Jimeng Text-to-Video Prompt Assistant

Problem it solves

When writing video prompts, the common failure is not missing vocabulary but treating a video like a still image. The prompt mixes subject, scene, camera, motion, style, and duration without saying what should move, how the camera should advance, or how the emotional beat changes over time. This is especially noticeable across Jimeng Dreamina models: Video 3.0 Pro favors natural-language full descriptions, Seedance 2.0 favors structured short sentences, reference assets, and multimodal inputs, while Smart Multi-Frame requires frame-level duration and camera planning. If you simply expand an image prompt, you can get missing motion, unstable camera movement, lost details, or inconsistent generation.

How the skill works

The skill decomposes video prompts into checkable components: subject, action, scene, camera, style, lighting, duration, and emotion. It first classifies the requested scenario, then loads the right reference material:

• motion.md: motion vocabulary for people, nature, animals, and machines
• scene-style.md: scene, quality, style, atmosphere, and pacing
• camera-basic.md / camera-advanced.md: push, pull, pan, tilt, track, crane moves, orbits, one-shot coverage, and camera emotion mapping
• model guides: constraints for Video 3.0 and Smart Multi-Frame

It then assembles the prompt using the model-specific formula and validates risky details: whether motion is explicit, whether camera and action are matched, whether duration can support the action complexity, whether lighting changes over time, and whether multiple alternatives differ enough. For Seedance 2.0, it also avoids overloading the prompt with too many action layers or combining complex multi-person interaction with aggressive camera work.

Boundaries

This is useful for drafting or optimizing text-to-video prompts where camera language, temporal pacing, and visual style matter. It is not for executing video generation commands, and it is not a replacement for image-to-video or text-to-image prompting. In practice, avoid replacing action with static description, avoid stacking strong camera moves with complex motion, avoid #RRGGBB color codes because the model may render them as text, and prefer Chinese camera terms when they feel more stable.