Fähigkeiten voice-ai-engine-development

📦

voice-ai-engine-development

Name: voice-ai-engine-development
Author: sickn33

Sicher

Build Real-Time Voice AI Engines

Create production-ready conversational AI voice engines with async worker pipelines, streaming transcription, LLM agents, and TTS synthesis. This skill provides complete guidance on building voice assistants with interrupt handling and multi-provider support.

Unterstützt: Claude Codex Code(CC)

🥈 77 Silber

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Verwendung von "voice-ai-engine-development". Create a voice AI engine with three workers: transcriber, agent, and synthesizer

Erwartetes Ergebnis:

A Python project structure with async queue-based workers, each processing audio/text independently and communicating via asyncio.Queue

Verwendung von "voice-ai-engine-development". Show me how to handle user interruptions in voice conversation

Erwartetes Ergebnis:

Code demonstrating InterruptibleEvent wrapper and get_message_up_to() method for cutting off AI speech when user speaks

Verwendung von "voice-ai-engine-development". Compare Deepgram vs Google Speech for transcription

Erwartetes Ergebnis:

A comparison table showing latency, accuracy, language support, and cost differences between providers

Sicherheitsaudit

Sicher

v1 • 2/25/2026

All 214 static findings are false positives. The skill is legitimate voice AI development documentation with example code. Detected patterns include: markdown code fences (not shell commands), legitimate audio data representation with \x00, standard network bindings (0.0.0.0), and placeholder configuration strings. No actual security issues found.

Gescannte Dateien

3,371

Analysierte Zeilen

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Gesamtzahl Audits

Keine Sicherheitsprobleme gefunden

Auditiert von: claude

Qualitätsbewertung

Architektur

100

Wartbarkeit

Inhalt

Community

100

Sicherheit

Spezifikationskonformität

Was du bauen kannst

Build a Voice Assistant

Create a real-time voice assistant that can listen, process, and respond to user queries with natural speech

Implement Voice Chatbot

Add voice conversation capability to existing chatbots for phone or video call applications

Create Multi-Provider Voice System

Build a flexible voice system that can switch between different transcription and TTS providers

Probiere diese Prompts

Basic Voice Pipeline

Use the voice-ai-engine-development skill to create a simple voice assistant that listens for speech, converts it to text, processes it with an LLM, and speaks the response back

Add Interrupt Handling

Using voice-ai-engine-development, show me how to add interrupt handling so users can stop the AI mid-response

Multi-Provider Setup

I want to use multiple TTS providers (ElevenLabs, Google, OpenAI) in my voice engine. Show me the factory pattern implementation from voice-ai-engine-development

Optimize Streaming Latency

Using voice-ai-engine-development best practices, how do I optimize the async pipeline to minimize latency between user speech and AI response?

Bewährte Verfahren

Use async worker queues for independent processing stages to enable true concurrency
Buffer LLM responses before TTS to ensure smooth audio output without truncation
Implement interrupt handling early as it affects the entire pipeline architecture

Vermeiden

Processing audio synchronously in a single thread (blocks real-time performance)
Sending incomplete chunks to transcription (causes accuracy issues)
Starting TTS before LLM response is complete (results in cut-off speech)

Häufig gestellte Fragen

What voice providers does this skill support?

The skill covers integration with Deepgram, Google Speech, and Whisper for transcription; OpenAI, Gemini, and Anthropic for LLM; and ElevenLabs, Google Cloud TTS, and OpenAI TTS for voice synthesis.

Do I need API keys to use this skill?

Yes, you will need API keys from your chosen providers (Deepgram, OpenAI, ElevenLabs, etc.). The skill provides configuration templates but does not include actual keys.

Can this run completely locally?

Some components can run locally (like Whisper transcription), but most production voice AI systems require cloud API calls for LLM processing and high-quality TTS.

How does interrupt handling work?

The skill uses an InterruptibleEvent wrapper that tracks whether the user has spoken. The get_message_up_to() method returns only the text spoken before the interruption.

What Python libraries are required?

Core dependencies include asyncio, FastAPI, websockets for real-time communication, and provider SDKs. The skill lists all required packages in documentation.

Is this suitable for production deployment?

The skill provides architectural patterns suitable for production, but you will need to implement the actual provider integrations and handle scaling considerations.