Overview

Empower your AI agent with a comprehensive suite of 135 ready-to-use scientific and research skills. This collection is designed to transform any AI agent supporting the open Agent Skills standard into a powerful research assistant. Covering domains from bioinformatics and drug discovery to physics, engineering, and scientific writing, these skills streamline complex multi-step workflows.

Key Features

• Extensive Skill Library: Access 135 meticulously crafted skills for a vast array of scientific disciplines, including genomics, cheminformatics, clinical research, AI/ML, materials science, physics, engineering, geospatial analysis, and scientific communication.
• Vast Database Integration: Seamlessly query over 100 scientific and financial databases (e.g., PubChem, ChEMBL, UniProt, FRED, ClinicalTrials.gov) through a unified lookup skill, plus dedicated access to resources like Hugging Science.
• Optimized Package Skills: Leverage pre-defined, well-documented skills for over 70 essential Python libraries (e.g., RDKit, Scanpy, PyTorch Lightning, BioPython, Qiskit, OpenMM), ensuring robust and reliable performance.
• Scientific Integration: Utilize specialized skills for seamless integration with platforms like Benchling, DNAnexus, and Protocols.io.
• Enhanced Workflow Capabilities: Execute complex, multi-step research pipelines with a single prompt, saving significant time and effort.
• Broad Compatibility: Works with any AI agent supporting the open Agent Skills standard, including Cursor, Claude Code, Codex, and Gemini CLI.
• Developer-Focused: Each skill comes with comprehensive documentation, practical code examples, best practices, and integration guides.

Typical Use Cases

• Accelerated Drug Discovery: Automate tasks like target identification, virtual screening, lead optimization, and ADMET analysis.
• Advanced Bioinformatics: Perform sequence analysis, single-cell RNA-seq processing, gene regulatory network inference, and phylogenetic analysis.
• Materials Science & Chemistry Research: Analyze crystal structures, predict material properties, and perform computational chemistry simulations.
• Engineering & Simulation: Optimize complex systems, model metabolic processes, and execute simulations for engineering design.
• Data Analysis & Visualization: Conduct statistical analysis, network analysis, time-series forecasting, and generate publication-quality figures.
• Scientific Writing & Communication: Streamline literature reviews, peer review processes, document generation, and visual content creation.
• Clinical Research & Healthcare AI: Analyze EHR data, interpret genetic variants, support clinical decision-making, and build predictive health models.