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 | $0.01766 |
| Opus 5.5 | $0.00017 | $0.00706 |
| Sonnet 5.5 | $0.00009 | $0.00353 |
| Haiku 4.5 | $0.00004 | $0.00177 |
Grade A, and why
tts-production scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 20d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl --fail-with-body -sS -X POST "https://api.elevenlabs.io/v1/text-to-speech/${VOICE_ID}/with-timestamps" \ How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TTS Production with ElevenLabs
Generate, audition and mix narration using the ElevenLabs Text-to-Speech API. Model guidance was checked on 2026-10-07 against the official ElevenLabs TTS skill and models documentation; re-check the live endpoint, voice eligibility, quota and price before execution.
This skill uses the official HTTP API; inspect the actual schema if an optional connector is used. Use narration audition for spoken phrasing, delivery and the audition decision. Keep voice identifiers and account receipts in the private execution record.
API Pattern
Text-to-Speech with Timestamps (recommended)
curl --fail-with-body -sS -X POST "https://api.elevenlabs.io/v1/text-to-speech/${VOICE_ID}/with-timestamps" \
-H "xi-api-key: ${ELEVENLABS_API_KEY}" \
-H "Content-Type: application/json" \
-d '{
"text": "Your text here",
"model_id": "eleven_v4",
"voice_settings": {
"stability": 0.75,
"similarity_boost": 0.80
}
}' \
--output /tmp/tts-response.json
Decode Response
import json, base64
with open('/tmp/tts-response.json', 'r') as f:
data = json.load(f)
audio_bytes = base64.b64decode(data['audio_base64'])
with open('output.mp3', 'wb') as f:
f.write(audio_bytes)
ends = data.get('alignment', {}).get('character_end_times_seconds', [])
print(f'Duration: {ends[-1]:.2f}s' if ends else 'No timestamps')
Simple TTS (no timestamps)
curl --fail-with-body -sS -X POST "https://api.elevenlabs.io/v1/text-to-speech/${VOICE_ID}" \
-H "xi-api-key: ${ELEVENLABS_API_KEY}" \
-H "Content-Type: application/json" \
-d '{ "text": "Your text here", "model_id": "eleven_v4", "voice_settings": { "stability": 0.75, "similarity_boost": 0.80 } }' \
--output output.mp3
Sound Effects Generation
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.
- 20d ago Changed · -3 lines d129d1aa5d0e
- 27d ago Changed · +6 lines 28f177ea3aba
- 1mo 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 4d ago), licensed MIT. It adds 43 tokens to every session and 1,766 once invoked, about $0.0002 per session on Opus 5.5. 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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