Teaching an AI Agent to Edit in Camtasia: From a 40-Minute Cut to 21 Minutes

Image source: https://www.youtube.com/watch?v=em2eoI5CsoA

Gen AI Spotlight (@GenAISpotlight), an AI news account on X, shared on October 4, 2026 how the author taught an AI agent to use the video editor Camtasia — cutting a roughly 40-minute original down to 21 minutes and even reorganizing the order of scenes.

The author calls the original "the extended version" and did the full edit by hand. The finished cut was then handed to the agent for feedback, which came back unimpressed, so the agent was challenged to build a better version in Camtasia. The account below is the author's first-person anecdote; no public repository, independent benchmark, or reproducible skill artifact was released alongside it.

From a 40-Minute Original to a 21-Minute "Alef Agent Cut"

According to the author, the agent already knew its way around Camtasia. The catch was that version 0.1 of the skill could only do ripple cuts — trimming pauses and, in the author's words, "when I start yapping too much." It could not cut tracks or reorganize scenes.

Those two structural operations were the gap. The agent did not know how to perform them, so the author set out to teach it. The result is the "Alef Agent Cut": a 21-minute version with some scenes reordered and others removed.

The author said the extended original and a video showing how the work was done would be posted later, and that it is still undecided who will edit that planned video.

The Two Steps That Taught the Agent Track Cuts and Scene Reordering

The author described a two-step teaching sequence.

1. Group all tracks into one: every track in the edit was grouped into a single track, simplifying the structure into something the agent could handle more easily.
2. Show sample cuts and let the agent watch: the agent — the one the author describes as powered by Sonnet 5.5 — asked the author to make a few sample cuts at random spots and observed what changed. Per the author, it figured it out "in no time."

What Extended Beyond Ripple Cuts, and What Remains Unverified

• Ripple cuts handle pause removal and filler trimming well, but cutting tracks and reordering scenes are structural edits that required separate teaching.
• The core of that teaching was a human demonstrating what changed once. The agent then learned the track-cut and scene-reordering behavior by observing the difference.
• The model name "Sonnet 5.5" and the way the Camtasia skill behaves are the author's claims, not something the vendor has confirmed. No public repository, independent benchmark, or reproducible skill artifact accompanied them.

A Related Gemini Notebook MCP Example From the Same Author

In a separate post that the author quoted, the same account described Google rebranding NotebookLM to Gemini Notebook and argued that the real value is not clicking through the web UI, but plugging the MCP into an AI agent and letting the model drive. The author connected the Gemini Notebook MCP to Claude Cowork and listed three demos.

1. PDF to video: a dense research PDF went in and, without touching a button, produced a finished one-minute explainer video.
2. Report to a local app: the agent built an interactive report for a 10-year-old, extracted the insights, and created a gamified learning app running locally on the computer.
3. Using its own repo: the agent used Gemini Notebook end to end to research, write, and produce a release video for the team's own repository.

The author presented this as evidence that agentic workflows beat clicking buttons in a browser. As with the Camtasia case, these demos are the author's own first-person account.

Original source

• Gen AI Spotlight (@GenAISpotlight) X Post: My Agent Edited This Video With Camtasia — a 40-Minute Original Cut to 21 Minutes
• Same author's quoted post: Claude Took Over Google's Gemini Notebook