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Agent skill

Chief AI Officer Advisor

by borghei in borghei/Claude-Skills

AI leadership advisor on AI strategy, governance, risk, investment, and org design.

MITAuto-check passedLegal & Compliance

Install Chief AI Officer Advisor

skills CLI
$ npx skills add borghei/Claude-Skills --skill chief-ai-officer-advisor -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install borghei/Claude-Skills chief-ai-officer-advisor --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/c-level-advisor/chief-ai-officer-advisor .claude/skills/chief-ai-officer-advisor && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
chief-ai-officer-advisor
GitHub stars
891
Token cost
~2.2k tokens
SKILL.md length
1,033 words
Files
7 (incl. scripts, references)
Skills in repo
354
Repo updated
First seen
Licence
MIT

At a glance

AI leadership advisor on AI strategy, governance, risk, investment, and org design.

  • Works in 4 steps: Pull the latest org context: portfolio,… → Run ai_maturity_assessor.py on a… → Review the dimension-level scores… → …
  • Defining an AI strategy
  • SKILL.md covers When to use this skill, Inputs the advisor expects, Workflows and Decision frameworks, plus 5 more sections
  • Runs Python scripts from its folder; calls python3

What it does

Chief AI Officer Advisor is an agent skill from borghei/Claude-Skills. AI leadership advisor on AI strategy, governance, risk, investment, and org design. Use when defining an AI strategy, building an AI governance program, scoring AI maturity, or drafting an AI risk register.

Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts and reference files (for example `references/ai-org-and-talent.md`, `references/ai-risk-and-governance.md` and `references/ai-strategy-framework.md`).

It sits in Legal & Compliance, covering AI governance. The repository describes itself as: 385 AI skills, 77 expert agents, and 900 stdlib Python tools for every team: engineering, PM, marketing, C-level, compliance, business ops, research, and a LinkedIn toolkit… The licence is MIT.

When your agent uses it

  • Defining an AI strategy
  • Building an AI governance program
  • Scoring AI maturity
  • Drafting an AI risk register

Example prompts

  • “/chief-ai-officer-advisor”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Pull the latest org context: portfolio, team, governance, infra, spend.
  2. Run ai_maturity_assessor.py on a populated input JSON.
  3. Review the dimension-level scores (strategy, data, MLOps, governance, people)
  4. Translate gaps into a quarterly OKR draft for the AI org.

What it can do on your machine

Read from SKILL.md and the folder at commit 4a698e8. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 3 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Chief AI Officer Advisor loads about 2.2k tokens when it runs, and up to ~9.8k if it reads all its reference files. Until then it costs about 58 tokens; SKILL.md has 1,033 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~58
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~9.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from borghei/Claude-Skills at commit 4a698e8, republished under its MIT licence (© borghei). 1,033 words, ~2,168 tokens.

Download SKILL.mdSave it as .claude/skills/chief-ai-officer-advisor/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
chief-ai-officer-advisor
description
AI leadership advisor on AI strategy, governance, risk, investment, and org design. Use when defining an AI strategy, building an AI governance program, scoring AI maturity, or drafting an AI risk register.
license
MIT + Commons Clause
metadata.version
1.0.0
metadata.author
borghei
metadata.category
executive-leadership
metadata.domain
c-level-advisor
metadata.updated
2026-05-27
metadata.tags
ai, strategy, governance, risk, mlops, org-design, investment

Chief AI Officer Advisor

The agent acts as a fractional Chief AI Officer, providing AI strategy and operating-model guidance grounded in modern AI governance frameworks (NIST AI RMF, ISO 42001, EU AI Act), MLOps maturity references, and enterprise AI investment heuristics.

When to use this skill

  • Defining the AI strategy for the next 12–24 months (themes, bets, KPIs)
  • Designing an AI operating model: centralized vs federated vs hybrid
  • Building an AI governance program that satisfies internal and regulatory expectations
  • Drafting an AI risk register and aligning it to NIST AI RMF / ISO 42001
  • Scoring AI maturity across strategy, data, MLOps, governance, and people
  • Planning AI investment: capex/opex split, build-vs-buy, infra vs talent vs tooling
  • Preparing AI updates for the board (results, risks, regulatory posture, asks)

Inputs the advisor expects

When invoking this skill, you should provide some combination of:

  • The company stage, sector, and regulatory exposure (e.g., financial services, healthcare, education)
  • Current AI portfolio (production use cases, pilots, evaluations, killed projects)
  • Data assets and constraints (data quality, governance maturity, sovereignty)
  • Existing AI/ML team composition (DS, MLE, MLOps, governance, product, legal/compliance)
  • Existing AI policies, model risk management framework, AUP, and acceptable-use policies
  • Spend posture: total AI spend (people + infra + tooling), trailing year + plan
  • Top stakeholders and current frictions (CEO, CTO, CISO, CFO, GC, business leaders)

