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 matteotitta/genesys-skills --skill video-pipelinegit clone --depth 1 https://github.com/matteotitta/genesys-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/matteotitta/genesys-skills/video-pipeline)<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/video-pipeline"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/video-pipeline/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/matteotitta/genesys-skills/video-pipeline"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/video-pipeline.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 | $0.00167 | $0.02091 |
| Opus 5 | $0.00084 | $0.01045 |
| Sonnet 5 | $0.00033 | $0.00418 |
| Haiku 4.5 | $0.00017 | $0.00209 |
Grade A, and why
video-pipeline 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 12d 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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/video-pipeline — raw-footage editing through AI plan + FFmpeg + Remotion + VideoDB
STATUS: scaffold only. This skill ships as v0.1 with the workflow documented but NOT yet runnable. Activation is gated on:
brew install ffmpeg(the CLI dependency for cuts)npm install -g @remotion/cli(the composition layer)- VideoDB API key registered in
.claude/apis/videodb-api-key.txt(the indexing layer) - First proving run: produce a 60-second LinkedIn vertical from a recent GTM Engineer Pulse podcast episode
Sourced from 2026-05-17 MCP Market /steal Item D — consolidates the four upstream patterns ai-video-production-pipeline, videodb-for-claude-code, ai-video-editing-workflow-1, video-editing-workflow-1 into one orchestrated Genesys skill.
Why this skill exists
product-ui-frames and onboarding-video render HTML compositions to MP4 via Hyperframes — perfect for product UI animation, useless for editing actual filmed footage. Anything that involves real video sources (podcast cuts, webinar highlights, founder interview clips, customer testimonial videos) falls outside our current stack.
This skill closes the real-footage gap. It's the production pipeline that gtme-podcast was always missing.
When to use
Invoke when user says:
- "Cut this podcast into a 60-second LinkedIn vertical"
- "Make a YouTube short from the [topic] section of [episode]"
- "Produce a cliplet of [guest] talking about [thing]"
- "Auto-edit this podcast episode for the punchiest segments"
- "Scrub this video for [keyword] and cut a 90-second clip"
Do NOT invoke when:
- The source is HTML/UI composition → use
/product-ui-framesor/onboarding-video - The source is just an audio file (no video) → use
/transcript-analysis+ downstream content skills - The user wants a script for a video that doesn't exist yet → use
/youtube-scriptsor/gtme-podcast
Input requirements
| Input | Required | Source |
|---|---|---|
| Source video file (mp4, mov) or URL | Required | User |
| Brand kit | Required | brand-kit output (the colors, fonts, overlay specs) |
| Intent: what segment, what platform | Required | User ("60-second LinkedIn vertical of the agent-dispatch section") |
| Transcript or transcript-search query | Recommended | transcript-analysis output or user query |
| Product messaging context | Recommended | product-messaging for hook framing |
| Target platform spec | Required | LinkedIn vertical (9:16, ≤60s), YouTube short (9:16, ≤60s), LinkedIn square (1:1, ≤90s), or custom |
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.
- 12d ago First seen · 174 lines · 167 tokens per session scan A 28feb6c2cf95
video-pipeline is a skill published in the GitHub repository matteotitta/genesys-skills (36 stars, last pushed 1mo ago), licensed MIT. It adds 167 tokens to every session and 2,091 once invoked, about $0.0008 per session on Opus 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 2026-08-30.
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