Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add bestagentkits/agency-skills --skill chief-ai-officer-advisorgit clone --depth 1 https://github.com/bestagentkits/agency-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/bestagentkits/agency-skills/chief-ai-officer-advisor)<a href="https://agentmods.dev/skills/bestagentkits/agency-skills/chief-ai-officer-advisor"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/chief-ai-officer-advisor/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/bestagentkits/agency-skills/chief-ai-officer-advisor"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/chief-ai-officer-advisor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00148 | $0.03527 |
| Opus 5 | $0.00074 | $0.01764 |
| Sonnet 5 | $0.00030 | $0.00705 |
| Haiku 4.5 | $0.00015 | $0.00353 |
Grade A, and why
chief-ai-officer-advisor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
# Decision B: Risk classification under EU AI Act + US state laws
python scripts/ai_risk_classifier.py # embedded hiring-AI sample
python scripts/ai_risk_classifier.py path/to/use_case.json
# Decision C: API vs self-hosted economics
python scripts/ai_cost_economics.py # embedded 5M tokens/day sample
python scripts/ai_cost_economics.py path/to/workload.json
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 233 B
- references/ai_cost_economics.md 10 KB
- references/ai_risk_governance.md 12 KB
- references/ai_team_org_evolution.md 12 KB
- references/model_buildvsbuy_strategy.md 7.8 KB
- scripts/ai_cost_economics.py 16 KB runs code
- scripts/ai_risk_classifier.py 20 KB runs code
- scripts/model_buildvsbuy_calculator.py 17 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 237 lines · 148 tokens per session scan A eead1069da4d
chief-ai-officer-advisor is a skill published in the GitHub repository bestagentkits/agency-skills (11 stars, last pushed 2mo ago), licensed MIT. It adds 148 tokens to every session and 3,527 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
analyze-generative-diffusion-model
Analyze pre-trained generative diffusion models (Stable Diffusion, DALL-E, Flux) by computing quality metrics (FID, IS, CLIP score, precision/recall), inspecting noise schedules, extracting and visualizing attention maps, and probing latent spaces. Use when evaluating a pre-trained generative diffusion model's output…
deploy-edge-ai-model
Deploy machine learning models to edge devices using Google AI Edge Gallery, TensorFlow Lite, ONNX Runtime, and MediaPipe. Covers model quantization (INT8/INT4), on-device inference with Gemma 4 models, Android/iOS deployment via AI Edge Gallery, hardware delegate selection (GPU/NPU/DSP), and performance benchmarking…
build-feature-store
Build a feature store using Feast for centralized feature management, configure offline and online stores for batch and real-time serving, define feature views with transformations, and implement point-in-time correct joins for ML pipelines. Use when managing features for multiple ML models, ensuring training-serving…
deploy-ml-model-serving
Deploy machine learning models to production serving infrastructure using MLflow, BentoML, or Seldon Core with REST/gRPC endpoints, implement autoscaling, monitoring, and A/B testing capabilities for high-performance model inference at scale. Use when deploying trained models for real-time inference, setting up REST…
design-serialization-schema
Design serialization schemas using JSON Schema, Protocol Buffer definitions, or Apache Avro. Covers schema versioning, backwards compatibility, validation rules, and evolution strategies for long-lived data formats. Use when defining a new API contract or data interchange format, adding fields to an existing schema…
数据搬运工
A general data-transfer and format-conversion tool for moving, cleaning, and reshaping files. It works with spreadsheets, CSV and TSV tables, JSON, XML, YAML, SQL inserts, and SQLite databases.