agent-management
Create, manage, and orchestrate AI agents using the AI Maestro CLI. Use when the user asks to "create agent", "list agents", "delete agent", "hibernate agent",…
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
$ npx -y skills add davila7/claude-code-templates --skill voice-agents --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/voice-agentsContext preview
The summary Claude sees to decide when to auto-load this skill.
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
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 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. Hu" source: vibeship-spawner-skills (Apache 2.0)
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
Direct audio-to-audio processing for lowest latency
Separate STT → LLM → TTS for maximum control
Detect when user starts/stops speaking
| 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: |
Works well with: `agent-tool-builder`, `multi-agent-orchestration`, `llm-architect`, `backend`
Ready-to-use configurations for Anthropic's Claude Code. A comprehensive collection of AI agents, custom commands, settings, hooks, external integrations (MCPs), and project templates to enhance your development workflow.
Repo: davila7/claude-code-templates
Create, manage, and orchestrate AI agents using the AI Maestro CLI. Use when the user asks to "create agent", "list agents", "delete agent", "hibernate agent",…
Send and receive cryptographically signed messages between AI agents using the Agent Messaging Protocol (AMP). Use when the user asks to "send a message to an…
Search auto-generated codebase documentation for function signatures, API docs, class definitions, and code comments. Use when the user asks to "search docs",…
Query the code graph database to understand component relationships, dependencies, and change impact. Use when the user asks to "find callers", "check…
Search conversation history and semantic memory to recall previous discussions, decisions, and context. Use when the user asks to "search memory", "what did we…