[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-6f5b0e59-277d-4faf-9a9c-1d2fb1fc4652":3,"$f7Pazfk7C3xcq62-jugVLW8WejkI64EQB_XS0sDVc_Eo":42},{"id":4,"title":5,"description":6,"categoryId":7,"moduleId":8,"tags":9,"prompt":10,"icon":11,"source":12,"sourceUrl":13,"authorId":14,"authorName":15,"isPublic":16,"stars":17,"runs":18,"createdAt":19,"updatedAt":19,"module":20,"category":27,"packages":33},"6f5b0e59-277d-4faf-9a9c-1d2fb1fc4652","chief-ai-officer-advisor","首席AI官为初创公司提供咨询：模型构建-购买决策（API vs 微调 vs 内部开发）、欧盟AI法案下的AI风险分类+美国州级拼图，AI成本经济学（API至自托管盈亏平衡），以及AI团队组织演变。在决定是否调用API或微调、对AI用例进行监管风险分类、计算何时自托管划算、安排AI招聘顺序或当用户提及CAIO、AI战略、模型选择、基础模型、微调、欧盟AI法案时使用。","cat_coding_backend","mod_coding","alirezarezvani,coding","---\nname: \"chief-ai-officer-advisor\"\ndescription: \"Chief AI Officer advisory for startups: model build-vs-buy decisions (API vs fine-tune vs in-house), AI risk classification under EU AI Act + US state patchwork, AI cost economics (API-to-self-hosted breakeven), and AI team org evolution. Use when deciding whether to call an API or fine-tune, classifying AI use cases for regulatory risk, calculating when self-hosting pays off, sequencing AI hires, or when user mentions CAIO, AI strategy, model selection, foundation model, fine-tuning, EU AI Act, NIST AI RMF, AI governance, model risk, or AI economics. Strategic only — does not duplicate engineering AI\u002FML skills.\"\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Alireza Rezvani\n  category: c-level\n  domain: chief-ai-officer-leadership\n  updated: 2026-05-12\n  python-tools: model_buildvsbuy_calculator.py, ai_risk_classifier.py, ai_cost_economics.py\n  frameworks: model-buildvsbuy, ai-risk-governance, ai-economics, ai-team-org\n---\n\n# Chief AI Officer Advisor\n\nStrategic AI leadership for startup CAIOs and founders without one. **Four decisions, no AI hype:**\n\n1. **Should we use an API, fine-tune, or build our own?** — model build-vs-buy with 3-year TCO\n2. **Is this AI use case high-risk under regulation, and how do we govern it?** — EU AI Act + NIST AI RMF + US state patchwork\n3. **When do we switch from API to self-hosted, and at what cost?** — token economics with breakeven analysis\n4. **What AI role do we hire next?** — stage-to-role map (AI engineer ≠ ML engineer ≠ research scientist)\n\nThis skill does **not** cover tactical AI\u002FML engineering. For RAG implementation, agent design, prompt engineering, eval infrastructure, model deployment, or cost optimization, see `engineering\u002Frag-architect\u002F`, `engineering\u002Fagent-designer\u002F`, `engineering\u002Fprompt-governance\u002F`, `engineering\u002Fself-eval\u002F`, `engineering\u002Fllm-cost-optimizer\u002F`.\n\n## Keywords\n\nCAIO, 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\n\n## Quick Start\n\n```bash\n# Decision A: API vs fine-tune vs build\npython scripts\u002Fmodel_buildvsbuy_calculator.py                          # embedded customer-support sample\npython scripts\u002Fmodel_buildvsbuy_calculator.py path\u002Fto\u002Fuse_case.json\n\n# Decision B: Risk classification under EU AI Act + US state laws\npython scripts\u002Fai_risk_classifier.py                                   # embedded hiring-AI sample\npython scripts\u002Fai_risk_classifier.py path\u002Fto\u002Fuse_case.json\n\n# Decision C: API vs self-hosted economics\npython scripts\u002Fai_cost_economics.py                                    # embedded 5M tokens\u002Fday sample\npython scripts\u002Fai_cost_economics.py path\u002Fto\u002Fworkload.json\n```\n\n## Key Questions (ask these first)\n\n- **What does this AI need to be good at, and how would you measure it?** (If no eval set, no ship.)\n- **What's the SLO on hallucination \u002F error rate?** (Without one, \"AI quality\" is a vibe.)\n- **What happens when the model is wrong?** (Fallback behavior, human-in-the-loop, blast radius.)\n- **What's the risk tier under EU AI Act, and is conformity assessment required?** (Determines product launch timeline.)\n- **At what monthly token volume does self-hosting beat API?** (Almost never below 100M tokens\u002Fmonth at frontier quality.)\n- **Are we hiring an AI engineer or an ML research scientist?