azure-ai-contentsafety-py

Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification.

Quality

61%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Advisory

Suggest reviewing before use

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Quality

Content

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 SDK reference skill with excellent actionability — all code examples are complete and executable with proper imports. However, it suffers from some verbosity (explanatory tables, generic best practices, boilerplate sections) and lacks error handling or validation steps for API operations. The structure is adequate but could benefit from trimming unnecessary content and adding error recovery patterns.

Suggestions

Remove the boilerplate 'When to Use' and 'Limitations' sections, the Harm Categories description column (Claude knows what hate speech and violence are), and the Severity Scale 'Meaning' column to improve conciseness.

Add error handling patterns (e.g., try/except for HttpResponseError, credential validation) and a brief workflow showing the blocklist creation-to-analysis pipeline as a sequenced process.

Trim the 'Best Practices' section to only non-obvious, SDK-specific guidance rather than generic software advice.

Dimension	Reasoning	Score
Conciseness	The skill is mostly efficient with executable code examples, but includes some unnecessary content: the Harm Categories table descriptions are things Claude already knows, the 'Best Practices' section is generic advice Claude can infer, and the boilerplate 'When to Use' and 'Limitations' sections add no value. The Severity Scale table also explains obvious meanings ('Safe', 'Mild references').	2 / 3
Actionability	All code examples are fully executable, copy-paste ready, with correct imports and proper SDK usage patterns. Authentication, text analysis, image analysis, and blocklist management all have complete, runnable code snippets with specific model classes and method calls.	3 / 3
Workflow Clarity	The skill presents individual operations clearly but lacks workflow sequencing for multi-step processes like blocklist creation → adding items → analyzing with blocklist. There are no validation checkpoints or error handling patterns shown, which matters for API operations that can fail (invalid credentials, rate limits, malformed requests).	2 / 3
Progressive Disclosure	The content is reasonably well-organized with clear section headers, but it's a long monolithic file (~180 lines) with no references to external files. The blocklist management section and reference tables could be split out. However, for an SDK reference skill with no bundle files, the inline structure is acceptable though not optimal.	2 / 3
	Total	9 / 12 Passed

Description

57%

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

The description identifies a clear niche (Azure AI Content Safety SDK for Python) and provides some functional context, but lacks depth in specific capabilities and natural trigger terms users might use. The 'Use for' clause is present but weak—it describes the general purpose rather than explicit trigger scenarios that would help Claude distinguish when to select this skill.

Suggestions

Expand the 'Use for' clause into an explicit 'Use when...' with trigger scenarios, e.g., 'Use when the user asks about content moderation, safety filtering, toxicity detection, or integrating Azure Content Safety into Python applications.'

Add more natural trigger terms users would say, such as 'content moderation', 'NSFW detection', 'hate speech filtering', 'content filtering', or 'safety API'.

List more specific concrete actions like 'analyze text for hate, violence, sexual, and self-harm categories', 'configure custom blocklists', 'interpret severity scores' to improve specificity.

Dimension	Reasoning	Score
Specificity	Names the domain (Azure AI Content Safety SDK) and some actions ('detecting harmful content in text and images with multi-severity classification'), but doesn't list multiple specific concrete actions like configuring blocklists, analyzing severity levels, or handling specific content categories.	2 / 3
Completeness	The 'what' is partially addressed (detecting harmful content) and 'Use for' provides a weak trigger clause, but it lacks explicit 'when' guidance such as 'Use when the user asks about content moderation, safety filtering, or integrating Azure Content Safety into their Python application'.	2 / 3
Trigger Term Quality	Includes some relevant keywords like 'Azure AI Content Safety', 'harmful content', 'text and images', and 'multi-severity classification', but misses common user variations like 'content moderation', 'toxicity detection', 'NSFW', 'hate speech', or 'content filtering'.	2 / 3
Distinctiveness Conflict Risk	The description is clearly scoped to Azure AI Content Safety SDK for Python specifically, which is a distinct niche unlikely to conflict with other skills. The combination of Azure, Content Safety, SDK, and Python creates a clear identity.	3 / 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: boisenoise/skills-collections
Commit: 47e0c5a

Reviewed: 2 days ago

Table of Contents

Discovery Implementation Validation

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