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 aTreeFrog/videolab-mcp --skill text-to-documentarygit clone --depth 1 https://github.com/aTreeFrog/videolab-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/atreefrog/videolab-mcp/text-to-documentary)<a href="https://agentmods.dev/skills/atreefrog/videolab-mcp/text-to-documentary"><img src="https://agentmods.dev/badge/skills/atreefrog/videolab-mcp/text-to-documentary/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/atreefrog/videolab-mcp/text-to-documentary"><img src="https://agentmods.dev/badge/skills/atreefrog/videolab-mcp/text-to-documentary.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 | $0.00110 | $0.02712 |
| Opus 5.5 | $0.00044 | $0.01085 |
| Sonnet 5.5 | $0.00022 | $0.00542 |
| Haiku 4.5 | $0.00011 | $0.00271 |
Grade A, and why
text-to-documentary scanned grade A with 0 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Text-to-Documentary Mode
This skill orchestrates the videolab-mcp to convert structured documents into engaging short-form documentaries. It's a separate behavior from the short-form promo flow — different defaults, different structural rules, different output shape.
When to invoke
Trigger on any of:
- "Convert this PDF into a documentary"
- "Turn this book/paper/chapter into videos"
- "Make a Netflix-style series from this PDF"
- "Generate a documentary from this text"
- User uploads a PDF and asks for video output
- User pastes long text and asks for an "engaging" or "ADHD-friendly" video
Do not invoke for short-form promo videos — that's a different workflow.
Input modes
The skill accepts three input shapes. Detect which one applies before starting.
| Mode | Trigger | Steps |
|---|---|---|
| Full PDF | User gives a PDF file path or URL | extract_pdf → split_chapters → loop chapters |
| Single chapter (text) | User pastes one chapter's worth of text, or says "just this chapter" | Skip extraction; treat input as one chapter |
| Whole book as text | User pastes a long document (no PDF) | split_chapters → loop chapters |
Defaults
These are the documentary-mode defaults. Override only if the user asks.
| Setting | Default | Notes |
|---|---|---|
targetMinutesPerChapter |
5 | Spec target is "5–10 min"; 5 is the sweet spot for retention |
sceneSecondsRange |
[15, 25] | Yields ~12–20 scenes per 5-min chapter |
visualMode |
broll |
All AI b-roll via Veo. Alternatives: slides (cheap, fast PNG cards) or mixed |
aspectRatio |
16:9 |
Documentaries default to landscape (NOT 9:16 mobile) |
platform |
desktop |
Matches 16:9 |
voiceId |
(use list_voices to pick a documentary-suited voice — calm authoritative narrator) |
Don't use the default short-form promo voice |
captionsWordsPerLine |
4 | Documentaries handle slightly denser caption blocks well |
musicId |
None (silence) by default; ask user if they want a bed | If yes, use list_music filter for "documentary" / "calm" mood, or generate_music |
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 · 157 lines · 110 tokens per session scan A e6656a305820
text-to-documentary is a skill published in the GitHub repository aTreeFrog/videolab-mcp (0 stars, last pushed 5mo ago), licensed MIT. It adds 110 tokens to every session and 2,712 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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