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ngs-dna-umi-panel-variants

Run or plan targeted DNA panel variant workflows that use UMIs, duplex consensus reads, molecular barcodes, low-frequency calling, target coverage, and panel-specific QC.

70

Quality

85%

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SKILL.md
Quality
Evals
Security

Quality

Content

87%Weight 40%Scale 1-3

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

A lean, actionable skill body that gives real executable commands and well-organized sections without explaining concepts Claude already knows. The main gap is the absence of an explicit validate→fix→retry feedback loop for the batch pipeline workflow, which leaves workflow clarity at 2.

Suggestions

Add an explicit feedback loop after the runner: e.g. inspect qc/umi_postrun_summary.tsv, and if on-target rate or family-size metrics fall below thresholds, adjust --umi-mode/filters and re-run — this would lift workflow clarity to 3.

Surface a short 'verify' checklist that maps each Decision Point (e.g. separate raw vs molecular depth, low-AF artifact review) to the specific qc/ output column to check, making the implicit verification steps explicit.

DimensionReasoningScore

Conciseness

The body is lean and assumes Claude's competence — it never explains what UMIs, duplex consensus, or target BEDs are, and uses compact checklists and commands; even the dense Route paragraph documents tool-specific outputs rather than padding with concept explanations, so it sits at the score-3 'every token earns its place' anchor.

3 / 3

Actionability

Provides fully executable, copy-paste-ready commands — the preflight 'python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline dna_umi_panel_variants --emit-install-plan' and the run_dna_umi_panel_variants.py invocation with concrete flags (--umi-mode duplex, --umi-tag RX, --execute) — matching the score-3 executable-code anchor.

3 / 3

Workflow Clarity

A sequence is present (confirm inputs → preflight → runner → review QC/outputs) with verification via the postrun summary and molecular-evidence contract, but there is no explicit validate→fix→retry feedback loop for this batch pipeline operation; the guidelines cap workflow clarity at 2 when such checkpoints are implicit rather than spelled out, so it is below the score-3 anchor and above the score-1 'steps unclear' anchor.

2 / 3

Progressive Disclosure

This is a single-file skill with no references/, scripts/, or assets/ bundle directories and well-organized sections (Essential Inputs, Route, Decision Points, Outputs) with no nested references; per the simple-skills scoring note, well-organized sections with no need for external references score 3.

3 / 3

Total

11

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12

Passed

Description

82%Weight 40%Scale 1-3

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-scoped description that names concrete UMI/duplex panel-variant capabilities and natural trigger terms with low conflict risk. Its main weakness is the absence of an explicit 'Use when...' trigger clause, which caps completeness at 2.

Suggestions

Add an explicit trigger clause such as 'Use when analyzing targeted DNA panels that rely on UMIs, duplex consensus, or low-frequency allele detection' to lift completeness from 2 to 3.

Include a few common user phrasings (e.g. 'umi error correction', 'duplex sequencing variants', 'low-AF panel calling') to broaden trigger-term coverage beyond the current technical terms.

DimensionReasoningScore

Specificity

Lists multiple concrete capabilities — 'Run or plan targeted DNA panel variant workflows', 'UMIs, duplex consensus reads, molecular barcodes, low-frequency calling, target coverage, and panel-specific QC' — matching the score-3 anchor of several specific concrete actions.

3 / 3

Completeness

Clearly states what the skill does, but there is no 'Use when...' clause or equivalent explicit trigger guidance; per the judging guidelines a missing explicit trigger caps completeness at 2, so it is not the 3 that requires both what and when.

2 / 3

Trigger Term Quality

Uses natural domain terms a genomics user would actually say — 'UMIs', 'duplex consensus reads', 'molecular barcodes', 'low-frequency calling', 'target coverage', 'panel' — giving good coverage of relevant trigger keywords rather than abstract jargon.

3 / 3

Distinctiveness Conflict Risk

The description carves a clear niche (UMI/duplex/low-frequency panel variants) distinct from ordinary germline calling, so it is unlikely to trigger for the wrong skill; it is well above the score-2 'could still overlap' anchor.

3 / 3

Total

11

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12

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
openai/plugins
Reviewed

Table of Contents

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