Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flow with sub-800ms latency while handling interruptions, background noise, and emotional nuance. This skill covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency, most natural) and pipeline (STT→LLM→TTS, more control, easier to debug). Key insight: latency is the constraint. Hu
5.1
Rating
0
Installs
AI & LLM
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The skill addresses a valuable domain (voice agents) with clear architectural distinctions (S2S vs pipeline) and identifies the critical constraint (latency). However, it is severely incomplete: the description is truncated mid-sentence, the SKILL.md body is cut off repeatedly, and the Sharp Edges table contains only generic 'Issue' placeholders without actual content. While the structure shows promise (Patterns, Anti-Patterns, Sharp Edges), the lack of concrete implementation guidance, code examples, or detailed solutions means a CLI agent would struggle to actually build or debug a voice agent using this skill. The novelty is moderate—voice agent architecture knowledge is useful but the skill doesn't provide enough depth to meaningfully reduce token costs compared to a direct LLM query.
davila7
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