meeting-to-text
PassAudited by VirusTotal on May 11, 2026.
Overview
Type: OpenClaw Skill Name: meeting-to-text Version: 1.0.0 The skill bundle provides a legitimate local workflow for transcribing audio and video files with speaker diarization. The Python script (meeting_to_text.py) utilizes standard machine learning libraries such as FunASR and Torch, and interacts with FFmpeg via safe subprocess calls to normalize media. No evidence of data exfiltration, malicious execution, or harmful prompt injection was found; the network activity is limited to downloading model weights from a legitimate repository (ModelScope) if they are not already present locally.
Findings (0)
Artifact-based informational review of SKILL.md, metadata, install specs, static scan signals, and capability signals. ClawScan does not execute the skill or run runtime probes.
A first run may contact an external model source and rely on downloaded model artifacts, which can surprise users expecting an offline-only tool and introduces supply-chain trust requirements.
If the speaker model cache is missing, the runtime can fetch a remote model artifact and then load the checkpoint, while the registry has no install spec and the skill is framed as a fully local workflow.
from modelscope.hub.snapshot_download import snapshot_download ... downloaded = snapshot_download(SPEAKER_MODEL_ID, revision=SPEAKER_MODEL_REVISION, cache_dir=str(THREE_D_SPEAKER_CACHE)) ... state_dict = torch.load(str(checkpoint_path), map_location="cpu")
Document the network/model download clearly, make it an explicit setup step or opt-in, pin and verify model artifacts where possible, and declare the required local dependencies in metadata or installation instructions.
The skill will execute local media-processing software against the file path you provide.
The skill runs a local FFmpeg executable on the user-selected media file; this is expected for audio/video normalization and is invoked without `shell=True`.
command = [str(FFMPEG_EXE), "-hide_banner", "-loglevel", "error", "-y", "-i", str(source_path), ...]; completed = subprocess.run(command, capture_output=True, text=True, encoding="utf-8", errors="replace")
Use a trusted FFmpeg binary and run the skill only on files you intend to process.