideation through structured collaborative dialogue.
Implements high-performance persistent memory and reinforcement learning patterns for AI agents using AgentDB.
Facilitates creative research ideation through hypothesis generation, interdisciplinary connections, and rigorous methodological development.
Accesses and interprets NCBI ClinVar data to evaluate the clinical significance and pathogenicity of human genetic variants.
Implements high-performance semantic vector search for RAG systems, knowledge bases, and intelligent similarity matching.
Accesses and interprets NCBI ClinVar data to evaluate human genetic variants, clinical significance, and phenotype relationships.
Streamlines the creation, versioning, and lifecycle management of Amazon Bedrock prompt templates with variable substitution and A/B testing.
Implements modern AI patterns including streaming responses, tool calling, and structured outputs using the Vercel AI SDK.
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