Chief AI Officer Advisor
Use for AI strategy decisions: API vs fine-tune vs build, EU AI Act or US state risk...
Install
npx skills add alirezarezvani/claude-skills --skill chief-ai-officer-advisor -g -a claude-code -y- Repository
- alirezarezvani/claude-skills29.1k
- Category
- Engineering
- Updated
- 4d ago
- License
- MIT
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- Yes, review before use
What it does
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.
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/.
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
# Decision A: API vs fine-tune vs build
python scripts/model_buildvsbuy_calculator.py # embedded customer-support sample
python scripts/model_buildvsbuy_calculator.py path/to/use_case.json
