Data Integrity Auditor

🌐Community
by monkey1sai · vlatest · Repository

Identifies data inconsistencies, errors, and anomalies within datasets to ensure accuracy and reliability for monkey1sai applications.

Install on your platform

We auto-selected Claude Code based on this skill’s supported platforms.

1

Run in terminal (recommended)

terminal
claude mcp add monkey1sai-data-integrity-auditor npx -- -y @trustedskills/monkey1sai-data-integrity-auditor
2

Or manually add to ~/.claude/settings.json

~/.claude/settings.json
{
  "mcpServers": {
    "monkey1sai-data-integrity-auditor": {
      "command": "npx",
      "args": [
        "-y",
        "@trustedskills/monkey1sai-data-integrity-auditor"
      ]
    }
  }
}

Requires Claude Code (claude CLI). Run claude --version to verify your install.

About This Skill

What it does

The Data Integrity Auditor skill analyzes datasets to identify inconsistencies, errors, and potential data quality issues. It can detect duplicate entries, missing values, and anomalies based on defined rules or statistical analysis. This helps ensure the reliability of data used for decision making and model training.

When to use it

  • Data Validation: Before feeding a dataset into a machine learning model, use this skill to verify its accuracy and completeness.
  • Error Detection in ETL Processes: Identify issues arising during Extract, Transform, Load (ETL) pipelines that might corrupt data.
  • Compliance Audits: Ensure datasets meet regulatory requirements for data quality and integrity.
  • Data Migration: Validate the integrity of data after migrating it from one system to another.

Key capabilities

  • Duplicate entry detection
  • Missing value identification
  • Anomaly detection based on rules
  • Statistical analysis for outlier identification

Example prompts

  • "Analyze this CSV file and report any duplicate entries."
  • "Check the 'customer_data' dataset for missing values in the 'email' column."
  • "Identify outliers in the 'sales_figures' data based on a standard deviation of 2 from the mean."

Tips & gotchas

The skill’s effectiveness depends heavily on clearly defined rules or statistical parameters. Ensure you provide sufficient context and instructions for accurate anomaly detection.

Tags

🛡️

TrustedSkills Verification

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Details

Version
vlatest
License
Author
monkey1sai
Installs
4

🌐 Community

Passed automated security scans.