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azure-ai-contentunderstanding-py

Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video.

73

11.11x
Quality

67%

Does it follow best practices?

Impact

100%

11.11x

Average score across 3 eval scenarios

SecuritybySnyk

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

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

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

SKILL.md
Quality
Evals
Security

Quality

Content

57%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 genuinely actionable SDK reference with executable code for every modality and a clear core workflow, held back by duplicated per-modality examples, absent error handling, an unguarded destructive delete, and no use of reference files to offload detail.

Suggestions

Consolidate the near-duplicate image/audio/video sections into one parameterized example (or a table of analyzer_id + result-access pattern) and add a note on how each differs, cutting substantial duplication.

Add error handling and validation checkpoints: check poller status, handle failed analyses before using results, and confirm an analyzer's identity/usage before `delete_analyzer` calls.

Split the per-modality result-access details and the model/client reference tables into references/ files (e.g. RESULT-ACCESS.md) so SKILL.md stays a lean overview, and replace the vacuous "When to Use" section with real trigger guidance or remove it.

DimensionReasoningScore

Conciseness

The body is code-dominant and free of concept-explaining padding, but the Analyze Image/Audio/Video sections are near-duplicates of the document example (only analyzer_id changes), Model Imports/Client Types restate inline imports, and the "When to Use" section is vacuous filler ("This skill is applicable to execute the workflow or actions described in the overview.").

3 / 5

Actionability

Real, executable Python covers every modality, custom analyzer creation, analyzer management, and the async client. However, the image/audio/video/custom sections reference `client` and `result` without defining them, so those examples are not copy-paste ready standalone.

4 / 5

Workflow Clarity

The three-step Core Workflow (begin_analyze, poll .result(), process contents) is coherent and consistently followed, but there is no error handling or result verification anywhere, and the destructive `delete_analyzer` operation has no validation checkpoint, capping this dimension at 3.

3 / 5

Progressive Disclosure

No bundle files exist, so all ~270 lines sit inline in SKILL.md. Section headers are well-organized, but the near-duplicate per-modality examples and the model/client reference tables belong in separate reference files for a skill of this length.

3 / 5

Total

13

/

20

Passed

Description

62%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 clearly identifies the Azure AI Content Understanding SDK and includes an explicit "Use for" clause, but it compresses everything into a single extraction action. Expanding to concrete capabilities (transcription, markdown for RAG, field extraction) and varied trigger phrases would raise both specificity and completeness.

Suggestions

List concrete capabilities instead of one umbrella action, e.g. "Extract markdown from documents for RAG, transcribe audio with timing, extract video frames and transcripts, extract invoice fields, build custom analyzers with field schemas."

Enrich the when-clause with natural trigger phrases and synonyms users would say: "Use when working with Azure Content Understanding, transcribing audio, extracting content from PDFs, images, or video, or building custom extraction analyzers."

Include file-format cues (.pdf, .mp3, .mp4) and task synonyms (transcribe, OCR, analyze) so the skill triggers on the phrasing users actually use.

DimensionReasoningScore

Specificity

"multimodal content extraction from documents, images, audio, and video" names the domain and one concrete action across four media types, but lists only a single action verb rather than several specific capabilities like transcription, markdown-for-RAG, or field extraction.

3 / 5

Completeness

A clear what ("SDK for... content extraction from documents, images, audio, and video") is paired with an explicit when ("Use for multimodal content extraction..."), but the when-clause merely mirrors the what rather than offering concrete trigger phrases.

4 / 5

Trigger Term Quality

Keywords like "Azure", "Content Understanding", "content extraction", and the four media types are relevant, but common user phrasings such as "transcribe", "OCR", "invoice extraction", or file extensions (.pdf, .mp4) are missing.

3 / 5

Distinctiveness Conflict Risk

The named Azure service carves a clear niche, but the broad "content extraction from documents, images, audio, video" when-clause could still overlap generic document or audio extraction skills.

4 / 5

Total

14

/

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

administrakt0r/AI-Agents-Safe-Coding-Skills

Path

plugins/AI-Agents-Safe-Coding-Skills/skills/azure-ai-contentunderstanding-py/SKILL.md

Reviewed

Table of Contents

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