home / skills / dmccreary / claude-skills / causal-loop-microsim-generator
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This skill generates interactive causal loop diagram MicroSims for systems thinking education with ready-to-use web assets.
npx playbooks add skill dmccreary/claude-skills --skill causal-loop-microsim-generatorReview the files below or copy the command above to add this skill to your agents.
---
name: causal-loop-microsim-generator
description: This skill generates interactive Causal Loop Diagram (CLD) MicroSims using the vis-network JavaScript library. Use this skill when users need to create causal loop diagrams for systems thinking education, showing feedback loops, reinforcing and balancing dynamics. The skill creates a complete MicroSim package with index.md, main.html, JavaScript, JSON data, and CSS files in the /docs/sims/ directory. This skill should be used when users request creating CLDs, causal diagrams, feedback loop visualizations, or systems thinking diagrams.
---
# Causal Loop MicroSim Generator
## Overview
This skill generates interactive Causal Loop Diagram (CLD) MicroSims for educational purposes in systems thinking. CLDs visualize cause-and-effect relationships, feedback loops, and system dynamics using nodes (variables) and edges (causal relationships with positive or negative polarity).
## When to Use This Skill
Use this skill when users request:
- Creating a causal loop diagram
- Visualizing feedback loops
- Building systems thinking diagrams
- Generating CLD visualizations
- Creating reinforcing or balancing loop diagrams
- Building system dynamics visualizations
## Workflow
### Step 1: Gather Requirements
Collect the following information from the user:
1. **MicroSim name** (kebab-case, e.g., `ai-flywheel`, `climate-feedback`)
2. **Title** for the diagram
3. **Description** of the system being modeled
4. **Nodes** (variables in the system) with their labels and descriptions
5. **Edges** (causal relationships) with polarity (positive/negative)
6. **Loops** (reinforcing R or balancing B) with descriptions
If the user provides a text description, parse it to identify:
- Key variables (become nodes)
- Causal relationships (become edges with polarity)
- Feedback loops (reinforcing or balancing)
### Step 2: Generate the MicroSim Files
Create the following directory structure in `/docs/sims/{{MICROSIM_NAME}}/`:
```
{{MICROSIM_NAME}}/
├── index.md # Documentation page
├── main.html # HTML container
├── {{MICROSIM_NAME}}.js # JavaScript code using vis-network
├── data.json # Node and edge data
└── style.css # Custom CSS styles
```
### Step 3: File Generation Details
#### 3.1 data.json
Generate the JSON data file following the CLD schema. Refer to `assets/rules.md` for the complete JSON schema and best practices.
Key structure:
```json
{
"metadata": {
"id": "{{MICROSIM_NAME}}-cld",
"title": "Title",
"archetype": "archetype-name",
"description": "Description",
"version": "1.0.0"
},
"nodes": [...],
"edges": [...],
"loops": [...]
}
```
**Node positioning guidelines:**
- Canvas center is approximately (300, 300)
- Space nodes 150-200 pixels apart
- Arrange nodes in a logical flow (clockwise for reinforcing, counter-clockwise for balancing)
- For 4-node loops: use positions like (300,150), (450,300), (300,450), (150,300)
#### 3.2 main.html
Create the HTML file using the template in `assets/templates/main.html`. The HTML should:
- Load vis-network from CDN
- Include the CSS file
- Reference the JavaScript file
- Have a container div for the network
#### 3.3 {{MICROSIM_NAME}}.js
Generate JavaScript using vis-network library. Refer to `assets/templates/microsim.js` for the template.
Key features to implement:
- Load data from data.json
- Configure node appearance (box shape, colors, fonts)
- Configure edge appearance (arrows, polarity colors: green for +, red for -)
- Disable physics for manual positioning
- Add click handlers for showing details
- Support URL parameters for iframe embedding
#### 3.4 style.css
Create CSS for the MicroSim layout. Use the template in `assets/templates/style.css`.
#### 3.5 index.md
Create the documentation page with:
- Title and description
- Learning objectives
- Iframe embed of the MicroSim
- Link to full-screen version
- Explanation of the system dynamics
### Step 4: Update mkdocs.yml
Add the new MicroSim to the navigation in `mkdocs.yml`:
1. Find the `MicroSims:` section in the nav
2. Add a new entry in **alphabetical order**: `- {{Title}}: sims/{{MICROSIM_NAME}}/index.md`
**Important:** Maintain alphabetical ordering of all MicroSim entries.
### Step 5: Remind User About Screenshot
After generating all files, remind the user:
> **Screenshot Required:** Please take a screenshot of the MicroSim and save it as `{{MICROSIM_NAME}}.png` in the `/docs/sims/{{MICROSIM_NAME}}/` directory. This image will be used for social sharing and documentation.
## CLD Design Best Practices
Refer to `assets/rules.md` for detailed rules on:
- JSON schema specification
- Node positioning algorithms
- Edge polarity and curve directions
- Loop labeling conventions
- vis-network configuration options
## Resources
### assets/
- `rules.md` - Comprehensive CLD generation rules and JSON schema
- `templates/main.html` - HTML template
- `templates/microsim.js` - JavaScript template
- `templates/style.css` - CSS template
- `templates/index.md` - Documentation template
- `templates/data.json` - Example JSON data structure
## Example Usage
**User request:** "Create a CLD showing how increased AI usage leads to more training data, which improves model accuracy, which increases AI usage."
**Generated MicroSim:**
- Name: `ai-usage-loop`
- Nodes: AI Usage, Training Data, Model Accuracy
- Edges: All positive polarity forming a reinforcing loop
- Loop: R - AI Improvement Cycle
This skill generates interactive Causal Loop Diagram (CLD) MicroSims using the vis-network JavaScript library. It produces a complete MicroSim package (HTML, JS, JSON, CSS, and documentation) placed in /docs/sims/{microsim_name}/. Use it to teach systems thinking, reveal feedback loops, and illustrate reinforcing or balancing dynamics.
Provide a MicroSim name, title, description, nodes (variables), edges (causal links with positive/negative polarity), and loop definitions. The skill creates data.json with node/edge/loop structures, a main.html container, a microsim JS file that loads vis-network and renders the CLD, a style.css, and an index.md documentation page. It also suggests node positions and visual conventions (green for positive links, red for negative) and adds interactivity for clicking nodes and embedding via iframe.
Can you parse a plain-text system description into nodes and edges?
Yes. Provide a concise paragraph describing variables and causal effects; I'll extract key variables, infer polarities when possible, and propose loops for your review.
How do you handle node positioning and layout?
I follow simple placement rules: center at roughly (300,300), space nodes 150–200px apart, and arrange loop nodes clockwise for reinforcing and counter-clockwise for balancing. You can request custom positions if needed.