famou-result-visualization

Visualize FaMou optimization solutions as self-contained HTML pages.

27|10|Updated Mar 15, 2026

Search Tags:#html #optimization #visualization #svg #web #famou #evolutionary-algorithm

Ready to run

npx skills add https://github.com/baidubce/skills --skill famou-result-visualization

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: famou-result-visualization
Source: https://github.com/baidubce/skills/tree/main/skills/famou-result-visualization
Command: npx skills add https://github.com/baidubce/skills --skill famou-result-visualization

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

FaMou 进化算法的最终解需要可视化展示，以直观呈现解的效果，而不仅仅是数值。

Core Features & Use Cases

收集输入：问题描述和以 Python 代码形式给出的最终解（FaMou 进化后的结果）。
理解解的语义：提取问题类型、核心数据结构与关键数值（如坐标、时间槽、资源分配等）。
确定可视化类型：path_map（路径/地图）、schedule_grid（排课/时间表）、packing_rect（装箱/矩形堆叠）、graph_color（图着色）、gantt（甘特图）、ml_viz、custom 等。
提取绘图数据：将解中的坐标、顺序、分配等信息整理为可绘制的数据结构。
生成 HTML 可视化：输出一个自包含的可视化页面，使用 CDN 加载的库，提供交互（如悬停提示、动画）。

Quick Start

提供你的 FaMou 问题描述和最终解的 Python 代码，技能将生成一个交互式可视化页面。

Frequently Asked Questions about famou-result-visualization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I visualize evolutionary algorithm optimization results for path planning and scheduling tasks?▼

You can visualize evolutionary algorithm optimization results by providing the problem description and final Python solution code. The system infers the problem type, extracts coordinates or allocations, and renders a self-contained interactive HTML visualization using CDN-based libraries.

What types of optimization solutions can be rendered as interactive HTML visualizations?▼

Optimization solutions suitable for HTML visualization include path planning maps, scheduling grids, packing rectangles, graph coloring, and Gantt charts. The system automatically determines the appropriate visualization type by extracting semantic data from your final solution.

How to generate a self-contained HTML page from a Python optimization solution without external dependencies?▼

To generate a self-contained HTML visualization, input your problem description and Python solution code. The system extracts coordinates, time slots, or resource allocations, then renders an interactive page using CDN-loaded libraries, eliminating the need for local dependencies.

Does the visualization support interactive features like hover tooltips for schedule grids and path maps?▼

Yes, the visualization supports interactive features such as hover tooltips and animations. The generated HTML page utilizes CDN-based libraries to provide an intuitive depiction of your optimization solution directly in the web browser.

Can I use this visualization approach for custom optimization problems outside of standard graph coloring or packing tasks?▼

Yes, the system supports custom problem types alongside standard categories like graph coloring and packing. It analyzes your Python code to extract specific data structures and renders a tailored HTML visualization to depict the unique optimization solution.

Why does my evolutionary algorithm solution need an HTML visualization instead of raw numerical output?▼

An HTML visualization is needed to intuitively present the effects of your evolutionary algorithm solution beyond raw numbers. By rendering interactive maps, grids, or charts, it allows immediate visual verification of path, schedule, or allocation optimization results.