| Best practices for ManimGL (Grant Sanderson's 3Blue1Brown version) - OpenGL-based animation engine with interactive development. Covers InteractiveScene, Tex with t2c, camera frame control, interactive mode (-se flag), 3D rendering, and checkpoint_paste() workflow. NOT for Manim Community Edition (which uses `manim` imports and `manim` CLI).
npx skills add https://github.com/adithya-s-k/manim_skill --skill manimgl-best-practices
Read individual rule files for detailed explanations and code examples:
-se flag, checkpoint_paste()Complete, tested example files demonstrating common patterns:
Copy and modify these templates to start new projects:
from manimlib import *
class MyScene(InteractiveScene):
def construct(self):
# Create mobjects
circle = Circle()
# Add to scene (static)
self.add(circle)
# Or animate
self.play(ShowCreation(circle)) # Note: ShowCreation, not Create
# Wait
self.wait(1)
# Render and preview
manimgl scene.py MyScene
# Interactive mode - drop into shell at line 15
manimgl scene.py MyScene -se 15
# Write to file
manimgl scene.py MyScene -w
# Low quality for testing
manimgl scene.py MyScene -l
| Feature | ManimGL (3b1b) | Manim Community |
|---------|----------------|-----------------|
| Import | from manimlib import * | from manim import * |
| CLI | manimgl | manim |
| Math text | Tex(R"\pi") | MathTex(r"\pi") |
| Scene | InteractiveScene | Scene |
| Create anim | ShowCreation | Create |
| Camera | self.frame | self.camera.frame |
| Fix in frame | mob.fix_in_frame() | self.add_fixed_in_frame_mobjects(mob) |
| Package | manimgl (PyPI) | manim (PyPI) |
ManimGL's killer feature is interactive development:
# Start at line 20 with state preserved
manimgl scene.py MyScene -se 20
In interactive mode:
# Copy code to clipboard, then run:
checkpoint_paste() # Run with animations
checkpoint_paste(skip=True) # Run instantly (no animations)
checkpoint_paste(record=True) # Record while running
# Get the camera frame
frame = self.frame
# Reorient in 3D (phi, theta, gamma, center, height)
frame.reorient(45, -30, 0, ORIGIN, 8)
# Animate camera movement
self.play(frame.animate.reorient(60, -45, 0))
# Fix mobjects to stay in screen space during 3D movement
title.fix_in_frame()
# Use raw strings with capital R
formula = Tex(R"\int_0^1 x^2 \, dx = \frac{1}{3}")
# Color mapping with t2c
equation = Tex(
R"E = mc^2",
t2c={"E": BLUE, "m": GREEN, "c": YELLOW}
)
# Isolate substrings for animation
formula = Tex(R"\sum_{n=1}^{\infty} \frac{1}{n^2} = \frac{\pi^2}{6}")
formula.set_color_by_tex("n", BLUE)
def construct(self):
circle = Circle()
self.play(ShowCreation(circle))
self.embed() # Drops into IPython shell here
self.set_floor_plane("xz") # Makes xy the viewing plane
text = Text("Label")
text.set_backstroke(BLACK, 5) # Black outline behind text
# Install ManimGL
pip install manimgl
# Check installation
manimgl --version
manimgl, not manim (community version)ShowCreation, not CreateTex with capital R raw stringsself.frame directly-se flag for interactive developmentThis skill contains example code adapted from 3Blue1Brown's video repository by Grant Sanderson.
License: CC BY-NC-SA 4.0
See LICENSE.txt for full details.
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
Best practices for Remotion - Video creation in React
Generate AI-powered podcast-style audio narratives using Azure OpenAI's GPT Realtime Mini model via WebSocket. Use when building text-to-speech features, audio narrative generation, podcast creation from content, or integrating with Azure OpenAI Realtime API for real audio output. Covers full-stack implementation from React frontend to Python FastAPI backend with WebSocket streaming.
Best practices for Remotion - Video creation in React
Port an existing Remotion (React) composition''s source to HyperFrames HTML. Use ONLY on an explicit ask to port/convert/migrate/translate a Remotion source — one-way, Remotion-only. A passing Remotion mention, reference-only code, or "make something like my Remotion video" is a fresh build (/general-video). Unclear → /hyperframes.
Use this skill when building applications with Gemini API hosted models, including Gemini and Gemma 4, working with multimodal content (text, images, audio, video), implementing function calling, using structured outputs, or needing current model specifications. Covers SDK usage...
Turn error logs, screenshots, voice notes, and rough bug reports into crisp, developer-ready GitHub issues with repro steps, impact, and evidence.
Take adithya-s-k/manimgl-best-practices from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference pip.
Without those the skill loads but fails at the first command.