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dfpalhano
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csv-pipeline

Filters, joins, aggregates, converts CSV/TSV/JSON Lines data via Python 3 and standard tools.

Updated Feb 26, 2026
Search Tags:#reporting#json#data analysis#data transformation#data processing#etl#csv
One-click install
npx skills add https://github.com/dfpalhano/openclaw-workspace --skill csv-pipeline-dfpalhano
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: csv-pipeline
Source: https://github.com/dfpalhano/openclaw-workspace/tree/main/skills/csv-pipeline
Command: npx skills add https://github.com/dfpalhano/openclaw-workspace --skill csv-pipeline-dfpalhano

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill streamlines the complex and time-consuming tasks of cleaning, transforming, analyzing, and reporting on data stored in CSV, TSV, and JSON Lines formats.

Core Features & Use Cases

  • Data Transformation: Filter rows, join datasets, rename columns, and convert data types.
  • Data Analysis: Compute aggregates, group data, and generate summary statistics.
  • Format Conversion: Easily convert between CSV, TSV, JSON, and JSON Lines.
  • Data Cleaning: Handle common data quality issues like whitespace and inconsistent empty values.
  • Reporting: Generate summary reports in Markdown format.

Quick Start

Use the csv-pipeline skill to filter the file 'data.csv' to keep only rows where the 'amount' column is greater than 100 and save the result to 'filtered.csv'.

Frequently Asked Questions about csv-pipeline

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

FAQPage Schema
How do I filter rows in a CSV file and save the results?▼

To filter rows in a CSV file, you can specify conditions like keeping rows where a column's value is greater than a certain number, then save the transformed output to a new file.

Can I convert CSV data to JSON Lines format?▼

Yes, you can convert data between CSV, TSV, JSON, and JSON Lines formats to structure tabular data into serialized objects for downstream processing.

How do I join two datasets and compute aggregates for data analysis?▼

You can join multiple datasets and compute aggregates by grouping data to generate summary statistics across the combined tabular files.

Do I need any special libraries to process TSV files?▼

No special libraries are needed to process TSV files; the data processing requires only standard command-line tools and Python 3.

What is the best way to clean inconsistent empty values in tabular data?▼

To clean tabular data, you can handle data quality issues like whitespace and inconsistent empty values to normalize records before analysis.

Can I generate summary reports from JSON Lines data?▼

Yes, you can generate summary reports in Markdown format from JSON Lines data to document your data analysis and aggregate findings.

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