Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add gertsylvest/meta-team --skill audio-fft-sanitygit clone --depth 1 https://github.com/gertsylvest/meta-teamWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/gertsylvest/meta-team/audio-fft-sanity)<a href="https://agentmods.dev/skills/gertsylvest/meta-team/audio-fft-sanity"><img src="https://agentmods.dev/badge/skills/gertsylvest/meta-team/audio-fft-sanity/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/gertsylvest/meta-team/audio-fft-sanity"><img src="https://agentmods.dev/badge/skills/gertsylvest/meta-team/audio-fft-sanity.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00052 | $0.00664 |
| Opus 5 | $0.00026 | $0.00332 |
| Sonnet 5 | $0.00010 | $0.00133 |
| Haiku 4.5 | $0.00005 | $0.00066 |
Grade A, and why
audio-fft-sanity scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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" $ARGUMENTS
Checks 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 1000
Typical 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 50
Effect 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 30
Impulse response capture: Feed a single-sample impulse, check output is non-silent and not clipping:
python3 analyse.py ir_output.wav
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 69 lines · 52 tokens per session scan A a09dee26fd14
audio-fft-sanity is a skill published in the GitHub repository gertsylvest/meta-team (5 stars, last pushed 1mo ago), licensed MIT. It adds 52 tokens to every session and 664 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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