How aicenturyclips Made This Viral Skeleton Video With GPTs And Kling — and How to Recreate It

This reel is a full creator tutorial for making viral skeleton videos, not just a poster or teaser. It starts with social-proof examples, moves into prompt engineering with GPTs, shows tool setup screens, and finishes with actual generated skeleton images that are ready to animate. That makes it a stronger PSEO case than a simple prompt library entry because the viewer can follow an observable production chain from idea to output.

The core content niche is easy to understand: surreal transparent skeleton characters placed into everyday, memeable scenes. In this example, the skeleton appears in lifestyle interiors and branded supplement-style setups while wearing a Santa hat, which adds instant seasonal character and repeatable visual identity.

Proof Hook

The first part of the reel uses proof before instruction. Viewers see skeleton examples, social posts, and creator-result screens before the tutorial properly begins. That ordering matters because it gives the workflow a reason to exist. The content is not presented as “a neat AI experiment” but as a format that can attract views, attention, and potentially revenue.

This is a useful growth lesson for indie creators. When teaching an AI workflow, start by proving the output has distribution value. If the viewer believes the end result can travel, they are more willing to stay through the technical setup.

Prompt Workflow

The reel makes it clear that the process begins with prompt structuring. The creator opens GPT tools, searches helpers, and works through a sequence of written instructions rather than improvising on the fly. The visible interfaces suggest a multi-prompt setup, likely used to standardize character description, scene logic, action ideas, and maybe style variations.

That is the right way to approach repeatable AI short-form content. Viral formats usually come from stable prompt systems, not one lucky output. By treating prompt writing like pre-production, the creator reduces variance and makes the niche scalable.

Image-to-Video Flow

After the prompt stage, the reel shifts into an image-to-video workflow. The creator first generates still images, then uses a video tool flow that includes reference-image controls, vertical aspect-ratio setup, and a cleaner output-resolution choice. This order is important because skeleton-style character consistency is easier to preserve when the still image is locked before motion begins.

The interface details shown in the reel, including 9:16 framing and 2K-style resolution choices, are exactly the kind of visible specifics that help small creators replicate the method. Instead of vague advice like “use AI video,” the reel shows the operational sequence: prompt, still image, reference, aspect ratio, resolution, then animation.

The resulting stills show why the workflow works. The skeleton character is recognizable, semi-transparent, slightly uncanny, and placed in normal environments with one strong prop or action. That balance between surreal subject and ordinary setting is what makes the final videos watchable and repeatable.

Why This Format Grows

This content format grows because it combines novelty with systemization. The skeleton gimmick is instantly attention-grabbing, but the tutorial frames it as a reusable template rather than a one-off idea. That is why it earns saves and comments: viewers believe they can adapt the same structure to other characters, props, or niches.

If you want to recreate this growth pattern, keep the production logic explicit. Show the examples, show the proof, show the prompt structure, show the settings, and show the output. The more visible the chain of evidence, the more your tutorial reads as a real workflow instead of content theater.