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.
55
62%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/antigravity-azure-ai-document-intelligence-dotnet/SKILL.mdQuality
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 strong in specificity and distinctiveness, clearly identifying the Azure AI Document Intelligence SDK for .NET and listing concrete extraction capabilities. However, it lacks an explicit 'Use when...' clause, which is critical for Claude to know when to select this skill, and could benefit from additional natural trigger terms users might employ (e.g., 'OCR', 'Form Recognizer', 'C#').
Suggestions
Add an explicit 'Use when...' clause, e.g., '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 such as 'OCR', 'Form Recognizer' (the former product name), 'C#', 'document analysis', and file types like '.pdf', '.tiff', '.jpeg'.
| 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') along with 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#', 'document analysis', or file type extensions like '.pdf', '.docx'. | 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
57%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill provides excellent, actionable code examples covering the full Azure Document Intelligence SDK surface area, with proper authentication patterns and field extraction. However, it suffers from being a monolithic document that tries to be both a quick-start guide and a comprehensive reference, resulting in a long file that would benefit from progressive disclosure through bundle files. Workflow clarity could be improved by adding validation steps, especially around custom model building and training data preparation.
Suggestions
Split detailed workflow examples (custom model building, classifier, model management) into separate bundle files, keeping only the most common operations (analyze invoice, extract layout) in SKILL.md with links to the rest.
Move reference tables (prebuilt models, key types, build modes) into a REFERENCE.md bundle file and link from the main skill.
Add validation checkpoints to the custom model building workflow (e.g., verify training data format, check model status, validate extraction results against expected fields).
Remove the generic 'When to Use' and 'Limitations' boilerplate sections that add no SDK-specific value.
| 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 table add bulk that could be in a separate reference file. However, the code examples themselves are lean and well-structured. | 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 independent code examples rather than sequenced multi-step processes. For the custom model building workflow (which involves training data preparation, building, and validation), there are no explicit validation checkpoints or error recovery loops. The error handling section is separate rather than integrated into workflows where failures are likely. | 2 / 3 |
Progressive Disclosure | This is a monolithic ~300-line file with no bundle files to offload content to. The prebuilt models table, key types reference, build modes table, and detailed code examples for 7 different workflows are all inline. The reference tables and multiple workflow examples would benefit greatly from being split into separate files with clear navigation from a concise overview. | 1 / 3 |
Total | 8 / 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.
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
- boisenoise/skills-collections
- Commit
8ac11ab
- Reviewed
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