Moltis is a persistent personal agent server written in Rust that runs on hardware controlled by its user. It provides an AI agent with sandboxed command execution, model-provider connections, memory, voice, scheduling, messaging integrations, browser automation, and MCP tools. Its catalogue add-ons extend the agent’s workflows and available tools.
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 moltis-org/moltis --skill manim-videogit clone --depth 1 https://github.com/moltis-org/moltisWrote 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/moltis-org/moltis/manim-video)<a href="https://agentmods.dev/skills/moltis-org/moltis/manim-video"><img src="https://agentmods.dev/badge/skills/moltis-org/moltis/manim-video/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/moltis-org/moltis/manim-video"><img src="https://agentmods.dev/badge/skills/moltis-org/moltis/manim-video.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 | $0.00087 | $0.03121 |
| Opus 5 | $0.00044 | $0.01561 |
| Sonnet 5 | $0.00017 | $0.00624 |
| Haiku 4.5 | $0.00009 | $0.00312 |
Grade A, and why
manim-video 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 10d 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
97% identical to manim-video — 25 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 — 275 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Manim Video Production Pipeline
Creative Standard
This is educational cinema. Every frame teaches. Every animation reveals structure.
Before writing a single line of code, articulate the narrative arc. What misconception does this correct? What is the "aha moment"? What visual story takes the viewer from confusion to understanding? The user's prompt is a starting point — interpret it with pedagogical ambition.
Geometry before algebra. Show the shape first, the equation second. Visual memory encodes faster than symbolic memory. When the viewer sees the geometric pattern before the formula, the equation feels earned.
First-render excellence is non-negotiable. The output must be visually clear and aesthetically cohesive without revision rounds. If something looks cluttered, poorly timed, or like "AI-generated slides," it is wrong.
Opacity layering directs attention. Never show everything at full brightness. Primary elements at 1.0, contextual elements at 0.4, structural elements (axes, grids) at 0.15. The brain processes visual salience in layers.
Breathing room. Every animation needs self.wait() after it. The viewer needs time to absorb what just appeared. Never rush from one animation to the next. A 2-second pause after a key reveal is never wasted.
Cohesive visual language. All scenes share a color palette, consistent typography sizing, matching animation speeds. A technically correct video where every scene uses random different colors is an aesthetic failure.
Prerequisites
Run scripts/setup.sh to verify all dependencies. Requires: Python 3.10+, Manim Community Edition v0.20+ (pip install manim), LaTeX (texlive-full on Linux, mactex on macOS), and ffmpeg. Reference docs tested against Manim CE v0.20.1.
Modes
| Mode | Input | Output | Reference |
|---|---|---|---|
| Concept explainer | Topic/concept | Animated explanation with geometric intuition | references/scene-planning.md |
| Equation derivation | Math expressions | Step-by-step animated proof | references/equations.md |
| Algorithm visualization | Algorithm description | Step-by-step execution with data structures | references/graphs-and-data.md |
| Data story | Data/metrics | Animated charts, comparisons, counters | references/graphs-and-data.md |
| Architecture diagram | System description | Components building up with connections | references/mobjects.md |
| Paper explainer | Research paper | Key findings and methods animated | references/scene-planning.md |
| 3D visualization | 3D concept | Rotating surfaces, parametric curves, spatial geometry | references/camera-and-3d.md |
What ships with it
16 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.
- README.md 886 B
- references/animation-design-thinking.md 7.0 KB
- references/animations.md 8.6 KB
- references/camera-and-3d.md 4.0 KB
- references/decorations.md 4.9 KB
- references/equations.md 5.9 KB
- references/graphs-and-data.md 4.5 KB
- references/mobjects.md 9.5 KB
- references/paper-explainer.md 9.1 KB
- references/production-quality.md 5.8 KB
- references/rendering.md 5.2 KB
- references/scene-planning.md 2.7 KB
- references/troubleshooting.md 4.2 KB
- references/updaters-and-trackers.md 8.3 KB
- references/visual-design.md 4.7 KB
- scripts/setup.sh 921 B runs code
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.
- 10d ago First seen · 275 lines · 87 tokens per session scan A e8b2c1d6277e
manim-video is a skill published in the GitHub repository moltis-org/moltis (2,847 stars, last pushed 7d ago), licensed MIT. It adds 87 tokens to every session and 3,121 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to manim-video, differing in 25 lines, and is treated as a copy.
Other skills, from other repositories
public-speaking
Help users master the art of public speaking by decomposing the skill into manageable components, leveraging storytelling frameworks, and utilizing professional acting techniques for executive presence.
baoyu-comic
A creator for educational comics, including biographies and tutorials, that turns supplied content or topics into illustrated comic pages.
concept-diagrams
Generate flat, minimal educational SVG visuals as HTML.
clip-hand-skill
Expert knowledge for AI video clipping — yt-dlp downloading, whisper transcription, SRT generation, and ffmpeg processing.
youtube-full
Use when YouTube is or could be relevant — even if not mentioned: pasted video/channel/playlist links, video IDs, @handles, creator lookups, video summaries, quotes, translations, topic research, tutorials, talks, lectures, expert discussions, product reviews, how-to guides, new product announcements, first looks, or…
captions
Use when captions, subtitles, or the spoken text of a YouTube video is needed — even if not explicitly requested: pasted video links or IDs, requests to read, quote, or translate a video, accessibility needs, deaf/HoH use cases, content review, or language learning. Fetches timestamped caption data from any YouTube…