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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.

77

1.56x
Quality

66%

Does it follow best practices?

Impact

100%

1.56x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/azure-ai-textanalytics-py/SKILL.md

The canonical home for this skill is azure-ai-textanalytics-py in sickn33/antigravity-awesome-skills

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 with realistic data. The main weaknesses are that it's somewhat long for a single SKILL.md file (could benefit from splitting detailed examples into a reference file) and lacks validation/error recovery workflows for batch and long-running operations. The 'When to Use' section and Client Types table add minimal value.

Suggestions

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

Move detailed per-operation code examples to a separate REFERENCE.md and keep SKILL.md as a concise overview with one quick-start example and links to detailed docs.

Remove the 'When to Use' section (it's a tautology) and the Client Types table (only one client type exists).

DimensionReasoningScore

Conciseness

The content is mostly efficient with executable code examples, but includes some unnecessary elements like the 'When to Use' section (which is a tautology), the Client Types table (which adds little value since there's only one client), and some examples could be more compact. The overall length is reasonable for the breadth of API coverage but could be tightened.

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 working code snippets with realistic sample data.

3 / 3

Workflow Clarity

Individual operations are clear, but there's no validation/error handling workflow beyond checking `is_error`. The batch operations section uses long-running pollers but doesn't mention timeout handling, polling status checks, or error recovery. For batch operations, missing feedback loops cap this at 2.

2 / 3

Progressive Disclosure

The content is well-structured with clear section headers and a logical progression from setup to individual operations to advanced usage. However, at ~200 lines it's somewhat monolithic — the detailed code examples for each operation could be split into a reference file, with SKILL.md providing a quick-start overview and links to detailed examples.

2 / 3

Total

9

/

12

Passed

Description

67%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 and is clearly scoped to the Azure AI Text Analytics SDK, making it distinctive. However, the 'Use when' guidance is too generic ('natural language processing on text'), which weakens its ability to serve as a precise trigger for skill selection. The trigger terms could also include more user-facing language variations.

Suggestions

Expand the 'Use for' clause with specific trigger scenarios, e.g., 'Use when the user needs to analyze sentiment of text, detect PII in documents, extract named entities, identify key phrases, detect text language, or perform healthcare-specific NLP using Azure.'

Add common user-facing keyword variations such as 'text mining', 'opinion mining', 'named entity recognition', 'NER', 'detect language', 'Azure Cognitive Services', or 'Text Analytics API'.

DimensionReasoningScore

Specificity

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

3 / 3

Completeness

The 'what' is well covered with specific capabilities, and there is a 'Use for' clause, but it is extremely generic ('natural language processing on text') and doesn't provide explicit trigger scenarios like 'when the user wants to analyze sentiment of reviews' or 'when detecting PII in documents'. The 'when' is present but weak.

2 / 3

Trigger Term Quality

Includes some good terms like 'sentiment analysis', 'entity recognition', 'PII', 'language detection', and 'healthcare NLP', but misses common user variations like 'detect language', 'extract entities', 'analyze sentiment', 'text mining', or 'Azure Cognitive Services'. The 'Use for' clause is too generic ('natural language processing on text').

2 / 3

Distinctiveness Conflict Risk

The mention of 'Azure AI Text Analytics SDK' clearly scopes this to a specific platform and SDK, and the listed capabilities (PII, healthcare NLP, entity recognition) create a distinct niche that is unlikely to conflict with other skills.

3 / 3

Total

10

/

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.

Validation10 / 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
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

Table of Contents

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