# Chief AI Officer Advisor

AI leadership advisor on AI strategy, governance, risk, investment, and org design.

- Repository: https://github.com/borghei/Claude-Skills (1026 stars)
- Category: Design
- License: MIT + Commons Clause
- Last updated: 2026-10-23
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add borghei/Claude-Skills --skill chief-ai-officer-advisor -g -a claude-code -y`
Codex: `npx skills add borghei/Claude-Skills --skill chief-ai-officer-advisor -g -a codex -y`
Cursor: `npx skills add borghei/Claude-Skills --skill chief-ai-officer-advisor -g -a cursor -y`
Gemini CLI: `npx skills add borghei/Claude-Skills --skill chief-ai-officer-advisor -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add borghei/Claude-Skills`

## Description

AI leadership advisor on AI strategy, governance, risk, investment, and org design. Use when defining an AI strategy, building an AI governance program, scoring AI maturity, or drafting an AI risk register.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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)

## Clarify First

AI advice depends most on regulatory exposure, what is already in production, and the size of the engineering org. Settle the inputs below before advising. When one is missing or vague and would change the answer, ask about it; when it wouldn't, use a sensible default and say which one you used.

The rest: https://github.com/borghei/Claude-Skills/blob/HEAD/c-level-advisor/chief-ai-officer-advisor/SKILL.md