Virtual Lab AgentSAFE

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.

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# COPYRIGHT NOTICE
# This file is part of the "Universal Biomedical Skills" project.
# Copyright (c) 2026 MD BABU MIA, PhD <[email protected]>
# All Rights Reserved.
#
# This code is proprietary and confidential.
# Unauthorized copying of this file, via any medium is strictly prohibited.
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# Provenance: Authenticated by MD BABU MIA

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---
name: 'virtual-lab-agent'
description: 'AI-powered virtual laboratory orchestrating multi-agent scientific research teams for autonomous hypothesis generation, experimental design, and validation in biomedical research.'
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
  - read_file
  - run_shell_command
---


# Virtual Lab Agent

The **Virtual Lab Agent** orchestrates AI-powered virtual scientific research teams consisting of specialized agents (Principal Investigator, Immunologist, Computational Biologist, Machine Learning Specialist) to autonomously conduct biomedical research. Inspired by Stanford's AI Scientist model, it enables hypothesis generation, experimental design, in silico validation, and research synthesis.

## When to Use This Skill

* When exploring new research hypotheses autonomously.
* For designing experiments with AI-generated protocols.
* To synthesize literature and generate research directions.
* When validating hypotheses through computational experiments.
* For multi-disciplinary research requiring diverse expertise.

## Core Capabilities

1. **Multi-Agent Research**: Coordinate specialized AI scientists.

2. **Hypothesis Generation**: Generate testable research hypotheses.

3. **Experimental Design**: Design in silico and wet lab experiments.

4. **Literature Synthesis**: Comprehensive research landscape analysis.

5. **Computational Validation**: Test hypotheses computationally.

6. **Research Documentation**: Auto-generate papers and reports.

## Virtual Lab Team

| Agent Role | Expertise | Responsibilities |
|------------|-----------|------------------|
| Principal Investigator | Strategy, oversight | Direction, prioritization |
| Immunologist | Immune biology | Domain expertise |
| Computational Biologist | Bioinformatics | Data analysis |
| Machine Learning Specialist | AI/ML methods | Model development |
| Scientific Critic | Validation | Quality control |

## Research Workflow

| Phase | Activities | Output |
|-------|------------|--------|
| Ideation | Literature review, gap identification | Hypotheses |
| Planning | Experimental design, resource allocation | Protocol |
| Execution | Computational experiments | Raw results |
| Analysis | Statistical analysis, interpretation | Findings |
| Synthesis | Paper writing, visualization | Publication-ready |

## Workflow

1. **Research Question**: Define the scientific question.

2. **Team Assembly**: Activate relevant specialist agents.

3. **Literature Review**: Synthesize existing knowledge.

4. **Hypothesis Generation**: Propose testable hypotheses.

5. **Experimental Design**: Design validation experiments.

6. **Execution**: Run computational experiments.

7. **Output**: Research findings, visualizations, manuscript.

## Example Usage

**User**: "Design a research project to discover nanobody-based therapies against emerging SARS-CoV-2 variants."

**Agent Action**:
```bash
python3 Skills/Clinical/Virtual_Lab_Agent/virtual_lab.py \
    --research_question "Design nanobodies against SARS-CoV-2 spike variants" \
    --team_config immunologist,comp_bio,ml_specialist \
    --literature_scope "nanobody,SARS-CoV-2,spike,variants" \
    --experimental_type computational,in_silico \
    --validation_method binding_prediction,md_simulation \
    --output_format research_report \
    --output virtual_lab_results/
```

## Input Parameters

| Parameter | Description | Options |
|-----------|-------------|---------|
| Research Question | Core scientific question | Free text |
| Team Config | Specialist agents needed | List of agents |
| Literature Scope | Search terms and databases | Keywords |
| Experimental Type | In silico, computational | Type list |
| Validation Method | How to test hypotheses | Method list |
| Output Format | Report, paper, presentation | Format |

## Output Components

| Output | Description | Format |
|--------|-------------|--------|
| Research Report | Comprehensive findings | .md, .pdf |
| Hypothesis Ranking | Prioritized hypotheses | .csv |
| Experimental Protocols | Detailed methods | .json |
| Computational Results | Simulation outputs | Various |
| Visualizations | Figures and plots | .png, .svg |
| Draft Manuscript | Publication-ready text | .docx, .tex |
| Supplementary Data | Raw data and code | .zip |

## AI Agent Interactions

| Interaction | Agents | Purpose |
|-------------|--------|---------|
| Debate | PI + Critic | Hypothesis refinement |
| Design Review | CompBio + ML | Method selection |
| Interpretation | All | Result synthesis |
| Quality Control | Critic | Validation |

## Research Domains Supported

| Domain | Example Questions | Key Agents |
|--------|-------------------|------------|
| Drug Discovery | Novel targets, compounds | CompBio, ML |
| Immunotherapy | CAR-T design, neoantigens | Immunologist |
| Genomics | Variant interpretation | CompBio, ML |
| Structural Biology | Protein design | CompBio, ML |
| Clinical | Biomarker discovery | All |

## AI/ML Components

**Literature Mining**:
- PubMed/bioRxiv search
- Entity extraction
- Knowledge graph construction

**Hypothesis Generation**:
- Gap analysis
- Analogy-based reasoning
- Causal inference

**Experimental Design**:
- Protocol templates
- Power calculations
- Control selection

**Result Interpretation**:
- Statistical analysis
- Visualization generation
- Narrative synthesis

## Validation Framework

| Validation Level | Method | Confidence |
|------------------|--------|------------|
| Computational | In silico prediction | Moderate |
| Literature | Existing evidence | Variable |
| Structural | AlphaFold 

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.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (1)

Gates applied: no_behavioural_pass.