Explore our collection of Agent Skills to enhance your AI workflow.
Performs state-of-the-art diffusion-based molecular docking to predict 3D binding poses of ligands to protein targets.
Automates the process of dispatching specialized subagents to review code changes and identify technical issues before merging.
Facilitates computational molecular biology tasks including sequence manipulation, NCBI database access, and structural bioinformatics analysis.
Automates protein testing and validation by connecting computational designs to cloud-based laboratory experiments and optimization tools.
Manipulates, analyzes, and visualizes phylogenetic trees with advanced support for evolutionary event detection and NCBI taxonomy integration.
Performs rigorous statistical modeling, econometric analysis, and hypothesis testing using the Python statsmodels library.
Implements advanced prompt design techniques to maximize LLM performance, reliability, and token efficiency across all interactions.
Implements robust saga patterns for managing distributed transactions and long-running business processes across microservices.
Processes and analyzes high-throughput sequencing data (NGS) to generate publication-quality visualizations and quality control metrics.
Simplifies molecular cheminformatics workflows by providing a Pythonic wrapper around RDKit with sensible defaults and parallel processing.
Infers gene regulatory networks from transcriptomics data using scalable machine learning algorithms like GRNBoost2 and GENIE3.
Analyzes and processes physiological signals including ECG, EEG, and EDA using the comprehensive NeuroKit2 Python library.
Automates laboratory workflows and hardware control through a unified, hardware-agnostic Python interface.
Facilitates molecular machine learning and drug discovery workflows using the DeepChem toolkit.
Manages comprehensive quality document control and regulatory compliance for medical device organizations using systematic change management and configuration control.
Performs robust differential gene expression analysis for bulk RNA-seq data using the Python implementation of DESeq2.
Accelerates Python development workflows by leveraging the ultra-fast uv package manager for dependency resolution, environment management, and project initialization.
Automates the creation of comprehensive JUnit 5 tests for static utility classes and pure functions.
Streamlines access to over 40 bioinformatics web services for integrated biological data retrieval and analysis.
Extends pandas with geometric operations and spatial data structures for advanced geospatial analysis and mapping.
Automates the validation of Java object mapping to JSON and vice versa using Spring Boot's @JsonTest and Jackson.
Builds and trains high-performance reinforcement learning agents using optimized vectorization and multi-agent simulation.
Performs comprehensive biological data analysis, including sequence manipulation, phylogenetics, and microbiome ecology statistics.
Generates and validates comprehensive unit tests for MapStruct mappers and custom data converters.
Automates scientific hypothesis generation and testing by combining observational data with literature-based insights using large language models.
Enables building, training, and optimizing quantum circuits and hybrid quantum-classical machine learning models with automatic differentiation.
Evaluates scientific manuscripts and grant proposals for methodological rigor, statistical accuracy, and reporting standards.
Simplifies testing of Jakarta Bean Validation constraints and custom validators using JUnit 5 and AssertJ.
Automates lead discovery and market analysis by examining your codebase to identify high-priority target companies and personalized outreach strategies.
Accelerates drug discovery and molecular modeling using graph neural networks and curated biological datasets within PyTorch.
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