Agent skill · alirezarezvani
cdo-review
/cs:cdo-review <plan> — Decision-driven Chief Data Officer interrogation of any plan that touches training data, data architecture, data productization, or data team hiring. Use when validating training-data rights before model work, choosing warehouse vs lakehouse vs mesh, or valuing data assets for productization or M&A.
What it needs
About 3k tokens when loaded.
What this skill does
/cs:cdo-review — CDO Forcing Questions Command: /cs:cdo-review <plan> The decision-driven CDO pressure-tests any plan that touches data strategy. Six questions before any commitment to a data architecture, AI training run, data productization, or data team hire. When to Run Before approving any new ML model training run that uses customer data Before signing a multi-year data-infrastructure SaaS contract (Snowflake, Databricks, Fivetran) Before productizing any customer data (benchmark report, embedding endpoint, license) Before a major data team hire (head of data, CDO, data PM, ML engineer) Before M&A diligence — yours or theirs When the founder uses the word "monetize" near "data" The Six CDO Questions 1. What decision does this data drive? If no decision is unblocked, why are we collecting / training on / productizing it? "We might need it later" is not a decision. "It feels like a moat" is not a decision. A real answer names a specific business call that requires this data. 2. What's the consent provenance for every source? For each data source: origin, consent flow, data class, intended use. 1st-party-TOS-only is weaker than 1st-party-explicit-opt-in. Bundled TOS doesn't cover material new purposes (training on PII for foundation models). Run aitrainingdataaudit.py if there's any AI use case in scope. 3. Who consumes this internally — and how many distinct functional domains? Drives the centralize-vs-embed and warehouse-vs-mesh decisions. <5 consumers: warehouse-only. 5-25 consumers: lakehouse. 25+ consumers + federated culture: mesh. Premature architecture choice is the #1 cause of data-team burnout. 4. What's the M&A diligence impact? If an acquirer asks about this data corpus tomorrow, are we ready? Is there a documented anonymization process? What % of customers have MSA carve-outs? Are training-data provenance logs current? Run dataassetvaluator.py quarterly. 5. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills alirezarezvani/cdo-review--754a41