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ngs-amplicon-microbiome

Kick off public 16S, 18S, ITS, COI, or other marker-gene amplicon microbiome workflows using nf-core/ampliseq, QIIME2, DADA2, and Cutadapt.

62

Quality

78%

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tessl review fix ./plugins/ngs-analysis/skills/ngs-amplicon-microbiome/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

78%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 highly actionable body with executable commands and genuine validation gating for batch pipeline runs, weakened by dense inline output inventories that belong in reference files and some changelog-flavored prose. Restructuring the per-backend details into one-level-deep references would address its main weakness.

Suggestions

Split the backend output inventories (tables/, visualizations/, resources/ file lists) into a reference file such as references/outputs.md and link to it from the main body.

Trim changelog-style phrasing like 'The runner now also emits...' into plain present-tense statements of what each command produces.

Add an explicit 'if validation fails, fix and re-run' step after the preflight and qc_verdict checks to close the feedback-loop gap.

DimensionReasoningScore

Conciseness

The body is dominated by concrete commands, flags, and file paths with zero explanation of concepts Claude already knows. However, changelog-style sentences like 'The runner now also emits methods/amplicon_methods.json plus a concrete backend handoff bundle...' and the long multi-clause paragraphs on outputs (lines 43, 64, 91, 93) could be tightened or trimmed.

4 / 5

Actionability

Multiple copy-paste-ready bash commands with concrete arguments (real primer sequences, a named classifier file, specific backends, explicit flags and output paths), covering preflight, FASTQ QC, QIIME2, DADA2, and nf-core paths. The common cases are covered with executable commands.

5 / 5

Workflow Clarity

A clear progression exists (essential inputs → preflight → FASTQ QC package → backend execution → visualization) with real validation checkpoints: preflight install plan, readiness verdicts, qc_verdict.json, gating that 'FASTQs alone' are not analysis-ready, and the review-only block on synthetic diversity data. It falls short of a 5 because there is no explicit fix-and-retry feedback loop for failed validation and the batch-run sequence is implied by section order rather than enumerated steps.

4 / 5

Progressive Disclosure

Sections are well organized, but no bundle files exist (no references/, scripts/, or assets/), so dense inline material — the backend output-file inventory (lines 66-75), the resource-plan flag detail (line 64), and the visualization output catalog (line 93) — is content that could live in one-level-deep reference files. At ~95 lines of detail across three backends, the inlining is more than a minor organization gap, matching the 'content that should be separate is inline' anchor.

3 / 5

Total

16

/

20

Passed

Description

66%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, well-targeted description with concrete tools and marker regions, but it lacks any explicit 'Use when...' trigger clause, capping its completeness. Adding trigger conditions and synonyms like 'metabarcoding' would round it out.

Suggestions

Add a 'Use when...' clause, e.g. 'Use when the user provides amplicon FASTQs or mentions 16S, ITS, metabarcoding, or microbiome analysis.'

Include common synonyms users say, such as 'metabarcoding' or 'amplicon sequencing', to broaden natural trigger coverage.

Mention 1-2 outcome actions (e.g. 'produces ASV tables, taxonomy assignments, and diversity plots') to lift action coverage toward comprehensive.

DimensionReasoningScore

Specificity

The description names the domain ('marker-gene amplicon microbiome workflows'), enumerates concrete region targets ('16S, 18S, ITS, COI'), and names the exact tools ('nf-core/ampliseq, QIIME2, DADA2, and Cutadapt'). The single verb 'Kick off' keeps it just below the comprehensive multi-action coverage of a 5.

4 / 5

Completeness

The 'what' is clear (kick off amplicon microbiome workflows with named tools and regions), but there is no 'Use when...' clause or equivalent explicit trigger guidance, which caps completeness at 3 per the judging guidelines. 'When' is only weakly implied by the domain terms.

3 / 5

Trigger Term Quality

Strong natural keywords users would actually say — '16S', 'ITS', 'amplicon', 'microbiome', 'marker-gene', plus tool names — but common synonyms like 'metabarcoding', 'metabarcodes', or FASTQ file mentions are missing, so coverage is good rather than comprehensive.

4 / 5

Distinctiveness Conflict Risk

The 16S/ITS/amplicon niche with named tools is clearly distinguishable from unrelated skills, but it sits inside a broader NGS/bioinformatics space (WGS, metagenomics) where a sibling skill could plausibly compete for the same request. Minor overlap risk, not a clear isolated niche.

4 / 5

Total

15

/

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-amplicon-microbiome/SKILL.md

Reviewed

Table of Contents

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