Workflows

Workflow 1 — Assess AI maturity (0-100, 5 dimensions)
  1. Pull the latest org context: portfolio, team, governance, infra, spend.
  2. Run ai_maturity_assessor.py on a populated input JSON.
  3. Review the dimension-level scores (strategy, data, MLOps, governance, people) and the prioritized gap list.
  4. Translate gaps into a quarterly OKR draft for the AI org.
bash
python3 chief-ai-officer-advisor/scripts/ai_maturity_assessor.py \
  --input company_ai_state.json --format markdown
Workflow 2 — Plan AI investment for the next budget cycle
  1. Collect candidate initiatives (existing + proposed) with cost, expected impact, risk tier (EU AI Act minimal/limited/high-risk) and dependencies.
  2. Run ai_investment_planner.py to allocate budget across themes using a strategic-fit × value × risk scoring model.
  3. Use the output to build the CFO submission and the board appendix.
bash
python3 chief-ai-officer-advisor/scripts/ai_investment_planner.py \
  --input ai_portfolio.json --budget 5000000 --format markdown
Workflow 3 — Stand up a baseline AI risk register
  1. Walk the AI portfolio and tag each system by risk tier, modality, data sensitivity, and business criticality.
  2. Run ai_risk_register_generator.py to seed a register aligned to NIST AI RMF (Govern/Map/Measure/Manage) and ISO 42001 (AIMS clauses).
  3. Assign owners and review cadences; route through the governance committee.
bash
python3 chief-ai-officer-advisor/scripts/ai_risk_register_generator.py \
  --input ai_systems.json --framework nist-ai-rmf --format markdown

Decision frameworks

Centralize vs federate AI
SignalLean centralizedLean federated
Regulatory exposureHigh (finance, health, public sector)Low/medium
Org size<500 engineers>1000 engineers, BU autonomy
MaturityEarly (need to set standards)Late (BUs have ML chops)
Risk appetiteConservativeAggressive, fast iteration

A typical pattern at scale is hub-and-spoke: a central AI/ML platform and governance team (the hub) sets standards, owns infra, and reviews high-risk systems; embedded ML squads (the spokes) own product outcomes inside business units. The advisor will recommend this as the default unless context says otherwise.

Build vs buy vs partner
  • Build when the capability is differentiating (proprietary data + workflow)
  • Buy when the capability is undifferentiated and well-served by SaaS (transcription, generic chat UI, vector store)
  • Partner when there's deep model IP you can't replicate and the partner is willing to accept your governance terms (e.g., a frontier-lab partnership with a data-residency contract)
When to declare a system "high-risk" under EU AI Act

Use ai_risk_register_generator.py --framework eu-ai-act to test classification against Annex III categories. If the system is in scope of one of the eight high-risk categories (e.g., employment screening, credit scoring, critical infrastructure), trigger the conformity assessment + post-market monitoring playbook from references/ai-risk-and-governance.md.

Common engagements

"Help me write the AI section of the board deck"
  1. Run the maturity assessor; pull dimension scores and 3-month delta.
  2. Pull top 3 wins and top 3 risks from the risk register output.
  3. Use the What changed / What's next / Asks structure (see c-level-advisor/board-deck-builder).
  4. Keep the section to one page; reserve detail for the appendix.
Show full SKILL.md (394 more words)Show less
"We're being asked to deploy a high-risk AI system in 6 months. What do we do?"
  1. Classify under EU AI Act Annex III + ISO 42001 risk categorization.
  2. Stand up the AI Impact Assessment (use ra-qm-team/audit-prep/aims-audit skill).
  3. Confirm the data is governed (lineage, consent, minimisation).
  4. Define the human oversight model and acceptance criteria.
  5. Plan post-market monitoring + incident reporting (Article 73).
  6. Get the AI governance committee sign-off before deployment.
"What should our AI org look like in 12 months?"
  1. Map current state to the target operating model (hub-and-spoke vs federated).
  2. Identify roles to hire/promote: AI platform lead, ML governance lead, applied ML squads.
  3. Define a RACI for: model approvals, infra spend, incident response, vendor reviews.
  4. Plan the L&D investment for non-ML engineers (prompt eng, eval design, AI literacy).

Anti-patterns to avoid

  • AI strategy that doesn't tie to a business outcome. Strategy without P&L attribution becomes a research project.
  • One governance committee for everything. Split: an exec AI council (strategy, spend) from a technical model review board (architectures, eval results).
  • Banning the LLM tool that everyone is already using. Set acceptable-use policies, provide a sanctioned tool, monitor — don't drive usage underground.
  • Treating AI risk as someone else's problem. The CAIO owns the model risk taxonomy; legal/compliance partners on enforcement.
  • Buying eight LLM platforms. Consolidate to one or two; the value is in eval, governance, and shared infra, not in tool sprawl.
  • Forgetting that 70% of "AI" cost is data + people. Infra is the noisy line; people and data quality are where you actually spend.

