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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.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
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 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.
Designs scheduling, reruns, and backfills safely for data systems. Use when changing orchestration, retries, dependency timing, historical reprocessing, or publish sequencing.
| name | data-resiliency-testing-and-failure-injection |
| description | 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. |
Use this skill when the goal is to prove that a data system recovers safely under failure, not only when everything goes right. It helps agents design controlled drills for retries, restarts, dependency outages, state recovery, duplicate prevention, backlog catch-up, and publish protection.
Do not treat resilience testing as random breakage. The point is to validate recovery behavior with explicit safety limits and evidence.
Define the failure modes that matter. Prioritize:
Define the resilience objectives. Include:
Choose the safest drill environment. Prefer:
Inject one failure mode at a time. Use controlled exercises such as:
Validate the recovery path. Check:
Record guardrails and automate the highest-value drills. The best resilience test is one the team can rerun after changes, not a one-time exercise that gets forgotten.
Load companion skills by failure mode.
safe-backfill-and-replay-orchestrationkafka-resilience-and-schema-evolutionspark-serverless-reliability-and-state-managementmcp-data-observability-integrationreferences/data-resiliency-testing-patterns.md| Rationalization | Reality |
|---|---|
| "If the job retries, we are resilient enough." | Retry alone does not prove replay safety, duplicate prevention, or publish protection. |
| "We can test recovery during a real incident." | Real incidents are the worst time to discover the recovery path is unclear or unsafe. |
| "Failure injection is too risky for data systems." | Uncontrolled failure is riskier than bounded, reviewable drills in safe environments. |
| "The scheduler health page already proves resilience." | Scheduler status does not prove data correctness, backlog catch-up, or downstream safety. |