Agent skill · product strategy · borghei
chief-ai-officer-advisor
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.
Why this skill is useful
Adds 3 executable scripts for assessing AI maturity, planning AI investment, and generating an AI risk register that are not commonly known or available in public documentation.
What it needs
Requires python3 installed locally. About 5k tokens when loaded. Last updated 2026-07-21. 462 stars on the source repository.
What this skill does
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) 1. Pull the latest org context: portfolio, team, governance, infra, spend. 2. Run aimaturityassessor.py on a populated input JSON. 3. Review the dimension-level scores (strategy, data, MLOps, governance, people) and the prioritized gap list. 4. Translate gaps into a quarterly OKR draft for the AI org. Workflow 2 — Plan AI investment for the next budget cycle 1. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills borghei/chief-ai-officer-advisor