VoiceTrust

Interpret VoiceTrust results for owner verification on voice/audio inputs. Use when you need the meaning of VoiceTrust fields, trust labels, command-gating d...

This skill appears to do what it claims: local speaker verification using SpeechBrain models. Before installing/running: 1) Inspect and verify the raw model URLs and the hard-coded SHA-256 checksums in scripts/ensure_models.py (the script will fail if checksums don't match). Prefer obtaining model checkpoints from the official upstream (SpeechBrain / HuggingFace) if you want stronger provenance guarantees. 2) Be aware the runtime will write enrollment/voiceprint files to runtime/data/owners and runtime/data/voiceprints — do not share those files if you want the enrolled identity private. 3) The runtime requires heavy ML deps (torch, speechbrain, etc.) and optionally ffmpeg; expect a large install and CPU/GPU resource use. 4) If you are concerned about network supply-chain risk, run ensure_models.py only after manually auditing the URLs or by populating the assets directory with checkpoints you obtained from an upstream release. Otherwise the skill is internally consistent and proportionate for its stated purpose.

Type: OpenClaw Skill Name: voicetrust Version: 0.4.1 The VoiceTrust skill bundle provides a legitimate implementation for speaker verification and trust scoring using the SpeechBrain framework. It includes well-structured code for managing local voice profiles, processing audio via soundfile or ffmpeg, and providing the AI agent with clear logic for command-gating decisions. Security best practices are observed, such as SHA256 hash verification for model assets downloaded by `scripts/ensure_models.py` and explicit documentation regarding the privacy and local-only storage of biometric enrollment data. No evidence of malicious intent, data exfiltration, or prompt injection was found.