Load CSV, JSON, or Excel data in memory for cleaning, statistics, and visualization.
npx skills add https://github.com/ntygod/ZhiWei --skill data-analyst-ntygod
Please help me install this Agent Skill. Skill: data-analyst Source: https://github.com/ntygod/ZhiWei/tree/main/src/main/resources/skills/data-analyst Command: npx skills add https://github.com/ntygod/ZhiWei --skill data-analyst-ntygod
This skill enables in-memory data analysis by loading CSV, JSON, or Excel data, performing clean-up and quality checks, executing descriptive statistics, hypothesis testing, and generating visualizations without repeatedly reading from disk or external services.
Upload a CSV, JSON, or Excel file and tell me the analysis you want, and I will load it in memory, clean it, analyze, and visualize it.
High-intent search queries and answers about installing and using this skill.
To analyze CSV data in memory, load your file into a shared kernel session. This maintains context across steps, allowing you to clean data, run statistics, and generate visualizations without repeated disk reads.
Yes, you can load Excel files into the in-memory session to perform data cleaning like missing value handling and type conversion, followed by descriptive statistics and hypothesis testing such as t-tests within the same workflow.
The best way to run descriptive statistics on JSON data is to load it in memory first. You can then apply describe and correlation functions, and generate charts directly within the kernel-enabled session for immediate reporting.
Yes, this in-memory data analysis approach ensures robust handling of large files. It includes input validation and graceful error reporting to manage exceptions during loading, cleaning, and statistical analysis.
To clean up a dataset before analysis, load it in memory and apply the built-in cleaning functions. This includes missing value handling, type conversion, and deduplication to ensure data quality prior to running statistics.
Using an in-memory kernel session for data analysis eliminates the latency of repeatedly reading from disk or external services. It maintains shared state across your cleaning, statistical testing, and visualization steps for faster workflows.
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