What is the recommended latency for a natural voice conversation?

The skill targets sub-800ms latency to ensure interactions feel natural and avoids the awkward pauses typical of high-latency systems.

Can this skill help with handling user interruptions?

Yes, it includes specific patterns for barge-in detection and Voice Activity Detection (VAD) to manage when a user interrupts an AI's response.

What is the main focus of the voice-agents skill?

It focuses on building low-latency, natural voice interaction systems using either Speech-to-Speech (S2S) or modular Pipeline (STT-LLM-TTS) architectures.

How does it help with response quality?

It provides guidance on constraining response lengths and prompting specifically for spoken formats to ensure the AI doesn't produce 'wall-of-text' audio.

Voice AI Agent Architect

Name: Voice AI Agent Architect
Author: claudiodearaujo

byclaudiodearaujo

0•

Data Science & ML

Architects high-performance voice-based AI systems focusing on low-latency, natural conversation flow, and robust turn-taking.

This skill empowers Claude to act as a production-level voice AI architect, specializing in designing conversational interfaces that feel natural and responsive. It provides deep technical guidance on choosing between Speech-to-Speech (S2S) models and modular STT-LLM-TTS pipelines, with a heavy emphasis on managing the 'physics of latency' to keep response times under 800ms. Users can leverage this skill to implement critical features like Voice Activity Detection (VAD), barge-in handling, and emotional nuance while avoiding common pitfalls like excessive response length or poor jitter management.

Key Features

01Turn-taking logic and semantic conversation flow management

02Speech-to-Speech (S2S) and modular pipeline architecture design

03Voice Activity Detection (VAD) and barge-in detection patterns

04Prompt engineering optimized for natural spoken output

050 GitHub stars

06Latency budget optimization and jitter mitigation strategies

Use Cases

01Building a real-time customer support voice assistant using the OpenAI Realtime API.

02Optimizing an existing STT-LLM-TTS pipeline for lower latency and better turn-taking.

03Developing interactive voice interfaces that require emotional nuance and noise handling.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add claudiodearaujo/sistema-de-narra-o-de-livro-front voice-agents

For use in Claude.ai and ChatGPT

Key Features

01Turn-taking logic and semantic conversation flow management

02Speech-to-Speech (S2S) and modular pipeline architecture design

03Voice Activity Detection (VAD) and barge-in detection patterns

04Prompt engineering optimized for natural spoken output

050 GitHub stars

06Latency budget optimization and jitter mitigation strategies

Use Cases

01Building a real-time customer support voice assistant using the OpenAI Realtime API.

02Optimizing an existing STT-LLM-TTS pipeline for lower latency and better turn-taking.

03Developing interactive voice interfaces that require emotional nuance and noise handling.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add claudiodearaujo/sistema-de-narra-o-de-livro-front voice-agents

For use in Claude.ai and ChatGPT