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Image Generation Skill

图像生成Skill

Create diagrams, charts, and visual assets for security documentation with support for network diagrams, data visualizations, and flowcharts.
为安全文档创建图表、图形和可视化资产，支持生成网络拓扑图、数据可视化图表和流程图。

Capabilities

功能特性

  • Diagrams: Generate network topology and architecture diagrams
  • Charts: Create data visualizations (bar, pie, line, heatmaps)
  • Flowcharts: Build process and workflow diagrams
  • Risk Matrices: Generate risk assessment visualizations
  • Timelines: Create incident and event timelines
  • Export: Save to PNG, SVG, and PDF formats
  • 图表生成：生成网络拓扑图和架构图
  • 数据图表：创建数据可视化图表（柱状图、饼图、折线图、热力图）
  • 流程图：构建流程和工作流图
  • 风险矩阵：生成风险评估可视化图
  • 时间线：创建安全事件和事件时间线
  • 导出功能：支持保存为PNG、SVG和PDF格式

Quick Start

快速开始

python
import matplotlib.pyplot as plt
python
import matplotlib.pyplot as plt

Create a simple bar chart

Create a simple bar chart

severities = ['Critical', 'High', 'Medium', 'Low'] counts = [3, 8, 15, 22]
plt.figure(figsize=(10, 6)) plt.bar(severities, counts, color=['#e74c3c', '#e67e22', '#f1c40f', '#3498db']) plt.title('Findings by Severity') plt.savefig('severity_chart.png', dpi=150)
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severities = ['Critical', 'High', 'Medium', 'Low'] counts = [3, 8, 15, 22]
plt.figure(figsize=(10, 6)) plt.bar(severities, counts, color=['#e74c3c', '#e67e22', '#f1c40f', '#3498db']) plt.title('Findings by Severity') plt.savefig('severity_chart.png', dpi=150)
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Usage

使用指南

Bar Charts

柱状图

Create bar charts for comparisons.
Example:
python
import matplotlib.pyplot as plt
from typing import Dict

def create_severity_chart(data: Dict[str, int], output_path: str = 'chart.png'):
    """Create a severity distribution bar chart."""
    colors = {
        'Critical': '#e74c3c', 'High': '#e67e22',
        'Medium': '#f1c40f', 'Low': '#3498db'
    }

    severities = list(data.keys())
    counts = list(data.values())
    bar_colors = [colors.get(s, '#333') for s in severities]

    plt.figure(figsize=(10, 6))
    plt.bar(severities, counts, color=bar_colors)
    plt.title('Findings by Severity', fontsize=14, fontweight='bold')
    plt.savefig(output_path, dpi=150, bbox_inches='tight')
    plt.close()
创建用于对比分析的柱状图。
示例:
python
import matplotlib.pyplot as plt
from typing import Dict

def create_severity_chart(data: Dict[str, int], output_path: str = 'chart.png'):
    """Create a severity distribution bar chart."""
    colors = {
        'Critical': '#e74c3c', 'High': '#e67e22',
        'Medium': '#f1c40f', 'Low': '#3498db'
    }

    severities = list(data.keys())
    counts = list(data.values())
    bar_colors = [colors.get(s, '#333') for s in severities]

    plt.figure(figsize=(10, 6))
    plt.bar(severities, counts, color=bar_colors)
    plt.title('Findings by Severity', fontsize=14, fontweight='bold')
    plt.savefig(output_path, dpi=150, bbox_inches='tight')
    plt.close()

Usage

Usage

create_severity_chart({'Critical': 3, 'High': 8, 'Medium': 15, 'Low': 22})
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create_severity_chart({'Critical': 3, 'High': 8, 'Medium': 15, 'Low': 22})
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Network Diagrams

网络拓扑图

Create network topology diagrams using Graphviz.
Example:
python
from graphviz import Digraph

def create_network_diagram(nodes: list, edges: list, output_path: str = 'network'):
    """Create a network topology diagram."""
    dot = Digraph()
    dot.attr(rankdir='TB')

    shapes = {'firewall': 'box3d', 'server': 'box', 'database': 'cylinder'}

    for node in nodes:
        dot.node(node['id'], node['label'],
                shape=shapes.get(node.get('type', 'server'), 'box'),
                style='filled', fillcolor=node.get('color', 'lightblue'))

    for edge in edges:
        dot.edge(edge['from'], edge['to'], label=edge.get('label', ''))

    dot.render(output_path, format='png', cleanup=True)
使用Graphviz创建网络拓扑图。
示例:
python
from graphviz import Digraph

def create_network_diagram(nodes: list, edges: list, output_path: str = 'network'):
    """Create a network topology diagram."""
    dot = Digraph()
    dot.attr(rankdir='TB')

    shapes = {'firewall': 'box3d', 'server': 'box', 'database': 'cylinder'}

    for node in nodes:
        dot.node(node['id'], node['label'],
                shape=shapes.get(node.get('type', 'server'), 'box'),
                style='filled', fillcolor=node.get('color', 'lightblue'))

    for edge in edges:
        dot.edge(edge['from'], edge['to'], label=edge.get('label', ''))

    dot.render(output_path, format='png', cleanup=True)

Usage

Usage

nodes = [ {'id': 'fw', 'label': 'Firewall', 'type': 'firewall', 'color': 'lightcoral'}, {'id': 'web', 'label': 'Web Server', 'type': 'server'}, {'id': 'db', 'label': 'Database', 'type': 'database'} ] edges = [{'from': 'fw', 'to': 'web'}, {'from': 'web', 'to': 'db'}] create_network_diagram(nodes, edges)
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nodes = [ {'id': 'fw', 'label': 'Firewall', 'type': 'firewall', 'color': 'lightcoral'}, {'id': 'web', 'label': 'Web Server', 'type': 'server'}, {'id': 'db', 'label': 'Database', 'type': 'database'} ] edges = [{'from': 'fw', 'to': 'web'}, {'from': 'web', 'to': 'db'}] create_network_diagram(nodes, edges)
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Flowcharts

