Tooluniverse Variant InterpretationSAFE

The largest open-source medical AI skills library for OpenClaw🦞.

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: tooluniverse-variant-interpretation
description: Systematic clinical variant interpretation from raw variant calls to ACMG-classified recommendations with structural impact analysis. Aggregates evidence from ClinVar, gnomAD, CIViC, UniProt, and PDB across ACMG criteria. Produces pathogenicity scores (0-100), clinical recommendations, and treatment implications. Use when interpreting genetic variants, classifying variants of uncertain significance (VUS), performing ACMG variant classification, or translating variant calls to clinical actionability.
---

---
name: tooluniverse-variant-interpretation
description: Systematic clinical variant interpretation from raw variant calls to ACMG-classified recommendations with structural impact analysis. Aggregates evidence from ClinVar, gnomAD, CIViC, UniProt, and PDB across ACMG criteria. Produces pathogenicity scores (0-100), clinical recommendations, and treatment implications. Use when interpreting genetic variants, classifying variants of uncertain significance (VUS), performing ACMG variant classification, or translating variant calls to clinical actionability.
---

# Clinical Variant Interpreter

Systematic variant interpretation skill using ToolUniverse - from raw variant calls to ACMG-classified clinical recommendations with structural impact analysis.

---

## Problem This Skill Solves

Clinical labs and researchers face critical challenges in variant interpretation:

1. **Variant classification uncertainty** - VUS (Variants of Uncertain Significance) comprise 40-60% of clinical variants
2. **Evidence aggregation burden** - Must integrate data from 10+ databases per variant
3. **Structural context missing** - Traditional annotation ignores 3D protein impact
4. **Clinical actionability unclear** - How does classification translate to patient care?

**This skill provides**: A systematic workflow that combines population databases, functional predictions, structural analysis (via AlphaFold2), and literature evidence into ACMG-compliant interpretations with clear clinical recommendations.

---

## Key Principles

1. **ACMG-Guided Classification** - Follow ACMG/AMP 2015 guidelines with explicit evidence codes
2. **Structural Evidence Integration** - Use AlphaFold2 for novel structural impact analysis
3. **Population Context** - gnomAD frequencies with ancestry-specific data
4. **Gene-Disease Validity** - ClinGen curation status for clinical relevance
5. **Actionable Output** - Clear recommendations, not just classifications
6. **English-first queries** - Always use English terms in tool calls (gene names, variant descriptions, disease names), even if the user writes in another language. Only try original-language terms as a fallback. Respond in the user's language

---

## Triggers

Use this skill when users:
- Ask about variant interpretation or classification
- Have VCF data needing clinical annotation
- Ask "what does this variant mean clinically?"
- Need ACMG classification for variants
- Want structural impact analysis for missense variants
- Ask about pathogenicity of specific variants

---

## Workflow Overview

```
┌─────────────────────────────────────────────────────────────────┐
│                    VARIANT INTERPRETATION                        │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  Phase 1: VARIANT IDENTITY                                       │
│  ├── Normalize variant notation (HGVS)                          │
│  ├── Map to gene, transcript, protein                           │
│  └── Get consequence type (missense, nonsense, etc.)            │
│                                                                  │
│  Phase 2: CLINICAL DATABASES                                     │
│  ├── ClinVar: Existing classifications                          │
│  ├── gnomAD: Population frequencies (all + ancestry)            │
│  ├── OMIM: Gene-disease associations                            │
│  ├── ClinGen: Gene validity + dosage sensitivity (ENHANCED)     │
│  │   └─ ClinGen_search_gene_validity, ClinGen_search_dosage     │
│  └── SpliceAI: Splice variant prediction (NEW)                  │
│                                                                  │
│  Phase 2.5: REGULATORY CONTEXT (NEW - for non-coding variants)  │
│  ├── ChIPAtlas: TF binding at position                          │
│  ├── ENCODE: Regulatory elements (enhancers, promoters)         │
│  ├── Conservation in regulatory regions                         │
│  └── Functional annotation of regulatory impact                 │
│                                                                  │
│  Phase 3: COMPUTATIONAL PREDICTIONS                              │
│  ├── SIFT/PolyPhen: Damaging predictions                        │
│  ├── CADD: Deleteriousness score                                │
│  ├── SpliceAI: Splice impact (if applicable)                    │
│  └── Conservation: Cross-species alignment                      │
│                                                                  │
│  Phase 4: STRUCTURAL ANALYSIS (for VUS/novel missense)          │
│  ├── Get protein structure (PDB or AlphaFold2)                  │
│  ├── Map variant to structure                                   │
│  ├── Assess domain/functional site impact                       │
│  └── Predict structural destabilization                         │
│                                                                  │
│  Phase 4.5: EXPRESSION CONTEXT (NEW)                            │
│  ├── CELLxGENE: Cell-type specific expression                   │
│  ├── Tissue relevance to phenotype                              │
│  └── Expression validation                                       │
│                                                                  │
│  Phase 5: LITERATURE EVIDENCE                                    │
│  ├── PubMed: Functional studies                                 

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

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Gates applied: no_behavioural_pass.