Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add benjaminasterA/antigravity-awesome-skills --skill azure-ai-document-intelligence-dotnetgit clone --depth 1 https://github.com/benjaminasterA/antigravity-awesome-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/benjaminastera/antigravity-awesome-skills/azure-ai-document-intelligence-dotnet)<a href="https://agentmods.dev/skills/benjaminastera/antigravity-awesome-skills/azure-ai-document-intelligence-dotnet"><img src="https://agentmods.dev/badge/skills/benjaminastera/antigravity-awesome-skills/azure-ai-document-intelligence-dotnet/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/benjaminastera/antigravity-awesome-skills/azure-ai-document-intelligence-dotnet"><img src="https://agentmods.dev/badge/skills/benjaminastera/antigravity-awesome-skills/azure-ai-document-intelligence-dotnet.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.02515 |
| Opus 5 | $0.00000 | $0.01257 |
| Sonnet 5 | $0.00000 | $0.00503 |
| Haiku 4.5 | $0.00000 | $0.00251 |
Grade A, and why
azure-ai-document-intelligence-dotnet scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
94% identical to azure-ai-document-intelligence-dotnet — 30 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 343 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Azure.AI.DocumentIntelligence (.NET)
Extract text, tables, and structured data from documents using prebuilt and custom models.
Installation
dotnet add package Azure.AI.DocumentIntelligence
dotnet add package Azure.Identity
Current Version: v1.0.0 (GA)
Environment Variables
DOCUMENT_INTELLIGENCE_ENDPOINT=https://<resource-name>.cognitiveservices.azure.com/
DOCUMENT_INTELLIGENCE_API_KEY=<your-api-key>
BLOB_CONTAINER_SAS_URL=https://<storage>.blob.core.windows.net/<container>?<sas-token>
Authentication
Microsoft Entra ID (Recommended)
using Azure.Identity;
using Azure.AI.DocumentIntelligence;
string endpoint = Environment.GetEnvironmentVariable("DOCUMENT_INTELLIGENCE_ENDPOINT");
var credential = new DefaultAzureCredential();
var client = new DocumentIntelligenceClient(new Uri(endpoint), credential);
Note: Entra ID requires a custom subdomain (e.g.,
https://<resource-name>.cognitiveservices.azure.com/), not a regional endpoint.
API Key
string endpoint = Environment.GetEnvironmentVariable("DOCUMENT_INTELLIGENCE_ENDPOINT");
string apiKey = Environment.GetEnvironmentVariable("DOCUMENT_INTELLIGENCE_API_KEY");
var client = new DocumentIntelligenceClient(new Uri(endpoint), new AzureKeyCredential(apiKey));
Client Types
| Client | Purpose |
|---|---|
DocumentIntelligenceClient |
Analyze documents, classify documents |
DocumentIntelligenceAdministrationClient |
Build/manage custom models and classifiers |
Prebuilt Models
| Model ID | Description |
|---|---|
prebuilt-read |
Extract text, languages, handwriting |
prebuilt-layout |
Extract text, tables, selection marks, structure |
prebuilt-invoice |
Extract invoice fields (vendor, items, totals) |
prebuilt-receipt |
Extract receipt fields (merchant, items, total) |
prebuilt-idDocument |
Extract ID document fields (name, DOB, address) |
prebuilt-businessCard |
Extract business card fields |
prebuilt-tax.us.w2 |
Extract W-2 tax form fields |
prebuilt-healthInsuranceCard.us |
Extract health insurance card fields |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 343 lines · 0 tokens per session scan A f5e13f66fd74
azure-ai-document-intelligence-dotnet is a skill published in the GitHub repository benjaminasterA/antigravity-awesome-skills (271 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,515 tokens. A static security scan graded it A with 0 findings. It is 94% identical to azure-ai-document-intelligence-dotnet, differing in 30 lines, and is treated as a copy.
Other skills, from other repositories
markitdown
Convert heterogeneous documents and selected URIs to Markdown with Microsoft MarkItDown for text analysis, search, and LLM/RAG ingestion. Covers safe local conversion, streams, Office/PDF/data formats, batch workflows, plugins, vision OCR, Azure extraction, and the official MCP server.
paddleocr-text-recognition
An optical character recognition tool configuration for extracting text from images, photos, scans, screenshots, and scanned PDFs. OCR means converting text visible in an image into machine-readable text.
azure-ai-document-intelligence-ts
Extract text, tables, and structured data from documents using Azure Document Intelligence (@azure-rest/ai-document-intelligence). Use when processing invoices, receipts, IDs, forms, or building custom document models.
azure-ai
Use for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR. WHEN: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, OCR, convert text to speech.
markitdown
Convert files and office documents to Markdown. Supports PDF, DOCX, PPTX, XLSX, images (with OCR), audio (with transcription), HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs and more.
ingest
Turn external URLs and documents into LLM-ready content — load + cache web pages (HQCC compression) and OCR PDFs/images to Markdown. Use whenever the user gives a URL, asks you to read a webpage, or attaches a PDF/scan that needs to be parsed before reasoning. Executes via the cloud load, cloud search, and cloud parse…