Voice Agents
AI and machine learning skill, available on Zeplik
Voice Agents is a ready-to-run AI and machine learning skill on Zeplik. Design low-latency voice agents: speech-to-speech (OpenAI Realtime) vs STT-LLM-TTS pipelines, sub-800ms turn-taking. Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.
The Voice Agents skill loads automatically when your request matches it, or you can invoke it directly by typing /voice-agents in any chat. It works with attachments, connectors, and any model that supports the task, so you get the same expert method every time without setting anything up.
What the Voice Agents skill can do
- Choose between speech-to-speech and STT-LLM-TTS pipeline architectures
- Budget and measure latency across each pipeline component
- Design semantic voice activity detection and barge-in handling
- Constrain prompt responses for natural sub-800ms turn-taking
Try these prompts on Zeplik
Pick a prompt to open it in the Zeplik app. If you are not signed in yet, your prompt is waiting for you the moment you do.
How the Voice Agents skill works
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
How to use the Voice Agents skill
Sign in to Zeplik
Create a free Zeplik account or sign in. New accounts start with free credits, so you can try the Voice Agents skill right away.
Describe your AI and machine learning task
Ask in plain language, or type /voice-agents to invoke the skill directly. Zeplik recognizes the Voice Agents skill and applies its method.
Review and refine the result
Zeplik returns a clear, structured answer. Ask follow-ups in the same chat to refine it or take the next step.
Source and credit
- Author
- davila7 (D7 Class-A standalone)
- License
- MIT
Adapted from the open-source davila7/claude-code-templates project and tuned to run natively on Zeplik. View source on GitHub.
Frequently asked questions
- What is the Voice Agents skill?
- Voice Agents is a ready-to-run AI and machine learning skill on Zeplik. Design low-latency voice agents: speech-to-speech (OpenAI Realtime) vs STT-LLM-TTS pipelines, sub-800ms turn-taking. Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.
- How do I use Voice Agents on Zeplik?
- Sign in to Zeplik and ask in plain language, or type /voice-agents in any chat to invoke it directly. The skill applies its method and returns a result you can refine in the same conversation.
- Which AI model does the Voice Agents skill use?
- Any model you choose. Zeplik works across every model in one chat, so the Voice Agents skill runs on your preferred model for the task.
- Where does the Voice Agents skill come from?
- The Voice Agents skill is adapted from the open-source davila7/claude-code-templates project (MIT) and tuned to run natively on Zeplik. The original source is linked on this page.
- How much does the Voice Agents skill cost?
- Using the skill is free to start. You only spend Zeplik credits when the assistant runs, and new accounts begin with free credits.
Related ai and machine learning skills
- Agent MemoryMemory and context management for LLM agents — short/long-term memory stores, conversation persistence, context-window strategies (summarization, trimming, retrieval) and prompt caching. Use for "give my agent memory / manage context"; for vector stores see vector-databases.
- AI Safety GuardrailsSafety and moderation for LLM apps — Constitutional AI, Llama Guard input/output moderation, and NeMo Guardrails runtime rails. Use for "add safety/moderation/guardrails to my LLM app"; for evaluating safety see llm-evaluation-harnesses.
- Autonomous Agent PatternsFramework-agnostic design patterns for autonomous coding agents — goal decomposition, planning, parallel/multi-agent orchestration and operational modes. Use for "how should I architect an autonomous agent"; for concrete frameworks see ai-agent-frameworks.
- Gemini CLIRun Gemini CLI (Gemini 3 Pro) for code/plan review and huge >200k-token context analysis in the terminal
- InterpretabilityMechanistic interpretability of neural networks — TransformerLens, NNsight remote access, sparse autoencoders (SAELens), and causal interventions (pyvene). Use for "probe/intervene on model internals" or "train an SAE"; for architecture basics see llm-architectures.
- LLM App PatternsProduction patterns for LLM applications — RAG architecture, embeddings, LLMOps, and end-to-end app design. Use for "architect a production LLM/RAG app"; for the vector store see vector-databases, for agents see ai-agent-frameworks.
More on Zeplik
Try Voice Agents on Zeplik
Every model, one chat. Bring the Voice Agents skill into your next conversation and let the assistant do the work.