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对CSV/Excel数据进行全面的统计描述、相关性分析和可视化；当用户需要数据分析、数据探索、统计摘要、特征分布分析或相关性分析时使用
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Search academic papers and conduct literature reviews using OpenAlex API (free, no key needed). Use when the user needs to find scholarly papers by topic/author/DOI, explore citation chains, get structured paper metadata (title, authors, abstract, citations, DOI, open access URL), fetch full text of open access papers, or conduct automated literature reviews with theme identification and synthesis. Triggers on requests involving academic search, paper lookup, citation analysis, literature review, research synthesis, or scholarly reference gathering.
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Use AI4Scholar to trace citation networks: citing papers, references, PubMed related papers, backward/forward citation search, classic paper discovery, mechanism literature expansion, and reviewer-style citation gap checks.
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| name | data-visualization-analysis |
| description | 对CSV/Excel数据进行全面的统计描述、相关性分析和可视化；当用户需要数据分析、数据探索、统计摘要、特征分布分析或相关性分析时使用 |
| dependency | {"python":["pandas>=2.0.0","numpy>=1.24.0","matplotlib>=3.7.0","seaborn>=0.12.0","openpyxl>=3.1.0"]} |
pandas>=2.0.0
numpy>=1.24.0
matplotlib>=3.7.0
seaborn>=0.12.0
openpyxl>=3.1.0
步骤1：数据验证
步骤2：执行数据分析
调用 scripts/analyze_data.py 脚本执行全面分析：
python /workspace/projects/data-visualization-analysis/scripts/analyze_data.py \
--input_file <用户数据文件路径> \
--output_dir <输出目录路径>
参数说明：
--input_file：必需，CSV或Excel文件的完整路径--output_dir：可选，分析结果输出目录，默认为 ./analysis_output--target_column：可选，目标变量列名（用于重点分析相关性）步骤3：查看HTML报告 脚本执行完成后，会生成一个完整的HTML分析报告文件：
analysis_report.html - 包含所有统计结果、图表和分析的HTML报告步骤4：智能解读与洞察 智能体基于HTML报告内容提供：
场景A：用户指定目标变量
--target_column 参数场景B：大数据集优化
场景C：非数值型数据处理
示例1：基础分析
python /workspace/projects/data-visualization-analysis/scripts/analyze_data.py \
--input_file ./sales_data.csv
示例2：指定目标变量
python /workspace/projects/data-visualization-analysis/scripts/analyze_data.py \
--input_file ./customer_data.xlsx \
--target_column purchase_amount \
--output_dir ./my_analysis
示例3：智能体解读流程
analysis_report.html 查看完整报告