Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/beel-collab/presets.dev/voice-agentsnpx skills add beel-collab/presets.dev --skill voice-agentsgit clone --depth 1 https://github.com/beel-collab/presets.devWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/beel-collab/presets.dev/voice-agents)<a href="https://agentmods.dev/skills/beel-collab/presets.dev/voice-agents"><img src="https://agentmods.dev/badge/skills/beel-collab/presets.dev/voice-agents.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00016 | $0.06300 |
| Opus 5 | $0.00008 | $0.03150 |
| Sonnet 5 | $0.00003 | $0.01260 |
| Haiku 4.5 | $0.00002 | $0.00630 |
Grade A, and why
voice-agents scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 988 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Voice Agents
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. Humans expect responses in 500ms. Every millisecond matters.
84% of organizations are increasing voice AI budgets in 2025. This is the year voice agents go mainstream.
Principles
- Latency is the constraint - target <800ms end-to-end
- Jitter (variance) matters as much as absolute latency
- VAD quality determines conversation flow
- Interruption handling makes or breaks the experience
- Start with focused MVP, iterate based on real conversations
- Combine best-in-class components (Deepgram STT + ElevenLabs TTS)
Capabilities
- voice-agents
- speech-to-speech
- speech-to-text
- text-to-speech
- conversational-ai
- voice-activity-detection
- turn-taking
- barge-in-detection
- voice-interfaces
Scope
- phone-system-integration → backend
- audio-processing-dsp → audio-specialist
- music-generation → audio-specialist
- accessibility-compliance → accessibility-specialist
Tooling
Speech_to_speech
- OpenAI Realtime API - When: Lowest latency, most natural conversation Note: gpt-4o-realtime-preview, native voice, sub-500ms
- Pipecat - When: Open-source voice orchestration Note: Daily-backed, enterprise-grade, modular
Speech_to_text
- OpenAI Whisper - When: Highest accuracy, multilingual Note: gpt-4o-transcribe for best results
- Deepgram Nova-3 - When: Production workloads, 54% lower WER Note: 150-184ms TTFT, 90%+ accuracy on noisy audio
- AssemblyAI - When: Real-time streaming, speaker diarization Note: Good accuracy-latency balance
Text_to_speech
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 988 lines · 16 tokens per session scan A 1b2ace33560a
voice-agents is a skill published in the GitHub repository beel-collab/presets.dev (2 stars, last pushed 3mo ago), licensed MIT. It adds 16 tokens to every session and 6,300 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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