manimce-best-practices

manimce-best-practices is a skill for Claude Code, Codex from adithya-s-k/manim_skill. It costs 149 tokens per session (1,567 once invoked), scanned A, original, MIT.

A set of guidance for Manim Community Edition, a community-maintained Python tool for creating mathematical animations.

In plain words
What is it for?
It covers scenes, animated objects, formulas, text, styling, positioning, grouping, coordinate systems, and graphing.
Why use it?
It helps developers use the correct classes and patterns for ManimCE rather than examples from the separate ManimGL project.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It covers scenes, animated objects, formulas, text, styling, positioning, grouping, coordinate systems, and graphing.

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Install with agentmods
npx agentmods add skills/adithya-s-k/manim_skill/manimce-best-practices
About the project

Manim Skills is a collection of agent instructions, examples, and practices for creating mathematical animations with Manim Community Edition or ManimGL, the animation framework associated with 3Blue1Brown. It helps agents produce educational and mathematical videos while accounting for the two incompatible Manim variants; the catalogue skills provide those workflows.

adithya-s-k/manim_skill · 1,100 stars · on GitHub · skills.sh

Install

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.

Any agent
npx skills add adithya-s-k/manim_skill --skill manimce-best-practices
Clone the repo
git clone --depth 1 https://github.com/adithya-s-k/manim_skill

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for manimce-best-practices

README.md
[![agentmods](https://agentmods.dev/badge/skills/adithya-s-k/manim_skill/manimce-best-practices/github.svg)](https://agentmods.dev/skills/adithya-s-k/manim_skill/manimce-best-practices)
Your own site
<a href="https://agentmods.dev/skills/adithya-s-k/manim_skill/manimce-best-practices"><img src="https://agentmods.dev/badge/skills/adithya-s-k/manim_skill/manimce-best-practices/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.

agentmods 80×15 button for manimce-best-practices

Your own site · 80×15
<a href="https://agentmods.dev/skills/adithya-s-k/manim_skill/manimce-best-practices"><img src="https://agentmods.dev/badge/skills/adithya-s-k/manim_skill/manimce-best-practices.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 149 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,567 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 18 Mar 2026
  • Snyk pass 17 Feb 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00149 $0.01567
Opus 5 $0.00075 $0.00783
Sonnet 5 $0.00030 $0.00313
Haiku 4.5 $0.00015 $0.00157

Measured 12d ago against content hash ae09751ec9e3, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

manimce-best-practices 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 12d ago.

The scan reads SKILL.md. This mod also ships 12 executable files (examples/3d_visualization.py, examples/attention/__init__.py, examples/attention/helpers.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

skills/manimce-best-practices/SKILL.md · 152 lines

How it starts

The opening of the file, as written. The whole thing — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.

How to use

Read individual rule files for detailed explanations and code examples:

Core Concepts

Creation & Transformation

Text & Math

Styling & Appearance

Positioning & Layout

Coordinate Systems & Graphing

Animation Control

Configuration & CLI

Read the full file on GitHub · 152 lines

Changes

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.

  1. 12d ago First seen · 152 lines · 149 tokens per session scan A ae09751ec9e3

Subscribe to this mod's changes

manimce-best-practices is a skill published in the GitHub repository adithya-s-k/manim_skill (1,100 stars, last pushed 7mo ago), licensed MIT. It adds 149 tokens to every session and 1,567 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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