Audio Fft Sanity
FFT-based sanity check for audio output files — verifies fundamental frequency, THD, SNR, clipping, and silence. Use after offline C tests or WASM OfflineAudioContext captures to confirm a signal chain is producing correct output.
Install
$ agentstack add skill-gertsylvest-meta-team-audio-fft-sanity ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-gertsylvest-meta-team-audio-fft-sanity) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming — see below.
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Audio FFT Sanity Check
Run a suite of FFT-based assertions on an audio output file to confirm a signal chain is behaving correctly. Suitable for both offline C test output and WASM OfflineAudioContext captures.
Requirements
- Python 3.8+
numpyandscipy:pip install numpy scipy
Instructions
Arguments are in $ARGUMENTS. Pass them directly to the analysis script:
python3 "$(dirname "$0")/analyse.py" $ARGUMENTSChecks performed
| Check | What it detects | |---|---| | CLIPPING | Any sample magnitude > 0.99 — indicates gain staging problem or overflow | | SILENCE | RMS below -60 dBFS — catches zero-output bugs (uninitialised buffer, wrong pointer) | | FUNDAMENTAL | Detected peak frequency vs --freq (if given) — verifies oscillator/filter tuning | | THD | Total harmonic distortion (harmonics 2–5 vs fundamental) — catches nonlinear distortion or aliasing | | SNR | Signal-to-noise ratio estimate — catches noise floor issues or quantisation problems |
Exit codes
0— all checks passed1— one or more checks failed
Examples
# Basic smoke test — check for clipping, silence, SNR, THD with defaults
python3 analyse.py output.wav
# Verify a 440 Hz sine from a synth, strict THD limit
python3 analyse.py output.wav --freq 440 --thd-max 1.0 --snr-min 60
# Raw float32 file at 48 kHz, stereo interleaved (uses first channel)
python3 analyse.py output.f32 --sr 48000 --channels 2 --freq 1000Typical usage patterns
Synthesizer test: Run the synth at a known pitch (e.g. MIDI A4 = 440 Hz), render N frames to a float32 file, then:
python3 analyse.py synth_output.wav --freq 440 --thd-max 2.0 --snr-min 50Effect processor test (e.g. filter, EQ): Feed a broadband noise or sweep, capture output — omit --freq and check only for clipping and SNR:
python3 analyse.py filtered_output.wav --snr-min 30Impulse response capture: Feed a single-sample impulse, check output is non-silent and not clipping:
python3 analyse.py ir_output.wavSource & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: gertsylvest
- Source: gertsylvest/meta-team
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.