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Get Started Free →AI-powered virtual laboratory orchestrating multi-agent scientific research teams for autonomous hypothesis generation, experimental design, and validation in biomedical research.
.claude/skills/virtual-lab-agent/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 28% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 28% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 7% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 82% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 64% | 0% |
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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.
| 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 |
| 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 |
User: "Design a research project to discover nanobody-based therapies against emerging SARS-CoV-2 variants."
Agent Action:
bashpython3 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/
| 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 | 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 |
| Interaction | Agents | Purpose | |-------------|--------|---------| | Debate | PI + Critic | Hypothesis refinement | | Design Review | CompBio + ML | Method selection | | Interpretation | All | Result synthesis | | Quality Control | Critic | Validation |
| 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 |
Literature Mining:
Hypothesis Generation:
Experimental Design:
Result Interpretation:
| Validation Level | Method | Confidence | |------------------|--------|------------| | Computational | In silico prediction | Moderate | | Literature | Existing evidence | Variable | | Structural | AlphaFold modeling | High (structure) | | Experimental | Wet lab validation | Highest |
| 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 |
| 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 |
| 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 |
| 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 |
AI Group - Biomedical AI Platform
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 34,947 | 36,042 | +3% | 1 | 1 | 0% | 6,218 | 7,963 | +28% | 0 | 0 | — |
case-02 | fail→pass | 33,792 | 33,128 | -2% | 1 | 1 | 0% | 6,205 | 7,950 | +28% | 0 | 0 | — |
case-03 | fail→pass | 28,481 | 35,418 | +24% | 1 | 1 | 0% | 6,207 | 7,952 | +28% | 0 | 0 | — |
case-04 | fail→fail | 14,458 | 13,623 | -6% | 1 | 1 | 0% | 1,234 | 4,290 | +248% | 0 | 0 | — |
case-05 | fail→fail | 26,272 | 28,500 | +8% | 1 | 1 | 0% | 5,023 | 7,926 | +58% | 0 | 0 | — |
case-06 | fail→fail | 16,947 | 26,162 | +54% | 1 | 1 | 0% | 3,400 | 7,081 | +108% | 0 | 0 | — |
case-11 | fail→pass | 15,597 | 6,220 | -60% | 1 | 1 | 0% | 2,580 | 2,752 | +7% | 0 | 0 | — |
case-07 | pass→pass | 7,401 | 2,884 | -61% | 1 | 1 | 0% | 1,258 | 2,240 | +78% | 0 | 0 | — |
case-08 | fail→pass | 6,183 | 1,926 | -69% | 1 | 1 | 0% | 1,128 | 2,049 | +82% | 0 | 0 | — |
case-09 | fail→pass | 8,281 | 2,202 | -73% | 1 | 1 | 0% | 1,265 | 2,080 | +64% | 0 | 0 | — |
case-10 | pass→pass | 25,847 | 5,061 | -80% | 1 | 1 | 0% | 2,240 | 2,581 | +15% | 0 | 0 | — |
case-12 | fail→pass | 9,130 | 2,883 | -68% | 1 | 1 | 0% | 1,458 | 2,330 | +60% | 0 | 0 | — |
case-13 | fail→pass | 5,782 | 2,779 | -52% | 1 | 1 | 0% | 964 | 2,230 | +131% | 0 | 0 | — |
case-14 | pass→pass | 9,629 | 2,054 | -79% | 1 | 1 | 0% | 1,444 | 2,055 | +42% | 0 | 0 | — |
case-15 | fail→pass | 7,761 | 2,019 | -74% | 1 | 1 | 0% | 1,345 | 2,052 | +53% | 0 | 0 | — |
case-16 | pass→pass | 18,467 | 6,005 | -67% | 1 | 1 | 0% | 3,050 | 2,700 | -11% | 0 | 0 | — |
case-17 | pass→pass | 11,054 | 2,032 | -82% | 1 | 1 | 0% | 1,871 | 2,073 | +11% | 0 | 0 | — |
case-18 | pass→pass | 10,011 | 2,195 | -78% | 1 | 1 | 0% | 1,625 | 2,107 | +30% | 0 | 0 | — |
case-19 | pass→pass | 14,107 | 5,426 | -62% | 1 | 1 | 0% | 2,310 | 2,759 | +19% | 0 | 0 | — |
case-20 | pass→pass | 12,672 | 4,027 | -68% | 1 | 1 | 0% | 1,918 | 2,438 | +27% | 0 | 0 | — |
case-21 | pass→pass | 5,782 | 1,880 | -67% | 1 | 1 | 0% | 949 | 1,959 | +106% | 0 | 0 | — |
case-22 | fail→pass | 8,605 | 2,857 | -67% | 1 | 1 | 0% | 1,491 | 2,323 | +56% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +41 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
| Model | Method | Date | Lift |
|---|---|---|---|
| gemini-3.6-flash | verified | 7/26/2026 | +9% |
Other measured skills in the registry, with their headline benchmark lift.