AI prompts that add motion interaction polish to your vibe-coded apps.
Copy-paste AI prompts that instruct Cursor, Lovable, and Claude to build fluid, complex UI animations. Skip the docs, get the vibe.
The Spring Physics Masterclass
A comprehensive set of 12 highly tuned prompts designed to instruct LLMs on generating perfect spring animations.
- Format.md / .txt
- Prompts Included12
CursorClaudeLovablev0ReplitGeminiFor Vibecoders & Solo Founders
You use AI to code because it's fast. But AI defaults to static, lifeless UI. These prompts are meticulously crafted context files that force standard LLMs to output code with agency-level motion design standards.
Copy the Prompt
Open the downloaded .md file. It contains strict instructions on easing curves, state management, and aesthetic intent.
Paste into Cursor/Claude
Append the prompt to your feature request. Example: "Build a pricing table. Follow the rules in [pasted_prompt]."
Watch it Move
The AI generates the complex Framer Motion or CSS animation logic correctly on the first shot, bypassing hours of tweaking.
Technical Details
What exactly am I buying?+
You are buying highly structured text files (.md) written in a specific syntax optimized for LLM comprehension. They act as "system prompts" or "rulesets" that you feed to your AI coding tool to constrain and guide its output specifically regarding UI animation.
Which tools do these work with?+
They are tested heavily with Claude and Codex (via Web UI or CLI). They also perform exceptionally well in agentic environments like Lovable and Bolt.new, provided you can inject custom prompt context.
Do I need to know how to code?+
No. That's the point. The prompt contains the technical knowledge. It instructs the AI on exactly which APIs and configuration values to use to achieve the desired effect.
License & Refunds+
Lifetime usage for unlimited personal or commercial projects. Do not redistribute the prompt files themselves. Due to the digital nature of the raw text products, we generally do not offer refunds, but contact us if a prompt fundamentally fails in modern LLMs.