home / skills / a5c-ai / babysitter / test-automation
This skill automates test equipment control and data acquisition to enable repeatable hardware validation with integrated uncertainty analysis.
npx playbooks add skill a5c-ai/babysitter --skill test-automationReview the files below or copy the command above to add this skill to your agents.
---
name: test-automation
description: Automated test equipment control and data acquisition skill for hardware validation, with VISA/SCPI instrument communication, test sequence scripting, and measurement uncertainty analysis.
allowed-tools: Read, Grep, Write, Bash, Edit, Glob
category: Testing
backlog-id: SK-018
metadata:
author: babysitter-sdk
version: "1.0.0"
---
# Test Equipment Automation Skill
Automated test equipment control and data acquisition for hardware validation and characterization.
## Purpose
This skill provides comprehensive capabilities for automating electronic test equipment, enabling consistent and repeatable hardware validation workflows. It supports instrument communication, automated test sequences, data logging, and analysis.
## Capabilities
### Instrument Communication
- VISA/SCPI instrument communication protocols
- Support for GPIB, USB-TMC, LAN/VXI-11, and serial interfaces
- Instrument discovery and resource management
- Error handling and timeout management
- Connection pooling for multi-instrument setups
### Oscilloscope Automation
- Keysight, Tektronix, Rohde & Schwarz oscilloscope control
- Channel configuration and coupling
- Trigger setup (edge, pulse, pattern, serial)
- Waveform acquisition and transfer
- Measurement extraction (rise time, frequency, duty cycle)
- Screenshot capture and documentation
### Spectrum Analyzer Control
- Frequency span and center frequency configuration
- Resolution and video bandwidth settings
- Marker operations and peak search
- Trace capture and data export
- Limit line testing and pass/fail determination
- EMC pre-compliance measurements
### Power Supply and Electronic Load Control
- Output voltage and current programming
- Protection limit configuration
- Sequencing and timing control
- Load transient generation
- Efficiency measurement automation
### DMM and SMU Automation
- DC/AC voltage and current measurements
- Resistance and continuity testing
- Source-measure unit (SMU) operations
- IV curve characterization
- Temperature coefficient measurements
### Data Logging and Analysis
- Real-time data acquisition
- Statistical analysis (mean, std, min, max)
- Data logging to CSV, JSON, HDF5
- Time-series trending
- Alarm and limit monitoring
### Test Sequence Scripting
- Sequential and parallel test execution
- Conditional branching based on results
- Loop and iteration support
- Test fixture abstraction
- Parameterized test cases
### Measurement Uncertainty Analysis
- Type A (statistical) uncertainty evaluation
- Type B (systematic) uncertainty estimation
- Combined uncertainty calculation
- Coverage factor and confidence intervals
- GUM-compliant uncertainty budgets
### Report Generation
- Automated test report creation
- Pass/fail summary tables
- Measurement data visualization
- Traceability documentation
- Export to PDF, HTML, Excel formats
## Prerequisites
### Installation
```bash
pip install pyvisa pyvisa-py numpy pandas matplotlib
```
### Optional Dependencies
```bash
# For HDF5 data storage
pip install h5py tables
# For advanced analysis
pip install scipy uncertainties
# For report generation
pip install jinja2 weasyprint
```
## Usage Patterns
### Basic Instrument Communication
```python
import pyvisa
# Initialize resource manager
rm = pyvisa.ResourceManager()
# List available instruments
print(rm.list_resources())
# Connect to oscilloscope
scope = rm.open_resource('TCPIP::192.168.1.100::INSTR')
scope.timeout = 5000 # 5 second timeout
# Query identification
idn = scope.query('*IDN?')
print(f"Connected to: {idn}")
# Configure and measure
scope.write(':CHANnel1:DISPlay ON')
scope.write(':CHANnel1:SCALe 1.0') # 1V/div
scope.write(':TIMebase:SCALe 0.001') # 1ms/div
scope.write(':TRIGger:EDGE:SOURce CHANnel1')
scope.write(':TRIGger:EDGE:LEVel 0.5')
# Read measurement
vpp = float(scope.query(':MEASure:VPP? CHANnel1'))
freq = float(scope.query(':MEASure:FREQuency? CHANnel1'))
print(f"Vpp: {vpp:.3f} V, Frequency: {freq:.2f} Hz")
scope.close()
```
### Automated Test Sequence
```python
from dataclasses import dataclass
from typing import List, Dict, Any
import time
@dataclass
class TestResult:
name: str
passed: bool
value: float
unit: str
limit_low: float
limit_high: float
class TestSequence:
def __init__(self, instruments: Dict[str, Any]):
self.instruments = instruments
self.results: List[TestResult] = []
def run_test(self, name: str, measure_func, limit_low: float,
limit_high: float, unit: str) -> TestResult:
value = measure_func()
passed = limit_low <= value <= limit_high
result = TestResult(name, passed, value, unit, limit_low, limit_high)
self.results.append(result)
return result
def generate_report(self) -> Dict:
return {
'total_tests': len(self.results),
'passed': sum(1 for r in self.results if r.passed),
'failed': sum(1 for r in self.results if not r.passed),
'results': [vars(r) for r in self.results]
}
# Example usage
def measure_output_voltage():
return float(dmm.query(':MEASure:VOLTage:DC?'))
