Data Product Design
Designs governed, domain-owned, discoverable and reusable data products. Use when packaging datasets as products, defining ownership, or reviewing product readiness.
Install
$ agentstack add skill-mmccalla-coding-agent-skill-library-data-product-design ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
About
Data Product Design
When to use
Use for reusable datasets, APIs, event streams, analytical products or source-to-refined outputs.
Objective
Produce a practical, concise, traceable architecture artefact that a coding agent can use to guide implementation or review.
Procedure
- Identify consumers and decisions.
- Define purpose, scope and non-goals.
- Assign owner and steward.
- Define interface and contract.
- Define quality SLOs.
- Define metadata, classification, lineage and access.
- Define lifecycle, support and deprecation.
- Define adoption/value metrics.
Required outputs
- Purpose and consumers
- Owner/steward
- Interface and contract
- Quality SLOs
- Classification/access model
- Lineage and support model
Best-practice alignment
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.
Quality checks
- Accountable owner exists.
- Consumers/use are clear.
- Quality and metadata are defined.
- Access and lineage controls exist.
Avoid
Do not call a dataset a product without ownership, consumers and operating expectations.
References
- DAMA-DMBOK (DAMA International)
- EDM Council CDMC (Cloud Data Management Capabilities)
- Data Mesh principles (Martin Fowler / Dehghani)
Verification
- [ ] Required artefacts produced and linked to scope.
- [ ] Decisions, assumptions and risks stated explicitly.
- [ ] Quality checks or validation performed.
- [ ] Files changed reported with traceability preserved.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: mmccalla
- Source: mmccalla/coding-agent-skill-library
- License: Apache-2.0
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.