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 ---
 lineage_type: import
-upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/9c9bd2e9/skills/bulk-rnaseq/SKILL.md
-upstream_sha: 9c9bd2e9
-imported_at: 2026-06-26
+upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/991bd993/skills/bulk-rnaseq/SKILL.md
+upstream_sha: 991bd993
+imported_at: 2026-08-08
 prompt_class: catalogue
 upstream_changes: accepted
 name: bulk-rnaseq
 description: End-to-end bulk RNA-seq orchestrator — takes raw FASTQ reads through QC and trimming (FastQC, fastp/Trim Galore), alignment and quantification (STAR, Salmon, featureCounts), assembles a gene-level counts matrix, then hands off to differential expression (pydeseq2), pathway/GSEA enrichment (pathway-enrichment), and publication figures (scientific-visualization). Use whenever the user has bulk RNA-seq reads or quant output and wants a complete, reproducible differential-expression workflow — e.g. "analyze my RNA-seq", "FASTQ to DESeq2", "run nf-core/rnaseq", "STAR/Salmon quantification", "build a counts matrix for DESeq2", or "go from reads to differentially expressed genes and enriched pathways". Routes between an nf-core/rnaseq (Nextflow) path and a standalone STAR/Salmon path, and covers experimental design, strandedness, and QC gates. For single-cell RNA-seq use the scanpy skill instead.
 license: MIT
-metadata: {"version": "1.0", "skill-author": "K-Dense Inc."}
+metadata:
+  version: "1.0"
+  skill-author: K-Dense Inc.
 ---

 # Bulk RNA-seq