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。
Simulates high-performance computational fluid dynamics using pseudospectral methods and Python-based analysis.
Builds, fits, and validates robust Bayesian models using PyMC's modern probabilistic programming interface.
Generates publication-quality statistical graphics and complex data visualizations using the Seaborn Python library.
Performs advanced molecular analysis, manipulation, and chemical informatics tasks using the RDKit toolkit.
Accesses the ClinicalTrials.gov API v2 to search, filter, and export clinical study data for medical research and patient matching.
Facilitates advanced mass spectrometry data analysis using the Python interface to the OpenMS library for proteomics and metabolomics.
Applies medicinal chemistry rules and structural filters to prioritize drug-like compounds in molecular discovery workflows.
Enables programmatic access to the RCSB Protein Data Bank for searching, retrieving, and analyzing 3D structures of biological macromolecules.
Queries the NCBI ClinVar database to interpret genetic variant pathogenicity and annotate genomic data for research and medicine.
Accesses the UniProt REST API to search, retrieve, and map protein sequence and functional data directly within scientific workflows.
Manages and analyzes microscopy data programmatically using the OMERO Python API and data management platform.
Automates complex Word document operations including precise tracked changes, raw XML manipulation, and scientific schematic integration.
Develops and trains Graph Neural Networks (GNNs) for node classification, link prediction, and geometric deep learning tasks.
Builds, optimizes, and executes quantum circuits and algorithms on simulators or real hardware using the Qiskit framework.
Facilitates automated protein testing and validation through the Adaptyv cloud laboratory platform.
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.
Processes and analyzes mass spectrometry data using the Matchms library for spectral similarity and metadata harmonization.
Parses and manipulates Flow Cytometry Standard (FCS) files, converting biological data into NumPy arrays and CSV formats for scientific analysis.
Implements comprehensive machine learning workflows using scikit-learn for classification, regression, clustering, and data preprocessing.
Accesses over 230 million purchasable chemical compounds for virtual screening, drug discovery, and molecular docking studies.
Accesses and analyzes the Ensembl REST API for gene lookups, sequence retrieval, and advanced variant effect predictions in genomic research.
Processes and analyzes high-performance genomic interval data for computational biology and machine learning applications.
Streamlines deep learning development by organizing PyTorch code into scalable, boilerplate-free LightningModules and automated training workflows.
Formulates testable, evidence-based scientific hypotheses and experimental designs from observations or literature synthesis.
Simplifies molecular cheminformatics workflows by providing a Pythonic wrapper for RDKit with sensible defaults and parallel processing.
Accesses the world's most comprehensive enzyme database to retrieve kinetic parameters, reaction equations, and biochemical property data.
Generates professional, publication-quality scientific research posters using LaTeX frameworks like beamerposter, tikzposter, and baposter.
Crafts competitive, agency-compliant research proposals for major federal funding bodies like the NSF, NIH, DOE, and DARPA.
Builds, analyzes, and visualizes complex networks and graph data structures using the comprehensive NetworkX library for Python.
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