data-analyzer

Process CSV, JSON, or in-memory datasets to generate mean, median, and distribution summaries.

3|1|Updated Feb 1, 2026

Search Tags:#automation #python #metrics #data #JSON #analyze #CSV

Ready to run

npx skills add https://github.com/dgriffin831/clawhub-skills --skill data-analyzer-dgriffin831

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-analyzer
Source: https://github.com/dgriffin831/clawhub-skills/tree/main/skill-scan/test-fixtures/resource-exhaustion-loop
Command: npx skills add https://github.com/dgriffin831/clawhub-skills --skill data-analyzer-dgriffin831

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps data scientists automate data analysis tasks by processing datasets to generate concise insights, reducing manual workload.

Core Features & Use Cases

Automated metrics generation: mean, median, standard deviation, and distribution summaries across CSV, JSON, or in-memory datasets.
Scalable analysis: handles large datasets with simple APIs suitable for pipelines.
Use Case: Quickly summarize a dataset to validate data quality before modeling.

Quick Start

Run the analyzer on a sample dataset: data.csv

Frequently Asked Questions about data-analyzer

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

FAQPage Schema

How do I automate generating mean and median metrics from a CSV file?▼

You can automate metrics generation from a CSV file by running a Python-based analyzer that processes datasets to calculate mean, median, standard deviation, and distribution summaries. It targets data science workflows by reducing manual workload through automated processing.

What is the best way to generate distribution summaries for in-memory datasets?▼

Generating distribution summaries for in-memory datasets is achieved by processing the data through an automated Python analysis skill. This approach calculates concise metrics like standard deviation and median without requiring manual workload, leveraging optional numpy or pandas for richer analysis.

Do I need pandas or numpy to analyze JSON datasets with Python?▼

You do not need pandas or numpy to analyze JSON datasets, as the skill operates using standard Python libraries. However, installing numpy or pandas is recommended if you require richer analysis capabilities and more advanced metrics generation.

Can I use Python to validate data quality before modeling across large datasets?▼

Yes, you can use Python to validate data quality before modeling across large datasets. The analyzer provides scalable analysis with simple APIs suitable for pipelines, quickly summarizing datasets to identify data quality issues prior to modeling.

Does this automated data analysis approach work with both CSV and JSON formats?▼

Yes, this automated data analysis approach works with both CSV and JSON formats, as well as in-memory datasets. It targets data science workflows across these formats to generate concise insights and metrics without manual intervention.