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complex-causal-analysis

by zmqnk · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install complex-causal-analysis
Description
Generates interactive D3.js Sankey-style causal network diagrams showing hierarchical cause-effect relationships with evidence and adjustable nodes.
README (SKILL.md)

Complex Causal Analysis

An interactive causal network visualization skill for analyzing cause-and-effect relationships in complex systems. Creates beautiful D3.js-based Sankey-style diagrams showing how factors connect and propagate through systems.

Features

  • Hierarchical Layout: Top-to-bottom flow from root causes to final outcomes
  • Interactive Nodes: Click nodes to see details (causes, results, evidence)
  • Draggable Elements: Drag any node to reposition; connections follow automatically
  • Color-coded Strength: Green = strong (8-10), Yellow = medium (5-7) causal relationships
  • Directional Arrows: Small arrows in the middle of each link showing causation direction
  • Evidence Support: Each node can include historical or factual evidence
  • Fixed Sidebar: Stays on screen while zooming/panning the graph
  • Zoom & Pan: Mouse wheel to zoom, drag canvas to pan

Use Cases

  • Historical event analysis (dynasty collapses, wars, revolutions)
  • Business cause-and-effect mapping
  • Scientific phenomenon chains
  • Problem root cause analysis
  • Decision tree visualization

Input Format

Provide JSON with this structure:

{
  "title": "Topic Name",
  "nodes": [
    { "id": "Node Name", "layer": 3, "type": "Category", "desc": "Description", "color": "#hex" }
  ],
  "links": [
    { "source": "Cause", "target": "Effect", "strength": 8 }
  ]
}

Layer Conventions

  • Layer 3: Root causes / Deep factors
  • Layer 2: Intermediate factors
  • Layer 1: Direct causes
  • Layer 0: Final outcomes

Strength Guidelines

  • 8-10: Strong causal relationship
  • 5-7: Medium causal relationship
  • 1-4: Weak (exclude)

Example Prompt

Create a causal network visualization for [TOPIC].

Nodes:
- [list with id, layer, type, description]

Links:
- [list source → target with strength 1-10]

Requirements:
- Top-to-bottom layout
- Only medium (5-7) and strong (8-10) relationships
- Add small arrow in middle of each link
- Node details show causes and results
- Include evidence/support for each node

Output

Generates a standalone HTML file with:

  • Embedded D3.js (via CDN)
  • No external dependencies
  • Works in any modern browser
  • Mobile-responsive

Skill Activation

When you activate this skill, provide:

  1. Your topic/theme
  2. List of causal factors (nodes)
  3. Relationships between factors (links)

The skill will generate the JSON and produce a clickable HTML file you can open in your browser.

Technical Details

  • Built with D3.js v7
  • Bezier curves for smooth link paths
  • Force-directed initial positioning with manual adjustment
  • SVG-based rendering for crisp graphics
  • Dark theme with gradient background

Tags: visualization, d3, causal-network, history, analysis, interactive

Usage Guidance
This skill appears to do what it says: it generates a standalone HTML D3 visualization from user-supplied JSON and includes a ready-made template and example data. Things to consider before installing/using: - Network note: the template loads D3 from https://d3js.org (CDN). If you need fully offline artifacts, ask the skill to embed a local copy of d3 or manually host d3 locally. The SKILL.md's "No external dependencies" claim is inconsistent with the CDN usage. - Data privacy: the generated HTML will contain your input data (nodes/links). If that data is sensitive, avoid opening the file in an environment that can access external networks or inspect the file first. - Review output before opening in a browser if you have strict security requirements — although the files provided contain only client-side visualization code, opening any HTML can cause your browser to fetch remote resources (the D3 CDN) or run JavaScript. - No credentials or system access are required, and there is no install step, so the risk surface is primarily the usual browser/network considerations when opening generated HTML.
Capability Analysis
Type: OpenClaw Skill Name: complex-causal-analysis Version: 1.0.0 The skill bundle is a legitimate visualization tool for creating interactive D3.js-based causal network diagrams. Analysis of 'SKILL.md' and 'template.html' shows the code is focused on rendering SVG graphics, handling node dragging, and displaying hierarchical data as described. There are no signs of data exfiltration, malicious execution, or prompt injection; the only external dependency is the official D3.js library from a reputable CDN (d3js.org).
Capability Assessment
Purpose & Capability
Name/description (interactive D3 Sankey-style causal diagrams) match the provided SKILL.md, README, and template.html. The included template and example data implement the declared features (layers, color-coded strengths, arrows, sidebar, detail panel). No unexpected credentials, binaries, or config paths are requested.
Instruction Scope
SKILL.md instructs the agent to generate JSON and produce a standalone HTML file; template.html contains the visualization code and an example dataset. One small contradiction: SKILL.md claims "No external dependencies" but explicitly embeds D3.js via a CDN — opening the output in a browser will cause a network fetch from d3js.org. Otherwise the instructions do not ask the agent to read unrelated files, exfiltrate data, or call unknown endpoints.
Install Mechanism
There is no install spec and no code to download or execute on the agent. This instruction-only skill will only produce an HTML file; nothing is written to disk by an installer. Low installation risk.
Credentials
The skill declares no required environment variables, no credentials, and no config paths. The runtime instructions and template do not reference secrets or external service tokens.
Persistence & Privilege
always is false and the skill does not request persistent or elevated platform privileges. It does not modify other skills or agent-wide settings.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install complex-causal-analysis
  3. After installation, invoke the skill by name or use /complex-causal-analysis
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
- Initial release of Complex Causal Analysis skill. - Interactive D3.js Sankey-style visualization for analyzing complex cause-and-effect networks. - Features draggable, hierarchical nodes with evidence support and color-coded strengths. - Directional arrows and fixed sidebar enhance usability. - Generates standalone, mobile-responsive HTML files for browser viewing.
Metadata
Slug complex-causal-analysis
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is complex-causal-analysis?

Generates interactive D3.js Sankey-style causal network diagrams showing hierarchical cause-effect relationships with evidence and adjustable nodes. It is an AI Agent Skill for Claude Code / OpenClaw, with 178 downloads so far.

How do I install complex-causal-analysis?

Run "/install complex-causal-analysis" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is complex-causal-analysis free?

Yes, complex-causal-analysis is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does complex-causal-analysis support?

complex-causal-analysis is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created complex-causal-analysis?

It is built and maintained by zmqnk (@zmqnk); the current version is v1.0.0.

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