Data leadership advisor on data strategy, governance, quality, and platform decisions.
SkillScore73Maintained
Automatic
◆Data Engineer pack
Last verified 8/20/2026
What it changes
Passed 16 of 22 test cases with the skill, and 10 of 22 without it.
Without skill45%
With skill73%
Checks it passed that the model alone missed
Recommends quantifying time-to-insight delays caused by fragmented platforms.
Recommends a hub-and-spoke hybrid model as the standard default operating structure.
States that lack of board access indicates a Head of Data role rather than a Chief Data Officer role.
and 3 more in the full report
6 fixed10 passed either way6 still failing0 broke
Pass-rate lift
+27 pp
Tokens
+59%
Agent turns
0%
Response time
-14%
Model gemini-3.6-flashMeasured 8/20/2026v116 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 6
Prompt
Our architecture board created platform_eval.json with footprint data and proposed alternatives. What CLI command runs the platform evaluation script to produce a weighted comparison in Markdown?
case-15 · gemini-3.6-flash · 8/20/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md162 lines7 filesincludes scripts
Chief Data Officer Advisor
The agent acts as a fractional Chief Data Officer, providing data strategy
and operating-model guidance grounded in DAMA-DMBOK, modern data-platform
patterns, and regulated-industry expectations (GDPR, HIPAA, sector data
governance regimes).
When to use this skill
Defining or refreshing the data strategy for the next 12–24 months
Designing the data operating model: central, federated, mesh, hybrid
Building a data governance program that holds up to internal + regulator review
Scoring data maturity across strategy, governance, quality, platform, and people
Auditing the data quality program (use jointly with engineering/data-quality-auditor)
Evaluating the data platform stack (warehouse, lake, lakehouse, governance)
Building the case for data monetization: products, services, internal apps
Preparing the data section of the board deck (assets, risks, returns, asks)
Inputs the advisor expects
Company stage, sector, regulatory exposure (e.g., financial services, healthcare, public sector)
Critical data domains (customer, product, transaction, employee, regulatory)
Current data platform (warehouse, lake, ingestion, transformation, BI, governance, ML/AI)
Data team composition (engineering, governance, analytics, stewardship, science)
Design and generate CI/CD pipelines from project stack signals across GitHub Actions, GitLab CI, CircleCI, and Buildkite. Use when bootstrapping CI, migrating pipelines, adding deployment gates, or optimizing build times.