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Found 72 Skills
Comprehensive data quality patterns using Great Expectations, DLT expectations, and custom validators for ensuring data reliability and trust.
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
Run a comprehensive data quality assessment and produce a scorecard across 6 dimensions: completeness, uniqueness, consistency, timeliness, accuracy, validity. Use when the user asks about data quality, mentions data issues, wants to audit a table, is onboarding a new data source, or needs to validate pipeline output.
Data Quality Checker - Auto-activating skill for Data Pipelines. Triggers on: data quality checker, data quality checker Part of the Data Pipelines skill category.
Assess construction data quality using completeness, accuracy, consistency, timeliness, and validity metrics. Automated validation with regex patterns, thresholds, and reporting.
Design and operate data quality programs for financial data — golden source architecture, validation rules, data lineage, exception management, profiling, and governance. Use when building validation rules for pricing or client data pipelines, designing a data quality monitoring framework, establishing golden source designations across systems, implementing data lineage for BCBS 239 or MiFID II, investigating reconciliation breaks or billing errors traced to bad data, preparing for regulatory exams on data accuracy, building data quality scorecards, or defining data stewardship roles. Trigger on: data quality, golden source, data lineage, data validation, data profiling, exception management, data governance, BCBS 239, data completeness, data accuracy, validation rules, data anomaly, data stewardship, data quality scorecard.
Assess data quality with checks for missing values, duplicates, type issues, and inconsistencies. Use for data validation, ETL pipelines, or dataset documentation.
Data quality framework covering completeness, accuracy, consistency, validation rules, and data contracts. Use when implementing data validation, setting up data quality checks, or defining data contracts.
Use this skill when implementing data validation, data quality monitoring, data lineage tracking, data contracts, or Great Expectations test suites. Triggers on schema validation, data profiling, freshness checks, row-count anomalies, column drift, expectation suites, contract testing between producers and consumers, lineage graphs, data observability, and any task requiring data integrity enforcement across pipelines.
Audit datasets for completeness, consistency, accuracy, and validity. Profile data distributions, detect anomalies and outliers, surface structural issues, and produce an actionable remediation plan.
Implement data quality checks, validation rules, and monitoring. Use when ensuring data quality, validating data pipelines, or implementing data governance.
Validate data quality in market analysis documents and blog articles before publication. Use when checking for price scale inconsistencies (ETF vs futures), instrument notation errors, date/day-of-week mismatches, allocation total errors, and unit mismatches. Supports English and Japanese content. Advisory mode -- flags issues as warnings for human review, not as blockers.