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CAIO Review (Chief AI Officer Interrogation)

Pressure-tests any AI plan with six CAIO questions covering eval sets, hallucination SLOs, EU AI Act tiering, cost trajectory and AI hiring.

Dev & CodingIntermediate★ 24,151⑂ 3,405AI score 7/10Last updated: Aug 9, 2026

What it does

Invoke /cs:caio-review <plan> and an eval-demanding Chief AI Officer persona interrogates your plan with six mandatory questions:

  1. What must this AI be good at, and how will you measure it? (50–100 representative inputs minimum)
  2. What's the SLO on hallucination/error rate, and what's the fallback? (detection, human-in-loop, blast radius)
  3. What's the EU AI Act risk tier, and is a conformity assessment required? (PROHIBITED / HIGH / LIMITED / MINIMAL)
  4. API, fine-tune, or build? (economic breakeven plus practical feasibility)
  5. What's the 12-month cost trajectory at expected scale? (self-host hidden costs, prompt caching)
  6. Which role unblocks this, and are prerequisite hires in place? (AI engineer vs ML engineer vs research scientist)

Output is a structured Markdown report with a 🟢 SHIP / 🟡 SHARPEN / 🔴 BLOCK verdict and three next steps. The quantitative parts call companion Python scripts (build-vs-buy calculator, risk classifier, cost economics) shipped in the repo.

Who it's for

  • Founders and PMs about to ship an AI-powered feature
  • CTOs/finance leads facing multi-year API or GPU infrastructure commitments
  • Teams launching in the EU or deploying AI in regulated domains (employment, credit, healthcare, education)
  • Companies making their first AI hire and unsure which role to open

Examples

  • Pre-launch gate: "We ship a customer support chatbot next week" → flags the missing eval set and undefined hallucination SLO, returns 🟡 SHARPEN with a fallback design.
  • Model sourcing: "Should we self-host our 300M-token/month summarization workload?" → compares breakeven volume, 3-year TCO and hidden ops costs, recommends staying on API.
  • Regulatory check: "Roll out resume-screening AI in the EU" → classifies HIGH risk, warns about conformity assessment, EU database registration, timeline and budget → 🔴 BLOCK.

· · · Install guide · · ·

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
↓ Download ZIP
Install in Claude Code

Let Claude do it — paste this into Claude Code

Install the skill I found on Claude Skill Mart.
Copy the .gemini/skills/caio-review folder from the GitHub repo alirezarezvani/claude-skills into my ~/.claude/skills/caio-review/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/alirezarezvani/claude-skills.git && mkdir -p ~/.claude/skills && cp -r claude-skills/.gemini/skills/caio-review ~/.claude/skills/

⚠ This is a third-party skill. Check the source repository before installing.

  1. Open a terminal.
  2. Clone the repository: git clone https://github.com/alirezarezvani/claude-skills.git
  3. Create the skills folder: mkdir -p ~/.claude/skills
  4. Copy this skill: cp -r claude-skills/.gemini/skills/caio-review ~/.claude/skills/
  5. To use the workflow scripts, also copy the companion skill: cp -r claude-skills/skills/chief-ai-officer-advisor ~/.claude/skills/ (adjust if the relative paths differ in your layout).
  6. Verify Python 3 is available: python3 --version
  7. Restart Claude Code, then run something like /cs:caio-review our chatbot launch plan.
  8. Prepare use_case.json and workload.json describing your use case and traffic volume so the calculators can produce real numbers.
View source on GitHub ↗License: MIT