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by makefinks · LLM Plugin · ★ 117
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AI - Manim Video Generator Automatic video generation using an agentic LLM flow in combination with the manim python library. Overview The project experiments with automated Manim video creation. An agent workflow delegates code drafting to a and validation to a , using LiteLLM for model routing so different models from different providers can be compared on the same task. The flow focuses on reducing render failures and improving visual consistency through iterative feedback and optional vision inputs. Manim Bench - Leaderboard 📊 The manim-generator harness now drives Manim Bench, a public leaderboard showcasing how latest AI models compare on full Manim video generation. manim-bench.makefinks.dev If you want to see examples of videos created with this project, or are interested in how different models stack up, visit the link above. Feedback appreciated! Current Video Generation Process Installation Clone the repository: Install the requirements With uv (recommended): Or using pip directly: bash python -m
| Stars | 117 |
| Forks | 24 |
| Language | Python |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 72.4824578177309/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-04 |
| Created | 2025-01-28 |
| Platforms | gemini, python |
| Est. Tokens | ~15k |
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manim-generator is Automatic LLM-based video generation using the manim library. Usage of a code-writer and code-reviewer feedback loop with execution logs.. It is categorized as a LLM Plugin with 117 GitHub stars.
manim-generator is primarily written in Python. It covers topics such as agents, ai, anthropic.
You can find installation instructions and usage details in the manim-generator GitHub repository at github.com/makefinks/manim-generator. The project has 117 stars and 24 forks, indicating an active community.
manim-generator is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to manim-generator on Agent Skills Hub include simplemind, Notate, log10. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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