** (Different jobs; founders confuse them.)\n\n## Core Responsibilities\n\n### 1. Model Build-vs-Buy\n\nThe decision is not \"use AI or not\" — it's **API vs fine-tune vs in-house** for each use case. Each path has a different TCO curve, latency profile, and capability ceiling.\n\n**Default path: API (frontier model)**\n- Use when: well-served by frontier (Claude, GPT, Gemini), QPS \u003C 100, latency budget > 1s, cost \u003C $50K\u002Fmonth\n- Why: frontier APIs are 10-100x more capable than what most teams can fine-tune in-house\n- Failure mode: API rate limits at scale, vendor lock-in, capability drift between model versions\n\n**Fine-tune a smaller model**\n- Use when: domain-specific behavior the API can't be prompted into (medical coding, legal redlining), high volume reducing API cost, latency budget \u003C 500ms, specific style\u002Fformat consistency required\n- Approaches: full fine-tune (rare), LoRA\u002FQLoRA (common), RLHF\u002FDPO (when alignment matters)\n- Failure mode: fine-tuned model lags frontier capability within 6-12 months; ongoing retraining cost\n\n**Build from scratch \u002F pre-train**\n- Use when: almost never. You're a foundation-model company, OR you have a unique data corpus, $50M+ funding, and 18+ month patience.\n- Failure mode: by the time you ship, frontier models have caught up and your sunk cost is unrecoverable\n\n**Run** `model_buildvsbuy_calculator.py` for a use-case-specific recommendation with 3-year TCO. See `references\u002Fmodel_buildvsbuy_strategy.md` for full decision tree.\n\n### 2. AI Risk Classification & Governance\n\nThe 2026 question every founder is facing: **does this AI use case trigger high-risk regulatory obligations?**\n\n**EU AI Act (in force 2026) tiers:**\n\n| Tier | Examples | Obligations |\n|---|---|---|\n| **Prohibited** | Social scoring, real-time biometric surveillance, manipulative AI | Cannot deploy in EU |\n| **High-risk** | Employment screening, credit scoring, education access, critical infrastructure, law enforcement, biometric ID | Conformity assessment, registration, post-market monitoring, transparency, human oversight |\n| **Limited-risk** | Chatbots, deepfakes, emotion recognition | Transparency: user must know they're interacting with AI |\n| **Minimal-risk** | Recommendation systems, spam filters, most B2B SaaS internals | No specific obligations |\n\n**Run** `ai_risk_classifier.py` to classify a use case and get the required-controls list.\n\n**US state patchwork (non-exhaustive):**\n\n- NYC LL 144 — Automated Employment Decision Tools (AEDTs) require annual bias audit + candidate notice\n- Colorado AI Act \u002F SB 21-169 — AI in consumer decisions (credit, insurance, employment, housing)\n- Illinois HB 53 — AI in interview\u002Fhiring\n- California SB 1001 — Bot disclosure\n- Texas TCPA — Biometric identifier capture\n- Federal NIST AI RMF — voluntary; increasingly referenced in contracts\n\n**Industry-specific overlays:**\n\n- Healthcare: FDA AI\u002FML guidance (2023), MDR (EU) for medical-device AI, 510(k) pathway for AI\u002FML-enabled medical devices\n- Financial: NYDFS Reg 23, FTC Section 5, ECOA for credit decisions\n- Insurance: NAIC model bulletin, state insurance commissioner rules\n\nSee `references\u002Fai_risk_governance.md` for the full regulatory landscape + governance program checklist.\n\n### 3. AI Cost Economics\n\n**The breakeven question:** at what monthly token volume does self-hosted inference beat API costs?\n\n**Key components:**\n\n- **API cost** — variable, per-token. Frontier models 2026: Claude Sonnet 4.6 ~$3\u002F$15 per M tokens (input\u002Foutput), GPT-4o ~$2.50\u002F$10, Gemini 2.5 ~$1.25\u002F$5\n- **Self-hosted cost** — fixed (GPU commitment) + variable (electricity). H100 spot ~$2-5\u002Fhour, A100 spot ~$1-3\u002Fhour. Llama 3.1 70B \u002F Qwen 2.5 72B: ~$0.50-2.00 per million output tokens at 70% utilization\n- **Hidden costs of self-hosting** — ops on-call, monitoring, model updates, scaling overhead, idle time penalty\n- **Hidden costs of API** — rate limits requiring multi-vendor failover, vendor lock-in, capability drift between versions, data residency\n\n**Typical breakeven (frontier-quality):** 100M–500M tokens\u002Fmonth, depending on model size and acceptable quality tradeoff. Below this, API wins. Above this, run the calculator.