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data-reliability

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업데이트2026년 7월 5일 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".

설치

Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.

Manus에서 실행

출처

Methasit-Pun

Methasit-Pun/data_engineer_claude_skills

GitHub 저장소 열기 Creator 저장소 보기

다운로드

Manus에서 실행

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]]

이 저장소의 다른 Skills

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data-lifecycle

Methasit-Pun/data_engineer_claude_skills

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data-sourcing

Methasit-Pun/data_engineer_claude_skills

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data-profiling

Methasit-Pun/data_engineer_claude_skills

Profile and map raw data BEFORE designing a schema. One-time exploratory analysis to learn the true shape of a dataset — row/column counts, null rates, cardinality, value distributions, ranges, data types, candidate keys, duplicates, referential relationships — then a source-to-target field mapping and an ER diagram. Use this skill whenever the user has data in hand and needs to understand it before modeling, is about to design a schema, asks "what does this data actually look like", needs to find the primary/composite key of an unfamiliar table, or must map source fields to a target model. This is exploratory and one-time; ongoing production validation is data-quality's job, and modeling patterns are schema-design's job.

2026-07-050

data-modeling

Methasit-Pun/data_engineer_claude_skills

Umbrella skill for shaping data once it has landed — warehouse schema design (star/snowflake/OBT/SCD/grain), SQL for analytics (window functions, CTEs, optimization), dbt model layers/tests/macros/incrementals, and Python transforms (pandas/Polars/PySpark performance). Use this whenever the user is designing tables, writing or reviewing analytical SQL, building dbt models, or writing Python data transformations and it isn't yet clear which tool dominates. This skill ROUTES to the focused sub-skills (schema-design, sql-patterns, dbt-patterns, python-data-patterns) and pulls in more than one when a task spans them. Trigger on: fact/dimension design, table grain, SCD, window functions, deduplication, dbt refs/materializations, or slow/memory-heavy DataFrame code.

2026-07-050

dbt-patterns

Methasit-Pun/data_engineer_claude_skills

dbt model design, ref chains, sources, tests, macros, incremental strategies, materializations, and documentation best practices. Use this skill whenever the user is writing or reviewing dbt models, configuring dbt tests, designing model layers (staging/intermediate/marts), asking about incremental models, choosing materializations, writing macros, setting up sources.yml, or troubleshooting dbt run/test failures. Also trigger when the user mentions dbt refs, lineage graphs, model dependencies, dbt Cloud, the dbt CLI, or when they want to transform data already in the warehouse using SQL. If the project uses dbt at all, this skill should be active for any transformation questions.

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python-data-patterns

Methasit-Pun/data_engineer_claude_skills

Pandas, Polars, and PySpark idioms for production data engineering — chunked reads, memory-safe transforms, vectorized operations, type optimization, and performance patterns. Use this skill whenever the user is writing a Python data transformation script and running into memory issues, slow performance, or correctness bugs with large datasets. Also trigger when the user asks how to handle large CSV/Parquet files, process data in batches, use Polars instead of Pandas, optimize a PySpark job, or reduce DataFrame memory usage. If you see someone iterating row-by-row over a DataFrame, this skill should trigger immediately.

2026-07-050

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]]