azure-ai-document-intelligence-dotnet
by microsoftazure-ai-document-intelligence-dotnet helps .NET developers install and use Azure AI Document Intelligence to extract text, tables, key-value pairs, and structured fields from invoices, receipts, IDs, and custom documents. It includes practical setup, authentication, and OCR Extraction guidance for reliable document analysis.
This skill scores 84/100, which means it is a solid directory listing candidate for users who need .NET-based Azure Document Intelligence workflows. The repository gives enough trigger cues, installation steps, auth/environment guidance, and example-oriented content that an agent can likely identify when to use it and execute it with less guesswork than a generic prompt.
- Explicit trigger phrases and use cases for Document Intelligence, invoice extraction, receipt OCR, and custom models.
- Concrete installation and environment setup, including required package names and endpoint/API key variables.
- Substantial body content with multiple headings, code fences, and repo/file references that support real workflow use.
- Description metadata is very short, so install decisions rely mainly on the body rather than a rich summary.
- No support files or supplemental references are present, so some edge cases may require reading the full SKILL.md.
Overview of azure-ai-document-intelligence-dotnet skill
What this skill does
The azure-ai-document-intelligence-dotnet skill helps you use the Azure AI Document Intelligence SDK for .NET to extract text, tables, key-value pairs, and structured fields from documents. It is a good fit when you need OCR Extraction plus layout-aware document analysis, not just plain text OCR. The azure-ai-document-intelligence-dotnet skill is especially useful for invoices, receipts, IDs, and custom document models where the output must be reliable enough for downstream code.
Who should use it
Use this skill if you are building a .NET app, agent, or script that calls Azure.AI.DocumentIntelligence and you want a practical azure-ai-document-intelligence-dotnet guide instead of piecing together auth and sample code from scratch. It is most valuable for engineers who already know the document type they want to process and need a fast, correct path to installation and first successful call.
What matters before adoption
The main decision factors are Azure setup, authentication choice, and whether your document type matches a prebuilt model or needs a custom model. This skill assumes you can provide an endpoint and, depending on auth, either an API key or Microsoft Entra access. If you only need generic OCR, a lighter prompt may be enough; if you need structured extraction with .NET, this skill is the better starting point.
How to Use azure-ai-document-intelligence-dotnet skill
Install and verify the package
For a normal azure-ai-document-intelligence-dotnet install, add the NuGet packages shown in the skill: Azure.AI.DocumentIntelligence and Azure.Identity. Then verify that your project targets a supported .NET version and that you can load Azure credentials before attempting document calls. If the package install succeeds but auth fails, the issue is usually configuration, not the skill itself.
Give the skill a concrete extraction goal
The best azure-ai-document-intelligence-dotnet usage starts with a document type, a target output shape, and the auth mode you want to use. A weak request is: “extract data from a PDF.” A stronger request is: “Use Azure.AI.DocumentIntelligence in .NET to extract invoice vendor name, invoice number, totals, and line items from PDF files using Entra auth in a service app.” That gives the skill enough context to choose the right model and code path.
Read the right source files first
Start with SKILL.md for installation, environment variables, and auth patterns. Then inspect the package-facing examples in the repository path for the skill, and look for any notes about DocumentIntelligenceClient, DefaultAzureCredential, and model selection. If your use case is production-bound, pay special attention to environment variable names and which values are required versus optional.
Use a workflow that matches the real app
A good workflow is: install packages, set DOCUMENT_INTELLIGENCE_ENDPOINT, choose API key or Entra auth, run one known document through a prebuilt model, then expand to custom extraction only after the basic call works. For OCR Extraction tasks, test with a small sample set first so you can confirm page breaks, tables, and field names before wiring the output into business logic.
azure-ai-document-intelligence-dotnet skill FAQ
Is this only for OCR?
No. The azure-ai-document-intelligence-dotnet skill covers OCR plus structured document analysis. If you only need raw text, you may be overusing it; if you need tables, key-value pairs, or model-driven fields, it is a strong fit.
Do I need Azure credentials before using it?
Yes, you need a valid Document Intelligence endpoint, and either an API key or Microsoft Entra setup depending on your chosen auth flow. If you plan to use DefaultAzureCredential, make sure your local and production environments are configured differently on purpose, not by accident.
Is it beginner friendly?
It is beginner friendly if you already know how to create a .NET project and install NuGet packages. It is less beginner friendly if you are new to Azure auth, because the hard part is usually endpoint and credential setup rather than the SDK call itself.
When should I not use this skill?
Do not use it if your task is unrelated to document parsing, or if you want a no-code summary of a PDF without any integration work. It is also a poor fit if you have not chosen a document source, output schema, or deployment environment yet.
How to Improve azure-ai-document-intelligence-dotnet skill
Specify the document and the fields
The biggest quality boost comes from naming the document class and the exact fields you need. For example, ask for invoice totals, vendor name, tax, dates, and line items instead of “important data.” Better field definitions produce better prompts, cleaner extraction logic, and fewer downstream mapping mistakes.
State auth and runtime constraints up front
If you want Microsoft Entra auth, say so in the request and mention whether the code must run locally, in CI, or in Azure. The azure-ai-document-intelligence-dotnet skill works best when it knows whether to show AzureKeyCredential, DefaultAzureCredential, or a production-safe pattern. This avoids code that looks correct but cannot run in your environment.
Test with one real sample before scaling
Start with one representative document and one model choice, then evaluate whether the output shape is stable enough for automation. If extraction is noisy, improve the input request by describing document variability, scanned quality, and whether you need tables preserved. That is usually more effective than asking for a broader prompt.
Iterate on output quality, not just code
If the first result is close but imperfect, refine the prompt with the exact failure: missing line items, merged fields, wrong date parsing, or page OCR errors. For azure-ai-document-intelligence-dotnet for OCR Extraction, this kind of iteration matters because small changes in document quality and model selection can change the result more than changes in code structure.