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 Galbaz1/video-research-mcp --skill tts-productiongit clone --depth 1 https://github.com/Galbaz1/video-research-mcpWrote 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/galbaz1/video-research-mcp/tts-production)<a href="https://agentmods.dev/skills/galbaz1/video-research-mcp/tts-production"><img src="https://agentmods.dev/badge/skills/galbaz1/video-research-mcp/tts-production/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/galbaz1/video-research-mcp/tts-production"><img src="https://agentmods.dev/badge/skills/galbaz1/video-research-mcp/tts-production.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 | $0.00043 | $0.01363 |
| Opus 5 | $0.00022 | $0.00681 |
| Sonnet 5 | $0.00009 | $0.00273 |
| Haiku 4.5 | $0.00004 | $0.00136 |
Grade A, and why
tts-production scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
> **Critical:** Use direct API calls (curl), NOT ElevenLabs MCP tools. The MCP `Text_To_Speech` tool returns 404 due to routing issues. Direct API is reliable and battle-tested. How it starts
The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TTS Production with ElevenLabs
Generate, tune, and mix voice-over audio using the ElevenLabs Text-to-Speech API.
Critical: Use direct API calls (curl), NOT ElevenLabs MCP tools. The MCP
Text_To_Speechtool returns 404 due to routing issues. Direct API is reliable and battle-tested.
API Pattern
Text-to-Speech with Timestamps (recommended)
curl -s -X POST "https://api.elevenlabs.io/v1/text-to-speech/${VOICE_ID}/with-timestamps" \
-H "xi-api-key: ${ELEVENLABS_API_KEY}" \
-H "Content-Type: application/json" \
-d '{
"text": "Your text here",
"model_id": "eleven_multilingual_v2",
"voice_settings": {
"stability": 0.75,
"similarity_boost": 0.80,
"style": 0.40,
"use_speaker_boost": true
}
}' \
--output /tmp/tts-response.json
Decode Response
import json, base64
with open('/tmp/tts-response.json', 'r') as f:
data = json.load(f)
audio_bytes = base64.b64decode(data['audio_base64'])
with open('output.mp3', 'wb') as f:
f.write(audio_bytes)
ends = data.get('alignment', {}).get('character_end_times_seconds', [])
print(f'Duration: {ends[-1]:.2f}s' if ends else 'No timestamps')
Simple TTS (no timestamps)
curl -s -X POST "https://api.elevenlabs.io/v1/text-to-speech/${VOICE_ID}" \
-H "xi-api-key: ${ELEVENLABS_API_KEY}" \
-H "Content-Type: application/json" \
-d '{ "text": "...", "model_id": "eleven_multilingual_v2", "voice_settings": {...} }' \
--output output.mp3
Sound Effects Generation
curl -s -X POST "https://api.elevenlabs.io/v1/sound-generation" \
-H "xi-api-key: ${ELEVENLABS_API_KEY}" \
-H "Content-Type: application/json" \
-d '{ "text": "short sharp underwater splash blip", "duration_seconds": 1.0, "prompt_influence": 0.8 }' \
--output sfx.mp3
Model Selection
| Model | Use Case | Speed | Quality |
|---|---|---|---|
eleven_multilingual_v2 |
Production — Dutch, English, mixed language | Slow | Highest |
eleven_flash_v2_5 |
Quick drafts, iteration | Fast | Good |
eleven_turbo_v2_5 |
Real-time, low latency | Fastest | Acceptable |
What ships with it
1 file 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.
- 13d ago First seen · 128 lines · 43 tokens per session scan A 9dd41bbed809
tts-production is a skill published in the GitHub repository Galbaz1/video-research-mcp (23 stars, last pushed 1mo ago), licensed MIT. It adds 43 tokens to every session and 1,363 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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