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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.

70

Quality

85%

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SecuritybySnyk

The risk profile of this skill

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

SKILL.md
Quality
Evals
Security

Quality

Content

71%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.

A well-structured, actionable strategic skill with strong progressive disclosure and clear workflows. The main weakness is conciseness: time-sensitive pricing and model versions are inlined rather than isolated, and the four decisions are restated across multiple sections.

Suggestions

Move time-sensitive specifics (2026 model versions, per-token pricing, 'EU AI Act in force 2026') into the existing references (e.g. ai_cost_economics.md) or a dated 'current rates' section, and keep the body version-agnostic to avoid staleness and reduce token weight.

Avoid restating the four decisions verbatim across the intro, Core Responsibilities, and Workflows — let the intro list them once and have later sections reference rather than re-enumerate.

Add an explicit validation/feedback step in the cost-economics and build-vs-buy workflows (e.g. 're-run the calculator after confirming GPU spot rates; abort if breakeven shifts beyond tolerance') to lift workflow clarity to the top anchor.

DimensionReasoningScore

Conciseness

The body is lean and opinionated, but it inlines time-sensitive specifics (model versions 'Claude Sonnet 4.6', 'GPT-4o', 'Gemini 2.5', 2026 dates, per-token dollar pricing) outside any deprecated/old-patterns section, and repeats the same four decisions across the intro, Core Responsibilities, and Workflows.

3 / 5

Actionability

It provides executable commands ('python scripts/model_buildvsbuy_calculator.py use_case.json') and concrete numeric thresholds (QPS < 100, latency > 1s, cost < $50K/month), though the full executable logic lives in the referenced scripts rather than inline.

4 / 5

Workflow Clarity

Four workflows are numbered with cross-check checkpoints against adjacent advisors and /cs:decide logging, giving a clear sequence with most checkpoints present; explicit validate->fix->retry feedback loops are absent but these are advisory rather than destructive/batch operations.

4 / 5

Progressive Disclosure

Clear overview of four decisions with well-signaled, one-level-deep references to four existing reference files and three executable scripts, each linked with a descriptive label in the References section — content is appropriately split and easy to navigate.

5 / 5

Total

16

/

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.

An exceptionally strong description: concrete actions, comprehensive natural trigger terms, explicit what/when structure, and a clear distinctiveness boundary. It hits the top anchor on every dimension.

DimensionReasoningScore

Specificity

Names multiple concrete advisory actions — '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' — with comprehensive coverage and no vague filler.

5 / 5

Completeness

It explicitly answers both what (four named advisory decisions) and when ('Use when deciding whether to call an API or fine-tune... or when user mentions CAIO, AI strategy...') with concrete trigger phrases, matching the top anchor.

5 / 5

Trigger Term Quality

The 'Use when' clause enumerates natural trigger phrases users would say — 'CAIO, AI strategy, model selection, foundation model, fine-tuning, EU AI Act, NIST AI RMF, AI governance, model risk, or AI economics' — covering synonyms and regulatory terms comprehensively.

5 / 5

Distinctiveness Conflict Risk

It carves a clear CAIO-strategy niche and explicitly states 'Strategic only — does not duplicate engineering AI/ML skills', minimizing overlap with adjacent engineering skills.

5 / 5

Total

20

/

20

Passed

Validation

100%

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

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
alirezarezvani/claude-skills
Reviewed

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