voice-agents
Architects voice AI agents focusing on latency, turn-taking, and speech processing.
Install
mkdir -p .claude/skills/voice-agents && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/775" && unzip -o skill.zip -d .claude/skills/voice-agents && rm skill.zipInstalls to .claude/skills/voice-agents
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
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. HuKey capabilities
- →Implement speech-to-speech architectures for low latency
- →Design pipeline architectures for STT, LLM, and TTS integration
- →Configure voice activity detection for turn-taking
- →Implement barge-in detection for natural interruptions
- →Optimize latency budgets for conversational AI
How it works
The skill focuses on managing the physics of latency by choosing between speech-to-speech models for natural flow or pipeline architectures for control. It provides patterns for voice activity detection and barge-in handling to ensure conversational responsiveness.
Inputs & outputs
When to use voice-agents
- →Design low-latency voice systems
- →Optimize STT/TTS pipelines
- →Implement barge-in detection
About this skill
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
When not to use it
- →Non-voice based AI interaction tasks
- →Applications where latency is not a primary constraint
Prerequisites
Limitations
- →Requires strict adherence to latency budgets
- →Speech-to-speech models offer less control than pipeline architectures
How it compares
This approach prioritizes sub-800ms latency and emotional nuance over generic chatbot implementations, treating conversation flow as a technical constraint.
Compared to similar skills
voice-agents side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| voice-agents (this skill) | 8 | 6mo | No flags | Intermediate |
| langchain-architecture | 8 | 2mo | Review | Intermediate |
| ai-agents-architect | 5 | 6mo | No flags | Advanced |
| llm-app-patterns | 3 | 6mo | No flags | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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