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>- Socket pass
- Snyk pass
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 5.5 | $0.00050 | $0.01076 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo 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 (15 stars, last pushed 4mo ago), licensed MIT. It adds 125 tokens to every session and 2,691 once invoked, about $0.0005 per session on Opus 5.5. 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.
Other skills, from other repositories
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.
math-modeling-solver
A Chinese-language guide for solving mathematical modeling competition problems. It covers China’s CUMCM and America’s MCM/ICM, where teams use mathematics and code to answer real-world problem statements.
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.
lean-formalize
Develop and verify a mathematical proof in Lean, continue an incomplete Lean project, or audit whether it proves the original statement. Connect actual inputs to intermediate lemmas, assemble the target theorem, check its transitive axioms, and provide a reproducible handoff. Use when Lean is requested or a specific…