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Applies governance, lineage, ownership, and sensitive-data controls to data changes. Use when a pipeline touches published datasets, regulated information, or shared business metrics.
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SOC 職業分類に基づく
Forces replay-safe rollout plans, reconciliation gates, and rollback paths before executing any data backfill or historical reprocessing. Use when running /backfill, rerunning pipelines, repairing publish windows, or any work that risks double-counting or downstream corruption.
Helps agents classify data engineering work, choose the right preset and skill bundle, and pick the safest next command. Use when starting a session, triaging an ambiguous request, or deciding how to proceed.
Guides agents through resiliency testing for data platforms. Use when designing or running failure drills, recovery validation, failover tests, replay-safety checks, dependency outage exercises, or fault injection for pipelines and publishes.
Guides agents through production data incidents. Use when a pipeline fails, publishes bad data, misses an SLA, partially loads, corrupts state, or requires rollback, replay, or stakeholder communication.
Enforces production Kafka guardrails including non-breaking schema evolution, dead-letter queues for poison messages, and acks=all producer durability. Use when designing or changing Kafka topics, producers, consumers, schema registry policies, or streaming recovery paths.
Guides agents to wire Model Context Protocol servers for live data platform observability including Spark execution plans, OOM diagnosis, Kafka consumer lag, and orchestration run state. Use when agents need cluster metrics, streaming lag, or job diagnostics instead of blind code changes.
| name | lineage-pii-and-governance |
| description | Applies governance, lineage, ownership, and sensitive-data controls to data changes. Use when a pipeline touches published datasets, regulated information, or shared business metrics. |
Governance is an engineering concern, not a cleanup exercise. This skill ensures that every meaningful data change accounts for ownership, lineage, access, and sensitive-data handling before release.
Identify ownership and consumers. Every published dataset should have:
Classify the data. Determine whether fields are:
Define required controls. Controls may include:
Update lineage and documentation. Record how the data flows from source to publish layer, including major transformations.
Verify policy enforcement in implementation. Do not stop at documentation. Check that access and masking rules are actually reflected in code or platform configuration.
| Rationalization | Reality |
|---|---|
| "It is only internal data." | Internal datasets still create exposure, misuse, and compliance risk. |
| "We will document lineage later." | Lineage that is not updated during change work becomes stale immediately. |
| "Security will handle masking downstream." | Sensitive data should be controlled as close to production as possible. |
