start
The start skill orients researchers to the bio-research plugin by displaying a welcome message, checking which MCP servers are connected across literature, drug discovery, and visualization categories, surveying available analysis skills like single-cell RNA QC and Nextflow pipelines, and explaining how to install optional binary servers. Use this when first accessing the plugin, verifying tool availability, or planning which analysis resources are ready for a new research project.
git clone --depth 1 https://github.com/openyak/openyak /tmp/start && cp -r /tmp/start/backend/app/data/plugins/bio-research/skills/start ~/.claude/skills/startSKILL.md
# Bio-Research Start > If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md). You are helping a biological researcher get oriented with the bio-research plugin. Walk through the following steps in order. ## Step 1: Welcome 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. ``` ## Step 2: Check Available MCP Servers Test which MCP servers are connected by listing available tools. Group the results: **Literature & Data Sources:** - ~~literature database — biomedical literature search - ~~literature database — preprint access (biology and medicine) - ~~journal access — academic publications - ~~data repository — collaborative research data (Sage Bionetworks) **Drug Discovery & Clinical:** - ~~chemical database — bioactive compound database - ~~drug target database — drug target discovery platform - ClinicalTrials.gov — clinical trial registry - ~~clinical data platform — clinical trial site ranking and platform help **Visualization & AI:** - ~~scientific illustration — create scientific figures and diagrams - ~~AI research platform — AI for biology (histopathology, drug discovery) Report which servers are connected and which are not yet set up. ## Step 3: Survey Available Skills 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 | ## Step 4: Optional Setup — Binary MCP Servers Mention that two additional MCP servers are available as separate installations: - **~~genomics platform** — Access cloud analysis data and workflows Install: Download `txg-node.mcpb` from https://github.com/10XGenomics/txg-mcp/releases - **~~tool database** (Harvard MIMS) — AI tools for scientific discovery Install: Download `tooluniverse.mcpb` from https://github.com/mims-harvard/ToolUniverse/releases These require downloading binary files and are optional. ## Step 5: Ask How to Help Ask the researcher what they're working on today. Suggest starting points based on common workflows: 1. **Literature review** — "Search ~~literature database for recent papers on [topic]" 2. **Analyze sequencing data** — "Run QC on my single-cell data" or "Set up an RNA-seq pipeline" 3. **Drug discovery** — "Search ~~chemical database for compounds targeting [protein]" or "Find drug targets for [disease]" 4. **Data standardization** — "Convert my instrument data to Allotrope format" 5. **Research strategy** — "Help me evaluate a new project idea" Wait for the user's response and guide them to the appropriate tools and skills.
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.
Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.
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Package an escalation for engineering, product, or leadership with full context. Use when a bug needs engineering attention beyond normal support, multiple customers report the same issue, a customer is threatening to churn, or an issue has sat unresolved past its SLA.