skills.rest
letitbk
letitbkCOMMUNITY
Explore all skills by this author →
GitHub

datacheck

Validate and summarize CSV, Excel, and JSON data files for encoding, structure, and quality issues.

Updated Feb 20, 2026
Search Tags:#validation#data#quality#Excel#pandas#inspection
One-click install
npx skills add https://github.com/letitbk/cursor-setup --skill datacheck
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: datacheck
Source: https://github.com/letitbk/cursor-setup/tree/main/skills/datacheck
Command: npx skills add https://github.com/letitbk/cursor-setup --skill datacheck

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

It helps data analysts and managers verify and validate data files, detecting common issues that could compromise analysis results.

Core Features & Use Cases

  • Raw file inspection: Checks encoding, line endings, and delimiters to identify common raw data issues.
  • Data loading & summary: Loads data into pandas and provides descriptive statistics, missing values, and data types.
  • Data quality assessment: Detects placeholder, erroneous, or inconsistent values, and suggests fixes to improve data reliability.
  • Data profiling: Summarizes distributions, correlations, and detects anomalies or structural issues.
  • Excel-specific diagnostics: Checks sheet info, merged cells, header positions, and formulas for Excel files.
  • Reporting: Outputs concise summaries of data health, issues found, and potential remediation steps for robust analysis.

Quick Start

Use the datacheck skill to quickly identify data issues in your CSV, Excel, or JSON file, allowing you to clean and prepare your dataset effectively.

Frequently Asked Questions about datacheck

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

FAQPage Schema
How do I validate data quality and inspect structural issues in a CSV or Excel file?▼

Data validation inspects raw file encoding, line endings, and delimiters to identify structural issues. It loads datasets via pandas to assess missing values, detect placeholders, and generate descriptive statistics for reliable analysis.

What is the best way to check Excel files for merged cells and header position errors?▼

Excel-specific diagnostics check sheet info, merged cells, header positions, and formulas to identify structural issues. This ensures your Excel files are well-formed, consistent, and ready for downstream data profiling or reporting.

Does data profiling with pandas detect inconsistent values and suggest fixes?▼

Data profiling with pandas summarizes distributions, correlations, and detects anomalies. It automatically identifies placeholder or inconsistent values and outputs concise reports suggesting potential remediation steps to improve data reliability.

Can I use this approach for raw file inspection before loading my dataset into pandas?▼

Raw file inspection checks basic file properties like encoding, line endings, and delimiters before loading data into pandas. This preliminary step prevents loading errors and ensures the dataset is well-formed for analysis.

What steps are needed to prepare a dataset for analysis if it contains anomalies?▼

Preparing a dataset requires loading it into pandas, running data quality assessments to detect anomalies, and reviewing generated remediation reports. This process ensures your data is clean, consistent, and ready for visualization.

Why are my datasets failing analysis due to encoding or delimiter issues?▼

Datasets often fail due to mismatched file encoding, incorrect line endings, or unrecognized delimiters. Raw file inspection identifies these specific structural issues before loading, preventing errors during your data analysis pipeline.

Related Skills

Explore More Data & Analytics Skills→
Citrus-bitCitrus-bit
135

data-analysis

Analyze Excel and CSV files with schema inspection, statistics, and SQL queries.

Community
Advanced
leonardodalinkyleonardodalinky
87

seaborn

Generate statistical visualizations from pandas DataFrames using seaborn.

Community
Basic
Wike-CHIWike-CHI
2

data-automation-service

Automate data cleaning and transformation for raw datasets.

Community
Intermediate
UlanxxUlanxx

canary

Monitor live URLs for console errors, performance regressions, and page failures.

Community
Advanced