References

  • references/ai-strategy-framework.md — strategy themes, operating models, prioritization heuristics
  • references/ai-risk-and-governance.md — NIST AI RMF, ISO 42001, EU AI Act mapping
  • references/ai-org-and-talent.md — org-design patterns, role definitions, hiring sequence
  • c-level-advisor/cto-advisor — for the technical platform decisions that intersect AI
  • c-level-advisor/ciso-advisor — for AI security risks (prompt injection, model theft, data exfil)
  • ra-qm-team/iso42001-ai-management — for the deep AIMS implementation
  • ra-qm-team/eu-ai-act-specialist — for high-risk AI system conformity
  • ra-qm-team/audit-prep/ai-act-readiness — for short-runway EU AI Act readiness sprints
  • engineering/senior-ml-engineer — for the implementation side of model deployment
  • engineering/senior-prompt-engineer — for LLM-specific patterns

Output expectations

When the advisor runs, the user should be able to walk away with:

  1. A clearly stated point of view (not "it depends")
  2. 2–4 concrete next actions with owners and timelines
  3. Open questions that materially change the recommendation
  4. References to relevant scripts and reference docs that deepen the analysis

© borghei, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 6 other files (scripts, references) in c-level-advisor/chief-ai-officer-advisor of borghei/Claude-Skills.

  • SKILL.md
  • references/ai-org-and-talent.md
  • references/ai-risk-and-governance.md
  • references/ai-strategy-framework.md
  • scripts/ai_investment_planner.py
  • scripts/ai_maturity_assessor.py
  • scripts/ai_risk_register_generator.py

Open the folder on GitHubat commit 4a698e8

Compare with similar skills

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AI Risk Managementbriiirussell/cybersecurity-skills413—~3.7kAutomated safety check: NotesMIT
EU AI Act System Inventoryanthropics/claude-for-legal9.6k3 repos~2.8kAutomated safety check: PassApache-2.0
Eu AI Act Readinessseb1n/awesome-ai-agent-skills206—~3.3kAutomated safety check: PassMIT
AI GovernanceHack23/cia239—~1.4kAutomated safety check: PassApache-2.0

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Categories

Questions about Chief AI Officer Advisor

What does Chief AI Officer Advisor do?

AI leadership advisor on AI strategy, governance, risk, investment, and org design. Chief AI Officer Advisor is an agent skill from borghei/Claude-Skills. AI leadership advisor on AI strategy, governance, risk, investment, and org design.

When should I use Chief AI Officer Advisor?

Chief AI Officer Advisor fits situations like: defining an AI strategy; building an AI governance program; scoring AI maturity; drafting an AI risk register.

How do I install Chief AI Officer Advisor in Claude Code?

Run `npx skills add borghei/Claude-Skills --skill chief-ai-officer-advisor -a claude-code`. Or copy the skill folder (c-level-advisor/chief-ai-officer-advisor in borghei/Claude-Skills) into .claude/skills/chief-ai-officer-advisor in your project. Claude Code loads it when a task matches its description.

How do I install Chief AI Officer Advisor in Codex?

Run `npx skills add borghei/Claude-Skills --skill chief-ai-officer-advisor -a codex`. Or copy the skill folder (c-level-advisor/chief-ai-officer-advisor in borghei/Claude-Skills) into .agents/skills/chief-ai-officer-advisor in your project. Codex loads it when a task matches its description.

Can I use Chief AI Officer Advisor in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add borghei/Claude-Skills --skill chief-ai-officer-advisor -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/chief-ai-officer-advisor, .gemini/skills/chief-ai-officer-advisor, .github/skills/chief-ai-officer-advisor and .opencode/skills/chief-ai-officer-advisor in your project.

What does Chief AI Officer Advisor need to run?

Going by SKILL.md and its folder, Chief AI Officer Advisor needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Chief AI Officer Advisor access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Chief AI Officer Advisor safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Chief AI Officer Advisor use?

Chief AI Officer Advisor is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Chief AI Officer Advisor use?

About 2.2k tokens (SKILL.md is roughly 8.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 7.7k tokens, read only when the agent opens those files.

What are the alternatives to Chief AI Officer Advisor?

Skills that share tags, products or a category with Chief AI Officer Advisor: Iso42001 (Sushegaad/Claude-Skills-Governance-Risk-and-Compliance, 946 stars), AI Risk Management (briiirussell/cybersecurity-skills, 413 stars), EU AI Act System Inventory (anthropics/claude-for-legal, 9.6k stars) and Eu AI Act Readiness (seb1n/awesome-ai-agent-skills, 206 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Chief AI Officer Advisor?

borghei (a GitHub user) maintains it in borghei/Claude-Skills, which has 891 GitHub stars. The repository holds 354 skills in this directory. The repository was last updated on October 7, 2026.

Source: borghei/Claude-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.