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World-class, in-depth Chief AI Officer skill covering 4 specific decisions
(not a generic AI strategy survey):
1. Should we use an API, fine-tune, or build our own? (3-yr TCO + breakeven)
2. Is this AI use case high-risk under regulation? (EU AI Act + US state +
industry overlays with Article-level citations)
3. When do we switch from API to self-hosted, and at what cost? (2026
pricing + GPU economics + hidden costs)
4. What AI role do we hire next? (5-stage map + 9-role definition table)
Built under karpathy-coder discipline (third in a row):
- Assumptions surfaced upfront before code (principle 1)
- Each tool/reference covers ONE decision; rejected generic-survey scope (#2)
- Surgical changes only; no scope creep (#3)
- All 3 tools smoke-tested with embedded samples before commit (#4)
- karpathy/complexity_checker.py: 0 findings on 3 new tools
- karpathy/diff_surgeon.py: 0 findings on staged diff
3 stdlib Python tools with deterministic logic:
- model_buildvsbuy_calculator.py — Returns API/FINE_TUNE/BUILD recommendation,
3-year TCO across 6 paths, breakeven analysis. Balances economic crossover
with practical feasibility (data availability, ML team capacity, compliance).
Embedded sample (B2B customer support, 4M queries/mo) -> API recommended
despite breakeven crossed, because no fine-tune data + 1-engineer ML team.
- ai_risk_classifier.py — Returns EU AI Act tier (PROHIBITED/HIGH/LIMITED/
MINIMAL) with 7 Article citations + US state triggers (NYC LL 144, CO AI
Act, IL HB 53, CA SB 1001, IL BIPA) + industry overlays (FDA, CFPB, NAIC,
ECOA, Fed SR 11-7). Sample (AI hiring in EU+NY+CO+IL+CA) -> HIGH,
conformity required, 3 US triggers, 14 controls.
- ai_cost_economics.py — Returns API costs (3 tiers) + self-hosted costs (low/
mid/high GPU rates with 24/7 warm + ops attribution) + breakeven analysis.
Reveals key insight: self-hosted floor makes API economics dominate at
typical B2B SaaS scale. Sample (5M tokens/day, 750M/mo) -> API at $1,500/mo
beats self-hosted at $13,450/mo by 9x; breakeven at 6.7B tokens/mo.
4 in-depth references, each citing 5+ authoritative sources:
- model_buildvsbuy_strategy.md — 3 paths with failure modes, 6 fine-tuning
approaches ranked by cost (RAG/LoRA/full FT/RLHF/DPO/continued pre-training),
decision tree, eval-first discipline. Cites Anthropic/OpenAI/Google/Meta
model cards, LoRA paper, RLHF paper, DPO paper, Stanford CRFM Foundation
Models report, Foundation Models and Fair Use (Henderson et al.).
- ai_risk_governance.md — Full EU AI Act tier map (Art. 5 prohibited, Art. 6
+ Annex III high-risk, Art. 50 limited-risk) with all 8 high-risk domains
+ 11 obligation articles. NIST AI RMF 1.0. US state patchwork (9 laws).
Industry overlays (FDA AI/ML, CFPB, NYDFS, NAIC). 10-item governance
program checklist. When-to-hire-AI-counsel criteria.
- ai_cost_economics.md — 2026 API pricing (4 tiers), GPU rental (A100/H100/
H200/B200), throughput estimates, GPU count by model size, utilization
reality (20-80%), 6 hidden costs of self-hosted, 6 hidden costs of API,
migration cost, prompt caching as economics lever. Cites vLLM paper,
DistServe, HELM, Artificial Analysis.
- ai_team_org_evolution.md — 5-stage role map (pre-seed -> late-stage),
9-role definition table (AI engineer != ML engineer != research scientist),
AI team vs data team contrast (8 dimensions), 7 anti-patterns, hiring
sequencing rule. Cites Huyen "Designing ML Systems" + "AI Engineering",
State of AI Report.
cs-caio-advisor agent (c-level-agents/agents/cs-caio-advisor.md):
- Eval-demanding realist voice
- Hard rule: does not duplicate engineering AI/ML skills (rag-architect,
agent-designer, prompt-governance, self-eval, llm-cost-optimizer)
- Treats every AI use case as a hiring decision; pushes back on AI hype
/cs:caio-review slash command:
- 6-question forcing interrogation: eval set, hallucination SLO, regulatory
tier, model selection, cost trajectory, role-that-unblocks
- Routes to /cs:cdo-review, /cs:gc-review, /cs:ciso-review, /cs:cfo-review,
/cs:chro-review
cs-caio-advisor voice spec added to persona-voices.md.
Updates:
- c-level plugin.json: v2.5.2 -> v2.5.3 (31 skills, 11 cs-* agents)
- c-level-agents plugin.json: v1.2.0 -> v1.3.0 (11 agents, 19 commands)
- marketplace.json: both c-level entries; new CAIO keywords (chief-ai-officer,
caio, ai-strategy, model-buildvsbuy, eu-ai-act, ai-cost-economics)
- c-level CLAUDE.md: CAIO row added; agent + count tables updated
- Root CLAUDE.md: 265->266 skills, 30->31 cs-* agents, 364->367 tools,
494->498 references, 51->52 commands; v2.5.3 highlight section
- CHANGELOG.md: v2.5.3 entry with full rationale
Known follow-up (out of scope this PR): cs-general-counsel-advisor voice spec
still missing from persona-voices.md (carried from v2.5.1); separate PR.
Disclaimer in every output: not legal advice; not a replacement for AI
counsel on EU AI Act conformity; not a tactical AI/ML engineering skill.
https://claude.ai/code/session_012WtZMm5NJHqkYoRqA9fHMN
13 lines
972 B
JSON
13 lines
972 B
JSON
{
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"name": "c-level-agents",
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"description": "Founder-mode executive team plugin: 11 cs-* C-suite agents (CFO, CMO, CRO, CPO, COO, CHRO, CISO, Chief of Staff, General Counsel, Chief Data Officer, Chief AI Officer) plus 19 /cs:* slash commands for forcing-question office hours (incl. /cs:cdo-review, /cs:caio-review), multi-role boardroom deliberation, strategic sprint pipeline, and meta routing. Wraps the 31 c-level skills (including chief-ai-officer-advisor with model build-vs-buy calculator + AI risk classifier covering EU AI Act + AI cost economics with API-vs-self-hosted breakeven) with cognitive gearing and artifact handoffs.",
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"version": "1.3.0",
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"author": {
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"name": "Alireza Rezvani",
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"url": "https://alirezarezvani.com"
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},
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"homepage": "https://github.com/alirezarezvani/claude-skills/tree/main/c-level-advisor/c-level-agents",
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"repository": "https://github.com/alirezarezvani/claude-skills",
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"license": "MIT",
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"skills": "./skills"
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}
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