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...
Scanned 6/1/2026
Install via CLI
openskills install christophacham/agent-skills-library---
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..."
risk: unknown
source: "vibeship-spawner-skills (Apache 2.0)"
date_added: "2026-02-27"
---
# 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.
No comments yet. Be the first to comment!
Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.
Adversarial multi-agent planning skill. Self-orchestrates 5 hostile category members (unspecified-low, unspecified-high, deep, ultrabrain, artistry) via team-mode for ruthless cross-critique debate, distills only the defensible insights, then MANDATORILY hands the distilled insight bundle to the `plan` agent for executable plan formalization. Use when planning needs maximum rigor and surfacing of weak assumptions, blind spots, and over-engineering. Triggers: 'hyperplan', 'hpp', '/hyperplan', ...
**Complete production-ready guide for Google Gemini embeddings API** This skill provides comprehensive coverage of the `gemini-embedding-001` model for generating text embeddings, including SDK usage, REST API patterns, batch processing, RAG integration with Cloudflare Vectorize, and advanced use cases like semantic search and document clustering. ---
Interview, source-challenge, verify, save, and ADR-gate fuzzy coding requests into Codex-ready implementation specs. Use when a feature, bugfix, refactor, migration, repo-wide change, or architecture task needs user-verified requirements, source-backed decisions, durable architecture decisions, acceptance criteria, validation commands, rollout notes, saved spec/ADR files, and a Codex execution prompt. Do not use when already fully specified or when the user wants direct implementation now.
Use when a repo needs CodeGraph plus ast-grep for Codex MCP setup, exploration, impact analysis, structural search, or safe refactor planning.