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brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or planning anything new, in software or out of it (a talk, a business, a renovation).
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voice-agents
voice-agentsVoice 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 Real…
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Documentation de la Skill
GitHubname: voice-agents description: "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" source: vibeship-spawner-skills (Apache 2.0)
Voice Agents
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
Capabilities
- voice-agents
- speech-to-speech
- speech-to-text
- text-to-speech
- conversational-ai
- voice-activity-detection
- turn-taking
- barge-in-detection
- voice-interfaces
Patterns
Speech-to-Speech Architecture
Direct audio-to-audio processing for lowest latency
Pipeline Architecture
Separate STT → LLM → TTS for maximum control
Voice Activity Detection Pattern
Detect when user starts/stops speaking
Anti-Patterns
❌ Ignoring Latency Budget
❌ Silence-Only Turn Detection
❌ Long Responses
⚠️ Sharp Edges
| 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: |
Related Skills
Works well with: agent-tool-builder, multi-agent-orchestration, llm-architect, backend