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data-analyzer

Calculate financial metrics and detect anomalies in CSV datasets using pandas.

Updated Mar 4, 2026

Search Tags:#audit #analysis #financial #csv #roi #dataset

One-click install

npx skills add https://github.com/ly2xxx/dot-claude-playground --skill data-analyzer-ly2xxx

Or copy as Structured Prompt for Agent▼

Please help me install this Agent Skill.
Skill: data-analyzer
Source: https://github.com/ly2xxx/dot-claude-playground/tree/main/.claude/skills/data-analyzer
Command: npx skills add https://github.com/ly2xxx/dot-claude-playground --skill data-analyzer-ly2xxx

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies the process of analyzing financial datasets by automating calculations and identifying anomalies, enabling quick decision-making.

Core Features & Use Cases

Data Analysis: Perform financial analysis on CSV datasets such as revenues and costs.
Auditing & Reporting: Detect irregularities like unusual ROI or anomalies in financial data.
Use Case: Suppose you have a CSV of quarterly revenue and expenses; you can use this Skill to compute the ROI and flag any suspicious trends or discrepancies.

Quick Start

Use the data-analyzer skill to analyze your uploaded CSV file for financial insights and anomalies.

Frequently Asked Questions about data-analyzer

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

FAQPage Schema

How do I analyze a CSV file for financial anomalies and ROI?▼

You can analyze a CSV dataset for financial anomalies and ROI by applying automated scripting logic to calculate key metrics and flag irregularities. This process automates calculations for quick auditing and data validation.

What is the best way to detect irregularities in a dataset for audit preparation?▼

Detecting irregularities in a dataset for audit preparation involves applying anomaly detection logic to financial records like revenues and costs. This identifies suspicious trends or discrepancies automatically during financial review.

Do I need pandas to calculate financial metrics from a CSV dataset?▼

Yes, you need pandas to calculate financial metrics from a CSV dataset. This dependency provides the data processing and scripting logic required to handle financial data and identify anomalies for auditing.

Can I use scripting logic to flag suspicious trends in quarterly revenue and expenses?▼

Yes, you can use scripting logic to flag suspicious trends in quarterly revenue and expenses. By computing metrics like ROI from your financial data, the process identifies unusual trends or discrepancies for reporting.

What types of financial data can I validate for unusual ROI during a financial review?▼

You can validate financial data such as quarterly revenue and expenses for unusual ROI during a financial review. The analysis computes these metrics from your CSV datasets to detect anomalies and support audit preparations.

Are there limitations when automating calculations for financial datasets?▼

Automating calculations for financial datasets requires structured CSV inputs and relies on pandas for processing. It is suited for financial review and data validation but does not replace comprehensive manual audit procedures.

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