data_quality

Name: data_quality
Availability: InStock
Author: benitorhuang-svg

Validate Taiwanese stock market data for completeness and cross-source consistency.

Updated Aug 23, 2026

Search Tags:#data quality #financial data #ETL #data validation #data reconciliation #taiwan stock market

Ready to run

npx skills add https://github.com/benitorhuang-svg/tw-stock-app --skill data-quality-benitorhuang-svg

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data_quality
Source: https://github.com/benitorhuang-svg/tw-stock-app/tree/main/.agents/skills/data_quality
Command: npx skills add https://github.com/benitorhuang-svg/tw-stock-app --skill data-quality-benitorhuang-svg

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the critical issue of data accuracy and reliability in financial datasets, preventing errors caused by incomplete, inconsistent, or invalid information.

Core Features & Use Cases

Data Validation: Implements checks for completeness, logical consistency (e.g., OHLCV rules), and format standardization across multiple data sources.
Cross-Source Reconciliation: Defines rules for prioritizing and resolving discrepancies between data from different providers (TWSE, MOPS, Yahoo).
Missing Data Handling: Establishes strategies for dealing with missing values, including flagging gaps, using previous data, or interpolation.
Use Case: Automatically verify that daily stock prices adhere to trading limits, ensure that institutional investor data sums correctly, and flag any date format inconsistencies before they corrupt analytical models.

Quick Start

Run a comprehensive data quality health check on the financial database.

Frequently Asked Questions about data_quality

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

FAQPage Schema

How do I validate financial data completeness and consistency for Taiwanese stock market datasets?▼

Financial data validation for Taiwanese stock market datasets is managed by implementing automated checks for completeness, logical OHLCV consistency, and format standardization across multiple sources to ensure data integrity.

What is the best way to reconcile discrepancies between TWSE, MOPS, and Yahoo financial data?▼

Cross-source reconciliation for TWSE, MOPS, and Yahoo financial data defines prioritization rules to resolve discrepancies, ensuring that institutional investor data sums correctly and conflicts between different providers are systematically addressed.

How do I handle missing values when processing ETL pipelines for stock market data?▼

Handling missing values during ETL involves establishing strategies that flag data gaps, use previous data points, or apply interpolation to prevent incomplete information from corrupting downstream analytical models.

Does this data validation approach check daily stock prices against trading limits?▼

Yes, data validation checks verify that daily stock prices adhere to defined trading limits and flag any date format inconsistencies, ensuring reasonableness and preventing invalid information from entering financial databases.

Can I use automated reconciliation for TDCC data alongside other Taiwanese stock market sources?▼

Automated reconciliation processes support TDCC data alongside TWSE, MOPS, and Yahoo sources, implementing cross-source consistency checks to maintain reliable financial datasets for analysis.