A specialized computational science co-pilot for stress-testing and architecting drug discovery and molecular modeling workflows.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install research-project-designer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install research-project-designer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Research Project Designer for AI Drug Discovery?
The Research Project Designer is a sophisticated agent capability tailored for computational scientists and biophysicists. It operates as an uncompromising co-pilot—part system architect and part expert reviewer—designed to bridge the gap between theoretical research and physical reality. By integrating this capability through Openclaw Skills, developers and researchers can ensure their workflows in Computer-Aided Drug Design (CADD) are grounded in mathematical rigor and physical laws.
Instead of merely validating user input, this skill is programmed to identify methodological blind spots and thermodynamic inconsistencies. It utilizes a specialized framework to evaluate molecular dynamics, binding affinities, and structural bioinformatics, making it an essential component for those leveraging Openclaw Skills in complex scientific environments.
Research Project Designer for AI Drug Discovery Use Cases
- Designing and auditing Computer-Aided Drug Design (CADD) workflows
- Debugging and optimizing MDAnalysis, RDKit, or OpenMM codebases
- Performing feasibility audits on molecular modeling methodologies
- Transitioning geometric pocket identification into physics-based simulations
- Drafting and peer-reviewing high-precision methodology sections for academic research
How Research Project Designer for AI Drug Discovery Works
- Classification: The skill identifies the request type, such as algorithm design, code debugging, or methodology peer review.
- Protocol Activation: It loads specific domain protocols to ensure the analysis follows standardized computational science frameworks.
- Fatal Flaw Audit: The agent runs a mandatory four-checkpoint audit covering the Static Fallacy, Thermodynamic Traps, Data Sparsity, and SOTA Obsolescence.
- Hierarchical Solution Matrix: It generates a tiered plan (Plans A, B, and C) ranging from fast Python-based solutions to high-precision molecular dynamics sampling.
- Structured Delivery: Findings are delivered with LaTeX equations and actionable next steps, maintaining a technical and objective tone.
Research Project Designer for AI Drug Discovery Setup
To deploy this skill, ensure your agent environment is configured to access the specialized reference library. Openclaw Skills require specific markdown reference files to be available in the project path.
# Initialize the research designer skill within your project
openclaw install research-project-designer
# Ensure reference files are in place
mkdir -p references/
cp standard-protocols.md references/protocols.md
Research Project Designer for AI Drug Discovery Data Schema & Taxonomy
The Research Project Designer organizes research data and analysis using a strict hierarchical taxonomy to ensure reproducibility within Openclaw Skills.
| Component | Format | Purpose |
|---|---|---|
| Category Code | String (A-E) | Defines the technical scope of the inquiry |
| Audit Checkpoints | List | Documents physical and thermodynamic constraints |
| Solution Matrix | Markdown Table | Compares Plan A (Fast), Plan B (Medium), and Plan C (Slow) |
| Equations | LaTeX | Provides mathematical justification for physical constraints |
Research Project Designer for AI Drug Discovery Advanced Features
- Multi-agent support for automated peer review and methodology auditing
- Built-in Fatal Flaw Audit to prevent common computational chemistry pitfalls
- Automated comparison against State-of-the-Art (SOTA) models like AlphaFold 3 and DiffDock
- Resource-aware solution planning based on compute overhead and academic payoff
- Deep integration with Openclaw Skills reference files for context-aware code optimization
SKILL.md
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