Discover Agent Skills for data science & ml. Browse 61 skills for Claude, ChatGPT & Codex.
Queries and analyzes clinical trial data from the official ClinicalTrials.gov API v2 for research and patient matching.
Automates end-to-end scientific research workflows from initial data analysis and hypothesis generation to the production of publication-ready LaTeX manuscripts.
Queries the ClinicalTrials.gov API to search for medical studies, retrieve trial details, and export structured clinical research data.
Accesses and analyzes comprehensive pharmaceutical data from DrugBank, including drug properties, interactions, targets, and chemical structures.
Automates scientific hypothesis generation and testing by combining observational data with literature-based insights using large language models.
Generates publication-quality scientific visualizations and data plots locally using Python's Matplotlib and Seaborn libraries.
Builds high-performance Retrieval-Augmented Generation systems using vector databases, semantic search, and advanced retrieval patterns.
Queries the Ensembl REST API to retrieve gene annotations, sequences, variants, and comparative genomics data for over 250 species.
Automates Next-Generation Sequencing (NGS) data processing, quality control, and publication-quality visualization.
Builds end-to-end MLOps pipelines for data preparation, model training, validation, and production deployment.
Implement high-performance semantic search and vector storage for intelligent document retrieval and RAG systems.
Trains and deploys sophisticated neural network architectures across distributed E2B sandbox environments.
Accesses and analyzes comprehensive pharmaceutical data from DrugBank including drug properties, interactions, and molecular structures.
Integrates the open-source embedding database to build AI-native applications with semantic search and retrieval-augmented generation (RAG) capabilities.
Predicts high-accuracy 3D protein-ligand binding poses using diffusion-based deep learning for structure-based drug design.
Automates complex scientific research workflows and tool discovery across bioinformatics, genomics, and drug discovery domains.
Builds end-to-end MLOps pipelines from data preparation and model training to production deployment and monitoring.
Builds end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment.
Automates the end-to-end scientific research lifecycle from data analysis and hypothesis generation to publication-ready LaTeX manuscripts.
Implements comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking.
Integrates Reactome's open-source curated pathway database for biological pathway analysis, gene mapping, and systems biology research.
Streamlines molecular machine learning workflows for drug discovery, property prediction, and graph neural network implementation.
Generates rigorous, testable scientific hypotheses and detailed experimental designs based on observations and existing literature.
Generates professional, multi-page PDF reports with formatted text, tables, and embedded visualizations using the ReportLab library.
Processes and analyzes mass spectrometry data using Python-based spectral similarity metrics and metadata harmonization.
Interfaces with the European Nucleotide Archive to programmatically retrieve DNA/RNA sequences, raw reads, and genome metadata.
Trains and deploys distributed neural networks within secure E2B sandbox environments using the Flow Nexus framework.
Enables advanced protein engineering, sequence generation, and structure prediction using Evolutionary Scale Modeling.
Simplifies PDF manipulation, data extraction, and document generation using industry-standard Python libraries and CLI tools.
Provides comprehensive cheminformatics capabilities for molecular analysis, property calculation, and 3D coordinate generation.
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