\n\n**Run** `ai_cost_economics.py` with workload characteristics for a breakeven point + sensitivity to GPU rates and model size.\n\nSee `references\u002Fai_cost_economics.md` for the full economics model and operational considerations.\n\n### 4. AI Team Org Evolution\n\n**The wrong question:** \"Should we hire an ML engineer or a research scientist?\"\n**The right question:** \"What's the next AI capability we need to ship, and what role unblocks that?\"\n\nStage-to-role map:\n\n| Stage | First AI hire | Then | Then |\n|---|---|---|---|\n| Pre-PMF | Founder + 1 ML-curious engineer playing with prompts | — | — |\n| Series A | **AI engineer** (applied, full-stack; owns prompts\u002Fevals\u002Fdeployment) | Second AI engineer for evals\u002Fquality | — |\n| Series B | AI\u002FML platform engineer (inference, evals, observability) | Third AI engineer for production reliability | Data scientist if model is core IP |\n| Series C | Manager of AI | ML research scientist (only if model IS the product) | AI safety \u002F red team (if customer-facing AI) |\n| Late-stage | Head of AI → CAIO | Multiple research scientists, platform team, safety\u002Fred team | Federated AI leads per business unit |\n\n**Critical distinctions:**\n\n- **AI engineer** ≠ **ML engineer** ≠ **research scientist**\n  - AI engineer: full-stack + prompts + evals + deployment. Most startups need this, not the others.\n  - ML engineer: production deployment, monitoring, retraining infrastructure. Hire after data engineer.\n  - Research scientist: model invention, novel architectures. Only at Series C+ if model is core IP.\n\n**Centralize-vs-embed for AI:** AI starts centralized (one team) and stays there longer than data team, because the surface area is smaller. Embed only when AI is being deployed in 4+ product surfaces.\n\nSee `references\u002Fai_team_org_evolution.md`.\n\n## Workflows\n\n### Workflow 1: Model Selection Decision (1 hour)\n**Goal:** Decide whether a specific use case should use API, fine-tune, or build.\n\n```bash\n# 1. Define use_case.json (volume, latency, accuracy, team size, budget)\npython scripts\u002Fmodel_buildvsbuy_calculator.py use_case.json\n# 2. Review 3-year TCO + breakeven\n# 3. Cross-check with cs-cfo-advisor on budget commitment\n# 4. Cross-check with cs-cto-advisor on engineering capacity (esp. for fine-tune)\n# 5. Log via \u002Fcs:decide; consider \u002Fcs:freeze 60 on multi-year vendor commitment\n```\n\n### Workflow 2: AI Risk Classification (2-4 hours)\n**Goal:** Classify a use case under EU AI Act + US state laws, identify required controls.\n\n```bash\n# 1. Define use_case.json (decisions affected, users, geography, sector)\npython scripts\u002Fai_risk_classifier.py use_case.json\n# 2. For HIGH-RISK: budget conformity assessment + registration\n# 3. For LIMITED-RISK: implement transparency requirements\n# 4. Cross-check with cs-general-counsel-advisor on contractual implications\n# 5. Cross-check with cs-ciso-advisor on technical safeguards\n# 6. Log via \u002Fcs:decide\n```\n\n### Workflow 3: API-to-Self-Hosted Breakeven (1 day)\n**Goal:** Decide when (and whether) to migrate from API to self-hosted inference.\n\n```bash\n# 1. Build workload.json (tokens\u002Fday, model size, latency, quality tolerance)\npython scripts\u002Fai_cost_economics.py workload.json\n# 2. Run sensitivity scenarios (low\u002Fmid\u002Fhigh GPU rates)\n# 3. Estimate migration cost (engineering time + risk)\n# 4. Cross-check with cs-cfo-advisor on capex commitment\n# 5. Cross-check with cs-cto-advisor on platform readiness\n# 6. Log via \u002Fcs:decide; pair with \u002Fcs:freeze if signing GPU commitment\n```\n\n### Workflow 4: AI Team Roadmap (1 week)\n**Goal:** Sequence next 18 months of AI hires aligned to capabilities to ship.