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/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 traini
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add alirezarezvani/claude-skills --skill cdo-review -a cursor -y
/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 <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.
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.
If no decision is unblocked, why are we collecting / training on / productizing it?
For each data source: origin, consent flow, data class, intended use.
ai_training_data_audit.py if there's any AI use case in scope.Drives the centralize-vs-embed and warehouse-vs-mesh decisions.
If an acquirer asks about this data corpus tomorrow, are we ready?
data_asset_valuator.py quarterly.Tests how much you depend on a specific data source.
Wrong hire (data scientist) when right answer (analytics engineer) is a 12-month productivity loss.
# 1. AI training audit (if any ML / AI use case)
python ../../../c-level-advisor/skills/chief-data-officer-advisor/scripts/ai_training_data_audit.py sources.json
# 2. Architecture decision (if changing the stack)
python ../../../c-level-advisor/skills/chief-data-officer-advisor/scripts/data_product_strategy_picker.py profile.json
# 3. Data asset valuation (if productizing or pre-M&A)
python ../../../c-level-advisor/skills/chief-data-officer-advisor/scripts/data_asset_valuator.py corpus.json
# 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]
/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-advisor agent — for data team hires (comp, ladder, leveling)/cs:decide — log the verdict/cs:freeze 90 — on multi-year infrastructure contractscs-cdo-advisorchief-data-officer-advisor../../../c-level-advisor/skills/general-counsel-advisor/ (contractual constraints), ../../../c-level-advisor/skills/cto-advisor/ (architecture capacity)Version: 1.0.0