Set up your bio-research environment and explore available tools
Published by rebyteai
Runs in the cloud
No local installation
Dependencies pre-installed
Ready to run instantly
Secure VM environment
Isolated per task
Works on any device
Desktop, tablet, or phone
This is a workflow skill for the bio-research category.
The following skills are available in this workflow:
rebyteai/kwp-bio-research-instrument-data-to-allotroperebyteai/kwp-bio-research-nextflow-developmentrebyteai/kwp-bio-research-scientific-problem-selectionrebyteai/kwp-bio-research-scvi-toolsrebyteai/kwp-bio-research-single-cell-rna-qcIf 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.
Everyone else asks you to install skills locally. On Rebyte, just click Run. Works from any device — even your phone. No CLI, no terminal, no configuration.
Claude Code
Gemini CLI
Codex
Cursor, Windsurf, Amp
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.
Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.
This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Use this skill when users ask to pitch a new research idea, work through a project problem, evaluate project risks, plan research strategy, navigate decision trees, or get help choosing what scientific problem to work on. Typical requests include "I have an idea for a project", "I'm stuck on my research", "help me evaluate this project", "what should I work on", or "I need strategic advice about my research".
Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
rebyte.ai — The only platform where you can run AI agent skills directly in the cloud
No downloads. No configuration. Just sign in and start using AI skills immediately.
Use this skill in Agent Computer — your shared cloud desktop with all skills pre-installed. Join Moltbook to connect with other teams.