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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.

68

Quality

82%

Does it follow best practices?

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

90%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 an efficient, executable Azure SDK cookbook with complete code examples, clear section structure, and per-document error validation. Its main gaps are the absence of an explicit batch-error feedback loop and the monolithic inlining of all API reference rather than progressive references to separate files.

Suggestions

Add an explicit validation feedback loop for batch operations (e.g. 'if any doc.is_error, inspect error.code, adjust input, and resubmit those documents') to reach a 5 on workflow clarity.

Consider moving the per-operation API details into a REFERENCES.md or API.md and keeping SKILL.md as an overview with quick-start, to improve progressive disclosure.

Resolve the dangling `endpoint` variable in the Async Client example (it is referenced but not defined in that snippet) so the code is truly copy-paste-ready.

DimensionReasoningScore

Conciseness

Lean, code-forward reference with minimal prose and no padding or explanation of NLP concepts Claude already knows; every section earns its place.

5 / 5

Actionability

Fully executable, copy-paste-ready code with real imports and result handling for every operation, covering the common cases comprehensively.

5 / 5

Workflow Clarity

Per-document `is_error` validation checkpoints and poller patterns are present throughout, plus a Best Practices note on handling document errors; lacks an explicit validate-fix-retry feedback loop for batch operations, so it falls short of a 5.

4 / 5

Progressive Disclosure

Well-organized with clear section headers, summary tables for client types and operations, and distinct per-operation sections; the full API reference is inlined in one file rather than split into reference files, a minor organization gap.

4 / 5

Total

18

/

20

Passed

Description

75%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, clearly enumerating six NLP capabilities, but its trigger guidance is generic ('natural language processing on text') rather than concrete user-facing phrases. Strengthening the 'Use when...' clause with natural trigger terms would raise completeness and trigger-term quality.

Suggestions

Replace the generic 'Use for natural language processing on text' with concrete trigger phrases like 'Use when analyzing sentiment, detecting entities or PII, extracting key phrases, detecting language, or processing healthcare text'.

Add common synonyms users might say such as 'NER', 'text mining', or 'redaction' to broaden trigger-term coverage.

Mirror a phrase the user would naturally say, e.g. 'Use when the user mentions sentiment analysis, PII detection, or key phrase extraction'.

DimensionReasoningScore

Specificity

Lists six concrete actions (sentiment analysis, entity recognition, key phrases, language detection, PII, healthcare NLP), matching the comprehensive-coverage anchor.

5 / 5

Completeness

Clear 'what' with six capabilities, but the 'when' clause ('Use for natural language processing on text') is generic and weakly explicit rather than concrete trigger phrases.

3 / 5

Trigger Term Quality

Good natural-term coverage (sentiment analysis, entity recognition, key phrases, PII, NLP) but missing common synonyms users might say like 'text mining' or 'NER'.

4 / 5

Distinctiveness Conflict Risk

'Azure AI Text Analytics SDK' names a specific niche with distinct triggers and minimal overlap risk with other skills.

5 / 5

Total

17

/

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.

Validation15 / 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
sickn33/antigravity-awesome-skills
Reviewed

Table of Contents

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