anthropics/life-sciences

What it solves

This project provides a specialized marketplace of plugins for Claude Code, enabling researchers in the life sciences to integrate professional research tools, academic databases, and bioinformatics workflows directly into their AI assistant. It bridges the gap between general-purpose LLMs and the highly specialized data sources and analysis tools used in biological research.

How it works

It utilizes the Model Context Protocol (MCP) to connect Claude Code to external services and local tools. It offers two main types of extensions:

• MCP Servers: These act as bridges to remote services like PubMed, BioRender, and Synapse, allowing the AI to search literature, access data management platforms, and interact with scientific illustration tools.
• Skills: These are domain-specific workflows, such as automated quality control for single-cell RNA-seq data or the execution of nf-core bioinformatics pipelines via Nextflow.

Who it’s for

• Bench scientists who need to run large-scale omics analyses without deep bioinformatics training.
• Life science researchers who seeking to streamline their literature search, data management, and project ideation.
• Bioinformaticians looking to integrate deep learning toolkits like scvi-tools into their AI-assisted workflow.

Highlights

• Academic Integration: Direct access to PubMed, Wiley Scholar Gateway, and Consensus for evidence-based research.
• Bioinformatics Pipelines: Support for nf-core pipelines (rnaseq, sarek, atacseq) and scvi-tools for deep learning omics analysis.
• Data Standardization: Tools for converting instrument data to the Allotrope Simple Model (ASM) format.
• Strategic Planning: A framework for scientific problem selection and risk assessment based on established methodologies.
• Industry Tooling: Connectivity to professional platforms like 10x Genomics Cloud, Cortellis, and AdisInsight.