\n\n1. List top 5 AI capabilities the product needs in 12 months\n2. Map each capability to the role that ships it (see `ai_team_org_evolution.md`)\n3. Sequence hires (one role at a time, ramp before next)\n4. Cross-check with cs-chro-advisor on comp + leveling\n5. Identify the centralize-vs-embed trigger\n\n## Output Standards\n\n```\n**Bottom Line:** [one sentence — decision and rationale]\n**The Decision:** [one of: model selection | risk classification | economics | next hire]\n**The Evidence:** [numbers from the tool, not adjectives]\n**How to Act:** [3 concrete next steps]\n**Your Decision:** [the call only the founder can make]\n```\n\n## Adjacent Skills\n\n- `..\u002Fchief-data-officer-advisor\u002F` — Training data rights, data product strategy (chains directly to model decisions)\n- `..\u002Fcto-advisor\u002F` — Architecture capacity, scaling cliffs (esp. for self-hosted inference)\n- `..\u002Fciso-advisor\u002F` — Threat modeling for AI (prompt injection, jailbreak, training data poisoning)\n- `..\u002Fgeneral-counsel-advisor\u002F` — AI contracts (vendor liability, output ownership, training-data licensing)\n- `..\u002Fcfo-advisor\u002F` — Build-vs-buy TCO math, multi-year vendor commitments\n- `..\u002Fchro-advisor\u002F` — AI team hiring + comp\n- `..\u002F..\u002F..\u002Fengineering\u002Frag-architect\u002F` — Tactical RAG implementation\n- `..\u002F..\u002F..\u002Fengineering\u002Fagent-designer\u002F` — Tactical agent architecture\n- `..\u002F..\u002F..\u002Fengineering\u002Fprompt-governance\u002F` — Tactical prompt management\n- `..\u002F..\u002F..\u002Fengineering\u002Fself-eval\u002F` — Tactical eval infrastructure\n- `..\u002F..\u002F..\u002Fengineering\u002Fllm-cost-optimizer\u002F` — Tactical inference cost optimization\n\n## References\n\n- [model_buildvsbuy_strategy.md](references\u002Fmodel_buildvsbuy_strategy.md) — Full decision tree + 3-year TCO components + when each path fails\n- [ai_risk_governance.md](references\u002Fai_risk_governance.md) — EU AI Act + NIST AI RMF + US state patchwork + industry overlays + governance program\n- [ai_cost_economics.md](references\u002Fai_cost_economics.md) — API pricing 2026 + GPU rental economics + utilization realities + migration cost\n- [ai_team_org_evolution.md](references\u002Fai_team_org_evolution.md) — Stage-to-role map + role definitions (AI engineer ≠ ML engineer ≠ scientist) + anti-patterns\n\n---\n\n**Version:** 1.0.0\n**Status:** Production Ready\n**Disclaimer:** AI regulation is evolving rapidly. This skill surfaces decisions and tradeoffs as of 2026 but cannot replace qualified AI counsel for binding compliance decisions, especially under EU AI Act conformity assessments.\n","","imported","https:\u002F\u002Fgithub.com\u002Falirezarezvani\u002Fclaude-skills","user_system_seed","SkillOPIC",true,60,240,"2026-05-16 13:49:54",{"id":8,"name":21,"slug":22,"icon":23,"description":24,"sort":25,"createdAt":26},"编程开发","coding","mdi-code-braces","代码生成、调试、审查，提升开发效率",2,"2026-05-16 12:53:40",{"id":7,"name":28,"slug":29,"icon":30,"description":31,"moduleId":8,"sort":25,"skillCount":32,"createdAt":26},"后端开发","backend","mdi-server","API、数据库、服务端架构",296,[34],{"id":35,"skillId":4,"version":36,"fileName":37,"fileSize":38,"filePath":39,"fileHash":40,"manifest":41,"createdAt":19},"f2b59090-1273-419d-a754-5875afede00b","1.0.0","chief-ai-officer-advisor.zip",42254,"uploads\u002Fskills\u002F6f5b0e59-277d-4faf-9a9c-1d2fb1fc4652\u002Fchief-ai-officer-advisor.zip","cdb43edbe68aa425029f31e46dbe57d46a64abb4e4dd84b34f1c5c661e3b912c","[{\"path\":\"SKILL.md\",\"isDirectory\":false,\"size\":14184},{\"path\":\"references\u002Fai_cost_economics.md\",\"isDirectory\":false,\"size\":10563},{\"path\":\"references\u002Fai_risk_governance.md\",\"isDirectory\":false,\"size\":12189},{\"path\":\"references\u002Fai_team_org_evolution.md\",\"isDirectory\":false,\"size\":11986},{\"path\":\"references\u002Fmodel_buildvsbuy_strategy.md\",\"isDirectory\":false,\"size\":8037},{\"path\":\"scripts\u002Fai_cost_economics.py\",\"isDirectory\":false,\"size\":16463},{\"path\":\"scripts\u002Fai_risk_classifier.py\",\"isDirectory\":false,\"size\":20175},{\"path\":\"scripts\u002Fmodel_buildvsbuy_calculator.py\",\"isDirectory\":false,\"size\":17081}]",{"code":43,"message":44,"data":45},200,"success",{"items":46,"stats":47,"page":50},[],{"averageRating":48,"totalRatings":48,"ratingCounts":49},0,[48,48,48,48,48],{"limit":51,"offset":48,"hasMore":52,"nextOffset":51,"ratedOnly":16},15,false]