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analyzing-data-html-report: Скилл для создания отчетов

Name: analyzing-data-html-report: Скилл для создания отчетов
Author: dtyq/magic

Навык разработки отчетов по анализу данных. Используется, когда пользователям необходимо разрабатывать отчеты по анализу данных, создавать проекты отчетов, формировать статические HTML-документы с результатами анализа или создавать разовые отчеты с визуализацией.

$ git log --oneline --stat

⭐ звёзды: 4,716

форки: 499

обновлено: 1 апреля 2026 года в 10:32

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SKILL.md

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--- name: analyzing-data-html-report description: Data analysis report development skill. Use when users need to develop data analysis reports, create analysis report projects, build static HTML analysis documents, or produce one-time analysis reports with visualization. name-cn: 数据分析报告开发技能 description-cn: 数据分析报告开发技能。当用户需要开发数据分析报告、创建分析报告项目、构建静态HTML分析文档、产出一次性分析报告时使用。 --- # 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:** ```python # 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:** ```html <!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: - TailwindCSS: https://cdn.tailwindcss.com/3.4.17 - ECharts: https://cdnjs.cloudflare.com/ajax/libs/echarts/5.6.0/echarts.min.js - FontAwesome: https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.7.2/css/all.min.css - Tabler Icons: https://cdnjs.cloudflare.com/ajax/libs/tabler-icons/3.34.1/tabler-icons.min.css - Google Fonts: https://fonts.googleapis.com/css2?family=Noto+Sans+SC:wght@400;500;700&display=swap --- ## 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

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