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table_collector

Extract structured data from CSV, TSV, XLS, and XLSX files into JSONL, JSON, or CSV.

541|171|Updated May 3, 2018
Search Tags:#data collection#csv#file conversion#jsonl#table data
One-click install
npx skills add https://github.com/cas-bigdatalab/piflow --skill table-collector
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: table_collector
Source: https://github.com/cas-bigdatalab/piflow/tree/main/workspace/skills/table_collector
Command: npx skills add https://github.com/cas-bigdatalab/piflow --skill table-collector

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openpyxl, pandas, and includes scripts (resource) components.

What problem does it solve?

This Skill simplifies the collection of data from structured tables, converting various file formats into standardized records.

Core Features & Use Cases

  • Table File Reading: Handles CSV, TSV, XLS, and XLSX files.
  • Recursive Collection: Can scan directories for supported files.
  • Metadata Adding: Includes file source information in output.
  • Output Formatting: Supports JSONL, JSON, CSV, and auto-detection.
  • Use Case: Perfect for researchers or data analysts needing to convert local tables into a uniform structure for further processing.

Quick Start

Run the table_collector skill with the input file path and desired output file path.

Frequently Asked Questions about table_collector

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I convert CSV and XLSX table files into JSONL format?▼

You can convert CSV and XLSX files into JSONL format by running a script that extracts structured table data and outputs standardized JSONL records. This handles file encoding and format conversion automatically for local data collection.

What is the best way to collect tabular data from multiple files in a directory?▼

Collecting tabular data from multiple files is done through recursive file scanning, which searches directories for supported table formats like CSV, TSV, XLS, and XLSX. It aggregates the structured data into a single standardized output file.

Does Pandas support reading TSV and XLS files for JSON conversion?▼

Pandas supports reading TSV and XLS files for JSON conversion when paired with openpyxl. These dependencies enable the script to parse tabular files and transform them into JSON, JSONL, or CSV output formats.

Can I add file source metadata when extracting table data into JSON?▼

You can add file source metadata when extracting table data into JSON. The collection process supports metadata tagging, ensuring original file source information is included alongside the structured records in the final output.

What are the limitations of converting local XLSX tables to CSV?▼

Limitations of converting local XLSX tables to CSV include the requirement to execute Python scripts and carefully handle file encoding during format conversion. It also depends on specific libraries like pandas and openpyxl to successfully process the tabular data.

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