data-quality

Installation
SKILL.md

Data Quality

Purpose

Guide the design and operation of data quality management programs for financial services firms. Covers the six dimensions of data quality (accuracy, completeness, timeliness, consistency, validity, uniqueness) applied to financial data domains, golden source architecture and master data management, data lineage and provenance tracking, validation rule design for security prices, client data, transaction data, and position data, data profiling and anomaly detection, exception management workflows, data quality governance frameworks, and regulatory requirements for data accuracy including BCBS 239, MiFID II, GIPS, and SEC recordkeeping obligations. Enables building or evaluating data quality infrastructure that ensures downstream systems — portfolio management, trading, compliance, reporting, and billing — operate on trustworthy data.

Layer

13 — Data Integration (Reference Data & Integration)

Direction

both

When to Use

  • Designing a data quality monitoring framework for a wealth management or asset management platform
  • Building validation rules for security pricing, client data, transaction data, or position data pipelines
  • Conducting a data quality assessment for regulatory reporting readiness (SEC filings, GIPS, AML/KYC)
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Installs
130
GitHub Stars
75
First Seen
Feb 19, 2026