virtual-lab-agent

by FreedomIntelligence

Install this Skill

curl -fsSL -o virtual-lab-agent.zip https://skill.seembox.com/api/v1/skills/virtual-lab-agent/download && unzip virtual-lab-agent.zip

Download virtual-lab-agent.zip

SKILL.md About

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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 modeling | High (structure) |
| Experimental | Wet lab validation | Highest |

## Stanford AI Scientist Reference

| Capability | Implementation | Status |
|------------|----------------|--------|
| Nanobody Design | SARS-CoV-2 variants | Validated |
| Binding Prediction | AF-based docking | Active |
| Lab Validation | Wet lab confirmation | Promising results |
| Generalization | Other domains | Expanding |

## Prerequisites

* Python 3.10+
* LLM APIs (Claude, GPT-4)
* Literature databases access
* Computational biology tools
* AlphaFold2/3 installation

## Related Skills

* Digital_Twin_Clinical_Agent - Patient simulation
* scFoundation_Model_Agent - Single-cell analysis
* CryoEM_AI_Drug_Design_Agent - Structure-based design
* PROTAC_Design_Agent - Degrader design

## Research Quality Control

| QC Check | Criterion | Action |
|----------|-----------|--------|
| Novelty | Not already published | Literature check |
| Feasibility | Resources available | Resource audit |
| Reproducibility | Clear methods | Protocol review |
| Statistical Power | Adequate samples | Power analysis |
| Bias | Confounders addressed | Critic review |

## Special Considerations

1. **Hallucination Risk**: Verify agent claims against literature
2. **Citation Accuracy**: Double-check all references
3. **Experimental Validity**: Wet lab confirmation needed
4. **Ethical Review**: Human subjects require IRB
5. **Novelty Assessment**: Ensure genuine contribution

## Limitations

| Limitation | Impact | Mitigation |
|------------|--------|------------|
| No Wet Lab | Computational only | Collaborator network |
| LLM Errors | Factual mistakes | Multi-agent verification |
| Creativity Bounds | Within training data | Human oversight |
| Domain Limits | Knowledge cutoffs | Database updates |

## Future Directions

| Enhancement | Timeline | Impact |
|-------------|----------|--------|
| Lab Automation | Present | Self-driving labs |
| Real-time Literature | Active | Current knowledge |
| Multi-modal Data | Emerging | Richer insights |
| Full Autonomy | Future | End-to-end research |

## Author

AI Group - Biomedical AI Platform