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-ml-pygit 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-ml-py)<a href="https://agentmods.dev/skills/benjaminastera/antigravity-awesome-skills/azure-ai-ml-py"><img src="https://agentmods.dev/badge/skills/benjaminastera/antigravity-awesome-skills/azure-ai-ml-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/benjaminastera/antigravity-awesome-skills/azure-ai-ml-py"><img src="https://agentmods.dev/badge/skills/benjaminastera/antigravity-awesome-skills/azure-ai-ml-py.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.01479 |
| Opus 5 | $0.00000 | $0.00740 |
| Sonnet 5 | $0.00000 | $0.00296 |
| Haiku 4.5 | $0.00000 | $0.00148 |
Grade A, and why
azure-ai-ml-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 8d 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.
Copies of this mod
7 near-identical copies found in the catalogue:
- azure-ai-ml-py — 97% identical, 6 lines differ
- azure-ai-ml-py — 97% identical, 6 lines differ
- azure-ai-ml-py — 91% identical, 11 lines differ
- azure-ai-ml-py — 91% identical, 11 lines differ
- azure-ai-ml-py — 91% identical, 11 lines differ
- azure-ai-ml-py — 91% identical, 11 lines differ
- azure-ai-ml-py — 89% identical, 13 lines differ
How it starts
The opening of the file, as written. The whole thing — 277 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Azure Machine Learning SDK v2 for Python
Client library for managing Azure ML resources: workspaces, jobs, models, data, and compute.
Installation
pip install azure-ai-ml
Environment Variables
AZURE_SUBSCRIPTION_ID=<your-subscription-id>
AZURE_RESOURCE_GROUP=<your-resource-group>
AZURE_ML_WORKSPACE_NAME=<your-workspace-name>
Authentication
from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential
ml_client = MLClient(
credential=DefaultAzureCredential(),
subscription_id=os.environ["AZURE_SUBSCRIPTION_ID"],
resource_group_name=os.environ["AZURE_RESOURCE_GROUP"],
workspace_name=os.environ["AZURE_ML_WORKSPACE_NAME"]
)
From Config File
from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential
# Uses config.json in current directory or parent
ml_client = MLClient.from_config(
credential=DefaultAzureCredential()
)
Workspace Management
Create Workspace
from azure.ai.ml.entities import Workspace
ws = Workspace(
name="my-workspace",
location="eastus",
display_name="My Workspace",
description="ML workspace for experiments",
tags={"purpose": "demo"}
)
ml_client.workspaces.begin_create(ws).result()
List Workspaces
for ws in ml_client.workspaces.list():
print(f"{ws.name}: {ws.location}")
Data Assets
Register Data
from azure.ai.ml.entities import Data
from azure.ai.ml.constants import AssetTypes
# Register a file
my_data = Data(
name="my-dataset",
version="1",
path="azureml://datastores/workspaceblobstore/paths/data/train.csv",
type=AssetTypes.URI_FILE,
description="Training data"
)
ml_client.data.create_or_update(my_data)
Register Folder
my_data = Data(
name="my-folder-dataset",
version="1",
path="azureml://datastores/workspaceblobstore/paths/data/",
type=AssetTypes.URI_FOLDER
)
ml_client.data.create_or_update(my_data)
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.
- 8d ago First seen · 277 lines · 0 tokens per session scan A c90ec495348e
azure-ai-ml-py is a skill published in the GitHub repository benjaminasterA/antigravity-awesome-skills (264 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,479 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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