sequence = TestSequence({'dmm': dmm, 'psu': psu})
sequence.run_test('Output Voltage', measure_output_voltage, 4.9, 5.1, 'V')
```
### Measurement Uncertainty Analysis
```python
from uncertainties import ufloat
import numpy as np
class UncertaintyAnalysis:
def __init__(self):
self.measurements = []
self.instrument_uncertainty = 0.0
def add_measurement(self, value: float):
self.measurements.append(value)
def set_instrument_uncertainty(self, uncertainty: float):
"""Set Type B uncertainty from instrument specifications"""
self.instrument_uncertainty = uncertainty
def calculate_combined_uncertainty(self, coverage_factor: float = 2.0):
# Type A uncertainty (statistical)
n = len(self.measurements)
mean = np.mean(self.measurements)
std = np.std(self.measurements, ddof=1)
type_a = std / np.sqrt(n)
# Type B uncertainty
type_b = self.instrument_uncertainty / np.sqrt(3) # Rectangular distribution
# Combined standard uncertainty
combined = np.sqrt(type_a**2 + type_b**2)
# Expanded uncertainty
expanded = coverage_factor * combined
return {
'mean': mean,
'type_a_uncertainty': type_a,
'type_b_uncertainty': type_b,
'combined_uncertainty': combined,
'expanded_uncertainty': expanded,
'coverage_factor': coverage_factor
}
# Usage
analysis = UncertaintyAnalysis()
for _ in range(10):
analysis.add_measurement(float(dmm.query(':MEASure:VOLTage:DC?')))
analysis.set_instrument_uncertainty(0.001) # 1mV from spec sheet
result = analysis.calculate_combined_uncertainty()
print(f"Voltage: {result['mean']:.4f} +/- {result['expanded_uncertainty']:.4f} V (k=2)")
```
## Usage Guidelines
### When to Use This Skill
- Automated hardware validation and characterization
- Production test development
- Design verification testing
- Environmental testing with data acquisition
- EMC pre-compliance measurements
### Best Practices
1. **Always verify instrument connections** before starting test sequences
2. **Use appropriate timeouts** for slow measurements
3. **Implement proper error handling** for instrument communication failures
4. **Document measurement conditions** (temperature, humidity, setup)
5. **Include calibration verification** in test procedures
6. **Use statistical analysis** for production testing decisions
### Process Integration
- ee-hardware-validation (all phases)
- ee-environmental-testing (data acquisition)
- ee-emc-design-testing (pre-compliance measurements)
## Dependencies
- PyVISA and PyVISA-py for instrument communication
- NI VISA or Keysight IO Libraries for driver support
- Instrument-specific drivers as needed
## References
- [PyVISA Documentation](https://pyvisa.readthedocs.io/)
- [SCPI Standard Reference](https://www.ivifoundation.org/scpi/)
- [GUM Uncertainty Guide](https://www.bipm.org/en/publications/guides/gum.html)
This skill automates control of electronic test equipment and captures measurement data for hardware validation and characterization. It provides VISA/SCPI communication, sequenced test execution, data logging, and built-in uncertainty analysis for repeatable, traceable testing. Designed for production, verification, and lab environments, it streamlines instrument orchestration and reporting.
The skill discovers and manages instrument resources over GPIB, USB-TMC, LAN/VXI-11, and serial interfaces using VISA/SCPI commands. It runs parameterized test sequences with sequential and parallel steps, reads waveforms and traces from oscilloscopes and spectrum analyzers, controls power supplies and SMUs, and logs measurements to CSV/JSON/HDF5. Measurement uncertainty is estimated using Type A and Type B methods and combined per GUM principles, and results are exported as PDF/HTML/Excel reports with pass/fail summaries.
Which instruments and interfaces are supported?
The skill targets instruments using VISA/SCPI across GPIB, USB-TMC, LAN/VXI-11, and serial. Vendor scopes, spectrum analyzers, DMMs, SMUs, power supplies, and electronic loads are supported via standard command sets and optional instrument drivers.
How is measurement uncertainty handled?
Uncertainty analysis includes Type A (statistical) and Type B (systematic) components. The skill computes combined standard uncertainty and expanded uncertainty with a configurable coverage factor, following GUM guidance and allowing instrument-specification inputs.