moa-explainer
Generate 3D animation scripts and lay explanations for drug mechanisms.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/aipoch-ai/moa-explainerWhat This Skill Does
The moa-explainer skill is a specialized agent utility designed to bridge the gap between complex pharmaceutical science and accessible visual communication. It transforms intricate Mechanism of Action (MoA) data into structured 3D animation scripts and clear, lay-person friendly explanations. By utilizing a rigorous, reproducible framework, this skill ensures that drug mechanism descriptions are not only accurate but also consistent with academic standards. It acts as a bridge for researchers, medical illustrators, and communication teams who need to distill pharmacodynamic principles into actionable production blueprints for visual media.
Installation
To integrate this skill into your OpenClaw environment, use the command-line interface to pull the package directly from the managed repository. Run the following command in your terminal:
clawhub install openclaw/skills/skills/aipoch-ai/moa-explainer
After installation, verify the environment integrity by running python -m py_compile scripts/main.py to ensure all Python 3.10+ dependencies are correctly mapped to your local system.
Use Cases
- Medical Animation Pre-production: Generating frame-by-frame narrative scripts for animators depicting drug-receptor binding events.
- Patient Education Materials: Translating technical clinical trial data into plain-language summaries for informed consent or patient brochures.
- Academic Presentations: Creating standardized explanation templates for pharmacological pathways, ensuring that all assumptions regarding drug distribution and clearance are explicitly documented.
- Scientific Documentation: Producing reproducible, audit-ready summaries of drug MoA for submission to regulatory bodies or internal review committees.
Example Prompts
- "Generate a 3D animation script for a monoclonal antibody targeting the PD-L1 receptor, ensuring the explanation is suitable for a high school biology class."
- "Review the provided drug data in /references/trial_data_x.json and extract the primary mechanism of action for a 60-second explainer video."
- "Create an academic summary of the drug's MoA, explicitly stating assumptions about the cellular binding affinity and molecular kinetics."
Tips & Limitations
- Explicit Assumptions: Always identify the scope of your pharmacological assumptions. The tool thrives on bounded inputs; vague prompts may lead to non-deterministic animation scripts.
- Validation: Always verify the generated script against the official Investigator's Brochure or equivalent source documents.
- Environment Control: The tool is optimized for Python 3.10+. If you encounter environment errors, check your local library versions against the skill prerequisites.
- Review Lifecycle: Use the structured output path to maintain a version history of your explainer drafts for easy collaborative iteration.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-aipoch-ai-moa-explainer": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Flags: file-read, file-write, code-execution
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