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analyzing-data-html-reportanalyzing 数据 HTML report

Agent Skill

用于辅助数据整理、表格处理、CSV/Excel 分析、指标计算和图表准备。它适合让 Agent 清洗字段、汇总数据、发现异常、生成统计口径或把分析结果转成可读说明。使用时需要确认数据来源、字段含义和时间范围,避免把样本数据当全量事实;涉及敏感数据、导出文件或批量写回时,应先确认权限和脱敏边界。

总安装

380

周安装

16

GitHub Stars

4,809

下载量

133
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:analyzing-data-html-report(analyzing 数据 HTML report)
来源仓库:https://github.com/dtyq/magic
仓库路径:skills/analyzing-data-html-report
安装命令:
npx skills add https://github.com/dtyq/magic --skill analyzing-data-html-report
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/dtyq/magic --skill analyzing-data-html-report

简介

用于辅助数据整理、表格处理和指标计算,生成可读的分析报告。

  • 适合清洗字段、汇总数据、发现异常或准备图表素材。
  • 通过 Python 脚本执行分析并输出 HTML 格式的报告文件。
  • 需确认数据来源和字段含义,避免将样本当作全量事实使用。
  • 涉及敏感数据导出时应先确认权限并进行脱敏处理。analyzing-data-html-report 属于开发类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

数据分析报告开发技能 / Data Analysis Report Development Skill

Provides data analysis report development capabilities, including creating analysis report projects, generating static HTML analysis reports, integrating visualization charts, and complete development workflows.


如何使用本文档 / How to Use This Document

This document provides quick guidance and core tool descriptions. For details, refer to the following reference documents:

  • Complete code examples → references/report-workflow.md
  • ECharts 5.6.0 specification → references/report-echarts-v5.md

代码执行方式 / Code Execution Method

Python analysis scripts are executed via Shell tool (e.g., python analyze.py).


快速开始 / Quick Start

Important: Before executing the following steps, it's recommended to read the corresponding reference documents to understand detailed specifications:

  • Before starting report development → Read reference/report-workflow.md for complete workflow and code examples
  • When configuring charts → Read reference/report-echarts-v5.md for ECharts 5.6.0 usage specification

Develop report project: Create report working directory and execute analysis:

# Step 1: 创建报告目录(目录名体现分析内容)/ Create report directory (name reflects analysis content)
import os
os.makedirs('销售数据分析', exist_ok=True)  # or 'Sales Data Analysis' for English

# Step 2: 编写分析脚本 / Write analysis script
# 脚本读取数据源,执行分析,输出 data.json

Develop HTML report:

<!DOCTYPE html>
<html>
  <head>
    <meta charset="UTF-8" />
    <script src="https://cdnjs.cloudflare.com/ajax/libs/echarts/5.6.0/echarts.min.js"></script>
  </head>
  <body>
    <div id="chart" style="width:800px;height:400px;"></div>
    <script data-type="report">
      var reportData = {"total": 12345, "chart": [...]};
    </script>
    <script>
      var chart = echarts.init(document.getElementById('chart'));
      chart.setOption({...});
    </script>
  </body>
</html>

主要流程 / Main Workflow

  1. Create report working directory: Must create dedicated directory at task start. Directory name must reflect analysis content (e.g., "Sales Data Analysis"). Place all report files (analyze.py, data.json, index.html, README.md) in this directory
  2. Execute data analysis: Use Python script to get accurate analysis results and output data.json file
  3. Develop HTML report: Create index.html file with ECharts visualization, data embedded via inline script tag
  4. Documentation: Write README.md documentation
  5. Complete delivery: Provide report summary

文件命名规范 / File Naming Rules

Directory Naming: Must reflect analysis content and business domain, intelligently determined by user preferred language, e.g.:

  • User preferred language is Chinese: "销售数据分析", "2024年财务报告", "用户行为分析"
  • User preferred language is English: "Sales Data Analysis", "2024 Financial Report", "User Behavior Analysis"

File Naming: Use standard names within directory, e.g.:

  • HTML report: index.html (main report file)
  • Data file: data.json (analysis result data)
  • Analysis script: analyze.py (data analysis script)
  • Documentation: README.md (project documentation)

Complete Examples:

  • Chinese project: 销售数据分析/index.html, 销售数据分析/data.json
  • English project: Sales Data Analysis/index.html, Sales Data Analysis/data.json

关键规则 / Key Rules

Staticization Principle

  • Single HTML file, all data inline, zero async loading
  • Data embedded via <script data-type="report"> tag: var reportData = {...};

Technical Constraints

  • Use ECharts 5.6.0 specification only
  • Python scripts for data analysis to get accurate results only, not for generating images
  • Charts must use ECharts, strictly prohibited to generate images via Python

决策树 / Decision Tree

Need analysis report? ├─ Yes → Report type? │ ├─ Static HTML single-page report → This skill │ └─ Interactive Dashboard → analyzing-data-dashboard skill └─ No → Only instant numeric answer → data-qa skill

Chart type? ├─ Line/Bar/Pie etc → ECharts 5.6.0 ├─ Table only → HTML table └─ Mixed → ECharts + table


技术栈 / Tech Stack

CDN Resources:


参考文档 / Reference

For detailed usage, refer to the reference directory:

  • report-workflow.md - Complete workflow and code examples
  • report-echarts-v5.md - ECharts 5.6.0 usage specification

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

34.39%
按下载量换算46

Claude

30.92%
按下载量换算41

Cursor

18.01%
按下载量换算24

Gemini CLI

8.12%
按下载量换算11

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/dtyq/magic --skill analyzing-data-html-report 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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