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Designs governed, domain-owned, discoverable and reusable data products. Use when packaging datasets as products, defining ownership, or reviewing product readiness.
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SOC 職業分類に基づく
Use when reviewing code before merge to assess correctness, tests, maintainability, security and user impact.
Use when reviewing plans, recommendations, prioritisation, risk registers, retrospectives or agent outputs for judgement distortion from cognitive bias — not for checking whether an argument's premises validly support its conclusion.
Use when evaluating whether premises in plans, ADRs, reviews, security justifications or agent recommendations validly support their conclusions — for logical fallacies, not judgement distortion from cognitive bias.
Use when a coding agent needs robust problem solving, explicit decomposition, alternative-path exploration, program-aided reasoning, ReAct loops, or self-correction.
Use when generated code, plans, tests, prompts or architecture need critique, repair and verification before completion; especially for coding tasks requiring self-review, test-driven repair or quality gates.
Identifies risks, scores likelihood and impact, selects treatment, assigns owners and review cadence. Use when building or updating a risk register, treating agent autonomy risks, or deciding mitigate, transfer, accept or avoid.
| name | data-product-design |
| description | Designs governed, domain-owned, discoverable and reusable data products. Use when packaging datasets as products, defining ownership, or reviewing product readiness. |
Use for reusable datasets, APIs, event streams, analytical products or source-to-refined outputs.
Produce a practical, concise, traceable architecture artefact that a coding agent can use to guide implementation or review.
Apply DAMA-DMBOK2-style separation of data governance, architecture, modelling, security, integration/interoperability, master/reference data, metadata and quality. For cloud/shared data, apply CDMC-style expectations: ownership, classification, entitlement/access evidence, lineage/provenance, lifecycle/retention, quality controls and auditable evidence.
Do not call a dataset a product without ownership, consumers and operating expectations.
