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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.

75

Quality

93%

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SecuritybySnyk

The risk profile of this skill

SKILL.md
Quality
Evals
Security

Quality

Content

86%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 advisory skill with real bundle files and clear one-level-deep references; its main weakness is mild verbosity from a Keywords block that duplicates the description's triggers.

Suggestions

Remove or drastically shrink the Keywords section — its trigger terms already appear in the frontmatter description, so it costs tokens without adding discovery signal.

Tighten the explanatory prose in Core Responsibilities (e.g. 'Why: frontier APIs are 10-100x more capable...') to decision-criteria-only phrasing, trusting Claude's knowledge of what fine-tuning and APIs are.

Add an explicit verification/checkpoint note to each workflow (e.g. 'Confirm the calculator output before logging via /cs:decide') to nudge workflow_clarity from good to exemplary.

DimensionReasoningScore

Conciseness

Mostly efficient and high-signal (concrete pricing, GPU rates, stage-to-role tables), but the standalone Keywords block largely duplicates trigger terms already present in the description and some explanatory prose could be trimmed.

4 / 5

Actionability

Copy-paste-ready Quick Start commands cover all four decisions (e.g. 'python scripts/model_buildvsbuy_calculator.py path/to/use_case.json'), scripts embed sample workloads, and decision criteria are backed by specific numeric thresholds rather than adjectives.

5 / 5

Workflow Clarity

Four workflows are clearly sequenced with cross-check checkpoints and conditional branches (e.g. 'For HIGH-RISK: budget conformity assessment'), but they lack explicit validate→fix→retry feedback loops; acceptable here since these are advisory decision workflows rather than destructive or batch operations.

4 / 5

Progressive Disclosure

Clear overview body points to four one-level-deep reference files and three scripts, all of which exist on disk; references are explicitly signaled inline (e.g. 'See references/model_buildvsbuy_strategy.md') and listed again in a References section for easy navigation.

5 / 5

Total

18

/

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 exemplary description: concrete capabilities, comprehensive natural trigger terms, explicit what-and-when guidance, and a clearly stated boundary that prevents conflicts with adjacent engineering skills.

DimensionReasoningScore

Specificity

Lists multiple concrete decision areas — '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 rather than vague abstractions.

5 / 5

Completeness

Explicitly answers both what ('Chief AI Officer advisory for startups: ...') and when ('Use when deciding whether to call an API or fine-tune, classifying AI use cases ...') with concrete trigger phrases, matching the top anchor.

5 / 5

Trigger Term Quality

Comprehensive natural trigger terms a CAIO or founder would actually say — 'CAIO, AI strategy, model selection, foundation model, fine-tuning, EU AI Act, NIST AI RMF, AI governance, model risk, or AI economics' — alongside action-oriented phrases like 'sequencing AI hires' and 'calculating when self-hosting pays off'.

5 / 5

Distinctiveness Conflict Risk

Clear CAIO-strategic niche with an explicit boundary — 'Strategic only — does not duplicate engineering AI/ML skills' — minimizing overlap with tactical AI/ML 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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