alirezarezvani/cdo-review
c-level-advisor/c-level-agents/skills/cdo-review/SKILL.md
/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.
$ install --global
skillsauthnpx skillsauth add alirezarezvani/claude-skills cdo-reviewInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- cdo-review
- description:
- /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.
/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
ai_training_data_audit.pyif 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
data_asset_valuator.pyquarterly.
5. Can the model / decision / report be retrained / re-run / re-published without this source?
Tests how much you depend on a specific data source.
- If yes → low blast radius; you can change consent posture later.
- If no → high blast radius; you've structurally committed to the source. Vet harder.
6. What role unblocks this — and is it the right next hire?
Wrong hire (data scientist) when right answer (analytics engineer) is a 12-month productivity loss.
- Map the decision being unblocked to the specific role.
- Confirm prerequisite roles are in place (data engineer before ML engineer, analyst before data scientist).
Workflow
# 1. AI training audit (if any ML / AI use case)
python ../../../skills/chief-data-officer-advisor/scripts/ai_training_data_audit.py sources.json
# 2. Architecture decision (if changing the stack)
python ../../../skills/chief-data-officer-advisor/scripts/data_product_strategy_picker.py profile.json
# 3. Data asset valuation (if productizing or pre-M&A)
python ../../../skills/chief-data-officer-advisor/scripts/data_asset_valuator.py corpus.json
Output Format
# CDO Review: <plan>
**Date:** YYYY-MM-DD
## The Decision Being Made
[one sentence — which of the four CDO decisions: training | architecture | asset | hire]
## Training Audit (if applicable)
- NO-GO sources: N
- MITIGATE sources: N
- GO sources: N
- Top remediation: <one line>
## Architecture (if applicable)
- Recommended: WAREHOUSE / LAKEHOUSE / MESH
- Build-vs-buy summary: <one line>
- Kill criteria: <when to revisit>
## Asset Value (if applicable)
- Strategic value: X/10 | Moat: STRONG / MEDIUM / WEAK
- M&A multiplier: X.Xx – X.Xx ARR
- Recommended productization path: <name>
## Org (if applicable)
- Next hire: <role>
- Why this, not that: <one line>
- Prerequisite hires in place: yes/no
## Verdict
🟢 SHIP | 🟡 SHARPEN | 🔴 BLOCK
## Next Steps
[3 concrete actions]
Routing
/cs:gc-review— for any productization or licensing path/cs:ciso-review— for any architecture change touching customer data/cs:cfo-review— for build-vs-buy TCO and M&A valuation mathcs-chro-advisoragent — for data team hires (comp, ladder, leveling)/cs:decide— log the verdict/cs:freeze 90— on multi-year infrastructure contracts
Related
- Agent:
cs-cdo-advisor - Skill:
chief-data-officer-advisor - Adjacent:
../../../skills/general-counsel-advisor/(contractual constraints),../../../skills/cto-advisor/(architecture capacity)
Version: 1.0.0
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