Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add iart-ai/manim-skills --skill manimgit clone --depth 1 https://github.com/iart-ai/manim-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/iart-ai/manim-skills/manim)<a href="https://agentmods.dev/skills/iart-ai/manim-skills/manim"><img src="https://agentmods.dev/badge/skills/iart-ai/manim-skills/manim/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/iart-ai/manim-skills/manim"><img src="https://agentmods.dev/badge/skills/iart-ai/manim-skills/manim.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00125 | $0.02691 |
| Opus 5 | $0.00063 | $0.01345 |
| Sonnet 5 | $0.00025 | $0.00538 |
| Haiku 4.5 | $0.00013 | $0.00269 |
Grade A, and why
manim scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to manim — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Manim (Programmatic Math Animation)
Manim (Mathematical Animation Engine, Community Edition) renders explanatory math/physics/CS videos from Python — the engine behind 3Blue1Brown-style motion. Every scene is declarative: you build vector objects, play timed animations over them, and render a deterministic MP4/GIF from the CLI. Best-in-class for moving equations, transforming formulas, animated graphs, and geometric intuition.
When to use
- Animate an equation: write it on, transform it term-by-term, highlight substitutions.
- Visualize a function/graph: axes, plotted curves, a dot tracing the curve, area under it.
- Geometric or conceptual explainers: a proof unfolding, vectors, transformations, number lines.
- 3D math: surfaces, parametric curves, a camera orbiting a shape.
- Any "show the math moving" educational clip rendered to MP4/GIF.
Not the tool for UI motion, social/marketing video, or data-from-CSV business charts — use the web-animation or Remotion video skills for those. Manim's edge is mathematical typesetting and intuition.
Setup & render
pip install manim # needs Python 3.9+, ffmpeg, and a LaTeX distribution (for Tex/MathTex)
manim -pql scene.py SquareToCircle # render one Scene class, preview at low quality
Render flags (the ones that matter):
-q l|m|h|k— quality: low (fast iteration) → 4K.-qlwhile building,-qhto ship.-p— play when done;-o name— output filename;--format=gif— render a GIF (README demos).-s— save the last frame as a PNG (no video encode — the verification lever, see below).-n a,b— render only animation index rangea..b(stop early to inspect a moment).- Output lands in
media/videos/<file>/<quality>/<Scene>.mp4(andmedia/images/...for-s).
The three building blocks
Everything is Scenes (orchestration) acting on Mobjects (objects) via Animations (timed change).
from manim import *
class SquareToCircle(Scene):
def construct(self): # ALL animation code lives in construct()
sq = Square().set_fill(BLUE, opacity=0.5)
self.play(Create(sq)) # animate it drawing on
self.play(sq.animate.rotate(PI / 4))
self.play(Transform(sq, Circle().set_fill(PINK, opacity=0.5)))
self.wait(0.5) # hold
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 137 lines · 125 tokens per session scan A 04c78c749f12
manim is a skill published in the GitHub repository iart-ai/manim-skills (5 stars, last pushed 2mo ago), licensed MIT. It adds 125 tokens to every session and 2,691 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to manim, differing in 0 lines, and is treated as a copy.
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mathmodel-skill
An end-to-end workflow for mathematical-modeling competitions, including CUMCM, MCM/ICM, and the Electrical Cup. It guides teams from choosing a problem through modeling, solving, checking, writing, rule compliance, and final review.
weight-loss-learn
A guided course on sustainable fat loss that adapts to your level and tracks your progress. It teaches the principles behind weight change and long-term weight maintenance.
manim-video
Create production-quality Manim Community Edition explainer videos, math animations, algorithm visualizations, paper/PDF explainers, data stories, and architecture diagrams. Use when a user asks for 3Blue1Brown-style programmatic animation, Manim scene planning/coding/rendering, Docker-based Manim setup, or…
graphs-dynamic-programming
Implement BFS/DFS, Dijkstra, Kruskal/Prim MST, topological sort, and DP (knapsack, Needleman-Wunsch, Smith-Waterman) in Python. Use for from-scratch alignment, PPI shortest paths, phylogenetic MST, gene-panel knapsack selection.
scaffold-exercises
Scaffold a graded problem set with sections, problems, worked solutions, and short "why this matters" explainers across analytical, empirical, and coding types. Use when user says "make a problem set on X", "scaffold exercises for this lecture", "create practice problems", "generate homework with a solution key"…