Genpark nano banana video orchestrator
GenPark Distilled Skill: nano-banana-video-orchestrator
npx -y skills add alphaparkinc/genpark-nano-banana-video-orchestratorAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
- 9 stars9 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Precision Video Orchestrator. Performs precise object/character masking and replacement in motion video using advanced AI vision workflows.
The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.
SKILL.md
1.3 KB, 187 tokens by cl100k_base, as published. Nobody here has run it
Nano Banana Video Orchestrator
This skill provides advanced video editing capabilities focused on character and object manipulation. It enables precise masking and replacement of elements within a video stream while maintaining temporal consistency.
Instructions
- Temporal Segmentation: Analyze video frames to track objects or characters across time.
- Precision Masking: Generate high-accuracy masks for specific subjects to be replaced or modified.
- Inpainting & Synthesis: Replace the masked subject with a new AI-generated element, ensuring it blends seamlessly with the original motion and background.
- Motion Consistency: Apply frame-to-frame smoothing to prevent flickering or artifacts in the final video.
Examples
- "Replace the main character in this video with a 3D-animated version of themselves."
- "Remove the background from this video and replace it with a futuristic city-scape while keeping the person in the foreground clear."
Gives 0 of the 12 instructions most agent orchestration skills give in 187 tokens
Counted across 742 of the 995 authors here whose files we hold, read 2026-08-06
- run the full test suite after integrating changesin 53 of 742, across 20 files
- reference existing artifacts by path or URLin 52 of 742, across 22 files
- dispatch one agent per independent problem domainin 50 of 742, across 17 files
- verify fixes do not conflictin 45 of 742, across 13 files
- include a suggested skills section in the documentin 45 of 742, across 15 files
- redact sensitive informationin 41 of 742, across 11 files
- save to the temporary directory of the operating systemin 39 of 742, across 9 files
- tailor the document to user-provided focus argumentsin 39 of 742, across 9 files
- spot check agent changes for systematic errorsin 34 of 742, across 7 files
- write a handoff document summarising the current conversationin 31 of 742, across 6 files
- assign each agent a specific scopein 23 of 742, across 8 files
- provide specific scope and clear goalin 23 of 742, across 5 files
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.