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azure-ai-vision-imageanalysis-py

Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks.

60

Quality

71%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./plugins/antigravity-awesome-skills-claude/skills/azure-ai-vision-imageanalysis-py/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

57%Scale 1-3

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

This is a comprehensive SDK reference with excellent actionability—every feature has executable code. However, it suffers from being overly long and monolithic, with repetitive code patterns across feature sections that could be condensed. The lack of progressive disclosure and a clear end-to-end workflow sequence are its main weaknesses.

Suggestions

Consolidate the per-feature sections: show one complete example with multiple VisualFeatures, then provide a concise reference table mapping each feature to its result attribute and data structure, rather than repeating the full analyze_from_url pattern 7 times.

Extract detailed per-feature examples into a separate REFERENCE.md or FEATURES.md file, keeping SKILL.md as a quick-start overview with links to the detailed content.

Remove the generic 'When to Use' and 'Limitations' boilerplate sections—they add no SDK-specific value and waste tokens.

DimensionReasoningScore

Conciseness

The skill is mostly efficient with executable code examples, but it's quite long (~200 lines) with repetitive patterns. Each feature section follows the same analyze_from_url + print pattern, which could be condensed into a single example showing multiple features with a reference table. The 'Visual Features' table duplicates information already shown in the code examples. The boilerplate 'When to Use' and 'Limitations' sections add no value.

2 / 3

Actionability

Every section provides fully executable, copy-paste ready Python code with proper imports, correct API usage, and result handling. Authentication setup, both sync and async clients, error handling, and all visual features are demonstrated with concrete, runnable examples.

3 / 3

Workflow Clarity

The skill presents individual API operations clearly but lacks a cohesive workflow sequence. There's no guidance on the typical flow (install → configure env vars → authenticate → analyze → handle results). Error handling is shown but not integrated into a validation/retry pattern. For an SDK reference skill this is acceptable, but the best practices section is a flat list rather than actionable workflow guidance.

2 / 3

Progressive Disclosure

The content is a monolithic wall of code examples with no references to external files and no layered structure. All features are presented at the same level of detail inline. The content would benefit from a concise quick-start section with detailed feature examples split into a separate reference file, especially given the repetitive per-feature sections.

1 / 3

Total

8

/

12

Passed

Description

85%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.

This is a solid description that clearly lists specific capabilities and includes an explicit 'Use for...' clause. Its main weakness is that the trigger terms could be more comprehensive, missing natural language variations users might employ when requesting image analysis tasks. The Azure-specific scoping provides good distinctiveness.

Suggestions

Expand trigger terms to include natural user phrases like 'read text from image', 'describe a photo', 'detect objects in picture', or file types like '.jpg', '.png'

Add more specific 'when' triggers such as 'Use when the user asks to analyze images, extract text from photos, detect faces, generate image descriptions, or perform optical character recognition using Azure'

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions: captions, tags, objects, OCR, people detection, and smart cropping. These are clearly defined capabilities.

3 / 3

Completeness

Clearly answers both what ('Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping') and when ('Use for computer vision and image understanding tasks'). The 'Use for...' clause serves as an explicit trigger.

3 / 3

Trigger Term Quality

Includes some good terms like 'OCR', 'image analysis', 'computer vision', and 'image understanding', but misses common user variations like 'read text from image', 'detect objects in photo', 'describe image', 'extract text from picture', or file extensions like '.jpg', '.png'.

2 / 3

Distinctiveness Conflict Risk

Clearly scoped to Azure AI Vision SDK specifically, with distinct capabilities listed. The mention of 'Azure AI Vision' and the specific SDK focus makes it unlikely to conflict with generic image processing or other cloud vision skills.

3 / 3

Total

11

/

12

Passed

Validation

90%

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

Validation — 10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

10

/

11

Passed

Repository
popey/claude-code-skills
Reviewed

Table of Contents

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