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。
Drafts, structures, and refines professional scientific manuscripts using standardized research formats and reporting guidelines.
Accesses and integrates the world's most comprehensive database for exploring somatic mutations in human cancer into bioinformatics workflows.
Performs rigorous statistical modeling and econometric analysis using regression, time series, and diagnostic testing.
Simplifies the implementation and training of reinforcement learning agents using the Stable Baselines3 framework.
Accesses and analyzes comprehensive USPTO patent and trademark data for intellectual property research and prior art discovery.
Accesses comprehensive pharmacogenomic data for precision medicine, including gene-drug interactions and CPIC clinical guidelines.
Creates publication-quality statistical graphics and exploratory data visualizations using a high-level Python interface.
Processes and analyzes mass spectrometry data using Python-based spectral similarity, metadata harmonization, and data filtering tools.
Processes digital pathology whole-slide images for computational research and deep learning dataset preparation.
Integrates Claude Code with the OMERO platform to manage, analyze, and automate microscopy image data workflows via the Python API.
Simplifies molecular cheminformatics and drug discovery workflows using a Pythonic abstraction layer over RDKit.
Automates comprehensive end-to-end testing for the Kosmos autonomous AI scientist using local models, external APIs, and Docker sandboxes.
Accesses the world's leading protein sequence and functional information resource via the UniProt REST API.
Performs comprehensive survival analysis and time-to-event modeling using the scikit-survival library in Python.
Streamlines deep learning development by organizing PyTorch code into scalable, high-performance Lightning modules and data pipelines.
Manages large-scale N-dimensional arrays with chunking, compression, and cloud-native storage for scientific computing.
Builds and validates advanced Bayesian probabilistic models using PyMC 5.x for scientific discovery and statistical inference.
Conducts high-performance computational fluid dynamics simulations using Python-based pseudospectral methods and MPI parallelization.
Applies unsupervised machine learning models to genomic interval data for region embeddings, single-cell analysis, and similarity searches.
Provides comprehensive Python tools for biological computation, sequence analysis, and bioinformatics database access.
Automates scientific hypothesis generation and empirical testing by synthesizing observational data with research literature.
Manipulates and processes DICOM medical imaging data for healthcare applications and scientific research.
Converts chemical structures into machine learning-ready numerical representations using over 100 specialized featurizers and pretrained embeddings.
Explains machine learning model predictions and feature importance using Shapley Additive exPlanations for transparent and interpretable AI.
Provides comprehensive cheminformatics capabilities for molecular analysis, manipulation, and property calculation within Claude Code.
Accesses and analyzes comprehensive FDA regulatory data for drugs, medical devices, and food safety through the openFDA API.
Automates the end-to-end scientific research lifecycle from data analysis and hypothesis generation to publishing LaTeX-formatted papers.
Scales Python data science workflows using parallel and distributed computing for larger-than-memory datasets.
Implements comprehensive machine learning workflows using scikit-learn, covering data preprocessing, model training, evaluation, and pipeline deployment.
Executes complex biomedical research tasks across genomics, drug discovery, and clinical analysis using autonomous AI reasoning.
Scroll for more results...