dls-particle-sizer
Dynamic Light Scattering skill for hydrodynamic size distribution and polydispersity analysis
Best use case
dls-particle-sizer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Dynamic Light Scattering skill for hydrodynamic size distribution and polydispersity analysis
Teams using dls-particle-sizer 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/dls-particle-sizer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How dls-particle-sizer Compares
| Feature / Agent | dls-particle-sizer | 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?
Dynamic Light Scattering skill for hydrodynamic size distribution and polydispersity analysis
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
# DLS Particle Sizer
## Purpose
The DLS Particle Sizer skill provides dynamic light scattering analysis for nanoparticle hydrodynamic size determination, enabling rapid, non-destructive measurement of size distributions and stability assessment.
## Capabilities
- Hydrodynamic diameter measurement
- Polydispersity index (PDI) calculation
- Size distribution analysis (intensity, volume, number)
- Temperature-dependent measurements
- Multi-angle DLS analysis
- Particle concentration estimation
## Usage Guidelines
### DLS Measurement
1. **Sample Preparation**
- Dilute to appropriate concentration
- Filter to remove dust
- Equilibrate temperature
2. **Data Analysis**
- Use cumulants for monomodal samples
- Apply CONTIN for multimodal
- Report intensity-weighted Z-average
3. **Quality Metrics**
- PDI < 0.1: Monodisperse
- PDI 0.1-0.3: Narrow distribution
- PDI > 0.3: Broad distribution
## Process Integration
- Statistical Particle Size Distribution Analysis
- Nanoparticle Synthesis Protocol Development
- Nanoparticle Drug Delivery System Development
## Input Schema
```json
{
"sample_id": "string",
"solvent": "string",
"temperature": "number (C)",
"refractive_index": "number",
"viscosity": "number (cP)"
}
```
## Output Schema
```json
{
"z_average": "number (nm)",
"pdi": "number",
"distribution": {
"intensity": {"peaks": [{"size": "number", "percent": "number"}]},
"volume": {"peaks": [{"size": "number", "percent": "number"}]},
"number": {"peaks": [{"size": "number", "percent": "number"}]}
},
"quality_metrics": {
"intercept": "number",
"baseline": "number"
}
}
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