Name: chief-ai-officer-advisor
Rating: 68 (1 reviews)
Author: alirezarezvani

chief-ai-officer-advisor

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/ML skills.

Quality

83%

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Securityby

Passed

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The canonical home for this skill is chief-ai-officer-advisor in alirezarezvani/claude-skills

Quality

Content

67%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is well-organized and highly actionable, with clear workflows and a clean one-level reference structure. Its main weaknesses are conciseness padding (a redundant Keywords block and embedded time-sensitive 2026 pricing) and the fact that all referenced reference/scripts bundle files are missing from the shipped skill.

Suggestions

Remove or shrink the Keywords section — its terms already appear in the description's trigger clause, so it duplicates context-window cost without adding skill-selection signal.

Move time-sensitive 2026 specifics (model pricing, 'EU AI Act in force 2026', version-dated figures) into a clearly labeled 'Current as of 2026 — verify before use' or deprecated-section callout so stale figures don't pollute the core guidance.

Ship the referenced bundle files (references/*.md and scripts/*.py) or drop the file-path references and inline the minimum needed decision logic so the Quick Start commands actually execute.

Dimension	Reasoning	Score
Conciseness	Mostly efficient but padded: a large Keywords block re-lists trigger terms already in the description, and time-sensitive 2026 specifics ('EU AI Act in force 2026', 'Claude Sonnet 4.6 ~$3/$15', 'Gemini 2.5') are woven into main content rather than isolated in a deprecated/old-patterns section, which the rubric penalizes.	3 / 5
Actionability	Copy-paste-ready bash commands with embedded samples ('python scripts/model_buildvsbuy_calculator.py path/to/use_case.json') and a concrete Output Standards template; the gap is that the referenced scripts/ files are absent from the bundle, so commands cannot actually run as shipped.	4 / 5
Workflow Clarity	Four workflows each carry a goal, time estimate, numbered steps, cross-checks with adjacent advisors, and decision-logging checkpoints ('Log via /cs:decide; consider /cs:freeze 60'); these are advisory rather than destructive/batch ops, so the absence of validate-fix-retry loops is only a minor gap.	4 / 5
Progressive Disclosure	Clear overview with well-signaled one-level-deep references (a References section lists model_buildvsbuy_strategy.md, ai_risk_governance.md, ai_cost_economics.md, ai_team_org_evolution.md each with a description); the gap is that none of these reference or scripts/ files exist in the actual bundle, so navigation breaks.	4 / 5
	Total	15 / 20 Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is exemplary: it states a clear strategic niche, enumerates four concrete decision domains, provides comprehensive natural trigger terms, and explicitly disambiguates from engineering AI/ML skills. Both the 'what' and 'when' are answered with concrete phrases.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete decision domains — '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' — giving comprehensive coverage of the CAIO remit.	5 / 5
Completeness	Explicitly answers both what (the four advisory domains) and when ('Use when deciding whether ... or when user mentions ...') with concrete trigger phrases, matching the top anchor exactly.	5 / 5
Trigger Term Quality	Comprehensive natural-term coverage with synonyms: 'Use when deciding whether to call an API or fine-tune ... 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.'	5 / 5
Distinctiveness Conflict Risk	Clear CAIO strategic niche with distinct triggers and explicit disambiguation — 'Strategic only — does not duplicate engineering AI/ML skills' — minimizing conflict with adjacent engineering skills.	5 / 5
	Total	20 / 20 Passed

Validation

87%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
relative_links	Relative link issues: 4 missing	Warning
referenced_paths_exist	Referenced path issues: 17 missing	Warning

	Total	14 / 16 Passed

Repository: alirezarezvani/claude-skills
Path: .gemini/skills/chief-ai-officer-advisor/SKILL.md
Commit: 19392f7

Reviewed: ~2 hours ago

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