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vertex-ai-api-dev

Guides the usage of Gemini API on Google Cloud Vertex AI with the Gen AI SDK. Use when the user asks about using Gemini in an enterprise environment or explicitly mentions Vertex AI. Covers SDK usage (Python, JS/TS, Go, Java, C#), capabilities like Live API, tools, multimedia generation, caching, and batch prediction.

74

Quality

92%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

85%Weight 40%Scale 1-3

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

A well-structured, highly actionable reference skill with excellent progressive disclosure and a clear core workflow. Its only real weakness is conciseness: the five full per-language quick-start examples and a marketing-style opener add tokens that could be trimmed without losing clarity.

Suggestions

Collapse the five near-identical language quick-starts into one fully worked example (e.g., Python) plus a short table or per-language one-liner showing only the client init + call differences, to cut repetitive boilerplate.

Remove the marketing opener 'Access Google's most advanced AI models built for enterprise use cases' and let the capability list and directive stand on their own.

The top 'Provide these key capabilities' bullet list partially duplicates the per-capability reference pointers at the bottom; consider merging them so each capability appears once with its reference link.

DimensionReasoningScore

Conciseness

The body is mostly efficient with concrete value (exact env vars, install commands, model names), but it pads tokens by repeating a full hello-world quick start across all five languages and opens with mild marketing fluff ('Access Google's most advanced AI models built for enterprise use cases'). It could be tightened to one representative example plus language-specific notes, so it is the score-2 'mostly efficient but could be tightened' rather than the lean score-3.

2 / 3

Actionability

Provides fully executable, copy-paste-ready guidance: install commands per language (pip/npm/go get/dotnet/gradle/maven), exact auth env vars with export commands, and complete generate_content examples in all five SDKs with concrete model names. This matches the score-3 'fully executable code/commands; copy-paste ready' anchor.

3 / 3

Workflow Clarity

The core flow is logically sequenced and unambiguous — install SDK, configure auth (ADC or Express Mode), initialize client, call generate_content — with the single primary action clearly demonstrated. The body contains no destructive or batch operation requiring validation checkpoints (those live in references), so no score-2 cap applies; the well-organized single-action flow qualifies for score-3 per the simple-skill note.

3 / 3

Progressive Disclosure

SKILL.md acts as a concise overview pointing to nine real, one-level-deep reference files (all verified present in references/), each clearly signaled as 'See [references/xxx.md]'. Content is appropriately split — getting-started stays inline while per-capability detail is externalized — matching the score-3 'clear overview with well-signaled one-level-deep references' anchor.

3 / 3

Total

11

/

12

Passed

Description

100%Weight 40%Scale 1-3

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

A strong description: it states a concrete capability set, provides an explicit 'Use when' trigger, names natural user-facing terms, and scopes itself clearly to the Vertex AI enterprise niche. No significant weaknesses to address.

DimensionReasoningScore

Specificity

Enumerates a comprehensive set of concrete capabilities rather than vague language: 'SDK usage (Python, JS/TS, Go, Java, C#), capabilities like Live API, tools, multimedia generation, caching, and batch prediction'. This lists multiple specific concrete capability areas, matching the score-3 anchor; it is not the score-2 'some actions but not comprehensive' because the coverage is broad and specific.

3 / 3

Completeness

Explicitly answers both 'what' ('Guides the usage of Gemini API on Google Cloud Vertex AI with the Gen AI SDK') and 'when' with an explicit 'Use when...' clause, matching the score-3 anchor. It is not score-2 because the trigger guidance is explicit rather than merely implied.

3 / 3

Trigger Term Quality

Includes natural terms a user would actually say — 'Gemini API', 'Vertex AI', 'enterprise environment', 'Gemini' — in the explicit trigger clause 'Use when the user asks about using Gemini in an enterprise environment or explicitly mentions Vertex AI'. These are good natural-voice triggers, not just technical jargon.

3 / 3

Distinctiveness Conflict Risk

The 'explicitly mentions Vertex AI' / 'enterprise environment' trigger carves a clear niche distinct from a non-Vertex Gemini Developer API skill, making wrong-skill triggering unlikely. This matches the score-3 'clear niche with distinct triggers' anchor.

3 / 3

Total

12

/

12

Passed

Validation

93%

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

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

Total

15

/

16

Passed

Repository
JetBrains/skills
Reviewed

Table of Contents

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