Audio SRT Workflow
v0.1.2Generate or align SRT subtitles from audio using this repository. Use when the user asks for subtitle generation, transcript-to-audio alignment, timing clean...
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description match the included scripts and requirements: align_to_srt.py, gui_app.py, srt_stats.py, make_preview_mp4.py and a requirements.txt listing faster-whisper. Required tools referenced in SKILL.md (Python 3.10+, ffmpeg, faster-whisper) are appropriate for subtitle/transcription work. One minor mismatch: the code reads an optional environment variable FASTER_WHISPER_MODEL_DIR to locate models but this env var is not documented in the registry's required env list.
Instruction Scope
SKILL.md gives concrete invocation templates and environment checks (python version, ffmpeg, faster_whisper import) and directs the agent to run only the included scripts on user-supplied audio/text files. The instructions do not ask for unrelated files, secrets, or to transmit outputs to unusual external endpoints. Note: faster-whisper/model usage may download model weights from remote hosts when a model path is not local — this is expected for ASR but is a network behavior to be aware of.
Install Mechanism
No install spec is present (instruction-only), and dependencies are limited to a pinned faster-whisper package in scripts/requirements.txt. No arbitrary URL downloads or archive extraction are performed by the skill itself. The README suggests using a venv and pip install -r requirements.txt which is standard.
Credentials
The skill declares no required credentials (none in registry) which matches the code. However, the code optionally consults FASTER_WHISPER_MODEL_DIR to locate local model files (not declared in requires.env). Also, faster-whisper / underlying HF tooling may use existing Hugging Face credentials (e.g., HF_TOKEN) from the environment if the user has them configured when fetching private models; that credential access is implicit and not declared. Overall requested privileges are minimal and appropriate for the task, but be aware of the implicit model-download behavior and any credentials present in your environment.
Persistence & Privilege
always:false and allow_implicit_invocation:false in the agent metadata; the skill does not request permanent system presence, nor does it modify other skills or system-wide configs. It only runs local scripts and writes output files specified by the user.
Assessment
This package appears to be what it says: an offline toolset to align/generate SRTs and render preview videos. Before installing/running:
- Run it in a dedicated Python virtualenv to avoid affecting your system Python.
- Confirm ffmpeg is the binary you expect (ffmpeg in PATH or pass --ffmpeg-bin). The preview tool shells out to ffmpeg to burn subtitles.
- If you want to avoid network downloads, pre-download or place Whisper model files in a local directory and set FASTER_WHISPER_MODEL_DIR (the code checks this env var) so faster-whisper won't fetch large weights at runtime.
- Be aware: when faster-whisper does need to fetch models it may use Hugging Face tooling which can pick up HF tokens from your environment; avoid running this skill in an environment that contains unexpected credentials you don't want used.
- The GUI requires tkinter; the code will exit if tkinter is unavailable.
- Inspect the included scripts (they are small and readable) and test with non-sensitive audio first. If you need higher assurance, run in an isolated environment or container to observe any network activity during model loading.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.