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azure-ai-vision-imageanalysis-java

Build image analysis applications with Azure AI Vision SDK for Java. Use when implementing image captioning, OCR text extraction, object detection, tagging, or smart cropping.

66

Quality

80%

Does it follow best practices?

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SecuritybySnyk

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tessl review fix ./skills/azure-ai-vision-imageanalysis-java/SKILL.md
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, executable Java SDK reference with concrete code for every feature. Its main weakness is progressive disclosure: all reference material is inlined in a single long file with no supporting bundle files.

Suggestions

Move the per-feature code patterns and the Visual Features table into a reference file (e.g. references/features.md) and signal it one level deep from SKILL.md to improve progressive disclosure.

Define `imageUrl` (or use a concrete URL string as the caption example does) in the object/tag/people/smart-crop/dense-caption/multi-feature snippets so every example is copy-paste ready.

Replace the generic 'When to Use' and 'Limitations' boilerplate with skill-specific guidance, or remove the redundant intro line that repeats the frontmatter description.

DimensionReasoningScore

Conciseness

Mostly lean code with minimal prose and no basic-concept padding, but the redundant intro line and generic 'When to Use'/'Limitations' boilerplate could be trimmed.

4 / 5

Actionability

Concrete, import-included Java snippets cover every visual feature, but later examples reference an undefined `imageUrl` variable, a recurring minor gap from fully copy-paste ready.

4 / 5

Workflow Clarity

The install → create client → analyze reading order is clear and error handling is shown; no validation checkpoints are required since this is a reference, not a destructive or batch workflow.

4 / 5

Progressive Disclosure

Section headers are well organized, but with no bundle files everything — per-feature examples and the features table that could be separate references — is inlined in one ~290-line file with no one-level-deep references signaled.

3 / 5

Total

15

/

20

Passed

Description

88%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.

A strong, third-person description that clearly states what it does and when to use it with concrete feature triggers. Trigger-term coverage is good but could add synonyms for full distinctiveness.

DimensionReasoningScore

Specificity

Enumerates multiple concrete actions — image captioning, OCR text extraction, object detection, tagging, and smart cropping — giving comprehensive coverage of the SDK's capabilities rather than just a few.

5 / 5

Completeness

Explicitly states both what it does ('Build image analysis applications with Azure AI Vision SDK for Java') and when to use it ('Use when implementing...'), with concrete trigger phrases.

5 / 5

Trigger Term Quality

Includes natural phrases users would say ('image captioning', 'OCR text extraction', 'object detection', 'smart cropping') but lacks synonyms (e.g. 'computer vision') for full coverage.

4 / 5

Distinctiveness Conflict Risk

The Azure AI Vision SDK for Java plus enumerated vision features carves a clear niche, but it could still overlap with generic OCR or computer-vision skills.

4 / 5

Total

18

/

20

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

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
sickn33/antigravity-awesome-skills
Reviewed

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