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 5.5 | $0.00018 | $0.00484 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo 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 (17 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 5.5. 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
video-prompting
Use when crafting video or motion prompts for any generative model — dramaturgy, camera, physics-safe motion, frame anchors, and clip chaining.
audio-prompting
Use when crafting TTS, music, or bed prompts for any generative audio model — director style, song structure, and post-production layering.
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.
image-prompt-engineer
Craft optimized prompts for AI image and short video generation across major models, visual formats, aspect ratios, and content goals. Triggers on image prompts, video prompts, prompt optimization, Flux, DALL-E, Midjourney, and Genfeed visual prompts.
amazon-qbusiness-anonymous-mcp
AI assistant for ingested content with anonymous access.