메뉴
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
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Convert files and office documents to Markdown. Supports PDF, DOCX, PPTX, XLSX, images (with OCR), audio (with transcription), HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs and more.
Use this skill when users need to search academic papers, download research documents, extract citations, or gather scholarly information. Triggers include: requests to "find papers on", "search research about", "download academic articles", "get citations for", or any request involving academic databases like arXiv, PubMed, Semantic Scholar, or Google Scholar. Also use for literature reviews, bibliography generation, and research discovery. Requires OpenClawCLI installation from clawhub.ai.
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.
Use AI4Scholar author tools to search scholars, inspect author profiles, retrieve an author's papers, compare experts, identify potential reviewers, map labs or collaborators, and verify whether an author is the correct person.
Use AI4Scholar auto_cite to add real citations to academic text and return formatted references and BibTeX. Trigger for automatic citation insertion, APA/IEEE/Vancouver/Nature citation support, reference generation, BibTeX export, or checking whether claims have real supporting papers.
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.
| 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