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Cdo review

Skill sinhoneyy/master-skills/plugins/master-skills-backend-engineer/skills/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.From its SKILL.md

Install
npx -y skills add sinhoneyy/master-skills --skill cdo-review

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SKILL.md

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/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.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 data_asset_valuator.py quarterly.

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 math
  • cs-chro-advisor agent — for data team hires (comp, ladder, leveling)
  • /cs:decide — log the verdict
  • /cs:freeze 90 — on multi-year infrastructure contracts

Related

Version: 1.0.0

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most review quality skills give in ~1.2k tokens

Counted across 1,048 of the 1,783 authors here whose files we hold, read 2026-08-07

  • Ask questions one at a timein 81 of 1048, across 64 files
  • Provide a recommended answer for each questionin 73 of 1048, across 50 files
  • Explore the codebase instead of asking answerable questionsin 66 of 1048, across 42 files
  • Resolve dependencies between decisions one-by-onein 42 of 1048, across 17 files
  • Interview the user relentlessly about the planin 38 of 1048, across 13 files
  • Order findings by severityin 31 of 1048
  • Resolve each branch of the decision treein 27 of 1048, across 5 files
  • Run a grilling sessionin 26 of 1048, across 5 files
  • Update CONTEXT.md immediately when a term is resolvedin 26 of 1048, across 11 files
  • Propose precise canonical terms for vague languagein 25 of 1048, across 7 files
  • Create documentation files lazilyin 24 of 1048, across 5 files
  • Assign severity to every findingin 24 of 1048

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