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data-validation

Validate CSV data, schemas, and business rules with M-5 reports.

Updated Apr 8, 2026
Search Tags:#documentation#schema#csv#quality-assurance#data-quality#data-validation#business-rules
One-click install
npx skills add https://github.com/Gami19/rd-strands-agents --skill data-validation-gami19
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-validation
Source: https://github.com/Gami19/rd-strands-agents/tree/main/strands-py/backend/agent/skills/pr/data-validation
Command: npx skills add https://github.com/Gami19/rd-strands-agents --skill data-validation-gami19

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Automates the validation of proposals, specifications, and CSV data to detect errors and ensure consistency across tables, schemas, and business rules.

Core Features & Use Cases

  • Automated checks for table structure, data quality, schema integrity, business rules, and numeric consistency
  • Workflow guidance: validation of 10_inbox, 20_notes, 30_proposal, 40_decision_log; cross-document consistency checks
  • Use Case: validate CSV data and detect duplicates, check schema compliance, identify rule conflicts.

Quick Start

Run the data-validation process on your dataset to produce a comprehensive report.

Frequently Asked Questions about data-validation

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

FAQPage Schema
How do I validate CSV data for schema integrity and business rule conflicts?▼

You can validate CSV data by running automated checks for table structure, schema compliance, and business rules to detect duplicates and rule conflicts. The process outputs a machine-readable M-5 report for auditing.

What is the best way to check cross-document consistency across proposals and specifications?▼

Cross-document consistency checking validates workflows like proposals and decision logs by enforcing schema definitions and business rules. It detects data quality gaps and table integrity issues across project documents.

Can I enforce custom business rules and schema definitions during data validation?▼

Yes, data validation enforces constraints via optional schema and rules definitions. It checks numeric consistency and identifies rule conflicts automatically across your enterprise datasets.

How does automated data quality validation detect table integrity issues?▼

Automated data quality validation detects table integrity issues by checking structure, schema, and business rules against defined constraints. It produces a comprehensive M-5 report suitable for downstream validation.

Does the validation process work without predefined schema or rules definitions?▼

Schema and rules definitions are optional for data validation. The process can check table structure and data quality automatically, but defining schemas and rules enables stricter constraint enforcement and conflict detection.

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