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These previews come from the published bundle files, so you can judge the method and writing before using it.
Example task
Draft requirements for a customer-health data product used by customer success managers. The user has not provided schemas, metric definitions, data owners, quality checks, or privacy classifications.
Without the bundle
Create a customer health score using usage, support tickets, NPS, and contract value. Build a dashboard showing score, churn risk, and recommended actions for each account. Track adoption and improve the model over time.
Using the bundle
Recommendation: draft a discovery-stage data product PRD, not an implementation-ready specification, because the source schemas, metric definitions, data owners, quality thresholds, privacy classification, and source-of-record policy are missing. Source note: user provided the target consumer and broad use case only. Needed sources include CRM/account schema, product usage event definitions, support-ticket source, contract/revenue source, existing health-score definitions, access policy, privacy classification, data quality checks, and owner approvals. Requirements Draft - Consumers: customer success managers. - Decision supported: identify accounts needing intervention.
Why this is better: The bundle-assisted output is better because it gives a direct readiness status, names missing evidence, avoids inventing schema and formula details, and adds quality, privacy, owner, source-of-record, and acceptance-criteria concerns.
Inspect this example in the repositoryRole guide
Data Product Manager
Defines the scope and expected behavior of a Data Product Manager agent.
Read the fileWorkflow
Define Data Product Opportunity
Turns a data need into a consumer- and outcome-centered product opportunity.
Read the fileWorkflow
Plan Data Product Roadmap
Prioritizes data-product work using value, consumer impact, quality risk, governance, and delivery evidence.
Read the fileWorkflow
Reconcile Metric Definition
Aligns conflicting metric values or definitions before recommending a source of record.
Read the fileIs this bundle right for your task?
Who it is for
- People performing or supporting Data Product Manager work, plus teams reviewing its decisions and outputs
- Teams working in software, saas, digital-products
When to use it
- A Data Product Manager task needs a structured plan, evidence checklist, or review-ready output.
- A recommendation needs its assumptions, owners, risks, dependencies, and success measures made explicit.
What you need to provide
- The task objective, intended audience, working context, constraints, source material, and decision owner.
- Relevant reports, exports, examples, policies, prior decisions, and success measures available for the task.
Tasks and expected outputs
Questions it helps answer
- define data product opportunities
- draft data product PRDs
- review data contracts
- reconcile metric definitions
- plan data product roadmaps
- evaluate data product trust and adoption
What it helps produce
- data product brief
- data product PRD
- data contract brief
- metric definition review
- data product roadmap note
Practical example
Use it with an agent
Load the bundle as context, provide the evidence named above, then adapt this example to your situation.
Provide the task objective, intended audience, working context, constraints, source material, and decision owner. Ask the agent to approach Data Product Manager work by producing data product brief with a prioritized plan, evidence checks, owners, risks, and unresolved questions. Inspect Data Product Manager before drafting.
Context path: bundles/roles/data-product-manager
What the bundle includes
Tools
- Jira
- Linear
- Confluence
- Notion
- dbt
- Snowflake
- BigQuery
- Databricks
- Looker
- Tableau
- Amplitude
- Mixpanel
- Monte Carlo
- Collibra
- Alation
- Atlan
- GitHub
Frameworks
- data-as-a-product
- data contracts
- data quality dimensions
- Jobs-to-be-Done
- OKRs
- RICE prioritization
- North Star metric
- lineage
- governance
Commands
/draft-data-product-prd
/review-data-contract
/reconcile-metric-definition
Evaluations
- data-product-management-quality-check
Limitations and safe use
Do not use this for
- Treating the bundle as a substitute for organization-specific authority, firsthand evidence, or accountable review.
Known limitations
- Data Product Manager responsibilities vary by organization; verify local ownership, decision rights, governance process, and product lifecycle.
- The O*NET mapping comes from the reviewed generator candidate and points to Advertising and Promotions Managers; treat it as candidate taxonomy context, not a precise role standard.
- The bundle does not include tool-specific API, workspace, schema, or admin procedures.
- It does not replace legal, privacy, security, data governance, compliance, or data engineering review.
Safety notes
- Confirm before modifying live schemas, data contracts, semantic layers, dashboards, pipeline schedules, access grants, tickets, docs, or roadmap systems.
- Treat customer data, personal data, restricted datasets, roadmap plans, data incidents, and governance records as confidential unless the user confirms they are safe to use.