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 lucaspmarie-a11y/claude-skills-vault --skill azure-ai-vision-imageanalysis-pygit clone --depth 1 https://github.com/lucaspmarie-a11y/claude-skills-vaultWrote 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/lucaspmarie-a11y/claude-skills-vault/azure-ai-vision-imageanalysis-py)<a href="https://agentmods.dev/skills/lucaspmarie-a11y/claude-skills-vault/azure-ai-vision-imageanalysis-py"><img src="https://agentmods.dev/badge/skills/lucaspmarie-a11y/claude-skills-vault/azure-ai-vision-imageanalysis-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/lucaspmarie-a11y/claude-skills-vault/azure-ai-vision-imageanalysis-py"><img src="https://agentmods.dev/badge/skills/lucaspmarie-a11y/claude-skills-vault/azure-ai-vision-imageanalysis-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 | $0.00040 | $0.01610 |
| Opus 5.5 | $0.00016 | $0.00644 |
| Sonnet 5.5 | $0.00008 | $0.00322 |
| Haiku 4.5 | $0.00004 | $0.00161 |
Grade A, and why
azure-ai-vision-imageanalysis-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 1mo 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
100% identical to azure-ai-vision-imageanalysis-py — 0 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 — 264 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Azure AI Vision Image Analysis SDK for Python
Client library for Azure AI Vision 4.0 image analysis including captions, tags, objects, OCR, and more.
Installation
pip install azure-ai-vision-imageanalysis
Environment Variables
VISION_ENDPOINT=https://<resource>.cognitiveservices.azure.com
VISION_KEY=<your-api-key> # If using API key
Authentication
API Key
import os
from azure.ai.vision.imageanalysis import ImageAnalysisClient
from azure.core.credentials import AzureKeyCredential
endpoint = os.environ["VISION_ENDPOINT"]
key = os.environ["VISION_KEY"]
client = ImageAnalysisClient(
endpoint=endpoint,
credential=AzureKeyCredential(key)
)
Entra ID (Recommended)
from azure.ai.vision.imageanalysis import ImageAnalysisClient
from azure.identity import DefaultAzureCredential
client = ImageAnalysisClient(
endpoint=os.environ["VISION_ENDPOINT"],
credential=DefaultAzureCredential()
)
Analyze Image from URL
from azure.ai.vision.imageanalysis.models import VisualFeatures
image_url = "https://example.com/image.jpg"
result = client.analyze_from_url(
image_url=image_url,
visual_features=[
VisualFeatures.CAPTION,
VisualFeatures.TAGS,
VisualFeatures.OBJECTS,
VisualFeatures.READ,
VisualFeatures.PEOPLE,
VisualFeatures.SMART_CROPS,
VisualFeatures.DENSE_CAPTIONS
],
gender_neutral_caption=True,
language="en"
)
Analyze Image from File
with open("image.jpg", "rb") as f:
image_data = f.read()
result = client.analyze(
image_data=image_data,
visual_features=[VisualFeatures.CAPTION, VisualFeatures.TAGS]
)
Image Caption
result = client.analyze_from_url(
image_url=image_url,
visual_features=[VisualFeatures.CAPTION],
gender_neutral_caption=True
)
if result.caption:
print(f"Caption: {result.caption.text}")
print(f"Confidence: {result.caption.confidence:.2f}")
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.
- 1mo ago First seen · 264 lines · 40 tokens per session scan A 9cf6a4b3a169
azure-ai-vision-imageanalysis-py is a skill published in the GitHub repository lucaspmarie-a11y/claude-skills-vault (5 stars, last pushed 4mo ago), licensed MIT. It adds 40 tokens to every session and 1,610 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to azure-ai-vision-imageanalysis-py, differing in 0 lines, and is treated as a copy.
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