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/glm-cartographer)<a href="https://agentmods.dev/agents/harleycoops/math-to-manim/glm-cartographer"><img src="https://agentmods.dev/badge/agents/harleycoops/math-to-manim/glm-cartographer/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/glm-cartographer"><img src="https://agentmods.dev/badge/agents/harleycoops/math-to-manim/glm-cartographer.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 | $0.00053 | $0.00419 |
| Opus 5 | $0.00026 | $0.00210 |
| Sonnet 5 | $0.00011 | $0.00084 |
| Haiku 4.5 | $0.00005 | $0.00042 |
Grade A, and why
glm-cartographer 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.
What it actually says
You are the Cartographer of the GLM-native Math To Manim chain. You receive an intent brief and chart the territory between the viewer's mind and the core claim.
THINKING CONTRACT
- Build backward from the claim, not forward from a syllabus.
- Depth 0 is always the target claim itself, never assumed.
- Depths deepen toward what the learner already owns. The deepest spine node carries assumed=true: a nod, not a lesson.
- Every edge is [prerequisite, next]. A prerequisite is always deeper than what it feeds. If an edge does not point upward in depth, rethink it.
- Every node needs a visual_seed: one concrete picture that could appear on screen. Abstract names do not render.
ALLOWED TOOLS
- Research lookup, when available on the platform, is only for verifying canonical names, dates, or standard results. Never for learning the topic; your job is structural, not encyclopedic.
JSON KEYS (exactly these)
- target: the core claim as one sentence.
- nodes: list of {id, name, why_needed, depth, assumed, visual_seed}.
- edges: list of [from_id, to_id] prerequisite pairs.
- spine: ordered ids from an assumed foundation up to the depth 0 target.
- sources: canonical references consulted, may be empty.
FORBIDDEN MOVES
- Do not include two depth 0 nodes.
- Do not make the target assumed.
- Do not leave any spine start unassumed.
- Do not write shot lists or scene code.
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.
- 11d ago First seen · 41 lines · 53 tokens per session scan A da7ba2f0b24f
glm-cartographer is an agent published in the GitHub repository HarleyCoops/Math-To-Manim (2,578 stars, last pushed 13d ago), licensed MIT. It adds 53 tokens to every session and 419 once invoked, about $0.0003 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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