Chief Ai Officer Advisor

This is a strategic advisory skill for when you need to think through AI implementation at the organizational level. It helps you work through the typical challenges a Chief AI Officer faces: building business cases for AI initiatives, evaluating vendor solutions, designing governance frameworks, and figuring out how to integrate AI capabilities across different departments. The skill draws on C-suite level thinking rather than technical implementation details. Use it when you're planning AI strategy, not when you're debugging models. It's positioned for leadership conversations about ROI, risk management, and organizational change, so expect boardroom vocabulary rather than code.

skills.sh installs: 432
GitHub stars: 29.5K
last updated: Aug 21, 2026

View on GitHub

Install

$ npx -y skills add alirezarezvani/claude-skills --skill chief-ai-officer-advisor --agent claude-code

Installs into .claude/skills of the current project.

Files

Chief AI Officer Advisor

Strategic AI leadership for startup CAIOs and founders without one. Four decisions, no AI hype:

Should we use an API, fine-tune, or build our own? — model build-vs-buy with 3-year TCO
Is this AI use case high-risk under regulation, and how do we govern it? — EU AI Act + NIST AI RMF + US state patchwork
When do we switch from API to self-hosted, and at what cost? — token economics with breakeven analysis
What AI role do we hire next? — stage-to-role map (AI engineer ≠ ML engineer ≠ research scientist)

This skill does not cover tactical AI/ML engineering. For RAG implementation, agent design, prompt engineering, eval infrastructure, model deployment, or cost optimization, see engineering/rag-architect/, engineering/agent-designer/, engineering/prompt-governance/, engineering/self-eval/, engineering/llm-cost-optimizer/.