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ngs-bulk-rnaseq-counts-qc

Run or plan bulk RNA-seq FASTQ-to-count processing with sample-sheet, strandedness, genome annotation, alignment or pseudoalignment, MultiQC, and count-matrix QC checks.

64

Quality

81%

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SecuritybySnyk

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SKILL.md
Quality
Evals
Security

Quality

Content

82%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 dense, command-driven skill body with executable runner and preflight invocations, explicit validation modes, and a clear routing decision between nf-core and a local path. Its weaknesses are modest: a confusing dry-run flag explanation, no explicit ordered workflow with error-recovery guidance, and reference-bundle details that would sit better in a separate file.

DimensionReasoningScore

Conciseness

The body is lean and assumes domain competence — no explanation of what RNA-seq, strandedness, or MultiQC are; every line carries operational content. A few dense passages could be trimmed, most notably the overlapping flag semantics ('Omit --execute for validation plus Snakemake workflow validation only. Use --no-dry-run only when...') which read as confusing rather than merely terse, keeping it below the level-5 anchor.

4 / 5

Actionability

Full copy-paste-ready commands with concrete arguments: the runner invocation with --sample-sheet, --fastq-root, reference flags, and --execute, plus two preflight invocations with explicit flags. Decision points give concrete, checkable actions ('Inspect per-sample reads, mapping rate, rRNA/mitochondrial fraction...'). This matches the fully-executable, covers-common-cases anchor.

5 / 5

Workflow Clarity

A coherent sequence is present — confirm essential inputs, run preflight, choose a route, run with or without --execute — with real validation checkpoints (dry-run/validation-only mode, Snakemake workflow graph validation, resource readiness checks, and a QC verdict JSON covering outlier samples). It falls short of the level-5 anchor because the sequence is organized by topic sections rather than an explicit ordered flow with feedback loops (what to do when validation or readiness fails is not stated).

4 / 5

Progressive Disclosure

The SKILL.md is a well-organized overview (~66 lines) with clearly delineated sections (inputs, route, decision points, outputs) and no nested or buried references — runner paths point at plugin scripts rather than bundle reference files. It misses the level-5 anchor partly because it slightly exceeds the simple-skill line, and the long Outputs/resource-bundle enumerations (resource plan, manifest, env exports, readiness summary) are exactly the kind of detail that could move one level deep into a reference file.

4 / 5

Total

17

/

20

Passed

Description

71%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.

A specific, tool-rich description of the what, weakened by the complete absence of an explicit 'Use when...' trigger clause, which also leaves the skill's boundary with a downstream differential-expression skill implicit. Adding a trigger phrase would lift the two dimensions currently held at 3 and 4.

Suggestions

Add an explicit trigger clause, e.g. 'Use when the user has raw bulk RNA-seq FASTQs (or aligned reads) and needs gene/transcript count matrices with QC; if they already have a count matrix and want contrasts or statistics, use the differential-expression skill instead.'

Include common synonyms users would naturally say — 'gene counts', 'read alignment', '.fastq files', 'expression matrix' — to improve trigger-term coverage.

State the boundary with the differential-expression skill directly in the description so the routing decision Claude makes in the body is visible at trigger time.

DimensionReasoningScore

Specificity

The description lists multiple concrete capabilities — 'FASTQ-to-count processing with sample-sheet, strandedness, genome annotation, alignment or pseudoalignment, MultiQC, and count-matrix QC checks' — naming both specific pipeline stages and tools. It matches the level-5 anchor for multiple specific concrete actions with comprehensive coverage of the processing stages.

5 / 5

Completeness

The 'what' is clear ('Run or plan bulk RNA-seq FASTQ-to-count processing...'), but there is no 'Use when...' clause or equivalent explicit trigger guidance — the when is at best weakly implied by the domain terms. Per the judging guidelines, a missing 'Use when...' clause caps completeness at 3; it is not 4 because the when is entirely absent rather than present-but-imprecise.

3 / 5

Trigger Term Quality

Natural terms like 'bulk RNA-seq', 'FASTQ', 'count-matrix', 'MultiQC', and 'QC' are present and would be said by users needing this skill. A few common variations are missing — 'gene counts', 'read alignment', '.fastq' extensions, and 'expression matrix' — so it sits between the 4 and 5 anchors, closer to 4.

4 / 5

Distinctiveness Conflict Risk

'Bulk RNA-seq FASTQ-to-count processing' plus named tools (MultiQC, pseudoalignment) carves a clear niche distinct from adjacent skills (e.g., differential expression, ChIP-seq, scRNA-seq). Minor overlap risk remains with a differential-expression or general-QC skill since the description does not explicitly bound the scope the way the body does; it is not 5 because that boundary ('if the user already has a count matrix...') appears only in the body, not the description.

4 / 5

Total

16

/

20

Passed

Validation

100%

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

openai/plugins

Path

plugins/ngs-analysis/skills/ngs-bulk-rnaseq-counts-qc/SKILL.md

Reviewed

Table of Contents

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