manim-video
Build reusable Manim explainers for technical concepts, graphs, system diagrams, and product walkthroughs, then hand off to the wider ECC video stack if needed. Use when the user wants a clean animated explainer rather than a generic talking-head script.
DeepseekModel
Curated skill
Quality Excellent · 90
v1.0.0
Get
https://deepseekmodel.com/api/download.php?id=affaan-m-ecc-skills-manim-video-skill-md&format=skill
Download .skill
Standard format with system_prompt and model_config, ready for any agent framework
The actual content of the system_prompt field in the .skill file.
name manim-video description Build reusable Manim explainers for technical concepts, graphs, system diagrams, and product walkthroughs, then hand off to the wider ECC video stack if needed. Use when the user wants a clean animated explainer rather than a generic talking-head script. metadata {"origin":"ECC"} Manim Video Use Manim for technical explainers where motion, structure, and clarity matter more than photorealism. When to Activate the user wants a technical explainer animation the concept is a graph, workflow, architecture, metric progression, or system diagram the user wants a short product or launch explainer for X or a landing page the visual should feel precise instead of generically cinematic Tool Requirements manim CLI for scene rendering ffmpeg for post-processing if needed video-editing for final assembly or polish remotion-video-creation when the final package needs composited UI, captions, or additional motion layers Default Output short 16:9 MP4 one thumbnail or poster frame storyboard plus scene plan Workflow Define the core visual thesis in one sentence. Break the concept into 3 to 6 scenes. Decide what each scene proves. Write the scene outline before writing Manim code. Render the smallest working version first. Tighten typography, spacing, color, and pacing after the render works. Hand off to the wider video stack only if it adds value. Scene Planning Rules each scene should prove one thing avoid overstuffed diagrams prefer progressive reveal over full-screen clutter use motion to explain state change, not just to keep the screen busy title cards should be short and loaded with meaning Network Graph Default For social-graph and network-optimization explainers: show the current graph before showing the optimized graph distinguish low-signal follow clutter from high-signal bridges highlight warm-path nodes and target clusters if useful, add a final scene showing the self-improvement lineage that informed the skill Render Conventions default to 16:9 landscape unless the user asks for vertical start with a low-quality smoke test render only push to higher quality after composition and timing are stable export one clean thumbnail frame that reads at social size Reusable Starter Use assets/network_graph_scene.py as a starting point for network-graph explainers. Example smoke test: manim -ql assets/network_graph_scene.py NetworkGraphExplainer Output Format Return: core visual thesis storyboard scene outline render plan any follow-on polish recommendations Related Skills video-editing for final polish remotion-video-creation for motion-heavy post-processing or compositing content-engine when the animation is part of a broader launch
Keywords that activate this skill. Click one to copy it.
This skill does not provide trigger words.
The downloaded .skill package contains the following fields.
| Field | Description |
|---|---|
| format | Format tag (skill/v1) |
| skill_id | Unique skill ID |
| name | Skill name |
| version | Version |
| description | Description |
| category | Categories (array) |
| trigger_words | Trigger words |
| tags | Tags |
| source | Source |
| source_url | Source URL (this page) |
| exported_at | Exported at (set per download) |
| system_prompt | System prompt body |
| model_config | Model config: provider / model / temperature / max_tokens / top_p |
| examples | Examples |
| install_guide | Import guide for Coze / Dify / Claude / custom frameworks |
The same skill can be exported in different platform formats.