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
Install with the open skills CLI (global, non-interactive — available in every Claude Code session):
npx skills add davila7/claude-code-templates --skill "voice-agents" -g -a claude-code -yOr manually — copy the SKILL.md below into:
~/.claude/skills/voice-agents-davila7/SKILL.md---
name: 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`
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
Roleplay the most difficult, tech-resistant user for your product. Browse the app as that persona, find every UX pain point, then filter complaints through a pragmatism layer to separate real problems from noise. Creates actionable tickets from genuine issues only.
Give the agent its own dedicated email inbox via AgentMail. Send, receive, and manage email autonomously using agent-owned email addresses (e.g. hermes-agent@agentmail.to).