What problem does it solve? Building a Manim explainer video from scratch involves fighting macOS toolchain traps (wrong LaTeX, broken dvisvgm, missing fonts), keeping narration timecodes in sync with animations, and avoiding rendering errors like Vietnamese text in LaTeX. This Skill provides a working scaffold and validation scripts so the pipeline renders correctly from the start. ## Core Features & Use Cases - Ready-to-render scaffold: Ships theme.py, build_final.py, render.sh, and manifest.py implementing a light-theme design system, a cue-based SRT generation pipeline, and a real-number data workflow where only data.py and scenes.py need writing. - Cue-synced subtitles and dubbing: self.cue() timestamps come from self.renderer.time, so SRT, narration TSV, and the timecoded script stay aligned with the video automatically; check-audio.sh verifies TTS output against cue marks before muxing. - Guardrails and macOS fixes: check.sh catches the three fatal rules (em-dash, Vietnamese in LaTeX, multi-argument MathTex) via AST parsing, and setup.sh resolves the four known macOS traps (standalone.cls, dvisvgm, Inter font, ffmpeg/libass). - Use Case: Produce a 1080p60 math explainer video with Vietnamese voiceover: run new-video.sh, write real computations in data.py, build scenes with ValueTracker animations, iterate at 480p15, then render the final with auto-muxed narration. ## Quick Start Ask the agent to create a new Manim explainer video project in a target folder and render a test scene with Vietnamese text and LaTeX to verify the pipeline works.