azure-ai-document-intelligence-dotnet

Azure AI Document Intelligence SDK for .NET. Extract text, tables, and structured data from documents using prebuilt and custom models.

Quality

66%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Advisory

Suggest reviewing before use

Optimize this skill with Tessl

npx tessl skill review --optimize ./skills/azure-ai-document-intelligence-dotnet/SKILL.md

Quality

Discovery

67%

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 technically specific and clearly identifies the technology stack and concrete capabilities, making it distinctive. However, it lacks an explicit 'Use when...' clause and misses common user-facing trigger terms like 'OCR', 'Form Recognizer' (the former name), 'C#', or specific file types, which limits its effectiveness for skill selection.

Suggestions

Add a 'Use when...' clause such as 'Use when the user needs to extract data from documents using Azure AI Document Intelligence, Form Recognizer, or OCR in a .NET/C# project.'

Include common trigger term variations like 'OCR', 'Form Recognizer', 'C#', '.pdf', '.docx', 'invoice extraction', 'receipt scanning' that users would naturally use when requesting this functionality.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: 'Extract text, tables, and structured data from documents' and specifies the technology stack ('Azure AI Document Intelligence SDK for .NET') and model types ('prebuilt and custom models').	3 / 3
Completeness	Clearly answers 'what does this do' (extract text, tables, structured data using Azure AI Document Intelligence SDK for .NET), but lacks an explicit 'Use when...' clause or equivalent trigger guidance, which caps this at 2 per the rubric.	2 / 3
Trigger Term Quality	Includes relevant keywords like 'Azure AI Document Intelligence', 'SDK', '.NET', 'extract text', 'tables', 'structured data', and 'documents', but misses common user variations like 'OCR', 'form recognizer', 'C#', 'PDF extraction', or file type extensions that users would naturally mention.	2 / 3
Distinctiveness Conflict Risk	The combination of 'Azure AI Document Intelligence SDK' and '.NET' creates a very specific niche that is unlikely to conflict with generic document processing skills or other cloud provider SDKs.	3 / 3
	Total	10 / 12 Passed

Implementation

64%

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, actionable SDK reference with excellent executable code examples covering the full API surface. Its main weaknesses are the monolithic structure (everything in one file at significant length) and the lack of validation/verification steps integrated into workflows. Some boilerplate sections and explanatory content could be trimmed to improve token efficiency.

Suggestions

Integrate validation checkpoints into workflows—e.g., after building a custom model, check model quality metrics before using it; after analysis, verify result.Documents is non-empty before processing fields.

Split detailed workflow examples (custom models, classifiers, model management) into a separate EXAMPLES.md or ADVANCED.md, keeping SKILL.md as a concise overview with the most common use case (analyze with prebuilt model).

Remove the generic 'When to Use' and 'Limitations' boilerplate sections, and trim the 'Related SDKs' table—these add little value for Claude.

Dimension	Reasoning	Score
Conciseness	The skill is fairly comprehensive but includes some unnecessary content like the 'When to Use' and 'Limitations' boilerplate sections, the 'Related SDKs' table, and explanatory notes Claude would already know (e.g., 'clients are thread-safe'). The prebuilt models table and key types reference are borderline—useful as reference but add significant length. Overall mostly efficient but could be tightened.	2 / 3
Actionability	All code examples are fully executable C# with proper using statements, concrete API calls, and realistic field extraction patterns. The examples cover the full range of operations (analyze, build, classify, manage) with copy-paste ready code including proper null/type checking patterns.	3 / 3
Workflow Clarity	The workflows are presented as numbered sections with clear code, but they lack validation checkpoints and error recovery feedback loops. For example, the 'Build Custom Model' workflow doesn't mention validating training data or checking model quality metrics after building. The error handling section is separate rather than integrated into workflows. No explicit 'verify result before proceeding' steps.	2 / 3
Progressive Disclosure	The content is a monolithic single file with no bundle files to reference. At ~250+ lines, the detailed code examples for all 7 workflows, multiple reference tables, and best practices could benefit from being split into separate files (e.g., a quick-start overview in SKILL.md with detailed examples in separate files). The Reference Links table at the end provides external navigation but internal structure is flat.	2 / 3
	Total	9 / 12 Passed

Validation

90%

Warnings & errors only

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

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	10 / 11 Passed

Repository: sickn33/antigravity-awesome-skills
Commit: 76aea27

Reviewed: 16 minutes ago

Table of Contents

Discovery Implementation Validation

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