| name | harness-runner |
| description | Run WaveCap-SDR test harness with automated parameter sweeps and validation. Use when regression testing, validating audio quality across configurations, testing SDR hardware, or benchmarking demodulation performance. |
Harness Runner for WaveCap-SDR
This skill helps run the WaveCap-SDR test harness with automated parameter sweeps, result collection, and regression testing.
When to Use This Skill
Use this skill when:
- Running regression tests after code changes
- Validating audio quality across different configurations
- Testing SDR hardware (RTL-SDR, SDRplay, etc.)
- Benchmarking demodulation modes (FM, AM, SSB)
- Comparing different AGC or filter settings
- Testing with multiple frequencies or channels
- Automated CI/CD testing
- Generating test reports for documentation
How It Works
The WaveCap-SDR harness (backend/wavecapsdr/harness.py) is a production-ready test tool that:
- Starts a server (optional) with specified configuration
- Creates a capture with SDR device or fake driver
- Adds channels with specified demodulation settings
- Captures audio for a duration and measures quality metrics
- Returns results with RMS levels, peak levels, and pass/fail status
This skill wraps the harness for automated testing scenarios.
Usage Instructions
Basic Harness Usage
Test with Fake Driver (Offline Testing):
cd backend
PYTHONPATH=. .venv/bin/python -m wavecapsdr.harness \
--start-server \
--driver fake \
--preset kexp \
--duration 3
Test with Real SDR (RTL-SDR):
cd backend
PYTHONPATH=. .venv/bin/python -m wavecapsdr.harness \
--start-server \
--driver soapy \
--device-args "driver=rtlsdr" \
--preset kexp \
--duration 5 \
--out harness_out
Test with SDRplay:
cd backend
PYTHONPATH=. .venv/bin/python -m wavecapsdr.harness \
--start-server \
--driver soapy \
--device-args "driver=sdrplay,serial=240309F070" \
--preset marine \
--duration 5
Advanced: Parameter Sweeps
Use the included script to run parameter sweeps:
PYTHONPATH=backend backend/.venv/bin/python .claude/skills/harness-runner/run_harness.py \
--preset kexp \
--duration 3 \
--driver fake \
--sweep gain --gain-values 10 20 30 40
Parameters:
--preset: Preset name (kexp, marine, aviation, tone, etc.)--duration: Seconds to capture per test (default: 3)--driver: Driver to use (fake, soapy, rtl)--device-args: Device selector string--output-dir: Directory for results (default: harness_results)--sweep: Parameter to sweep (gain, bandwidth, frequency)--gain-values: List of gain values to test (for gain sweep)--bandwidth-values: List of bandwidth values (for bandwidth sweep)--frequency-offsets: List of frequency offsets (for frequency sweep)--parallel: Run tests in parallel (default: sequential)--report: Generate HTML/JSON report
Example Workflows
1. Regression Testing After Code Changes:
# Run quick smoke test with fake driver
PYTHONPATH=backend backend/.venv/bin/python .claude/skills/harness-runner/run_harness.py \
--preset kexp \
--duration 2 \
--driver fake \
--output-dir regression_test_$(date +%Y%m%d_%H%M%S) \
--report
Expected: All channels should have audio RMS > -40 dB (validates demodulation works)
2. Find Optimal Gain for SDR Device:
# Sweep gain from 0 to 50 dB in 10 dB steps
PYTHONPATH=backend backend/.venv/bin/python .claude/skills/harness-runner/run_harness.py \
--preset kexp \
--duration 5 \
--driver soapy \
--device-args "driver=rtlsdr" \
--sweep gain \
--gain-values 0 10 20 30 40 50 \
--output-dir gain_sweep_kexp \
--report
Review report to find gain with best audio level (typically -20 to -10 dB RMS)
3. Test All Presets with Fake Driver:
# Test each preset to ensure they work
for preset in kexp marine aviation noaa tone; do
echo "Testing preset: $preset"
PYTHONPATH=backend backend/.venv/bin/python -m wavecapsdr.harness \
--start-server \
--driver fake \
--preset $preset \
--duration 3 \
--out harness_out_$preset
done
4. Compare FM Demodulation Quality:
# Test FM demodulation with different configurations
# (Requires modifying harness to support demod parameter sweep)
PYTHONPATH=backend backend/.venv/bin/python .claude/skills/harness-runner/run_harness.py \
--preset kexp \
--driver fake \
--duration 5 \
--output-dir fm_demod_test \
--report
5. Hardware Stress Test:
# Long-duration test to check for memory leaks or crashes
PYTHONPATH=backend backend/.venv/bin/python -m wavecapsdr.harness \
--start-server \
--driver soapy \
--device-args "driver=rtlsdr" \
--preset kexp \
--duration 300 \
--out stress_test
Monitor server logs and system resources during test.
