安全扫描（ClawHub）

• VirusTotal ：良性
• OpenClaw ：可疑

OpenClaw 评估

The skill largely implements a local PK/PD digital-twin simulator (no declared credentials or installers), but documentation and risk statements claim network/API behavior and high risk without corresponding code or declared endpoints, and the shipped code contains quality bugs and lacks explicit privacy controls — proceed only after code review and isolation.

安装（复制给龙虾 AI）

将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。

请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「Digital Twin Patient Builder」。简介：Build digital twin patient models to test drug efficacy and toxicity in virtual…。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/aipoch-ai/digital-twin-patient-builder/SKILL.md
（来源：yingzhi8.cn 技能库）

SKILL.md

打开原始 SKILL.md（GitHub raw）

---
name: digital-twin-patient-builder
description: Build digital twin patient models to test drug efficacy and toxicity
  in virtual environments
version: 1.0.0
category: AI/Tech
tags: []
author: AIPOCH
license: MIT
status: Draft
risk_level: High
skill_type: Hybrid (Tool/Script + Network/API)
owner: AIPOCH
reviewer: ''
last_updated: '2026-02-06'
---

# Digital Twin Patient Builder (ID: 208)

## Function Overview

Build a "digital twin" model of a patient, integrating genotype, clinical history, and imaging data to test the efficacy and toxicity of different drug doses in a virtual environment.

## Use Cases

- Personalized drug treatment plan design
- Drug dose optimization
- Adverse reaction risk assessment
- Clinical trial virtual simulation

## Input

| Data Type | Description | Format |
|---------|------|------|
| `genotype` | Patient genotype data (SNPs, CNVs) | JSON |
| `clinical_history` | Clinical history and laboratory indicators | JSON |
| `imaging_features` | Imaging features (MRI, CT, etc.) | JSON |

## Output

| Output Type | Description |
|---------|------|
| `efficacy_prediction` | Efficacy prediction results |
| `toxicity_prediction` | Toxicity reaction prediction |
| `optimal_dose` | Optimal dose recommendation |

## Usage

### Command Line Usage

```bash
python scripts/main.py --patient patient_data.json --drug drug_profile.json --doses "[50, 100, 150]"
```

## Parameters

| Parameter | Type | Default | Required | Description |
|-----------|------|---------|----------|-------------|
| `--patient` | string | - | Yes | Path to patient data JSON file |
| `--drug` | string | - | Yes | Path to drug profile JSON file |
| `--doses` | string | - | Yes | Dose range to test (JSON array format) |
| `--output`, `-o` | string | - | No | Output file path for simulation results |
| `--simulation-days` | int | 30 | No | Number of days to simulate |
| `--timestep` | float | 0.5 | No | Simulation timestep in days |

### Python API

```python
from scripts.main import DigitalTwinBuilder

builder = DigitalTwinBuilder()
twin = builder.build_twin(patient_data)
results = twin.simulate_drug_regimen(drug_profile, dose_range)
```

## Technical Architecture

```
digital-twin-patient-builder/
├── SKILL.md              # This file
├── scripts/
│   └── main.py           # Core implementation
│
├── Core Components:
│   ├── PatientProfile    # Patient profile management
│   ├── GenotypeModel     # Genotype modeling
│   ├── ClinicalModel     # Clinical data modeling
│   ├── ImagingModel      # Imaging feature modeling
│   ├── DigitalTwin       # Digital twin main class
│   ├── PharmacokineticModel  # Pharmacokinetic model
│   └── DrugSimulator     # Drug simulator
```

## Dependencies

- numpy >= 1.21.0
- scipy >= 1.7.0
- pandas >= 1.3.0

## Example Data Format

### Patient Data (patient_data.json)
```json
{
  "patient_id": "P001",
  "genotype": {
    "CYP2D6": "*1/*4",
    "TPMT": "*1/*3C",
    "SNPs": {"rs12345": "AG", "rs67890": "CC"}
  },
  "clinical": {
    "age": 58,
    "weight": 70.5,
    "height": 170,
    "lab_values": {"creatinine": 1.2, "alt": 45, "ast": 38},
    "comorbidities": ["hypertension", "diabetes"]
  },
  "imaging": {
    "tumor_volume": 45.2,
    "perfusion_rate": 0.85,
    "texture_features": {"entropy": 5.2, "uniformity": 0.45}
  }
}
```

### Drug Profile (drug_profile.json)
```json
{
  "drug_name": "ExampleDrug",
  "drug_class": "chemotherapy",
  "metabolizing_enzymes": ["CYP2D6", "CYP3A4"],
  "target_genes": ["EGFR", "KRAS"],
  "pk_params": {
    "clearance": 15.5,
    "volume_distribution": 45.0,
    "half_life": 8.0
  },
  "efficacy_biomarkers": ["tumor_reduction", "survival_rate"],
  "toxicity_markers": ["neutropenia", "hepatotoxicity"]
}
```

## Model Principles

1. **Genotype Modeling**: Parse drug metabolizing enzyme genotypes to predict metabolic phenotypes (ultrarapid/normal/poor metabolizer)
2. **Physiological Modeling**: Calculate personalized pharmacokinetic parameters based on age, weight, and organ function
3. **Imaging Modeling**: Extract tumor features to predict drug responsiveness
4. **Integrated Model**: Multi-modal data fusion to build a comprehensive digital twin
5. **Drug Simulation**: PBPK (physiologically-based pharmacokinetics) + PD (pharmacodynamics) model

## References

- PBPK modeling guidelines (FDA, 2018)
- Pharmacogenomics in precision medicine (Nature Reviews, 2020)

## Risk Assessment

| Risk Indicator | Assessment | Level |
|----------------|------------|-------|
| Code Execution | Python scripts with tools | High |
| Network Access | External API calls | High |
| File System Access | Read/write data | Medium |
| Instruction Tampering | Standard prompt guidelines | Low |
| Data Exposure | Data handled securely | Medium |

## Security Checklist

- [ ] No hardcoded credentials or API keys
- [ ] No unauthorized file system access (../)
- [ ] Output does not expose sensitive information
- [ ] Prompt injection protections in place
- [ ] API requests use HTTPS only
- [ ] Input validated against allowed patterns
- [ ] API timeout and retry mechanisms implemented
- [ ] Output directory restricted to workspace
- [ ] Script execution in sandboxed environment
- [ ] Error messages sanitized (no internal paths exposed)
- [ ] Dependencies audited
- [ ] No exposure of internal service architecture
## Prerequisites

```bash
# Python dependencies
pip install -r requirements.txt
```

## Evaluation Criteria

### Success Metrics
- [ ] Successfully executes main functionality
- [ ] Output meets quality standards
- [ ] Handles edge cases gracefully
- [ ] Performance is acceptable

### Test Cases
1. **Basic Functionality**: Standard input → Expected output
2. **Edge Case**: Invalid input → Graceful error handling
3. **Performance**: Large dataset → Acceptable processing time

## Lifecycle Status

- **Current Stage**: Draft
- **Next Review Date**: 2026-03-06
- **Known Issues**: None
- **Planned Improvements**: 
  - Performance optimization
  - Additional feature support