nanosensor-calibration-manager
Nanosensor characterization skill for calibration, sensitivity analysis, and selectivity validation
Best use case
nanosensor-calibration-manager is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Nanosensor characterization skill for calibration, sensitivity analysis, and selectivity validation
Teams using nanosensor-calibration-manager 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/nanosensor-calibration-manager/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How nanosensor-calibration-manager Compares
| Feature / Agent | nanosensor-calibration-manager | 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?
Nanosensor characterization skill for calibration, sensitivity analysis, and selectivity validation
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
# Nanosensor Calibration Manager
## Purpose
The Nanosensor Calibration Manager skill provides comprehensive characterization of nanomaterial-based sensors, enabling systematic calibration, sensitivity optimization, and selectivity validation for analytical applications.
## Capabilities
- Calibration curve generation
- Limit of detection (LOD) calculation
- Sensitivity and dynamic range analysis
- Selectivity and interference testing
- Response time characterization
- Long-term stability assessment
## Usage Guidelines
### Sensor Calibration
1. **Calibration Curve**
- Prepare standard solutions
- Measure sensor response
- Fit calibration model
2. **Performance Metrics**
- Calculate LOD (3 sigma method)
- Determine linear range
- Assess sensitivity (slope)
3. **Selectivity Testing**
- Test interferents
- Calculate selectivity coefficients
- Validate in complex matrices
## Process Integration
- Nanosensor Development and Validation Pipeline
## Input Schema
```json
{
"sensor_id": "string",
"analyte": "string",
"concentration_range": {"min": "number", "max": "number", "unit": "string"},
"interferents": ["string"],
"matrix": "buffer|serum|environmental"
}
```
## Output Schema
```json
{
"calibration": {
"equation": "string",
"r_squared": "number",
"linear_range": {"min": "number", "max": "number"}
},
"performance": {
"lod": "number",
"loq": "number",
"sensitivity": "number",
"response_time": "number (seconds)"
},
"selectivity": [{
"interferent": "string",
"selectivity_coefficient": "number"
}]
}
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