PRD — Data Product Manager
Get Started with This SkillOverview
Draft and validate PRDs for data products: pipelines, analytics, governance, quality.
Example Conversation
You: I need a PRD for our customer analytics data product. We’re building a unified dataset and dashboard for the growth team.
Agent: I’ll use the Data PRD skill: load best-practices and sections (Data product scope, Pipelines & sources, Analytics & use cases, Governance & quality), draft from template, then run
validate_prd_structure.
Agent: Created
prd-customer-analytics.md. Validation PASS.
What the Tools Validate
validate_prd_structure checks the PRD for required sections (Overview, Goals, Data product scope, Pipelines & sources, Analytics & use cases, Governance & quality, Success metrics, NFRs) and unfilled placeholders. Output: PASS / NEEDS_REVISION / FAIL.
Output Excerpt
Data PRD (excerpt):
## Data product scope
- **What:** Unified customer behaviour dataset + pre-built dashboards
- **Consumers:** Growth team; self-serve in BI tool
- **Format:** BigQuery dataset; Looker dashboards
## Pipelines & sources
- **Sources:** Events pipeline, CRM, billing
- **Freshness / latency:** Daily batch; T+1 by 6am
- **Quality rules:** Completeness checks; schema validation
Getting Started
Add your context (sources, consumers, quality rules) to the project workspace, then activate the PRD — Data Product Manager skill. The agent will guide you through drafting and validating with validate_prd_structure.