A specialized tool that converts Python-based optimization solutions from FaMou evolutionary algorithms into interactive, high-fidelity visual dashboards.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install famou-result-visualization
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install famou-result-visualization using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is FaMou Result Visualization?
The FaMou Result Visualization skill is a technical bridge between complex mathematical optimization results and human-readable insights. Instead of simply outputting raw Python code or console logs, this skill analyzes the semantic structure of a solution—such as TSP paths, scheduling grids, or knapsack allocations—and generates a standalone, interactive HTML5 page. This allows developers and researchers using Openclaw Skills to see the physical or logical manifestation of their algorithm's best-found solution.
The generated visualizations are built with a modern dark-themed aesthetic, utilizing React, Tailwind CSS, and SVG for sharp, responsive rendering. By extracting data literals directly from Python code, the skill provides a zero-dependency visualization layer that focuses on the quality of the solution rather than the history of the evolution process.
FaMou Result Visualization Use Cases
- Visualizing path planning and Route Optimization (TSP/VRP) results as interactive maps.
- Rendering complex academic or industrial course schedules and timetables.
- Illustrating bin packing or knapsack problem loading configurations.
- Displaying graph coloring and community detection results.
- Generating Gantt charts for job scheduling and project management optimization.
How FaMou Result Visualization Works
- The skill identifies optimization triggers such as FaMou visualization or evolution results in the user's prompt.
- It parses the provided Python code to extract core data structures, coordinates, time slots, and fitness values.
- Based on the problem logic, it selects a specific visualization type such as path_map, schedule_grid, or gantt.
- It maps the extracted numerical data into a hardcoded SOLUTION_DATA constant within an HTML template.
- It generates a self-contained famou_viz_result.html file featuring interactive React components and smooth CSS animations.
FaMou Result Visualization Setup
To use this within the Openclaw Skills environment, ensure you have a Python solution generated by the FaMou framework. Simply prompt the agent:
# Example prompt to trigger the skill
Show me the visualization for this FaMou evolution result: [Paste Python Code]
The skill will automatically generate the HTML file in your current workspace.
FaMou Result Visualization Data Schema & Taxonomy
The skill organizes output data into a structured JSON-like constant within the HTML file to ensure offline portability:
| Data Key | Description | Type |
|---|---|---|
problem_type |
Identified category (e.g., TSP, Packing) | String |
metrics |
Key performance indicators (Fitness, Cost) | Object |
nodes |
Coordinates or entities for rendering | Array |
solution_path |
The sequence or mapping representing the optimal result | Array/Object |
highlights |
Specific strengths of the current solution | Array |
FaMou Result Visualization Advanced Features
- Multi-metric comparison bars to visualize improvements between initial and evolved solutions.
- Dynamic SVG path rendering with step-by-step drawing animations.
- Interactive Tooltips providing granular data on hover for every node or task.
- Automatic sampling logic to handle large-scale datasets (100+ nodes) without performance degradation.
- Fallback 'Custom Dashboard' mode for unique or complex optimization strategies that don't fit standard templates.
SKILL.md
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