voice-agents

$npx mdskill add benjaminasterA/antigravity-awesome-skills/voice-agents

Voice Agents enable natural human-AI conversations via speech recognition and synthesis.

  • Capture and process audio in real-time for immediate responses.
  • Deploys speech-to-text (STT) to convert spoken words into text.
  • Uses text-to-speech (TTS) to generate audible responses from text input.
  • Delivers conversational results directly through voice interfaces.

SKILL.md

.github/skills/voice-agentsView on GitHub ↗
---
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 flo..."
source: vibeship-spawner-skills (Apache 2.0)
risk: unknown
---

# 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 to Use
This skill is applicable to execute the workflow or actions described in the overview.

More from benjaminasterA/antigravity-awesome-skills

SkillDescription
2d-games2D game development principles. Sprites, tilemaps, physics, camera.
3d-games3D game development principles. Rendering, shaders, physics, cameras.
3d-web-experienceExpert in building 3D experiences for the web - Three.js, React Three Fiber, Spline, WebGL, and interactive 3D scenes. Covers product configurators, 3D portfolios, immersive websites, and bringing ...
accessibility-compliance-accessibility-auditYou are an accessibility expert specializing in WCAG compliance, inclusive design, and assistive technology compatibility. Conduct audits, identify barriers, and provide remediation guidance.
active-directory-attacksThis skill should be used when the user asks to \"attack Active Directory\", \"exploit AD\", \"Kerberoasting\", \"DCSync\", \"pass-the-hash\", \"BloodHound enumeration\", \"Golden Ticket\", ...
activecampaign-automationAutomate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first for current schemas.
address-github-commentsUse when you need to address review or issue comments on an open GitHub Pull Request using the gh CLI.
agent-framework-azure-ai-pyBuild Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code int...
agent-manager-skillManage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
agent-memory-mcpA hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).