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.

Quality

96%

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Quality

Content

92%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 strategic skill: executable commands and specific quantitative thresholds, four clearly sequenced workflows with checkpoints, and a clean one-level reference layout verified against the bundle. The only weakness is mild verbosity in the Keywords line and repeated cross-skill invitations.

Suggestions

Collapse the long Keywords buzzword line into a compact comma list or move it into a reference file; it is the densest token block and largely duplicates the trigger terms already in the description.

Trim the repeated 'Cross-check with cs-*-advisor' invitations in the workflows to a single Adjacent-Skills pointer to reduce redundancy.

Consider moving the Output Standards template block into a reference file so the main body stays a pure decision overview.

Dimension	Reasoning	Score
Conciseness	Dense and decision-oriented with tables and thresholds and no concept-explaining padding, but the long Keywords buzzword dump and repeated cross-skill cross-check invitations could be trimmed; efficient with minor over-content.	4 / 5
Actionability	Provides copy-paste-ready commands (python scripts/ai_training_data_audit.py sources.json) backed by real bundled scripts with embedded samples, plus concrete thresholds (k-anonymity ≥ 5, 1.2x–2x ARR uplift) and a stage-to-role table covering the common cases.	5 / 5
Workflow Clarity	Four numbered workflows each with a goal, sequenced steps, explicit MITIGATE/NO-GO handling, cross-skill cross-checks, and /cs:decide logging checkpoints forming feedback loops; the operations are advisory rather than destructive batch ops so no validation cap applies.	5 / 5
Progressive Disclosure	SKILL.md is an overview with one-level-deep references; all four referenced references/*.md files and three referenced scripts exist and are linked with descriptions, yielding clear navigation.	5 / 5
	Total	19 / 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.

An exemplary description: it specifies four concrete decision domains, gives rich natural trigger terms with synonyms, and draws an explicit boundary against adjacent tactical skills. Every dimension lands at the top anchor with no over-claims or fluff.

Dimension	Reasoning	Score
Specificity	Names four concrete, distinct decision 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; third-person voice throughout.	5 / 5
Completeness	Explicitly answers both what (four advisory domains) and when ("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…") with concrete trigger phrases.	5 / 5
Trigger Term Quality	Comprehensive natural-term coverage including synonyms ("CDO, chief data officer, data strategy, data mesh, lakehouse, training data, data product, data monetization, or customer data asset") plus decision-verb triggers users would naturally voice.	5 / 5
Distinctiveness Conflict Risk	The explicit boundary "NOT a tactical data engineering skill — strategic decisions only" carves a clear C-level strategic niche with minimal overlap risk against engineering data skills.	5 / 5
	Total	20 / 20 Passed

Validation

100%

Warnings & errors only

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: alirezarezvani/claude-skills
Path: c-level-advisor/skills/chief-data-officer-advisor/SKILL.md
Commit: 19392f7

Reviewed: ~12 hours ago

Table of Contents

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