Interpreting Harness Results
The harness outputs JSON reports with channel statistics:
{
"captureId": "cap_abc123",
"channels": [
{
"channelId": "ch1",
"label": "KEXP 90.3 FM",
"offsetHz": -600000,
"rmsDb": -18.5,
"peakDb": -6.2,
"status": "PASS"
},
{
"channelId": "ch2",
"label": "KNHC 89.5 FM",
"offsetHz": 800000,
"rmsDb": -65.2,
"peakDb": -58.1,
"status": "FAIL"
}
]
}
Pass/Fail Criteria:
- PASS: RMS > -40 dB (audio is present and audible)
- FAIL: RMS < -40 dB (silence, noise, or tuning issue)
- CLIP: Peak > -0.5 dB (clipping detected, reduce gain)
Typical Good Audio Levels:
- RMS: -20 to -10 dB (strong, clear audio)
- RMS: -30 to -20 dB (moderate audio, acceptable)
- RMS: -40 to -30 dB (weak audio, may have noise)
- RMS: < -40 dB (too quiet, indicates problem)
Common Presets
KEXP (FM Broadcast):
- Center: 90.3 MHz
- Sample rate: 2 MHz
- Channels: KEXP 90.3, KNHC 89.5
- Use for: FM demodulation testing
Marine VHF:
- Center: 156.8 MHz (Channel 16)
- Sample rate: 250 kHz
- Channels: Ch 16, Ch 9, Ch 6
- Use for: Narrowband FM testing
Aviation:
- Center: 118-137 MHz
- Channels: Tower, ATIS, Ground
- Use for: AM demodulation testing
NOAA Weather:
- Center: 162.550 MHz
- Channels: WX1, WX2
- Use for: Continuous broadcast testing
Tone (Test Signal):
- Fake driver generates 1 kHz test tone
- Use for: Offline testing without SDR hardware
Harness Command-Line Options
--start-server Start embedded server (required for offline testing)
--driver Driver: fake, soapy, rtl (default: soapy)
--device-args Device selector (e.g., "driver=rtlsdr")
--preset Preset name (kexp, marine, aviation, etc.)
--center-hz Override center frequency
--sample-rate Override sample rate
--offset Channel offset Hz (repeatable)
--duration Seconds to capture (default: 10)
--gain RF gain in dB
--bandwidth RF bandwidth in Hz
--out Output directory for WAV files
--auto-gain Auto-select optimal gain
--probe-seconds Seconds for auto-gain probing (default: 2)
Integration with CI/CD
GitHub Actions Example:
name: Test Harness
on: [push, pull_request]
jobs:
harness:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Setup Python
uses: actions/setup-python@v4
with:
python-version: '3.11'
- name: Install dependencies
run: |
cd backend
python -m venv .venv
.venv/bin/pip install -e .
- name: Run harness tests
run: |
cd backend
PYTHONPATH=. .venv/bin/python -m wavecapsdr.harness \
--start-server \
--driver fake \
--preset kexp \
--duration 3
- name: Check exit code
run: |
if [ $? -eq 0 ]; then
echo "Harness tests PASSED"
else
echo "Harness tests FAILED"
exit 1
fi
Troubleshooting
Issue: Harness fails with "No audio detected"
- Check if preset exists in config:
cat backend/config/wavecapsdr.yaml | grep -A 10 "preset-name" - Verify device connection:
SoapySDRUtil --find - Check antenna is connected
- Try fake driver first:
--driver fake
Issue: RMS levels very low (< -60 dB)
- Increase gain:
--gain 30 - Use auto-gain:
--auto-gain - Check if frequency is correct
- Verify antenna tuned for frequency range
Issue: Clipping (peak > -0.5 dB)
- Reduce gain:
--gain 10 - Check for strong nearby signals
- Use AGC in demodulation
Issue: Server port already in use
- Change port:
--port 8088 - Kill existing server:
pkill -f wavecapsdr
Files in This Skill
SKILL.md: This file - instructions for using the skillrun_harness.py: Parameter sweep automation script
Notes
- Harness always returns non-zero exit code if any channel fails
- Use
--duration 3for quick tests,--duration 10+for reliable results - Fake driver is deterministic (same output every time)
- Real SDR results vary based on signal conditions
- Save WAV files with
--outfor manual inspection - Auto-gain feature requires multiple iterations (slower but finds optimal gain)