data-quality

Name: data-quality
Availability: InStock
Author: nima

Implement data validation gates for automated pipelines using Pandera or Great Expectations.

25|3|Updated Jul 14, 2026

Search Tags:#testing #validation #profiling #pipelines #data-quality #anomaly-detection

Ready to run

npx skills add https://github.com/nimadorostkar/Claude-Skills-collection --skill data-quality-nimadorostkar

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-quality
Source: https://github.com/nimadorostkar/Claude-Skills-collection/tree/main/skills/data/data-quality
Command: npx skills add https://github.com/nimadorostkar/Claude-Skills-collection --skill data-quality-nimadorostkar

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandera, pandas.

What problem does it solve?

This skill prevents the silent propagation of corrupt or invalid data through your pipelines, ensuring that downstream systems and stakeholders rely on accurate, verified information.

Core Features & Use Cases

Automated Validation: Implements schema enforcement, constraint checks, and distribution profiling to catch anomalies before they reach production.
Pipeline Guardrails: Provides patterns for quarantine and alerting, ensuring that bad data is isolated rather than silently dropped or processed.
Use Case: When ingesting third-party API data, use this skill to define strict schema expectations and volume thresholds that automatically halt the pipeline if the incoming data is stale, empty, or malformed.

Quick Start

Use the data-quality skill to implement a validation gate for the incoming orders dataset that checks for schema compliance and volume anomalies.

Frequently Asked Questions about data-quality

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

FAQPage Schema

How do I prevent bad data from propagating through automated data pipelines?▼

You can prevent bad data propagation in data pipelines by implementing automated validation gates that enforce strict schema compliance, detect distribution anomalies, and halt execution upon failure. This isolates corrupt records before they reach production.

What is data profiling and how does it detect anomalies in ingestion workflows?▼

Data profiling analyzes incoming datasets to establish baseline distribution patterns and volume thresholds. In ingestion workflows, it detects anomalies by flagging records that violate these established schema constraints or distribution expectations before storage.

How do I set up schema validation checks for a pandas dataframe using pandera?▼

To set up schema validation for a pandas dataframe, use pandera to define strict data contracts specifying expected column types and constraints. This framework validates the dataframe against your rules and halts the pipeline if validation fails.

Can I quarantine invalid records instead of dropping them during data transformation?▼

Yes, you can quarantine invalid records during data transformation instead of dropping them. The skill provides pipeline guardrails and patterns for isolating bad data into a separate quarantine area while triggering alerts for stakeholder review.

What is the best way to enforce data contracts for third-party API ingestion?▼

The best way to enforce data contracts for third-party API ingestion is to define strict schema expectations and volume thresholds. This automatically halts the pipeline if the incoming API data is stale, empty, or malformed.

Does this data validation approach work for detecting schema drift in storage workflows?▼

Yes, this data validation approach works for detecting schema drift in storage workflows by continuously enforcing strict data contracts. It targets ingestion, transformation, and storage stages to catch schema violations and business rule breaks during execution.