harleycoops/math-to-manim

Overview

This skill converts any mathematical or scientific concept into a complete Manim animation workflow using a six-agent pipeline. It builds pedagogically sound sequences by recursively discovering prerequisites (the Reverse Knowledge Tree) and then generating LaTeX-enriched narratives and runnable Manim code. The output includes a verbose prompt, a knowledge-tree JSON, and a ready-to-run Python scene.

How this skill works

The system parses user intent (concept, domain, level, goal) then the PrerequisiteExplorer recursively asks what must be understood first, producing a directed acyclic knowledge tree. Each node is enriched with equations, definitions, worked examples, and visual specs. A NarrativeComposer orders content from foundations to target and creates a detailed verbose prompt. Finally, a CodeGenerator emits Community Manim Python code implementing the specified visuals, transitions, colors, and timings.

When to use it

• Create educational math or physics animations from a concept description or image
• Generate runnable Manim code for classroom videos or lecture supplements
• Visualize abstract concepts by converting them into stepwise visual narratives
• Produce pedagogy-driven sequences by discovering and teaching prerequisites
• Request verbose prompt generation for assisted manual production or further editing

Best practices

• Provide a clear core concept, desired depth (beginner/intermediate/advanced), and learning goal
• Limit initial scope to one target concept per request to keep tree manageable
• Accept the generated knowledge tree and prune or extend nodes as needed before code generation
• Use consistent color palettes and naming conventions in follow-up edits to preserve coherence
• Test generated scenes incrementally (render single nodes) before assembling full-length videos

Example use cases

• Turn 'quantum tunneling' into a 3–5 minute Manim video that starts from wave basics
• Create an animation explaining the Pythagorean theorem with stepwise prerequisites and proofs
• Generate a study video for the chain rule by building from limits and derivative definitions
• Produce a classroom-ready scene that visualizes eigenvectors starting from linear maps
• Export a verbose prompt and Python file when you want editable, production-quality assets