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data-analyzer

Compute count, sum, mean, min, and max for numeric datasets.

17.6k|2.0k|Updated Mar 17, 2023
Search Tags:#data#insights#statistics#exploration#analyze#dataset
One-click install
npx skills add https://github.com/camel-ai/camel --skill data-analyzer-camel-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-analyzer
Source: https://github.com/camel-ai/camel/tree/main/examples/toolkits/skill_toolkit_example/.camel/skills/data-analyzer
Command: npx skills add https://github.com/camel-ai/camel --skill data-analyzer-camel-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill helps users understand data quickly by computing fundamental statistics and summarizing patterns, reducing manual calculations and guesswork.

Core Features & Use Cases

  • Statistical Summary: Compute count, sum, mean, min, max for numeric data.
  • Pattern Discovery: Identify trends and anomalies across small to moderate datasets.
  • Use Case: Analyze a dataset of daily measurements to obtain a concise overview and detect outliers.

Quick Start

Load numeric data as a Python list and pass it to the analyze function. Example: analyze([10, 20, 30, 40]) will return count, sum, mean, min, and max values for quick decision support.

Frequently Asked Questions about data-analyzer

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

FAQPage Schema
How do I compute basic statistics for a numeric dataset?▼

To compute basic statistics for a numeric dataset, you pass a Python list of numbers to the analyze function. It returns count, sum, mean, min, and max values to support quick exploratory data analysis.

Can I use this for exploratory data analysis on small to moderate datasets?▼

Yes, you can use it for exploratory data analysis on small to moderate datasets. It validates input data and computes fundamental statistics to help you identify trends, anomalies, and outliers.

How do I identify outliers and trends in daily measurement data?▼

To identify outliers and trends in daily measurement data, run the dataset through the analyzer. It provides a concise statistical summary including min and max values to help spot anomalies.

Does the data analyzer require any external dependencies to run?▼

No, the data analyzer requires no external dependencies to run. It operates independently to validate input data and calculate statistical summaries without needing additional libraries.

What are the limitations of using basic statistics for pattern discovery?▼

The limitation of using basic statistics for pattern discovery is that it only returns count, sum, mean, min, and max. It does not compute advanced statistical distributions for complex pattern discovery.

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