Agent skill · product strategy · alirezarezvani
chief-ai-officer-advisor
Use for AI strategy decisions: API vs fine-tune vs build, EU AI Act or US state risk classification, when self-hosting pays off, or AI hiring order. Runs model_buildvsbuy_calculator.py, ai_risk_classifier.py and ai_cost_economics.py.
Why this skill is useful
Adds 3 executable scripts for model build-vs-buy analysis, AI risk classification, and cost economics that provide strategic insights not commonly found in public resources.
What it needs
Requires python installed locally. About 8k tokens when loaded. Last updated 2026-08-05. 23,939 stars on the source repository.
What this skill does
Chief AI Officer Advisor Strategic AI leadership for startup CAIOs and founders without one. Four decisions, no AI hype: 1. Should we use an API, fine-tune, or build our own? — model build-vs-buy with 3-year TCO 2. Is this AI use case high-risk under regulation, and how do we govern it? — EU AI Act + NIST AI RMF + US state patchwork 3. When do we switch from API to self-hosted, and at what cost? — token economics with breakeven analysis 4. 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/. Keywords CAIO, chief AI officer, AI strategy, model selection, foundation model, fine-tuning, RLHF, DPO, LoRA, QLoRA, build vs buy, AI build-vs-buy, model risk tier, EU AI Act, AI Act Article 6, Article 9, Article 10, Annex III, prohibited AI, high-risk AI, NIST AI RMF, AI risk management framework, NYC Local Law 144, Colorado SB 21-169, Illinois HB 53, model card, eval set, eval harness, hallucination rate, jailbreak risk, prompt injection, AI red team, AI safety, alignment, model lifecycle, model registry, API-to-self-hosted breakeven, GPU economics, A100, H100, inference cost, fine-tuning cost, AI team, AI engineer, ML engineer, research scientist, MLOps, AI platform Quick Start Key Questions (ask these first) What does this AI need to be good at, and how would you measure it? (If no eval set, no ship.) What's the SLO on hallucination / error rate? (Without one, "AI quality" is a vibe.) What happens when the model is wrong? (Fallback behavior, human-in-the-loop, blast radius.) What's the risk tier under EU AI Act, and is conformity assessment required? (Determines product launch timeline.) At what monthly token vo …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills alirezarezvani/chief-ai-officer-advisor--771e39