data-analyst

Name: data-analyst
Availability: InStock
Author: openocta

Analyze CSV and JSON tabular data to generate insights and reproducible steps.

3.1k|368|Updated Feb 26, 2026

Search Tags:#json #data analysis #business intelligence #statistics #csv #anomaly detection #trend analysis

Ready to run

npx skills add https://github.com/openocta/openocta --skill data-analyst-openocta

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-analyst
Source: https://github.com/openocta/openocta/tree/main/src/embed/employee_skills/data-analyst/data-analyst
Command: npx skills add https://github.com/openocta/openocta --skill data-analyst-openocta

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you quickly analyze tabular data (CSV, JSON) to answer business questions, identify trends, and detect anomalies without needing deep data science expertise.

Core Features & Use Cases

Data Cleaning & Preparation: Handles missing values, duplicates, and type inconsistencies.
Statistical Analysis: Performs aggregations, group-by operations, and calculates key metrics.
Insight Generation: Identifies top performers, analyzes funnels, calculates retention, and detects outliers.
Reproducible Steps: Provides clear, step-by-step instructions in pseudocode, SQL, or Python for analysis.
Use Case: You have a CSV file of customer orders and want to know the top 5 selling products last month, including their total revenue and percentage contribution.

Quick Start

Analyze the attached customer_orders.csv file to find the top 5 selling products by revenue for last month and provide the analysis steps.

Frequently Asked Questions about data-analyst

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

FAQPage Schema

How do I analyze CSV data to identify trends and detect anomalies?▼

You can analyze CSV data for trend analysis and anomaly detection using this Skill, which performs statistical summarization and group comparisons on tabular datasets. It identifies outliers, calculates metrics, and outputs reproducible analytical steps in pseudocode, SQL, or Python.

What is the best way to clean and prepare JSON data for business intelligence reporting?▼

The best way to clean JSON data for business intelligence is to use a data analysis tool that handles missing values, duplicates, and type inconsistencies. This Skill prepares and aggregates data to generate insights like top performers and retention rates.

Can I generate Python code for statistical analysis from a raw customer orders dataset?▼

Yes, you can generate Python code for statistical analysis from a raw dataset. This Skill analyzes your CSV or JSON files and provides clear, step-by-step analytical instructions and reproducible code in Python, SQL, or pseudocode.

Does this data analysis approach work without requiring deep data science expertise?▼

Yes, this data analysis approach works without requiring deep data science expertise. It helps you quickly analyze tabular data to answer business questions, track performance, and identify trends, handling statistical computations automatically.

How do I calculate total revenue and percentage contribution for top selling products from a CSV file?▼

To calculate total revenue and percentage contribution for top selling products from a CSV file, use a tool that performs group-by operations and aggregations. This Skill processes your customer orders data to output these exact metrics and step-by-step logic.