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azure-ai-textanalytics-py

Azure AI Text Analytics SDK for sentiment analysis, entity recognition, key phrases, language detection, PII, and healthcare NLP. Use for natural language processing on text.

55

Quality

62%

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SecuritybySnyk

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

Quality

Content

64%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 solid API reference skill with excellent actionability — every operation has complete, executable code examples. However, it's somewhat monolithic for the breadth of operations covered, and lacks workflow guidance around error handling, retry logic for long-running operations, and validation steps. The boilerplate 'When to Use' and 'Limitations' sections add no value.

Suggestions

Add explicit error handling and retry guidance for long-running operations (begin_analyze_healthcare_entities, begin_analyze_actions), including polling status checks and timeout handling.

Split detailed operation examples into a separate reference file and keep SKILL.md as a concise overview with authentication setup and one representative example, linking to the full reference.

Remove the boilerplate 'When to Use' and 'Limitations' sections and the trivial 'Client Types' table to improve conciseness.

DimensionReasoningScore

Conciseness

The content is mostly efficient with executable examples, but includes some unnecessary elements like the 'When to Use' and 'Limitations' boilerplate sections that add no value, and the Client Types table is trivial (both rows are the same client). The 'Best Practices' section is reasonably concise but some points are things Claude already knows.

2 / 3

Actionability

Every section provides fully executable, copy-paste ready Python code with concrete examples. Authentication, each API operation, batch processing, and async usage all have complete, runnable code snippets with realistic sample data.

3 / 3

Workflow Clarity

The skill covers individual operations clearly but lacks validation checkpoints. Error handling is mentioned only as 'check doc.is_error' inline in examples, with no explicit guidance on what to do when errors occur, how to handle rate limits, or retry logic for the long-running healthcare/batch operations that use pollers.

2 / 3

Progressive Disclosure

The content is a monolithic file with all API operations inline. For a skill covering 8+ distinct operations, the detailed code examples for each could be split into referenced files, with SKILL.md serving as a concise overview with quick-start and links to detailed operation guides. No bundle files exist to offload content.

2 / 3

Total

9

/

12

Passed

Description

60%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.

The description excels at listing specific capabilities (sentiment analysis, entity recognition, key phrases, etc.) and names the specific SDK, which aids identification. However, the 'Use for' clause is too generic ('natural language processing on text') and fails to provide meaningful trigger guidance for skill selection. The description would benefit from more natural user-facing trigger terms and a more specific 'when' clause.

Suggestions

Expand the 'Use for' clause with specific trigger scenarios, e.g., 'Use when the user asks to analyze sentiment, detect PII, extract key phrases, identify entities, detect language, or perform healthcare text analysis using Azure.'

Add common user-facing synonyms and variations such as 'opinion mining', 'named entity recognition', 'NER', 'text mining', 'detect language', 'redact personal information'.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions: sentiment analysis, entity recognition, key phrases, language detection, PII detection, and healthcare NLP. These are clear, well-defined capabilities.

3 / 3

Completeness

The 'what' is well covered with specific capabilities. There is a 'Use for...' clause but it's extremely generic ('natural language processing on text') and doesn't provide meaningful trigger guidance. The 'when' essentially restates the domain rather than providing actionable selection criteria.

2 / 3

Trigger Term Quality

Includes good technical terms like 'sentiment analysis', 'entity recognition', 'PII', and 'language detection' that users would naturally use. However, it misses common variations and synonyms users might say (e.g., 'detect language', 'extract entities', 'text mining', 'opinion mining', 'named entity recognition', 'NER').

2 / 3

Distinctiveness Conflict Risk

Mentioning 'Azure AI Text Analytics SDK' provides some distinctiveness by naming the specific platform/SDK. However, the generic 'Use for natural language processing on text' trigger could overlap with other NLP-related skills. The specific SDK name helps but the trigger clause undermines distinctiveness.

2 / 3

Total

9

/

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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