data-handler

Clean and analyze CSV, XLSX, or JSON tabular data into charts and reports.

Updated May 1, 2026

Search Tags:#data visualization #report generation #statistics #xlsx #csv #data cleaning #charting

Ready to run

npx skills add https://github.com/picsky/flowos --skill data-handler

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-handler
Source: https://github.com/picsky/flowos/tree/main/templates/skills/data-handler
Command: npx skills add https://github.com/picsky/flowos --skill data-handler

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It turns messy datasets into clear insights by cleaning data, running statistics, and producing charts so you can understand results without manual spreadsheet work.

Core Features & Use Cases

Data cleaning & validation: handle missing, abnormal, and duplicate values while standardizing formats.
Statistical analysis & reporting: compute core metrics and derive 1–3 key findings backed by the data.
Chart generation & delivery: create the right visualization (trend, comparison, distribution) and save outputs for sharing.

Quick Start

Ask the system: “Analyze this sales dataset in ~/Downloads/Q1_sales.xlsx and show the monthly trend with a clean report and a chart.”

Frequently Asked Questions about data-handler

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

FAQPage Schema

How do I clean and analyze CSV data to generate visual charts and a findings report?▼

Yes, you can clean and analyze CSV data by providing the dataset file path, allowing the system to validate structure, handle missing values, compute statistics, and generate chart images alongside a written findings report.

What is the best way to automate statistical analysis and trend visualization for an XLSX spreadsheet?▼

Automating statistical analysis and trend visualization for XLSX involves loading the spreadsheet, computing core metrics, deriving key findings, and producing trend or comparison charts with a non-destructive cleaned output and a written report.

Does data cleaning and report generation work with JSON formats as well as CSV files?▼

Yes, data cleaning and report generation supports JSON formats alongside CSV and XLSX files, validating data quality, standardizing formats, and producing non-destructive cleaned outputs with visual charts and statistical summaries.

Can I run grouped comparisons and distribution analysis on tabular data without modifying the original dataset?▼

Yes, you can run grouped comparisons and distribution analysis on tabular data while preserving the original dataset, as the process applies non-destructive cleaning and outputs results separately to a shared workspace.

What do I need to start visualizing data trends and generating a statistics report?▼

To start visualizing data trends and generating a statistics report, you need to provide a readable file path to your dataset in CSV, XLSX, or JSON format, ensuring the structure can be validated for data quality and analysis.