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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Passed

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Quality

Content

92%Weight 40%Scale 1-5

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

A well-structured orchestrator skill: executable commands for both upstream paths, a clearly sequenced stage workflow with validation checkpoints and QC feedback loops, and clean progressive disclosure to verified reference files and scripts. The only minor weakness is occasional restatement of domain knowledge that Claude already has.

Suggestions

Tighten the 'Common Pitfalls' and stage descriptions by trimming domain restatements Claude already knows (e.g. 'DESeq2 needs raw counts', why strandedness halves reads) and keeping only the skill-specific decision guidance.

Consider moving the full Path B inline commands into references/upstream-manual.md (already referenced) and leaving only the canonical one-liner per stage in SKILL.md to further reduce token load.

Dimension	Reasoning	Score
Conciseness	Largely lean — it delegates depth to references, links sibling skills rather than re-explaining them, and uses compact tables and a Mermaid diagram — but a few sentences restate domain knowledge Claude already has (e.g. elaborating why strandedness matters, restating 'DESeq2 needs raw counts') that could be trimmed.	4 / 5
Actionability	Provides copy-paste-ready commands for both paths (validated samplesheet check, nf-core run with pinned -r, fastp/salmon invocations, the build_counts_matrix.py bridge call) and points to executable bundled scripts, covering the common cases concretely.	5 / 5
Workflow Clarity	The 8-stage numbered workflow is clearly sequenced with an explicit validation checkpoint (validate_samplesheet.py before the run), a smoke-test step, QC gates framed as feedback loops ('re-run FastQC to confirm', 'always look at the PCA before trusting DE'), and a Common Pitfalls section guiding error recovery — matching the explicit-validation anchor.	5 / 5
Progressive Disclosure	SKILL.md is an overview that routes to four real one-level-deep reference files (all present under references/) and two real scripts (present under scripts/), each self-contained and clearly signaled by name in the body and a dedicated Reference Files section; no nested-reference chains.	5 / 5
	Total	19 / 20 Passed

Description

100%Weight 40%Scale 1-5

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

An exemplary description: it states precisely what the skill does, names the concrete tools and stages, gives multiple natural trigger phrases, and explicitly carves out its niche against neighboring skills. Both the 'what' and 'when' are answered concretely with minimal conflict risk.

Dimension	Reasoning	Score
Specificity	Enumerates concrete actions and tools across the full pipeline — '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)', giving comprehensive coverage with named tools per stage.	5 / 5
Completeness	Explicitly answers both 'what' (the orchestration from FASTQ through QC, alignment/quantification, counts, DE, enrichment, figures) and 'when' via a concrete 'Use whenever…' clause with example trigger phrases and a routing distinction between nf-core and standalone paths.	5 / 5
Trigger Term Quality	Includes natural user phrasings and synonyms — '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' — covering the realistic ways a user would request this.	5 / 5
Distinctiveness Conflict Risk	Occupies a clear end-to-end bulk RNA-seq niche and explicitly disambiguates from adjacent skills ('For single-cell RNA-seq use the scanpy skill instead'; 'for the DE statistics alone use pydeseq2'; 'for enrichment alone use pathway-enrichment'), minimizing wrong-skill triggering.	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: b2a92ba

Reviewed: 3 days ago

Table of Contents

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