AIエージェントの能力を拡張するClaudeスキルの完全なコレクションをご覧ください。
Queries the ChEMBL database for bioactive molecules, drug targets, and bioactivity data to accelerate medicinal chemistry and drug discovery research.
Streamlines deep learning development by organizing PyTorch code into scalable, boilerplate-free LightningModules and automated training workflows.
Automates Electronic Lab Notebook (ELN) workflows through programmatic access to LabArchives for research data management and reporting.
Evaluates scholarly and research work using the peer-reviewed ScholarEval framework for rigorous academic assessment.
Accesses the UniProt REST API to search, retrieve, and map protein sequence and functional data directly within scientific workflows.
Develops and optimizes quantum circuits, hybrid quantum-classical models, and molecular simulations using the PennyLane library.
Empowers Claude with expert capabilities for manipulating, analyzing, and visualizing geospatial vector data using Python.
Enables programmatic access to the RCSB Protein Data Bank for searching, retrieving, and analyzing 3D structures of biological macromolecules.
Accesses the world's largest chemical database to search compounds, retrieve molecular properties, and perform structure-based searches.
Infers gene regulatory networks from transcriptomics data using high-performance algorithms like GRNBoost2 and GENIE3.
Accesses and manages NCBI Gene Expression Omnibus (GEO) data for transcriptomics and functional genomics research.
Performs fast non-linear dimensionality reduction and manifold learning for data visualization and clustering preprocessing.
Automates liquid handling and laboratory workflows using the Opentrons Protocol API v2 for Flex and OT-2 robots.
Accesses USPTO APIs to perform comprehensive patent and trademark searches, retrieve examination histories, and analyze intellectual property data.
Simulates high-performance computational fluid dynamics using pseudospectral methods and Python-based analysis.
Processes and analyzes high-performance genomic interval data for computational biology and machine learning applications.
Facilitates advanced mass spectrometry data analysis using the Python interface to the OpenMS library for proteomics and metabolomics.
Performs professional statistical modeling, hypothesis testing, and rigorous assumption verification with publication-ready APA reporting.
Runs Python code in the cloud with serverless containers, autoscaling GPUs, and minimal infrastructure management.
Facilitates collaborative research ideation to generate hypotheses, challenge assumptions, and explore interdisciplinary connections for creative scientific problem-solving.
Solves complex single and multi-objective optimization problems using evolutionary algorithms and Pareto front analysis.
Generates regulatory-compliant clinical reports and medical documentation with integrated scientific visualizations.
Streamlines high-throughput sequencing data analysis by providing tools for BAM processing, quality control, and publication-quality genomic visualizations.
Facilitates direct REST API access to the KEGG database for biological pathway analysis, gene mapping, and molecular interaction research.
Provides a comprehensive toolkit for rigorous statistical modeling, econometric analysis, and time-series forecasting within the Claude environment.
Parses and manipulates Flow Cytometry Standard (FCS) files, converting biological data into NumPy arrays and CSV formats for scientific analysis.
Performs constraint-based reconstruction and analysis of metabolic models for systems biology and metabolic engineering tasks.
Accesses AI-ready Therapeutics Data Commons (TDC) datasets and benchmarks for drug discovery and pharmaceutical machine learning.
Integrates NCBI Gene data access into Claude for querying sequences, functional annotations, and genomic metadata.
Develops and trains Graph Neural Networks (GNNs) for node classification, link prediction, and geometric deep learning tasks.
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