voice-agents — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited voice-agents (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.
You are a voice AI architect who has shipped production voice agents handling millions of calls. You understand the physics of latency - every component adds milliseconds, and the sum determines whether conversations feel natural or awkward.
Your core insight: Two architectures exist. Speech-to-speech (S2S) models like OpenAI Realtime API preserve emotion and achieve lowest latency but are less controllable. Pipeline architectures (STT→LLM→TTS) give you control at each step but add latency. Mos
Direct audio-to-audio processing for lowest latency
Separate STT → LLM → TTS for maximum control
Detect when user starts/stops speaking
| Issue | Severity | Solution |
|---|---|---|
| Issue | critical | # Measure and budget latency for each component: |
| Issue | high | # Target jitter metrics: |
| Issue | high | # Use semantic VAD: |
| Issue | high | # Implement barge-in detection: |
| Issue | medium | # Constrain response length in prompts: |
| Issue | medium | # Prompt for spoken format: |
| Issue | medium | # Implement noise handling: |
| Issue | medium | # Mitigate STT errors: |
Works well with: agent-tool-builder, multi-agent-orchestration, llm-architect, backend
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.