Explore our collection of Agent Skills to enhance your AI workflow.
Accesses and analyzes comprehensive pharmaceutical data from DrugBank for drug discovery, pharmacology research, and interaction analysis.
Processes and analyzes high-performance genomic interval data using Rust-powered algorithms and Python bindings.
Streamlines the development, deployment, and management of serverless bioinformatics pipelines using the LatchBio SDK and cloud infrastructure.
Performs advanced time series machine learning tasks including classification, forecasting, and anomaly detection using a scikit-learn compatible interface.
Queries the Monarch Initiative knowledge graph to explore complex disease-gene-phenotype associations across multiple species.
Automates protein sequence optimization and experimental validation through cloud-based laboratory assays and API integration.
Automates cloud-based quantum chemistry workflows, molecular property predictions, and protein-ligand modeling via a unified Python API.
Provides programmatic access to the ChEMBL database for bioactive molecule research and medicinal chemistry.
Generates publication-ready clinical decision support documents and biomarker-stratified cohort analyses for pharmaceutical and clinical research.
Simplifies digital pathology workflows by automating tissue detection and tile extraction from gigapixel whole slide images (WSI).
Provides a unified Python interface to over 40 bioinformatics web services and databases for integrated biological data analysis.
Facilitates programmatic access to 110M+ chemical compounds and bioactivity data via the PubChem API and PubChemPy.
Query, visualize, and download large-scale public cancer imaging datasets from the National Cancer Institute.
Automates searching and retrieving life sciences preprints from the bioRxiv database using keywords, authors, and DOI lookups.
Enables advanced whole-slide image analysis, nucleus segmentation, and machine learning workflows for computational pathology.
Builds, fits, and validates robust Bayesian statistical models using PyMC and ArviZ for advanced probabilistic programming.
Manages large N-dimensional arrays using chunked, compressed storage for cloud-native and parallel scientific computing.
Analyzes protein sequences for glycosylation sites and provides tools for the rational engineering of therapeutic proteins and vaccines.
Performs comprehensive differential gene expression analysis on bulk RNA-seq data using the PyDESeq2 Python framework.
Streamlines the analysis of Neuropixels high-density neural recordings using SpikeInterface and industry-standard curation workflows.
Queries the Genome Aggregation Database (gnomAD) for population allele frequencies, variant consequences, and gene constraint metrics.
Accesses and analyzes over 200 million AI-predicted protein structures from the DeepMind and EMBL-EBI repository.
Applies medicinal chemistry rules, structural alerts, and drug-likeness filters to prioritize molecular libraries for drug discovery.
Transforms academic papers into interactive websites, professional presentation videos, and print-ready conference posters using an autonomous pipeline.
Interfaces with the openFDA API to analyze regulatory data for drugs, medical devices, food safety, and substances.
Implements eval-driven development principles to benchmark agent reliability and ensure high-quality task completion.
Enforces technical rigor and verification when processing code review feedback to ensure architectural integrity over performative agreement.
Develops and trains quantum machine learning models with hardware-agnostic automatic differentiation and hybrid framework integration.
Generates publication-quality infographics using a multi-model AI pipeline for automated research, visual design, and iterative quality review.
Analyzes system hardware to provide optimized strategy recommendations for high-performance scientific computing and data processing tasks.
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