CtrlK
BlogDocsLog inGet started
Tessl Logo

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.

84

1.56x
Quality

76%

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/skills/azure-ai-textanalytics-py/SKILL.md

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

SKILL.md
Quality
Evals
Security

Quality

Content

72%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.

A well-structured, highly actionable SDK reference with copy-paste-ready code for every common operation. It loses points for missing validation/feedback loops on batch and long-running operations and for inlining all detail rather than progressively disclosing it to separate reference files.

Suggestions

Add explicit validation checkpoints for long-running operations — check poller status (e.g. poller.done() / poller.wait()) and handle a failed poller before calling poller.result(), with a fix-and-retry loop for batch failures.

Move the bulk per-operation examples and the Available Operations table into a separate reference file (e.g. references/operations.md) and keep SKILL.md as a lean overview that links out to it, improving progressive disclosure.

Trim redundant filler — drop the intro line that restates the description and replace the vague 'When to Use' line with concrete triggers (e.g. 'Use when the user needs sentiment analysis, PII detection, or entity recognition on text').

DimensionReasoningScore

Conciseness

The body is lean — mostly executable code under clear headers with almost no concept explanation — but the intro line restates the description, the Available Operations table repeats the code above, and the 'When to Use' line is filler, so minor trimming is possible.

4 / 5

Actionability

Every section provides fully executable, copy-paste-ready code with imports, real method calls, and result-processing loops covering all common operations, matching the 5 anchor for concrete executable guidance.

5 / 5

Workflow Clarity

Batch and long-running operations (begin_analyze_actions, begin_analyze_healthcare_entities) call poller.result() without explicit status validation or error-recovery feedback loops; the per-doc 'is_error' checks are only partial validation, so the batch-operation cap of 3 applies.

3 / 5

Progressive Disclosure

Sections are well-organized, but ~230 lines of per-operation examples and the operations table are all inlined in SKILL.md with no split into separate reference files, which is content that could appropriately live one level deeper.

3 / 5

Total

15

/

20

Passed

Description

80%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.

A strong, specific description that names the SDK and enumerates its concrete capabilities with an explicit use-clause. Its main weakness is a generic 'when' trigger that could name concrete scenarios (sentiment, PII, entities) instead of the broad 'natural language processing on text'.

DimensionReasoningScore

Specificity

Lists six concrete capabilities — 'sentiment analysis, entity recognition, key phrases, language detection, PII, and healthcare NLP' — giving comprehensive, specific coverage rather than vague language.

5 / 5

Completeness

Has a clear 'what' (the SDK and its six capabilities) and an explicit 'Use for natural language processing on text' when-clause, but the when is generic rather than concrete trigger phrases, so it stops short of the 5 anchor.

4 / 5

Trigger Term Quality

Includes natural terms users would say ('sentiment analysis', 'PII', 'language detection', 'NLP') but lacks synonyms/extensions like 'opinion mining', 'NER', or the package name, so a few natural terms are missing.

4 / 5

Distinctiveness Conflict Risk

The 'Azure AI Text Analytics SDK' naming carves a clear niche distinct from generic skills, but the broad 'natural language processing on text' trigger creates minor overlap risk with other NLP skills.

4 / 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
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.