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/f-amine/vibe-stack/text-to-speechnpx skills add f-amine/vibe-stack --skill text-to-speechgit clone --depth 1 https://github.com/f-amine/vibe-stackWrote 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/f-amine/vibe-stack/text-to-speech)<a href="https://agentmods.dev/skills/f-amine/vibe-stack/text-to-speech"><img src="https://agentmods.dev/badge/skills/f-amine/vibe-stack/text-to-speech.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 | $0.00040 | $0.02038 |
| Opus 5 | $0.00020 | $0.01019 |
| Sonnet 5 | $0.00008 | $0.00408 |
| Haiku 4.5 | $0.00004 | $0.00204 |
Grade A, and why
text-to-speech scanned grade A with 1 finding 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 5d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -X POST "https://api.elevenlabs.io/v1/text-to-speech/JBFqnCBsd6RMkjVDRZzb" \ This is a copy
86% identical to text-to-speech — 19 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ElevenLabs Text-to-Speech
Generate natural speech from text - supports 70+ languages, multiple models for quality vs latency tradeoffs.
Setup: See Installation Guide. For JavaScript, use
@elevenlabs/*packages only.
Quick Start
Python
from elevenlabs import ElevenLabs
client = ElevenLabs()
audio = client.text_to_speech.convert(
text="Hello, welcome to ElevenLabs!",
voice_id="JBFqnCBsd6RMkjVDRZzb", # George
model_id="eleven_multilingual_v2"
)
with open("output.mp3", "wb") as f:
for chunk in audio:
f.write(chunk)
JavaScript
import { ElevenLabsClient } from "@elevenlabs/elevenlabs-js";
import { createWriteStream } from "fs";
const client = new ElevenLabsClient();
const audio = await client.textToSpeech.convert("JBFqnCBsd6RMkjVDRZzb", {
text: "Hello, welcome to ElevenLabs!",
modelId: "eleven_multilingual_v2",
});
audio.pipe(createWriteStream("output.mp3"));
cURL
curl -X POST "https://api.elevenlabs.io/v1/text-to-speech/JBFqnCBsd6RMkjVDRZzb" \
-H "xi-api-key: $ELEVENLABS_API_KEY" -H "Content-Type: application/json" \
-d '{"text": "Hello!", "model_id": "eleven_multilingual_v2"}' --output output.mp3
Models
| Model ID | Languages | Latency | Best For |
|---|---|---|---|
eleven_v3 |
70+ | Standard | Highest quality, emotional range |
eleven_multilingual_v2 |
29 | Standard | High quality, long-form content |
eleven_flash_v2_5 |
32 | ~75ms | Ultra-low latency, real-time |
eleven_flash_v2 |
English | ~75ms | English-only, fastest |
eleven_turbo_v2_5 |
32 | ~250-300ms | Balanced quality/speed |
eleven_turbo_v2 |
English | ~250-300ms | English-only, balanced |
Voice IDs
Use pre-made voices or create custom voices in the dashboard.
Popular voices:
JBFqnCBsd6RMkjVDRZzb- George (male, narrative)EXAVITQu4vr4xnSDxMaL- Sarah (female, soft)onwK4e9ZLuTAKqWW03F9- Daniel (male, authoritative)XB0fDUnXU5powFXDhCwa- Charlotte (female, conversational)
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 227 lines · 40 tokens per session scan A 85f02ab82ac8
text-to-speech is a skill published in the GitHub repository f-amine/vibe-stack (21 stars, last pushed 2mo ago), licensed MIT. It adds 40 tokens to every session and 2,038 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 86% identical to text-to-speech, differing in 19 lines, and is treated as a copy.
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