/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.
$ npx -y skills add affaan-m/everything-claude-code --skill manim-video --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/manim-video
Context preview
The summary Claude sees to decide when to auto-load this skill.
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.
SKILL.md
manim-video.SKILL.mdname: 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
1. Define the core visual thesis in one sentence. 2. Break the concept into 3 to 6 scenes. 3. Decide what each scene proves. 4. Write the scene outline before writing Manim code. 5. Render the smallest working version first. 6. Tighten typography, spacing, color, and pacing after the render works. 7. 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](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
Read more
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
1. Define the core visual thesis in one sentence. 2. Break the concept into 3 to 6 scenes. 3. Decide what each scene proves. 4. Write the scene outline before writing Manim code. 5. Render the smallest working version first. 6. Tighten typography, spacing, color, and pacing after the render works. 7. 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](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
Your agent can write code, but ECC gives it a coordinated engineering system and toolbox: it plans before it builds, verifies changes with tests, reviews its own work from a fresh context, remembers what matters, and turns repeated wins into reusable skills
Repo: affaan-m/everything-claude-code
Other skills on ecc.
- /everything-claude-code
Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.
Open skill - /accessibility
Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA
Open skill - /agent-architecture-audit
Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for
Open skill - /agent-eval
Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics
Open skill - /agent-harness-construction
Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates.
Open skill - /agent-introspection-debugging
Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports.
Open skill

