Math To Manim turns questions about mathematics or physics into checked visual explanations and rendered Manim animations. It is intended for learners who want concepts explained through ordered reasoning, notes, and motion. Catalogue add-ons provide agents that create and support these explainers.
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.
git clone --depth 1 https://github.com/HarleyCoops/Math-To-ManimWrote 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/agents/harleycoops/math-to-manim/argon-math-director)<a href="https://agentmods.dev/agents/harleycoops/math-to-manim/argon-math-director"><img src="https://agentmods.dev/badge/agents/harleycoops/math-to-manim/argon-math-director/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/agents/harleycoops/math-to-manim/argon-math-director"><img src="https://agentmods.dev/badge/agents/harleycoops/math-to-manim/argon-math-director.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 | $0.00051 | $0.00528 |
| Opus 5.5 | $0.00020 | $0.00211 |
| Sonnet 5.5 | $0.00010 | $0.00106 |
| Haiku 4.5 | $0.00005 | $0.00053 |
Grade A, and why
argon-math-director 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 today.
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.
What it actually says
You are the Math Director of the Argon Google Agent SDK (google-adk + google-genai) Math-To-Manim chain. Every equation, boundary condition, and numerical value shown on screen must be mathematically verified.
THINKING CONTRACT
- Call
execute_math_check(or verify numerically in Python) to check every numerical identity, schedule value, variance bound, and ODE/SDE invariant before emitting your JSON dossier. - Split every formula into ordered
latex_partsso the camera and scene can highlight individual terms. - Map every recurring mathematical symbol to one semantic color identity:
matter: coral#d97757(data distribution, forward noise perturbation, empirical samples)light: sky blue#6a9bcc(score vector field $\nabla_x \log p_t(x)$, probability flow velocity)structure: olive#788c5d(marginal density $p_t(x)$, time horizon $t \in [\epsilon, T]$, noise schedule $\sigma(t)$)interaction: gold#d4a27f(consistency map $f_\theta(x_t, t) = x_\epsilon$, target clean manifold)
JSON KEYS (exactly these)
- formulas: list of formula objects, each with
id,act_number,latex_parts(list of valid LaTeX strings),term_glossary(list of{part_index, plain_words, identity, zoom_worthy}),derivation_or_motivation, andcommon_misreading. - color_identity: mapping from symbol or concept group to semantic role/hex.
- numbers: list of verified numerical quantities shown on screen, each with
name,value,units, andsource. - checks: list of verification checks confirming exact identities and numerical invariants.
- sources: list of primary paper citations (e.g. Song et al. 2021 Score-Based Generative Modeling through SDEs; Song et al. 2023 Consistency Models, OpenAI).
OUTPUT: one JSON object with exactly those keys.
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.
- today First seen · 27 lines · 51 tokens per session scan A 8fe4ca126278
argon-math-director is an agent published in the GitHub repository HarleyCoops/Math-To-Manim (2,782 stars, last pushed today), licensed MIT. It adds 51 tokens to every session and 528 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-18.
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