data-analysis

Name: data-analysis
Availability: InStock
Author: IbIFACE

Analyze CSV, JSON, and Excel datasets for statistics and anomalies.

Updated Dec 12, 2025

Search Tags:#data visualization #insights #data analysis #pattern recognition #statistics #anomaly detection #correlation

Ready to run

npx skills add https://github.com/IbIFACE-Tech/paracle --skill data-analysis-ibiface-tech

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-analysis
Source: https://github.com/IbIFACE-Tech/paracle/tree/main/content/templates/.parac-template/agents/skills/builtin/data-analysis
Command: npx skills add https://github.com/IbIFACE-Tech/paracle --skill data-analysis-ibiface-tech

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scipy, matplotlib, seaborn, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the process of analyzing datasets, identifying patterns, detecting anomalies, and generating meaningful insights, transforming raw data into actionable intelligence.

Core Features & Use Cases

Descriptive Statistics: Calculate summary statistics (mean, median, standard deviation, etc.) for numeric and categorical data.
Pattern Recognition: Identify trends and patterns in time-series or sequential data.
Anomaly Detection: Detect outliers using methods like IQR or Z-score.
Correlation Analysis: Understand relationships between different variables in a dataset.
Data Cleaning: Handle missing values and duplicates to prepare data for analysis.
Visualization: Generate plots like histograms, box plots, and correlation heatmaps.
Insight Generation: Provide textual insights based on the analysis results.

Quick Start

Use the data-analysis skill to analyze the attached file 'sales_data.csv' and generate a summary of its statistics.

Frequently Asked Questions about data-analysis

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

FAQPage Schema

How do I find patterns and detect outliers in CSV data?▼

To find patterns and detect outliers in CSV data, use automated data analysis to calculate descriptive statistics and apply IQR or Z-score methods. This process identifies statistical anomalies and sequential trends within the dataset.

What is the best way to generate visualizations from an Excel dataset?▼

Generating visualizations from an Excel dataset involves loading the file into a data environment to create histograms, box plots, and correlation heatmaps. These visual plots translate raw spreadsheet data into actionable visual intelligence.

Do I need pandas and scipy to perform statistical analysis on JSON files?▼

Yes, you need pandas and scipy to perform statistical analysis on JSON files. Pandas handles data manipulation and cleaning of JSON structures, while scipy provides the advanced statistical computations required for correlation analysis.

How do I identify correlations between variables in a dataset?▼

To identify correlations between variables in a dataset, perform a correlation analysis using statistical libraries like scipy. This calculates statistical relationships between different data columns and generates a correlation heatmap for visual confirmation.

Can I clean missing values and duplicates before generating statistics?▼

Yes, you can clean missing values and duplicates before generating statistics. Data cleaning is an essential preparatory step that handles incomplete records and duplicate entries, ensuring resulting descriptive statistics and anomaly detection remain accurate.