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data-reliability

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Mis à jour5 juillet 2026 à 11:17

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".

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Methasit-Pun

Methasit-Pun/data_engineer_claude_skills

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Développeurs de logicielsProfessions informatiques et mathématiques·SOC 15-1252

SKILL.md

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name

data-reliability

description

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-governance immediately, often alongside data-quality.
"Small" upstream schema change about to ship → data-contracts to 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]]

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name

data-reliability

description

Data Reliability, Contracts & Governance (Router)

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-governance immediately, often alongside data-quality.
"Small" upstream schema change about to ship → data-contracts to 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]]