Data Viz Suite

数据可视化套件 - 企业级BI工具，支持图表生成、数据报表、交互式仪表盘。支持 Plotly/Matplotlib/Seaborn 多种引擎。

Security Scan

The package largely matches its stated data-visualization purpose, but there are documentation/manifest inconsistencies and a few implementation issues that warrant caution before installing or running it.

Purpose & Capability

Code files implement plotting, dashboard, and report export functionality consistent with the skill description (Plotly/Matplotlib/Seaborn). However SKILL.md/README mention additional files (scripts/data_connector.py, tests/test_dashboard.py, tests/test_report_generator.py) that are not present in the provided file manifest—this mismatch could indicate incomplete packaging or stale documentation.

Instruction Scope

Runtime instructions are limited to installing requirements and using the provided APIs. The example scripts write output files to /tmp and the dashboard/report generators write files to disk (expected for this purpose). No code attempts to read unrelated system config or external credentials. One implementation bug: dashboard._generate_html embeds fig.to_json() directly into JS as if it were an object (uses '{fig_json}.data' and '{fig_json}.layout'), which will produce invalid JS and break interactive charts; this looks like sloppy code rather than malicious behavior.

Install Mechanism

This is an instruction-only skill with a requirements.txt (pip). No install spec downloads arbitrary archives or runs remote installers. The dependency list is moderately large (plotly, matplotlib, seaborn, pandas, numpy, kaleido, reportlab, jupyter, openpyxl) which is plausible for a BI toolkit but should be installed in an isolated virtual environment.

Credentials

No environment variables, credentials, or external config paths are required. The report generator checks for common font files on the host (standard system font paths) to register Chinese fonts — this is reasonable for PDF rendering and does not require secrets.

Persistence & Privilege

Skill does not request permanent or elevated privileges (always: false). It does write files when used (HTML/PDF/Excel output), which is expected for this type of tool. There is no code that alters other skills or system-wide agent settings.

What to consider before installing

This package appears to implement a normal data-visualization toolkit, but take these precautions before installing or running it: - Run in an isolated virtualenv or container to avoid polluting your system Python environment and to limit dependency impact. - Note the manifest/documentation mismatches (missing data_connector.py and some tests). Ask the publisher or inspect the repo for the missing files before trusting the package. - Expect the examples to write files to disk (examples use /tmp). Ensure that is acceptable in your environment. - The dashboard HTML generation has a bug (embedding fig.to_json() incorrectly) — interactive charts may be broken; review/patch the HTML generation before using in production. - Dependencies include jupyter/kaleido/reportlab/openpyxl; if you only need a subset of features, consider trimming dependencies. - Because the source is marked "unknown", prefer to run tests (pytest/unittests) locally in an isolated environment and review code for any added network calls or logging before using with sensitive data. What would change my assessment: presence of the missing files (data_connector) that legitimately explain the documentation, or additional evidence of external network calls or credential usage would raise the concern level. If the missing files are intentionally omitted or there are unexpected outbound network endpoints, treat the skill as higher risk.

Data Viz Suite - 数据可视化套件

专业的数据可视化解决方案，支持静态图表、交互式仪表盘和企业级报表。

功能特性

• 📊 多种图表类型：折线图、柱状图、饼图、散点图、热力图、箱线图
• 🎨 三大可视化引擎：Plotly(交互式)、Matplotlib(静态)、Seaborn(统计)
• 📈 交互式仪表盘：支持拖拽布局、实时数据更新
• 📄 报表导出：支持 PDF、PNG、HTML、Excel 格式
• 🔗 数据源支持：CSV、Excel、JSON、SQL 数据库
• 🌐 Web 展示：生成交互式 HTML 报告

安装

pip install -r requirements.txt

快速开始

1. 基础图表

from scripts.chart_engine import ChartEngine

engine = ChartEngine(backend='plotly')

# 创建折线图
data = {'月份': ['1月', '2月', '3月'], '销售额': [100, 150, 200]}
fig = engine.line_chart(data, x='月份', y='销售额', title='月度销售趋势')
fig.write_html('sales.html')

2. 交互式仪表盘

from scripts.dashboard import Dashboard

dash = Dashboard(title='业务监控大屏')
dash.add_chart('sales', engine.line_chart(data, x='月份', y='销售额'))
dash.add_chart('users', engine.bar_chart(users, x='日期', y='新增用户'))
dash.save('dashboard.html')

3. 数据报表

from scripts.report_generator import ReportGenerator

report = ReportGenerator()
report.add_section('销售分析', charts=[fig1, fig2])
report.add_table('明细数据', dataframe=df)
report.export('report.pdf')

目录结构

data-viz-suite/
├── SKILL.md                  # 本文件
├── README.md                 # 详细文档
├── requirements.txt          # 依赖
├── examples/                 # 示例
│   └── basic_usage.py
├── scripts/                  # 核心脚本
│   ├── chart_engine.py
│   ├── dashboard.py
│   ├── report_generator.py
│   └── data_connector.py
└── tests/                    # 测试
    ├── test_chart_engine.py
    ├── test_dashboard.py
    └── test_report_generator.py

配置说明

主题配置

from scripts.chart_engine import Theme

engine = ChartEngine(theme=Theme.DARK)  # DARK, LIGHT, CORPORATE

数据源配置

# CSV/Excel
conn = DataConnector()
df = conn.load_csv('data.csv')
df = conn.load_excel('data.xlsx', sheet='Sheet1')

# SQL
config = {
    'host': 'localhost',
    'port': 3306,
    'user': 'root',
    'password': 'pass',
    'database': 'analytics'
}
df = conn.load_sql('SELECT * FROM sales', config)

许可证

MIT License