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complex-causal-analysis

作者 zmqnk · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install complex-causal-analysis
功能描述
Generates interactive D3.js Sankey-style causal network diagrams showing hierarchical cause-effect relationships with evidence and adjustable nodes.
使用说明 (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

安全使用建议
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.
功能分析
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).
能力评估
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.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install complex-causal-analysis
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /complex-causal-analysis 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
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.
元数据
Slug complex-causal-analysis
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

complex-causal-analysis 是什么?

Generates interactive D3.js Sankey-style causal network diagrams showing hierarchical cause-effect relationships with evidence and adjustable nodes. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 178 次。

如何安装 complex-causal-analysis?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install complex-causal-analysis」即可一键安装,无需额外配置。

complex-causal-analysis 是免费的吗?

是的,complex-causal-analysis 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

complex-causal-analysis 支持哪些平台?

complex-causal-analysis 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 complex-causal-analysis?

由 zmqnk(@zmqnk)开发并维护,当前版本 v1.0.0。

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