Data reliability
Skill Methasit-Pun/data_engineer_claude_skills/06-reliability/data-reliability
Practical guides, prompts, and Python code for applying Anthropic's Claude Skills to data engineering and pipeline automation
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Umbrella skill for making data correct, trustworthy, and compliant — validation and quality checks (Great Expectations, dbt tests, anomaly/null/range/referential assertions), producer↔consumer schema contracts (versioning, breaking-change detection), and governance (PII tagging, lineage, access control, retention, audit for PDPA/GDPR/HIPAA). Use this whenever the concern is "is this data right, safe, and allowed" rather than moving or shaping it. This skill ROUTES to the focused sub-skills (data-quality, data-contracts, data-governance) and pulls in more than one when a task spans them. Trigger on: bad data in a pipeline, silent upstream schema changes breaking downstream, PII/compliance/audit, or "how do I make sure my data is correct".
SKILL.md
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Data Reliability, Contracts & Governance (Router)
This is a router skill. It groups the three skills that make data trustworthy: correctness checks, cross-team schema guarantees, and regulatory/access governance. Diagnose which sub-area(s) the task touches, then invoke the matching sub-skill(s) with the Skill tool.
How to route
| If the task is about… | Invoke sub-skill |
|---|---|
| Validation rules, Great Expectations suites, dbt tests, anomaly detection, null/type/range/referential assertions, monitoring bad data in a pipeline | data-quality |
| Formalizing what a dataset promises across team boundaries: field types, nullability, allowed values, versioning, breaking vs. non-breaking changes, schema registries, downstream-impact assessment | data-contracts |
| PII tagging/classification, column-level access control, data catalog metadata, lineage, retention, right-to-erasure, audit logging, PDPA/GDPR/HIPAA compliance | data-governance |
Routing rules
- Quality vs. contracts overlap (both validate schema). Same-owner pipeline correctness →
data-quality. Two teams/services sharing a dataset where upstream keeps breaking downstream →data-contracts. When both apply (a contract enforced by quality tests), invoke both. - Any mention of PII, compliance, audit, or regulation →
data-governanceimmediately, often alongsidedata-quality. - "Small" upstream schema change about to ship →
data-contractsto assess downstream impact before it lands. - Invoke via the Skill tool by name, e.g.
Skill(skill="data-quality"). Combine outputs; don't paraphrase from memory.
Related groups
- Where these checks run in the pipeline → [[data-pipelines]]
- The models being validated → [[data-modeling]]