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 Ghosteken/agent-harness --skill azure-ai-contentunderstanding-pygit clone --depth 1 https://github.com/Ghosteken/agent-harnessWrote 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/ghosteken/agent-harness/azure-ai-contentunderstanding-py)<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.
<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>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 | $0.00032 | $0.01730 |
| Opus 5 | $0.00016 | $0.00865 |
| Sonnet 5 | $0.00006 | $0.00346 |
| Haiku 4.5 | $0.00003 | $0.00173 |
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.
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.
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:
- Begin Analysis — Start the analysis operation with
begin_analyze()(returns a poller) - Poll for Results — Poll until analysis completes (SDK handles this with
.result()) - 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)
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.
- 7d ago First seen · 282 lines · 32 tokens per session scan A 25ae5841561c
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.
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