bulk-rnaseq

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.

Quality

96%

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Quality

Content

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Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-architected skill body: executable commands for both upstream paths and the bridge, a validated numbered workflow with QC checkpoints, and clean one-level-deep progressive disclosure into self-contained references. The only weakness is minor non-essential prose (the framing block and citing boilerplate) that keeps conciseness just short of perfectly lean.

Suggestions

Tighten the 'Defensible' overview block — the three-bullet Reproducible/Quality-gated/Statistically-sound expansion restates values already enforced throughout the stages; a single sentence would preserve the framing while trimming tokens.

Move the boilerplate 'Citing Scientific Agent Skills' section into a shared references file so it does not consume SKILL.md context on every load.

A couple of placeholder arguments in Path B (salmon_index, s1_R1.fq.gz) could carry a one-line note that they are per-sample placeholders, to remove any ambiguity for direct copy-paste.

Dimension	Reasoning	Score
Conciseness	The body is dense and assumes Claude's competence — it does not explain what RNA-seq, alignment, or FastQC are — but the 'Defensible' framing block and the required citing boilerplate add some tokens that could be trimmed, so it sits at efficient-with-minor-over-explanation rather than perfectly lean.	4 / 5
Actionability	Provides copy-paste-ready commands covering both paths and the bridge — 'nextflow run nf-core/rnaseq -r 3.26.0 ... --aligner star_salmon', 'salmon quant -i salmon_index -l A ... --gcBias --seqBias', 'python scripts/build_counts_matrix.py --from salmon --quant-dir quant/ --tx2gene tx2gene.tsv --output-dir counts/' — matching the score-5 anchor for fully executable, common-case coverage.	5 / 5
Workflow Clarity	A numbered stage-by-stage sequence with explicit validation checkpoints — 'Validate the samplesheet first (catches the most common failures early)', 'Re-run FastQC to confirm', 'Always look at the PCA and sample-distance heatmap before trusting DE' — plus a pitfalls checklist; validation is present so the batch-operation cap at 3 does not apply.	5 / 5
Progressive Disclosure	A clear overview in SKILL.md pointing to four well-signaled, verified one-level-deep references (references/upstream-nfcore.md, upstream-manual.md, counts-and-handoff.md, design-and-qc.md) and two scripts, with depth appropriately split out and each reference self-contained — matching the score-5 anchor.	5 / 5
	Total	19 / 20 Passed

Description

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Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A highly specific, third-person description that comprehensively covers capabilities, includes natural trigger phrases a user would say, and explicitly answers both what and when while cleanly distinguishing itself from adjacent skills. No meaningful gaps to penalize.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions with named tools — '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)' — comprehensive coverage matching the score-5 anchor.	5 / 5
Completeness	Explicitly answers both what ('End-to-end bulk RNA-seq orchestrator...') and when ('Use whenever the user has bulk RNA-seq reads or quant output and wants a complete, reproducible differential-expression workflow') with concrete trigger phrases, matching the score-5 anchor; not below because the 'when' clause is explicit rather than weakly implied.	5 / 5
Trigger Term Quality	Includes natural phrases a user would actually say — 'analyze my RNA-seq', 'FASTQ to DESeq2', 'run nf-core/rnaseq', 'STAR/Salmon quantification', 'build a counts matrix for DESeq2', 'go from reads to differentially expressed genes and enriched pathways' — comprehensive coverage matching the score-5 anchor.	5 / 5
Distinctiveness Conflict Risk	Clear niche (bulk RNA-seq) with explicit de-confliction — 'For single-cell RNA-seq use the scanpy skill instead' and routing to pydeseq2/pathway-enrichment — giving minimal conflict risk, matching the score-5 anchor.	5 / 5
	Total	20 / 20 Passed

Validation

93%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
referenced_paths_exist	Referenced path issues: 1 missing	Warning

	Total	15 / 16 Passed

Repository: K-Dense-AI/scientific-agent-skills
Path: skills/bulk-rnaseq/SKILL.md
Commit: 49c6e97

Reviewed: 9 days ago

Table of Contents

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