“CSV”

Convert between JSON and CSV.

Read a CSV or XLSX file and return columns, shape, dtypes, and first N rows as JSON.

用 Python 标准库生成 CSV 字段和缺失值摘要

Summarize CSV files, count data rows, and total numeric columns.

Parse, query, filter, sort, transform, and summarize CSV and JSON data files.

Profile a CSV file and produce a Markdown report with row/column counts, fill rates, unique values, and numeric or categorical summaries.

在浏览器 Python 中把 CSV 分类数值生成可下载 SVG 柱状图

Merges, appends, joins, and deduplicates multiple CSV, TSV, and Excel files (including multi-sheet workbooks) with pandas - mapping mismatched column names to…

Answer small CSV questions accurately with careful tabular reasoning.

Summarize CSV files into per-column stats (type, count, nulls, min/max/mean for numbers).

Print row and column counts for a CSV file

This skill should be used when users need to analyze CSV or Excel files, understand data patterns, generate statistical summaries, or create data…

Summarize CSV files with statistics and previews.

Use this skill when the user gives you a path to a CSV file and wants a summary — number of rows, columns, inferred column types, and per-column descriptive…

Convert CSV files to JSON with schema inference and validation

Analyzes CSV files, generates summary stats, and plots quick visualizations using Python and pandas.

Import CSV test cases into PostgreSQL using an LLM-derived column mapping and a streaming transformation pipeline.

读取、写入、转换和分析CSV文件，支持过滤、排序、聚合和格式转换。

No description from the publisher yet.

Clean a messy tabular dataset by fixing value-level data-quality problems — duplicate rows, missing-value placeholders, text-encoding and casing noise, and…

Convert per-case Abaqus FEA outputs into ML-ready (X, Y) wide-table CSVs.

Triggered when the user needs to convert data formats, such as CSV to JSON, JSON to YAML, or XML to JSON.

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV using native allotropy or…

Use when the user has tabular data (pandas DataFrame, parquet, csv, Arrow, json) and wants to filter, group, aggregate, join, or speed up slow pandas.