Nanosensor characterization skill for calibration, sensitivity analysis, and selectivity validation
Scanned 9/2/2026
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npx -y skills add a5c-ai/babysitter --skill nanosensor-calibration-manager --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: nanosensor-calibration-manager
description: Nanosensor characterization skill for calibration, sensitivity analysis, and selectivity validation
allowed-tools:
- Read
- Write
- Glob
- Grep
- Bash
metadata:
specialization: nanotechnology
domain: science
category: applications
priority: high
phase: 6
tools-libraries:
- Sensor calibration software
- Data analysis tools
graph:
domains: [domain:nanotechnology]
skillAreas: [skill-area:mathematical-reasoning, skill-area:physics-simulation, skill-area:data-analysis]
workflows: [workflow:experiment-design]
roles: [role:research-engineer]
---
# 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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