data-quality
data-quality is a Data warehousing & modelling skill for AI agents, published by JoelLewis in the finance_skills repository on GitHub. Skillspace rates data-quality D (68/100) and it passed the safety scan; the rating is based on static analysis only.
Design and operate data quality programs for financial data — validation rules, pricing validation, data lineage, exception management, profiling, and governance. Use when building validation rules for pricing or client data pipelines, detecting stale prices, designing a data quality monitoring framework, calibrating validation thresholds, 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, pricing validation, stale prices, 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.
- Grade
- D
- Quality
- 68 / 100
- Safety
- Passed
- GitHub stars
- 151
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 100 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
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- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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