azure-ai-contentunderstanding-py

azure-ai-contentunderstanding-py is a skill for Claude Code from Ghosteken/agent-harness. It costs 32 tokens per session (1,730 once invoked), scanned A, a copy of azure-ai-contentunderstanding-py, MIT.

A Python SDK for Azure AI Content Understanding, a service that extracts meaningful, structured information from documents, images, audio, and video. It can also produce document text in Markdown for RAG, a method of giving AI answers information from a document collection.

In plain words
What is it for?
Use it to analyze documents, images, audio, and video, then process the returned structured content for search, RAG, or automated workflows.
Why use it?
It reduces the work needed to turn different media formats into data that an application or AI workflow can use. Long-running analysis is handled through the SDK's polling workflow.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the agent-harness plugin — 173 skills, 11 commands, 12 agents shipped together

Good fit Use it to analyze documents, images, audio, and video, then process the returned structured content for search, RAG, or automated workflows.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ghosteken/agent-harness/azure-ai-contentunderstanding-py
Install

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.

Any agent
npx skills add Ghosteken/agent-harness --skill azure-ai-contentunderstanding-py
Clone the repo
git clone --depth 1 https://github.com/Ghosteken/agent-harness

Made for: Claude Code.

Or install agent-harness, the plugin that ships this one along with the rest of its 173 skills, 11 commands, 12 agents.

Wrote 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.

agentmods badge for azure-ai-contentunderstanding-py

README.md
[![agentmods](https://agentmods.dev/badge/skills/ghosteken/agent-harness/azure-ai-contentunderstanding-py/github.svg)](https://agentmods.dev/skills/ghosteken/agent-harness/azure-ai-contentunderstanding-py)
Your own site
<a href="https://agentmods.dev/skills/ghosteken/agent-harness/azure-ai-contentunderstanding-py"><img src="https://agentmods.dev/badge/skills/ghosteken/agent-harness/azure-ai-contentunderstanding-py/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.

agentmods 80×15 button for azure-ai-contentunderstanding-py

Your own site · 80×15
<a href="https://agentmods.dev/skills/ghosteken/agent-harness/azure-ai-contentunderstanding-py"><img src="https://agentmods.dev/badge/skills/ghosteken/agent-harness/azure-ai-contentunderstanding-py.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 32 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,730 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 92% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00032 $0.01730
Opus 5 $0.00016 $0.00865
Sonnet 5 $0.00006 $0.00346
Haiku 4.5 $0.00003 $0.00173

Measured 7d ago against content hash 25ae5841561c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

azure-ai-contentunderstanding-py 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 7d 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.

Origin

This is a copy

92% identical to azure-ai-contentunderstanding-py — 11 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.

archive/skills-community/azure-ai-contentunderstanding-py/SKILL.md · 282 lines

How it starts

The opening of the file, as written. The whole thing — 282 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Azure AI Content Understanding SDK for Python

Multimodal AI service that extracts semantic content from documents, video, audio, and image files for RAG and automated workflows.

Installation

pip install azure-ai-contentunderstanding

Environment Variables

CONTENTUNDERSTANDING_ENDPOINT=https://<resource>.cognitiveservices.azure.com/

Authentication

import os
from azure.ai.contentunderstanding import ContentUnderstandingClient
from azure.identity import DefaultAzureCredential

endpoint = os.environ["CONTENTUNDERSTANDING_ENDPOINT"]
credential = DefaultAzureCredential()
client = ContentUnderstandingClient(endpoint=endpoint, credential=credential)

Core Workflow

Content Understanding operations are asynchronous long-running operations:

  1. Begin Analysis — Start the analysis operation with begin_analyze() (returns a poller)
  2. Poll for Results — Poll until analysis completes (SDK handles this with .result())
  3. Process Results — Extract structured results from AnalyzeResult.contents

Prebuilt Analyzers

Analyzer Content Type Purpose
prebuilt-documentSearch Documents Extract markdown for RAG applications
prebuilt-imageSearch Images Extract content from images
prebuilt-audioSearch Audio Transcribe audio with timing
prebuilt-videoSearch Video Extract frames, transcripts, summaries
prebuilt-invoice Documents Extract invoice fields

Analyze Document

import os
from azure.ai.contentunderstanding import ContentUnderstandingClient
from azure.ai.contentunderstanding.models import AnalyzeInput
from azure.identity import DefaultAzureCredential

endpoint = os.environ["CONTENTUNDERSTANDING_ENDPOINT"]
client = ContentUnderstandingClient(
    endpoint=endpoint,
    credential=DefaultAzureCredential()
)

# Analyze document from URL
poller = client.begin_analyze(
    analyzer_id="prebuilt-documentSearch",
    inputs=[AnalyzeInput(url="https://example.com/document.pdf")]
)

result = poller.result()

# Access markdown content (contents is a list)
content = result.contents[0]
print(content.markdown)

Read the full file on GitHub · 282 lines

Changes

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.

  1. 7d ago First seen · 282 lines · 32 tokens per session scan A 25ae5841561c

Subscribe to this mod's changes

azure-ai-contentunderstanding-py is a skill published in the GitHub repository Ghosteken/agent-harness (2 stars, last pushed 6d ago), licensed MIT. It adds 32 tokens to every session and 1,730 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to azure-ai-contentunderstanding-py, differing in 11 lines, and is treated as a copy.

Related

Other skills, from other repositories

library-rag

Semantic search over a personal library using Nemotron-3-Embed-1B embeddings + sqlite-vec. Index books, documents, any text corpus; query by meaning. Includes EPUB→Markdown conversion and MCP server for auto-available search tools.

moonlight-lupin/agent-skills · 54 tokens

document-qa

A document question-and-answer tool for PDFs, Word files, text, Markdown, CSV, and Excel files. It finds relevant passages in one file or a whole folder and uses a language model to answer questions.

xyva-yuangui/XyvaClaw · 53 tokens

rag-knowledge-base

A local document search system that lets you add PDFs, Word files, Excel files, Markdown, and text, then ask questions about their contents. It finds relevant passages using both meaning-based and keyword search, then prepares context for an AI model.

xyva-yuangui/XyvaClaw · 0 tokens

recipe-backup-sheet-as-csv

Export a Google Sheets spreadsheet as a CSV file for local backup or processing.

sagebynature/team-nexus · 23 tokens

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.

K-Dense-AI/scientific-agent-skills · 61 tokens

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.

microsoft/skills · 76 tokens