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chief-data-officer-advisor

Chief Data Officer advisory for startups: AI training data rights and consent provenance, data product strategy (warehouse vs lakehouse vs mesh, build-vs-buy), B2B customer-data-as-asset valuation and M&A readiness, data team org evolution. Use when deciding whether to train models on customer data, choosing data architecture, valuing data for fundraising or M&A, sequencing data hires, or when user mentions CDO, chief data officer, data strategy, data mesh, lakehouse, training data, data product, data monetization, or customer data asset. NOT a tactical data engineering skill — strategic decisions only.

72

Quality

89%

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SecuritybySnyk

The risk profile of this skill

The canonical home for this skill is chief-data-officer-advisor in alirezarezvani/claude-skills

SKILL.md
Quality
Evals
Security

Quality

Content

78%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-structured, action-oriented skill body that leans on tables and executable commands and cleanly splits detail into four real reference files. Slight conciseness and validation-loop gaps keep it just below the top anchors.

Suggestions

Trim the Keywords section and the intro's restatement of the four decisions to reduce token overlap with the description and the Core Responsibilities section.

Show the expected output/schema of each script (or a one-line sample result) so the actionability and workflow anchors fully cover execution, not just invocation.

Add an explicit validate-fix-retry checkpoint in Workflow 1 (e.g., re-run the audit after remediation until no MITIGATE remains) to strengthen workflow clarity.

DimensionReasoningScore

Conciseness

Mostly lean — tables and stage-driven thresholds assume Claude's competence without explaining what a warehouse or lakehouse is — but the Keywords section restates body terms and the intro re-lists the four decisions already in the description, minor padding that could be trimmed.

4 / 5

Actionability

Copy-paste-ready bash commands with arguments and concrete decision tables (build-vs-buy per layer, stage-to-role map) cover the common cases, but several workflows end in cross-checks with other skills without showing the scripts' expected output, a minor gap.

4 / 5

Workflow Clarity

Four numbered workflows each have a stated goal and sequenced steps, with MITIGATE/NO-GO remediation and a diligence checklist as checkpoints; these are advisory rather than destructive/batch ops, and explicit validate-fix-retry loops are absent, leaving a minor validation gap.

4 / 5

Progressive Disclosure

The body is a clear overview pointing to four real, one-level-deep reference files, each signaled with what it contains, plus separated scripts and well-organized sections, matching the clear-overview-with-well-signaled-references anchor.

5 / 5

Total

17

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, third-person description that names four concrete decision domains, gives explicit "Use when" triggers with natural synonyms, and draws a clear boundary against tactical engineering. It is comprehensive and distinctive with minimal conflict risk.

DimensionReasoningScore

Specificity

Lists four concrete, specific advisory domains ("AI training data rights and consent provenance", "warehouse vs lakehouse vs mesh, build-vs-buy", "B2B customer-data-as-asset valuation and M&A readiness", "data team org evolution") with comprehensive coverage of the skill's capabilities, matching the multiple-specific-actions anchor.

5 / 5

Completeness

Explicitly answers both what (the four advisory areas) and when (a clear "Use when deciding..." clause with concrete trigger phrases), and adds a boundary ("NOT a tactical data engineering skill").

5 / 5

Trigger Term Quality

Comprehensive natural-term coverage including synonyms ("CDO, chief data officer") and domain phrases users actually say ("data mesh, lakehouse, training data, data product, data monetization, customer data asset"), plus decision-based triggers.

5 / 5

Distinctiveness Conflict Risk

Clear CDO strategic niche with an explicit scope boundary distinguishing it from tactical data-engineering skills, minimizing conflict risk.

5 / 5

Total

20

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
alirezarezvani/claude-skills
Reviewed

Table of Contents

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