Auto Video Cut
v1.0.0Automatically trims single-speaker videos by detecting and removing silence and filler to produce a rough cut with quality scoring and deduplication.
⭐ 0· 31·1 current·1 all-time
MIT-0
Download zip
LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
The name/description (auto-trim single-speaker video, silence/filler removal, dedup, scoring) aligns with the code and SKILL.md. The script uses FFmpeg for audio/video processing and invokes Whisper for transcription, which is exactly what this functionality requires. Sample work files included are consistent with expected inputs/outputs.
Instruction Scope
SKILL.md instructs the agent/user to run `python3 video_editor_auto.py` but the repository contains `video_editor_auto_v4.6.py` (filename mismatch) — you'll need to run the actual filename or rename it. The instructions and script operate only on supplied video files and a local work directory; they do not reference or require any unrelated system paths or credentials. Note: running Whisper may download model weights (network access) on first run; the SKILL.md does not explicitly warn about model downloads.
Install Mechanism
There is no install spec; dependencies are installed via normal package managers (pip/brew) as documented. requirements.txt only lists openai-whisper. No remote arbitrary archive downloads or installers are present in the manifest. The only external tooling invoked is FFmpeg and the Whisper package.
Credentials
The skill declares no required environment variables, credentials, or config paths and the code does not attempt to read secrets or unrelated env vars. All runtime needs (ffmpeg, whisper) are proportional to the stated purpose.
Persistence & Privilege
always:false and no install-time or runtime behavior attempts to persist the skill into system-wide agent settings. The script writes output and temporary files to the provided work/output directories only.
Scan Findings in Context
[no_findings] expected: Static pre-scan reported no injection or suspicious regex matches. That matches expectation for a local video-processing script that shells out to ffmpeg and whisper.
Assessment
This skill appears to do what it says: it uses FFmpeg to detect silence and OpenAI Whisper to transcribe, then scores and trims segments. Before installing/running: (1) fix the filename mismatch in the README or call the provided script (video_editor_auto_v4.6.py). (2) Ensure FFmpeg is installed and on PATH. (3) Be aware Whisper may download model weights the first time (it will use network and disk cache). (4) The script runs locally and reads/writes files in the work/output folders you supply — review those outputs and the script if you want to confirm no unintended file access. (5) If you will run this on sensitive videos, test on non-sensitive material first and review the transcript files it generates. If you want, I can point out exact lines to change for the filename or help inspect the remainder of the source for further hardening.Like a lobster shell, security has layers — review code before you run it.
latestvk97ddm8gxcw96xfcgdc1r619rs844hnq
License
MIT-0
Free to use, modify, and redistribute. No attribution required.