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.

1.56x

Quality

75%

Does it follow best practices?

Impact

97%

1.56x

Average score across 3 eval scenarios

Securityby

Advisory

Suggest reviewing before use

Optimize this skill with Tessl

npx tessl skill review --optimize ./skills/azure-ai-vision-imageanalysis-py/SKILL.md

Quality

Discovery

85%

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 identifies the specific SDK (Azure AI Vision) and enumerates six concrete capabilities. The 'Use for...' clause provides trigger guidance, though it could be more detailed with natural user language variations. The main weakness is that the trigger terms lean slightly technical and could better capture how users naturally phrase image analysis requests.

Suggestions

Expand trigger terms with natural user phrases like 'read text from image', 'describe a photo', 'detect objects in picture', or mention common image file types (.jpg, .png, .bmp).

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: captions, tags, objects, OCR, people detection, and smart cropping. These are clearly defined capabilities within the Azure AI Vision domain.	3 / 3
Completeness	Clearly answers 'what' (Azure AI Vision SDK for captions, tags, objects, OCR, people detection, smart cropping) and 'when' ('Use for computer vision and image understanding tasks'). The 'Use for...' clause serves as an explicit trigger guidance.	3 / 3
Trigger Term Quality	Includes some good terms like 'OCR', 'image analysis', 'computer vision', '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, which distinguishes it from generic image processing skills or other cloud provider vision APIs. The mention of the specific SDK and its capabilities creates a distinct niche.	3 / 3
	Total	11 / 12 Passed

Implementation

64%

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

This is a solid API reference skill with excellent actionability—every feature has complete, executable Python code. Its main weakness is length; the repetitive pattern of analyze_from_url calls for each feature inflates the token cost without proportional value. The content would benefit from a more concise overview with references to detailed examples.

Suggestions

Consolidate the per-feature sections into a compact reference table with one comprehensive example showing multiple features, and move individual feature examples to a separate EXAMPLES.md file.

Remove the 'When to Use' boilerplate sentence and the Visual Features table (which duplicates what the code examples already demonstrate).

Integrate error handling and image requirements validation into the main workflow rather than listing them as separate sections—e.g., show a complete analyze-with-validation pattern.

Dimension	Reasoning	Score
Conciseness	The skill is mostly efficient with executable code examples, but it's quite long (~200 lines) with repetitive patterns. The Visual Features table duplicates information already evident from the code examples, and the 'Best Practices' section contains some obvious guidance. The 'When to Use' footer sentence is meaningless filler.	2 / 3
Actionability	Every section provides fully executable, copy-paste ready Python code with proper imports, correct API usage, and result handling. Authentication, URL analysis, file analysis, and each visual feature are demonstrated with complete working examples.	3 / 3
Workflow Clarity	The skill is essentially a reference/cookbook rather than a multi-step workflow, so sequencing is less critical. However, there's no validation checkpoint guidance—e.g., checking if the endpoint is reachable before analyzing, or verifying image meets requirements before sending. The error handling section exists but isn't integrated into a workflow with feedback loops.	2 / 3
Progressive Disclosure	The content is well-organized with clear section headers, but it's a monolithic document that could benefit from splitting detailed per-feature examples into a separate reference file. The main SKILL.md could show just authentication + one analyze example and link out to feature-specific details.	2 / 3
	Total	9 / 12 Passed

Validation

90%

Warnings & errors only

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

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	10 / 11 Passed

Repository: sickn33/antigravity-awesome-skills
Commit: 33fc69f

Reviewed: 3 days ago

Table of Contents

Discovery Implementation Validation

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