Analyze screen-recorded Unity UI animations into animation_clips JSON keyframes.
npx skills add https://github.com/igot-ai/os-twin --skill detect-anim
Please help me install this Agent Skill. Skill: detect-anim Source: https://github.com/igot-ai/os-twin/tree/main/.agents/skills/roles/game-ui-analyst/detect-anim Command: npx skills add https://github.com/igot-ai/os-twin --skill detect-anim
Analyse a screen-recorded video of a Unity UI animation (popup appear, heart refill, level complete, etc.) and generate animation_clips JSON with per-object keyframe tracks. Uses a 2-phase pipeline -- Python/OpenCV extracts motion data, then Claude performs semantic analysis matching tracks to objects from a detection JSON. Triggers on 'analyse animation', 'detect animation', 'extract animation', 'animation from video', 'video to animation', 'detect-anim'. Use this skill whenever the user has a screen recording of a UI animation and wants to extract structured animation data from it.
Run the CV pipeline on a screen recording and supply the detection JSON to generate an animation_clips JSON.
High-intent search queries and answers about installing and using this skill.
Extract Unity UI animation keyframes from a screen recording by running a two-phase pipeline that uses OpenCV to track motion and maps it to semantic UI objects, outputting an animation_clips JSON file with per-object keyframes.
Yes, you can convert a video of a popup animation into a structured Unity animation clip by detecting visual motion with OpenCV and matching it to your UI objects to generate a schema 6.1.0 animation_clips JSON payload.
Yes, the pipeline requires a detection JSON file to map the computer-vision-tracked motion data to semantic UI objects, ensuring the extracted keyframes accurately represent the individual elements in your Unity interface.
The analysis supports Unity UI animations including popup appearances, heart fills, level-complete sequences, and overlay fades, extracting structured keyframe data and easing hints from each recorded interaction.
Computer vision tracks UI motion using OpenCV and numpy to extract raw visual movement data from the screen recording, which is then matched to semantic UI objects to produce per-object keyframe tracks.
Yes, you need OpenCV and numpy installed in your Python environment, as these dependencies handle the computer-vision motion extraction phase before the semantic analysis generates the final animation_clips JSON.
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