drug-encapsulation-optimizer
Drug delivery formulation skill for optimizing drug loading, encapsulation efficiency, and release kinetics
Best use case
drug-encapsulation-optimizer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Drug delivery formulation skill for optimizing drug loading, encapsulation efficiency, and release kinetics
Teams using drug-encapsulation-optimizer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/drug-encapsulation-optimizer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How drug-encapsulation-optimizer Compares
| Feature / Agent | drug-encapsulation-optimizer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Drug delivery formulation skill for optimizing drug loading, encapsulation efficiency, and release kinetics
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Drug Encapsulation Optimizer
## Purpose
The Drug Encapsulation Optimizer skill provides systematic optimization of nanoparticle drug delivery formulations, enabling maximized drug loading, controlled release kinetics, and formulation stability.
## Capabilities
- Drug loading optimization
- Encapsulation efficiency calculation
- Release kinetics modeling (zero-order, Higuchi, Korsmeyer-Peppas)
- Stability testing protocols
- Size optimization for target application
- Payload-to-carrier ratio optimization
## Usage Guidelines
### Formulation Optimization
1. **Loading Optimization**
- Screen loading methods
- Optimize drug:carrier ratio
- Maximize encapsulation efficiency
2. **Release Kinetics**
- Characterize release profile
- Fit to kinetic models
- Design sustained release
3. **Stability Assessment**
- Monitor size over time
- Track drug leakage
- Accelerated stability testing
## Process Integration
- Nanoparticle Drug Delivery System Development
- Nanomaterial Safety Assessment Pipeline
## Input Schema
```json
{
"drug": "string",
"carrier_type": "liposome|polymer|micelle|lipid_np",
"target_loading": "number (wt%)",
"release_target": "immediate|sustained|triggered",
"route": "iv|oral|topical|inhalation"
}
```
## Output Schema
```json
{
"optimized_formulation": {
"drug_loading": "number (wt%)",
"encapsulation_efficiency": "number (%)",
"particle_size": "number (nm)",
"pdi": "number"
},
"release_kinetics": {
"model": "string",
"parameters": {},
"t50": "number (hours)",
"t90": "number (hours)"
},
"stability": {
"shelf_life": "number (months)",
"storage_conditions": "string"
}
}
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