data-quality-platforms-and-rule-management — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited data-quality-platforms-and-rule-management (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Use this skill when the question is not only what checks to write, but how the quality program should operate across tools, teams, and publish stages. It helps agents design rule ownership, severity, evidence, and enforcement across multiple quality frameworks.
dbt tests, Great Expectations, Deequ, Cuallee, Soda, or warehouse-native checksDo not assume more checks automatically improve quality. The operating model matters as much as the framework.
Clarify:
Typical groups:
Decide where each type of rule belongs:
dbt tests for warehouse-native model validationGreat Expectations, Deequ, Cuallee, or Soda for reusable framework-based checksRequire:
Review overlapping, stale, noisy, or low-value checks so quality stays credible and maintainable.
| Rationalization | Reality |
|---|---|
| "We should standardize on one tool for everything." | Different rule types often fit different execution surfaces and operating models. |
| "If a rule fails, we can decide the impact later." | Publish and incident behavior must already be defined when the rule is introduced. |
| "More rules always mean better quality." | Noisy or duplicate checks reduce trust and slow triage. |
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.