dls-particle-sizer

Dynamic Light Scattering skill for hydrodynamic size distribution and polydispersity analysis

509 stars

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

$curl -o ~/.claude/skills/dls-particle-sizer/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/nanotechnology/skills/dls-particle-sizer/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/dls-particle-sizer/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How dls-particle-sizer Compares

Feature / Agentdls-particle-sizerStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/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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