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tooluniverse-variant-interpretation

Clinical variant interpretation from raw variant calls to ACMG-classified recommendations with structural impact analysis. Use for VUS classification, pathogenicity assessment with cited criteria, structure-based variant impact (AlphaFold/PDB), non-coding/regulatory variant effect prediction with sequence deep-learning models (AlphaGenome, Enformer, Borzoi, ChromBPNet, Evo 2), and producing clinical-grade variant reports for return of results or molecular tumor boards. Use this whenever a user asks about a variant's significance, an intronic/promoter/enhancer/UTR non-coding variant's functional impact, or needs ACMG classification — even if they don't say "ACMG".

76

Quality

94%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

The canonical home for this skill is tooluniverse-variant-interpretation in mims-harvard/ToolUniverse

SKILL.md
Quality
Evals
Security

Quality

Content

88%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, well-sequenced clinical workflow with executable code, specific tool guidance, and strong validation checkpoints. The main gaps are minor over-explanation in rationale prose and reference files that are signaled but absent from the bundle.

Suggestions

Ship the referenced bundle files (ACMG_CLASSIFICATION.md, CODE_PATTERNS.md, CHECKLIST.md, EXAMPLES.md, TOOLS_REFERENCE.md) alongside SKILL.md, or inline the essential parts and drop the dangling references, so signaled navigation actually resolves.

Tighten the rationale prose in Phase 2.5 and the 'Handling Conflicting Evidence' section to instruction-first guidance, trimming explanatory sentences that don't change what Claude does.

Verify the cross-skill path 'skills/tooluniverse-sequence-analysis/scripts/amino_acids.py' resolves from the skill's install location, or replace it with a self-contained snippet.

DimensionReasoningScore

Conciseness

Largely efficient and tool-specific (exact parameters, return shapes, thresholds), but several rationale paragraphs — Phase 2.5's 'this is what turns the variant is in an enhancer into...' and the multi-paragraph 'Handling Conflicting Evidence' prose — over-explain reasoning that could be tightened without losing actionability.

4 / 5

Actionability

Provides copy-paste-ready executable code (the complete classify_acmg function with a worked PALB2 example), exact tool names with parameters and return shapes, concrete fallback chains, and specific thresholds (e.g. BS1 ~0.0001 for high-penetrance genes) covering the common cases.

5 / 5

Workflow Clarity

Clear phased sequence (Phases 1–6 plus 2.5/2.9/4.2/4.5) with explicit validation checkpoints — the 'LOOK UP, DON'T GUESS' gate, the Phase 2.9 short-circuit check, the Quantified Minimums checklist, and tool-failure fallbacks that act as feedback loops for the database operations.

5 / 5

Progressive Disclosure

Excellent disclosure structure — a concise overview with well-signaled, one-level-deep references each given a one-line description in the References section — but the referenced files (ACMG_CLASSIFICATION.md, CODE_PATTERNS.md, CHECKLIST.md, EXAMPLES.md, TOOLS_REFERENCE.md) and the cross-skill amino_acids.py script are not present in the bundle, so the signaled navigation does not resolve.

4 / 5

Total

18

/

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.

A strong, specific description that clearly states capabilities, provides comprehensive natural trigger terms, explicitly covers both what and when, and occupies a distinct niche. Written in third-person imperative trigger form consistent with the accepted 'Use when...' pattern.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — ACMG-classified recommendations, VUS classification, pathogenicity assessment, structure-based variant impact, non-coding/regulatory effect prediction, and clinical-grade reports — giving comprehensive coverage rather than vague language.

5 / 5

Completeness

Explicitly answers both what ('Clinical variant interpretation from raw variant calls to ACMG-classified recommendations...') and when ('Use this whenever a user asks about a variant's significance...') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Comprehensive natural terms users would say — 'variant's significance', 'VUS', 'pathogenicity', 'intronic/promoter/enhancer/UTR', 'ACMG' — plus the explicit 'even if they don't say ACMG' guidance captures synonyms and unstated intent.

5 / 5

Distinctiveness Conflict Risk

Occupies a clear clinical-genomics niche (ACMG variant interpretation with structural/regulatory analysis) with distinct, specific triggers and minimal overlap with other skills.

5 / 5

Total

20

/

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
mims-harvard/ToolUniverse
Reviewed

Table of Contents

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