流程图

Create process flowcharts.
Example:
python
from graphviz import Digraph

def create_flowchart(steps: list, output_path: str = 'flowchart'):
    """Create a process flowchart."""
    dot = Digraph()
    dot.attr(rankdir='TB')

    shapes = {'start': 'ellipse', 'end': 'ellipse',
              'process': 'box', 'decision': 'diamond'}
    colors = {'start': 'lightgreen', 'end': 'lightcoral',
              'process': 'lightblue', 'decision': 'lightyellow'}

    for step in steps:
        dot.node(step['id'], step['label'],
                shape=shapes.get(step.get('type', 'process'), 'box'),
                style='filled',
                fillcolor=colors.get(step.get('type', 'process'), 'white'))

        if 'next' in step:
            for n in (step['next'] if isinstance(step['next'], list) else [step['next']]):
                if isinstance(n, dict):
                    dot.edge(step['id'], n['to'], label=n.get('label', ''))
                else:
                    dot.edge(step['id'], n)

    dot.render(output_path, format='png', cleanup=True)
创建流程流程图。
示例:
python
from graphviz import Digraph

def create_flowchart(steps: list, output_path: str = 'flowchart'):
    """Create a process flowchart."""
    dot = Digraph()
    dot.attr(rankdir='TB')

    shapes = {'start': 'ellipse', 'end': 'ellipse',
              'process': 'box', 'decision': 'diamond'}
    colors = {'start': 'lightgreen', 'end': 'lightcoral',
              'process': 'lightblue', 'decision': 'lightyellow'}

    for step in steps:
        dot.node(step['id'], step['label'],
                shape=shapes.get(step.get('type', 'process'), 'box'),
                style='filled',
                fillcolor=colors.get(step.get('type', 'process'), 'white'))

        if 'next' in step:
            for n in (step['next'] if isinstance(step['next'], list) else [step['next']]):
                if isinstance(n, dict):
                    dot.edge(step['id'], n['to'], label=n.get('label', ''))
                else:
                    dot.edge(step['id'], n)

    dot.render(output_path, format='png', cleanup=True)

Risk Heatmaps

风险热力图

Create risk assessment heatmaps.
Example:
python
import matplotlib.pyplot as plt
import numpy as np

def create_risk_heatmap(data: list, x_labels: list, y_labels: list, output_path: str):
    """Create a risk assessment heatmap."""
    fig, ax = plt.subplots(figsize=(10, 8))
    im = ax.imshow(data, cmap='RdYlGn_r')

    ax.set_xticks(np.arange(len(x_labels)))
    ax.set_yticks(np.arange(len(y_labels)))
    ax.set_xticklabels(x_labels)
    ax.set_yticklabels(y_labels)

    for i in range(len(y_labels)):
        for j in range(len(x_labels)):
            ax.text(j, i, data[i][j], ha='center', va='center',
                   color='white' if data[i][j] > 5 else 'black')

    ax.set_title('Risk Matrix')
    plt.colorbar(im)
    plt.savefig(output_path, dpi=150)
    plt.close()
创建风险评估热力图。
示例:
python
import matplotlib.pyplot as plt
import numpy as np

def create_risk_heatmap(data: list, x_labels: list, y_labels: list, output_path: str):
    """Create a risk assessment heatmap."""
    fig, ax = plt.subplots(figsize=(10, 8))
    im = ax.imshow(data, cmap='RdYlGn_r')

    ax.set_xticks(np.arange(len(x_labels)))
    ax.set_yticks(np.arange(len(y_labels)))
    ax.set_xticklabels(x_labels)
    ax.set_yticklabels(y_labels)

    for i in range(len(y_labels)):
        for j in range(len(x_labels)):
            ax.text(j, i, data[i][j], ha='center', va='center',
                   color='white' if data[i][j] > 5 else 'black')

    ax.set_title('Risk Matrix')
    plt.colorbar(im)
    plt.savefig(output_path, dpi=150)
    plt.close()

Configuration

配置说明

Environment Variables

环境变量

VariableDescriptionRequiredDefault
IMAGE_OUTPUT_DIR
Output directoryNo
./output
IMAGE_DPI
Default DPINo
150
环境变量描述是否必填默认值
IMAGE_OUTPUT_DIR
输出目录否
./output
IMAGE_DPI
默认DPI否
150

Limitations

限制说明

  • Interactive: Static images only
  • 3D: Limited 3D support
  • Graphviz: Required for diagrams
  • 交互性：仅支持生成静态图像
  • 3D支持：仅提供有限的3D功能
  • Graphviz依赖：生成拓扑图需要安装Graphviz

Troubleshooting

问题排查

Graphviz Not Found

找不到Graphviz

Install the system package:
bash
apt-get install graphviz  # Ubuntu
brew install graphviz      # macOS
安装系统包：
bash
apt-get install graphviz  # Ubuntu
brew install graphviz      # macOS

Related Skills

相关技能

  • pptx: Embed images in presentations
  • docx: Include visuals in reports
  • pdf: Add charts to PDF reports
  • pptx：在演示文稿中嵌入图像
  • docx：在报告中插入可视化内容
  • pdf：在PDF报告中添加图表

References

参考资料