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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".

70

Quality

85%

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SecuritybySnyk

Low

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SKILL.md
Quality
Evals
Security

Quality

Content

71%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.

The body is a knowledgeable, well-sequenced domain guide with genuinely executable code and unusually good error-recovery guidance. Its main weaknesses are broken progressive disclosure — five referenced bundle files that do not exist, with the ACMG algorithm inlined anyway — and minor over-explanation in the regulatory and conflicting-evidence sections.

Suggestions

Ship the five referenced bundle files (or remove the references): no references/ directory exists, so ACMG_CLASSIFICATION.md, CODE_PATTERNS.md, CHECKLIST.md, EXAMPLES.md, and TOOLS_REFERENCE.md all fail to resolve, breaking both navigation and the pre-delivery verification step.

Move the Bayesian point table, the full classify_acmg implementation, and the gene-specific BS1 thresholds out of Phase 6 into ACMG_CLASSIFICATION.md — the body already directs readers there 'for the complete algorithm', so keep only the classification cutoffs and evidence-code summary inline.

Tighten Phase 2.5's prediction rationale and the 'Handling Conflicting Evidence' narrative into short rule-style guidance, and inline the calling signatures for Phases 4-5 tools (or ensure TOOLS_REFERENCE.md exists with them).

DimensionReasoningScore

Conciseness

The body is dense with genuinely non-obvious domain content (ToolUniverse tool parameters, gnomAD two-step workflow, fallback chains) that Claude cannot know, so it earns its tokens. But Phase 2.5's mechanism rationale paragraph ('annotation says an element is present, not whether this specific allele disrupts it...') and the discursive 'Handling Conflicting Evidence' narrative include explanation that could be tightened to direct tool-selection rules. This matches 'efficient; minor instances of over-explanation that could be trimmed', not the lean anchor at 5.

4 / 5

Actionability

Strong concrete guidance: copy-paste-ready `classify_acmg` and `ESM_explain_variant_mechanism` code, parameter tables for regulatory-prediction tools, and explicit fallback orderings. Falls short of fully executable because Phases 4 and 5 list bare tool names with the parameter details deferred to `TOOLS_REFERENCE.md`, which is not present in the bundle, and many tool invocations (e.g. `ClinVar_search_variants`) never show their calling signature.

4 / 5

Workflow Clarity

A clearly sequenced 6-phase pipeline (with 2.5/2.9/4.2/4.5 sub-phases), feedback loops via the 'Tool Failure Fallbacks' section, and checkpoints via 'Quantified Minimums' and a short-circuit ClinVar check. Minor validation gaps remain: the pre-delivery verification checklist is delegated to `CHECKLIST.md` which is absent, and per-phase 'capture/verify before proceeding' checkpoints are implicit. Anchor 4 ('clear sequence with most checkpoints present; minor validation gaps'), not 5, since the explicit validation checklist does not resolve.

4 / 5

Progressive Disclosure

The References section signals five one-level-deep files with one-line descriptions (good pattern), but no bundle files exist — `ACMG_CLASSIFICATION.md`, `CODE_PATTERNS.md`, `CHECKLIST.md`, `EXAMPLES.md`, and `TOOLS_REFERENCE.md` are all missing from the skill directory, so every reference is a dead end. Additionally, ~100 lines of Phase 6 (Bayesian point table, full `classify_acmg` implementation, gene-specific BS1 thresholds) inline the very content the body says lives in `ACMG_CLASSIFICATION.md` ('See ACMG_CLASSIFICATION.md for the complete algorithm'), which is the 'content that should be separate is inline' pattern of anchor 3.

3 / 5

Total

15

/

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: concrete end-to-end capability list, natural trigger phrasing including anticipated synonyms, explicit what-and-when, and a well-bounded clinical niche. No changes needed.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions covering the full pipeline: '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'. Coverage spans raw calls to report output with no meaningful gaps, matching the comprehensive anchor rather than the 'minor gaps' anchor at 4.

5 / 5

Completeness

Explicitly answers both questions: what it does ('Clinical variant interpretation from raw variant calls to ACMG-classified recommendations with structural impact analysis') and when to use it ('Use this whenever a user asks about a variant's significance... or needs ACMG classification'). Voice is third person throughout, so no specificity penalty applies.

5 / 5

Trigger Term Quality

Natural user phrasing is comprehensively covered: "a variant's significance", "VUS classification", "pathogenicity", "intronic/promoter/enhancer/UTR non-coding variant's functional impact", "ACMG classification — even if they don't say 'ACMG'", plus named models and 'molecular tumor boards'. The explicit anticipation of users who omit the jargon ('even if they don't say ACMG') is exactly the synonym coverage the top anchor rewards.

5 / 5

Distinctiveness Conflict Risk

Clear clinical variant-interpretation niche (ACMG guidelines, ClinVar-style curation, return of results, molecular tumor boards) with triggers anchored to variant-significance questions; adjacent skills (e.g. general sequence analysis) are even routed via a separate cross-skill reference, keeping conflict risk minimal.

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
mims-harvard/ToolUniverse
Reviewed

Table of Contents

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