ffmpeg-audio — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited ffmpeg-audio (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
One ffmpeg shape recurs across the voiceprint, diarization, whisper, and training lanes: mono · 16 kHz · signed-16-bit PCM WAV — ffmpeg -ac 1 -ar 16000 -c:a pcm_s16le. This skill is that convention as a single idempotent CLI, so the same bytes come out every time regardless of who runs it or what container went in.
Defaults are the convention (SR=16000, CH=1, CODEC=pcm_s16le). Change them in one place at the top of ffmpeg_audio.py if a lane ever needs a different target.
ffmpeg command it wouldrun and changes nothing until --apply.
is skipped — re-running a batch is free. --force rebuilds anyway.
python3 ~/.claude/skills/ffmpeg-audio/ffmpeg_audio.py normalize output.m4a -o ian.wav --applyBatch a roster with globs + --suffix (no -o):
python3 .../ffmpeg_audio.py normalize ian.* brandon.* stephen.* --suffix .16k.wav --apply--start/--duration then normalize (the enroll-clippattern: superwhisper/recordings/<ts>/output.wav → ~/enroll/ian.wav, 22s).
python3 .../ffmpeg_audio.py trim output.wav --start 0 --duration 22 -o ~/enroll/ian.wav --apply python3 .../ffmpeg_audio.py concat a.wav b.wav c.wav -o joined.wav --apply python3 .../ffmpeg_audio.py probe ian.wavFor a fixed set of jobs (e.g. the enroll roster), declare them once and reconcile — idempotent, so it's safe to re-run after adding one person.
python3 .../ffmpeg_audio.py reconcile jobs.yaml # dry-run
python3 .../ffmpeg_audio.py reconcile jobs.yaml --apply # executeSee examples/enroll_roster.yaml for the shape:
jobs:
- op: trim
input: ~/superwhisper/recordings/2026-06-05/output.wav
start: 0
duration: 22
output: ~/enroll/ian.wav
- op: normalize
input: ~/recordings/brandon.m4a
output: ~/enroll/brandon.wav
- op: concat
inputs: [~/clips/a.wav, ~/clips/b.wav]
output: ~/enroll/merged.wav--apply — actually run ffmpeg (default: dry-run preview)--force — rebuild even if the output is up-to-date--json — machine-readable result array (for chaining)--suffix — output name when -o is omitted (normalize/trim batch mode)brew install ffmpeg · Ubuntu: apt-get install -y ffmpeg. The scripthard-fails on a missing ffmpeg/ffprobe rather than running half a batch.
reconcile needs PyYAML.de-duped); a missing input surfaces as a per-job error, never silent.
Apache 2.0
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.