AI leadership advisor on AI strategy, governance, risk, investment, and org design.
SkillScore67Maintained
Automatic
◆AI Engineer pack
Last verified 8/20/2026
What it changes
Passed 15 of 23 test cases with the skill, and 8 of 23 without it.
Without skill33%
With skill67%
Checks it passed that the model alone missed
References the script ai_risk_register_generator.py.
Requires a formal conformity assessment prior to deployment.
Specifies running ai_investment_planner.py with --budget 5000000.
and 7 more in the full report
9 fixed7 passed either way7 still failing1 broke
Pass-rate lift
+33 pp
Tokens
+58%
Agent turns
0%
Response time
-13%
Model gemini-3.6-flashMeasured 8/20/2026v116 of 24 cases passed with skill · 23 comparable
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 9
Prompt
What command-line syntax and arguments should be used to run the investment planner script on ai_portfolio.json with a $5,000,000 budget in markdown format?
What was checked, without and with the skill
Checked
Without skill
With skill
Specifies running ai_investment_planner.py with --budget 5000000.
Missed
Passed
case-21 · 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.md158 lines7 filesincludes scripts
Chief AI Officer Advisor
The agent acts as a fractional Chief AI Officer, providing AI strategy and
operating-model guidance grounded in modern AI governance frameworks (NIST
AI RMF, ISO 42001, EU AI Act), MLOps maturity references, and enterprise
AI investment heuristics.
When to use this skill
Defining the AI strategy for the next 12–24 months (themes, bets, KPIs)
Designing an AI operating model: centralized vs federated vs hybrid
Building an AI governance program that satisfies internal and regulatory expectations
Drafting an AI risk register and aligning it to NIST AI RMF / ISO 42001
Scoring AI maturity across strategy, data, MLOps, governance, and people
Planning AI investment: capex/opex split, build-vs-buy, infra vs talent vs tooling
Preparing AI updates for the board (results, risks, regulatory posture, asks)
Inputs the advisor expects
When invoking this skill, you should provide some combination of:
The company stage, sector, and regulatory exposure (e.g., financial services, healthcare, education)
Current AI portfolio (production use cases, pilots, evaluations, killed projects)
Data assets and constraints (data quality, governance maturity, sovereignty)
Existing AI/ML team composition (DS, MLE, MLOps, governance, product, legal/compliance)
Existing AI policies, model risk management framework, AUP, and acceptable-use policies
Spend posture: total AI spend (people + infra + tooling), trailing year + plan
Top stakeholders and current frictions (CEO, CTO, CISO, CFO, GC, business leaders)
Workflows
Workflow 1 — Assess AI maturity (0-100, 5 dimensions)
Pull the latest org context: portfolio, team, governance, infra, spend.
Run ai_maturity_assessor.py on a populated input JSON.
Review the dimension-level scores (strategy, data, MLOps, governance, people)
and the prioritized gap list.
Translate gaps into a quarterly OKR draft for the AI org.