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 skills add oyi77/1ai-skills --skill voice-aigit clone --depth 1 https://github.com/oyi77/1ai-skillsWrote 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/oyi77/1ai-skills/voice-ai)<a href="https://agentmods.dev/skills/oyi77/1ai-skills/voice-ai"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/voice-ai/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/oyi77/1ai-skills/voice-ai"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/voice-ai.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00044 | $0.01209 |
| Opus 5 | $0.00022 | $0.00605 |
| Sonnet 5 | $0.00009 | $0.00242 |
| Haiku 4.5 | $0.00004 | $0.00121 |
Grade B, and why
voice-ai scanned grade B with 2 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 8d 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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
resp = requests.post("https://api.elevenlabs.io/v1/voices/add", Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
resp = requests.post("https://api.elevenlabs.io/v1/voices/add", How it starts
The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Voice AI covers TTS, STT, voice cloning, and real-time conversational agents. Integrates ElevenLabs, OpenAI TTS, Whisper, and Vapi/Bland/Retell.
Capabilities
- Text-to-speech (ElevenLabs, OpenAI TTS)
- Audio transcription (Whisper, Deepgram)
- Voice cloning from samples
- Real-time voice agents (Vapi, Bland, Retell)
- Audio processing (pydub)
When to Use
Trigger phrases:
-
"voice ai"
-
"Voice AI — text-to-speech (ElevenLabs, OpenAI TTS), speech-to-text (Whisper), vo"
-
Voiceovers for videos/podcasts
-
Voice-based customer support
-
Transcribing meetings/interviews
-
Audiobook generation
When NOT to Use
- Task is about content strategy, not creation (use strategy skills)
- Task is about content distribution (use distribution skills)
- You need to analyze content performance (use analytics skills)
- Task is about content moderation (use moderation tools)
- You don't have content guidelines
- Task requires domain expertise (consult experts)
Pseudo Code
The voice-ai workflow follows a standard pipeline pattern.
Core flow:
# voice-ai primary flow
input = prepare(raw_data)
result = process(input, config={agents, cloning, elevenlabs, openai, real})
validate(result)
deliver(result)
Error handling:
on error:
log(error_details)
retry_with_backoff(max=3)
if still_failing: alert_and_escalate()
Core Workflow
# voice-ai primary flow
input = prepare(raw_data)
result = process(input, config={agents, cloning, elevenlabs, openai, real})
validate(result)
deliver(result)
Error Handling
on error:
log(error_details)
retry_with_backoff(max=3)
if still_failing: alert_and_escalate()
OpenAI TTS
from openai import OpenAI
client = OpenAI()
resp = client.audio.speech.create(model="tts-1-hd", voice="nova", input="Hello!")
resp.stream_to_file("output.mp3")
ElevenLabs Clone
import requests
resp = requests.post("https://api.elevenlabs.io/v1/voices/add",
headers={"xi-api-key": KEY}, files={"files": [open("sample.mp3","rb")]}, data={"name":"Clone"})
voice_id = resp.json()["voice_id"]
resp = requests.post(f"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}",
headers={"xi-api-key": KEY}, json={"text":"Hello!","model_id":"eleven_multilingual_v2"})
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.
- 8d ago First seen · 172 lines · 44 tokens per session scan B 08d787740c31
voice-ai is a skill published in the GitHub repository oyi77/1ai-skills (12 stars, last pushed today), licensed MIT. It adds 44 tokens to every session and 1,209 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
prism
Consultant for NotebookLM steering prompt design. Optimizes Audio/Video/Slide/Infographic output quality through source preparation, prompt engineering, and Custom Goals persona design.
skill-smart-clip-detection
Use for AI-assisted clip detection from transcripts, livestreams, videos, podcasts, calls, or long-form content, including scored candidates, timestamps, batching, validation, prompt versioning, review queues, idempotent reprocessing, consent, and publishing-ready metadata.
audio-prompting
Use when crafting TTS, music, or bed prompts for any generative audio model — director style, song structure, and post-production layering.
video-prompting
Use when crafting video or motion prompts for any generative model — dramaturgy, camera, physics-safe motion, frame anchors, and clip chaining.
generation-diversity
Use when writing any generative prompt — ritual seed, explicit structure, scenario axes, and quality gates before paid API calls.
image-prompting
Use when crafting still-image prompts for any generative model — composition, identity sheets, edits, try-on, and photoreal personas.