genomics plugins

by Shanghua Gao

1000+ scientific research tools for biology, chemistry, medicine, and data science. Access PubMed, UniProt, PubChem, TCGA, NHANES, GWAS Catalog, and hundreds more databases through a unified MCP interface. Includes specialized research skills for genomics, drug discovery, clinical analysis, protein variant interpretation, and more.

by Shanghua Gao

1000+ scientific research tools for biology, chemistry, medicine, and data science. Access PubMed, UniProt, PubChem, TCGA, NHANES, GWAS Catalog, and hundreds more databases through a unified MCP interface. Includes 120+ specialized research skills for genomics, drug discovery, clinical analysis, protein variant interpretation, and more.

by Manuel Corpas

Bioinformatics-native AI agent skill library: pharmacogenomics, clinical variant interpretation, GWAS and polygenic scores, ancestry, RNA-seq and single-cell, metagenomics, proteomics and nf-core pipeline wrappers. 97 Agent Skills, 50 with a deterministic CLI entry point and demo data, every runnable skill writing a SHA-256 reproducibility bundle. Local-first.

by Dr. Alex M. Mawla, PhD

20 ENCODE API tools + 47 expert skills for genomics research. Search experiments, download files with MD5 verification, run pipelines, and cross-reference 14 databases.

by Dr. Alex M. Mawla, PhD

20 ENCODE API tools + 47 expert skills for genomics research. Search experiments, download files with MD5 verification, run pipelines, and cross-reference 14 databases.

by Matt Corbett

Bioinformatics & genomics-pipeline team — agents (bioinformatics-workflow-architect, genomics-pipeline-engineer) for the layer answering 'which workflow engine, reference, and compute strategy, and how do we build a reproducible, validated genomics pipeline?': engine choice (Nextflow/nf-core, Snakemake, WDL+Cromwell/miniwdl, CWL), reference build (GRCh38 vs T2T-CHM13) + core steps (QC, trimming, alignment BWA-MEM2/minimap2/STAR/Salmon, dedup, variant calling GATK/DeepVariant, joint genotyping), RNA-seq (DESeq2/edgeR) and single-cell (Scanpy/Seurat), reproducibility (Docker/Apptainer, Conda/Bioconda, pinned versions, FAIR, provenance), HPC-vs-cloud scaling and cost, and validation vs GIAB/hap.py truth sets. skills, a knowledge bank (pipeline decision tree + 2026 workflow-patterns reference), and templates. Distinct from ml-engineering (generic MLOps/model training), clinical-trials (trial operations/regulatory), and data-platform (warehouse/BI). Requires ravenclaude-core@>=0.7.0.