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azure-ai-contentsafety-py

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

56

Quality

64%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./skills/antigravity-azure-ai-contentsafety-py/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

63%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.

The body is a well-organized, largely executable SDK reference with good sectioning and concrete code for every operation. Its main weaknesses are token inefficiency (repeated client setup per snippet, filler sections) and a monolithic single-file structure with no progressive disclosure into reference files.

Suggestions

Move blocklist management and the category/severity reference tables into a references/ file and link them from SKILL.md, keeping the main file as a lean overview with the core analyze-text and analyze-image flows.

Show client construction once in the Authentication section and omit repeated imports/client setup from subsequent snippets to cut significant duplication.

Replace the vague "When to Use" and boilerplate "Limitations" sections with concrete error-handling guidance (e.g. handling 401/429 responses and retrying) to add real value in the same tokens.

DimensionReasoningScore

Conciseness

The body is code-forward and mostly efficient, but every snippet re-imports and re-constructs the client, and the "When to Use" section ("This skill is applicable to execute the workflow or actions described in the overview") and generic Limitations boilerplate are filler that could be trimmed.

3 / 5

Actionability

Concrete, near copy-paste-ready code covers the common cases (analyze text, analyze image, blocklist CRUD, severity output types). Minor gaps: later snippets use undefined `endpoint` and `key` variables after the Authentication section showed env-var-based construction.

4 / 5

Workflow Clarity

Logical progression from installation, environment setup, and authentication through each operation, with result handling shown (e.g. iterating `categories_analysis`, checking `blocklists_match`). Validation is implicit rather than explicit — no error-handling or retry guidance — keeping it below a 5.

4 / 5

Progressive Disclosure

A single ~215-line SKILL.md with no references/ bundle and no pointers to separate material; the blocklist management and reference tables are inlined API reference that could live in a separate file, matching the anchor example of inlined reference content despite good section headers.

3 / 5

Total

14

/

20

Passed

Description

66%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.

The description is specific and distinctive thanks to the named Azure product and clear task statement, with an explicit "Use for" clause. Its weaknesses are trigger-term coverage (missing natural synonyms like "content moderation" or "toxicity") and a when-clause that restates the what rather than describing concrete trigger situations.

Suggestions

Rewrite the trigger clause to name concrete situations and synonyms, e.g. "Use when moderating or filtering user-generated or AI-generated content, screening for toxicity, or when the user mentions content moderation, safety thresholds, or Azure Content Safety."

Mention the blocklist management capability ("manage custom text blocklists") in the description so the what covers the skill's full scope.

Trim jargon like "multi-severity classification" in favor of natural phrasing such as "with severity ratings (safe to severe)".

DimensionReasoningScore

Specificity

Names the domain and 1-2 concrete actions ("detecting harmful content in text and images", "multi-severity classification") but omits capabilities the body covers (blocklists, per-category analysis, thresholds), so coverage is not comprehensive.

3 / 5

Completeness

Both what ("detecting harmful content in text and images with multi-severity classification") and when ("Use for detecting harmful content...") are explicitly present, but the when-clause merely restates the what instead of giving distinct trigger situations, so it could be more explicit.

4 / 5

Trigger Term Quality

"Harmful content" and "text and images" are natural terms, but common user phrases like "content moderation", "toxicity", or "filtering user-generated content" are missing, and "multi-severity classification" is jargon rather than a user-facing trigger.

3 / 5

Distinctiveness Conflict Risk

"Azure AI Content Safety SDK for Python" names a specific product with a clear niche (harmful-content detection in text/images), making it highly distinguishable from other skills with minimal conflict risk.

5 / 5

Total

15

/

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.

Validation — 15 / 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
boisenoise/skills-collections
Reviewed

Table of Contents

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