From bio-research
Orients bio-researchers: displays welcome, checks MCP server connections for literature/drug-discovery/visualization, lists analysis skills like scRNA QC and scvi-tools, suggests workflows.
npx claudepluginhub anthropics/knowledge-work-plugins --plugin bio-researchThis skill uses the workspace's default tool permissions.
> If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md).
Autonomously executes multi-step biomedical research tasks across genomics, drug discovery, molecular biology, and clinical analysis using LLM reasoning, code execution, and integrated databases. For CRISPR screening, scRNA-seq analysis, ADMET prediction, GWAS, and rare disease diagnosis.
Autonomously executes complex biomedical research tasks in genomics, drug discovery, molecular biology, and clinical analysis using LLM reasoning, code execution, and integrated databases. For CRISPR screening, scRNA-seq analysis, ADMET prediction, GWAS, rare disease diagnosis.
Build AI scientist systems using ToolUniverse Python SDK to access 1000+ scientific tools for drug discovery, protein/genomics analysis, literature research, and computational biology workflows.
Share bugs, ideas, or general feedback.
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
You are helping a biological researcher get oriented with the bio-research plugin. Walk through the following steps in order.
Display this welcome message:
Bio-Research Plugin
Your AI-powered research assistant for the life sciences. This plugin brings
together literature search, data analysis pipelines,
and scientific strategy — all in one place.
Test which MCP servers are connected by listing available tools. Group the results:
Literature & Data Sources:
Drug Discovery & Clinical:
Visualization & AI:
Report which servers are connected and which are not yet set up.
List the analysis skills available in this plugin:
| Skill | What It Does |
|---|---|
| Single-Cell RNA QC | Quality control for scRNA-seq data with MAD-based filtering |
| scvi-tools | Deep learning for single-cell omics (scVI, scANVI, totalVI, PeakVI, etc.) |
| Nextflow Pipelines | Run nf-core pipelines (RNA-seq, WGS/WES, ATAC-seq) |
| Instrument Data Converter | Convert lab instrument output to Allotrope ASM format |
| Scientific Problem Selection | Systematic framework for choosing research problems |
Mention that two additional MCP servers are available as separate installations:
txg-node.mcpb from https://github.com/10XGenomics/txg-mcp/releasestooluniverse.mcpb from https://github.com/mims-harvard/ToolUniverse/releasesThese require downloading binary files and are optional.
Ask the researcher what they're working on today. Suggest starting points based on common workflows:
Wait for the user's response and guide them to the appropriate tools and skills.