feat: Add Official Microsoft & Gemini Skills (845+ Total)
🚀 Impact Significantly expands the capabilities of **Antigravity Awesome Skills** by integrating official skill collections from **Microsoft** and **Google Gemini**. This update increases the total skill count to **845+**, making the library even more comprehensive for AI coding assistants. ✨ Key Changes 1. New Official Skills - **Microsoft Skills**: Added a massive collection of official skills from [microsoft/skills](https://github.com/microsoft/skills). - Includes Azure, .NET, Python, TypeScript, and Semantic Kernel skills. - Preserves the original directory structure under `skills/official/microsoft/`. - Includes plugin skills from the `.github/plugins` directory. - **Gemini Skills**: Added official Gemini API development skills under `skills/gemini-api-dev/`. 2. New Scripts & Tooling - **`scripts/sync_microsoft_skills.py`**: A robust synchronization script that: - Clones the official Microsoft repository. - Preserves the original directory heirarchy. - Handles symlinks and plugin locations. - Generates attribution metadata. - **`scripts/tests/inspect_microsoft_repo.py`**: Debug tool to inspect the remote repository structure. - **`scripts/tests/test_comprehensive_coverage.py`**: Verification script to ensure 100% of skills are captured during sync. 3. Core Improvements - **`scripts/generate_index.py`**: Enhanced frontmatter parsing to safely handle unquoted values containing `@` symbols and commas (fixing issues with some Microsoft skill descriptions). - **`package.json`**: Added `sync:microsoft` and `sync:all-official` scripts for easy maintenance. 4. Documentation - Updated `README.md` to reflect the new skill counts (845+) and added Microsoft/Gemini to the provider list. - Updated `CATALOG.md` and `skills_index.json` with the new skills. 🧪 Verification - Ran `scripts/tests/test_comprehensive_coverage.py` to verify all Microsoft skills are detected. - Validated `generate_index.py` fixes by successfully indexing the new skills.
This commit is contained in:
Ahmed Rehan
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skills/official/microsoft/python/foundry/ml/SKILL.md
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skills/official/microsoft/python/foundry/ml/SKILL.md
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---
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name: azure-ai-ml-py
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description: |
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Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines.
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Triggers: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".
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package: azure-ai-ml
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---
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# Azure Machine Learning SDK v2 for Python
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Client library for managing Azure ML resources: workspaces, jobs, models, data, and compute.
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## Installation
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```bash
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pip install azure-ai-ml
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```
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## Environment Variables
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```bash
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AZURE_SUBSCRIPTION_ID=<your-subscription-id>
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AZURE_RESOURCE_GROUP=<your-resource-group>
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AZURE_ML_WORKSPACE_NAME=<your-workspace-name>
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```
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## Authentication
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```python
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from azure.ai.ml import MLClient
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from azure.identity import DefaultAzureCredential
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ml_client = MLClient(
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credential=DefaultAzureCredential(),
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subscription_id=os.environ["AZURE_SUBSCRIPTION_ID"],
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resource_group_name=os.environ["AZURE_RESOURCE_GROUP"],
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workspace_name=os.environ["AZURE_ML_WORKSPACE_NAME"]
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)
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```
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### From Config File
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```python
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from azure.ai.ml import MLClient
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from azure.identity import DefaultAzureCredential
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# Uses config.json in current directory or parent
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ml_client = MLClient.from_config(
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credential=DefaultAzureCredential()
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)
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```
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## Workspace Management
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### Create Workspace
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```python
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from azure.ai.ml.entities import Workspace
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ws = Workspace(
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name="my-workspace",
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location="eastus",
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display_name="My Workspace",
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description="ML workspace for experiments",
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tags={"purpose": "demo"}
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)
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ml_client.workspaces.begin_create(ws).result()
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```
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### List Workspaces
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```python
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for ws in ml_client.workspaces.list():
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print(f"{ws.name}: {ws.location}")
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```
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## Data Assets
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### Register Data
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```python
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from azure.ai.ml.entities import Data
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from azure.ai.ml.constants import AssetTypes
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# Register a file
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my_data = Data(
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name="my-dataset",
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version="1",
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path="azureml://datastores/workspaceblobstore/paths/data/train.csv",
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type=AssetTypes.URI_FILE,
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description="Training data"
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)
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ml_client.data.create_or_update(my_data)
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```
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### Register Folder
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```python
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my_data = Data(
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name="my-folder-dataset",
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version="1",
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path="azureml://datastores/workspaceblobstore/paths/data/",
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type=AssetTypes.URI_FOLDER
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)
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ml_client.data.create_or_update(my_data)
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```
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## Model Registry
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### Register Model
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```python
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from azure.ai.ml.entities import Model
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from azure.ai.ml.constants import AssetTypes
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model = Model(
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name="my-model",
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version="1",
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path="./model/",
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type=AssetTypes.CUSTOM_MODEL,
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description="My trained model"
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)
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ml_client.models.create_or_update(model)
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```
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### List Models
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```python
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for model in ml_client.models.list(name="my-model"):
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print(f"{model.name} v{model.version}")
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```
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## Compute
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### Create Compute Cluster
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```python
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from azure.ai.ml.entities import AmlCompute
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cluster = AmlCompute(
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name="cpu-cluster",
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type="amlcompute",
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size="Standard_DS3_v2",
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min_instances=0,
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max_instances=4,
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idle_time_before_scale_down=120
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)
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ml_client.compute.begin_create_or_update(cluster).result()
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```
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### List Compute
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```python
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for compute in ml_client.compute.list():
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print(f"{compute.name}: {compute.type}")
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```
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## Jobs
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### Command Job
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```python
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from azure.ai.ml import command, Input
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job = command(
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code="./src",
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command="python train.py --data ${{inputs.data}} --lr ${{inputs.learning_rate}}",
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inputs={
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"data": Input(type="uri_folder", path="azureml:my-dataset:1"),
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"learning_rate": 0.01
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},
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environment="AzureML-sklearn-1.0-ubuntu20.04-py38-cpu@latest",
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compute="cpu-cluster",
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display_name="training-job"
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)
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returned_job = ml_client.jobs.create_or_update(job)
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print(f"Job URL: {returned_job.studio_url}")
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```
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### Monitor Job
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```python
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ml_client.jobs.stream(returned_job.name)
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```
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## Pipelines
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```python
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from azure.ai.ml import dsl, Input, Output
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from azure.ai.ml.entities import Pipeline
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@dsl.pipeline(
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compute="cpu-cluster",
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description="Training pipeline"
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)
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def training_pipeline(data_input):
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prep_step = prep_component(data=data_input)
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train_step = train_component(
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data=prep_step.outputs.output_data,
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learning_rate=0.01
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)
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return {"model": train_step.outputs.model}
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pipeline = training_pipeline(
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data_input=Input(type="uri_folder", path="azureml:my-dataset:1")
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)
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pipeline_job = ml_client.jobs.create_or_update(pipeline)
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```
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## Environments
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### Create Custom Environment
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```python
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from azure.ai.ml.entities import Environment
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env = Environment(
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name="my-env",
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version="1",
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image="mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04",
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conda_file="./environment.yml"
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)
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ml_client.environments.create_or_update(env)
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```
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## Datastores
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### List Datastores
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```python
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for ds in ml_client.datastores.list():
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print(f"{ds.name}: {ds.type}")
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```
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### Get Default Datastore
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```python
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default_ds = ml_client.datastores.get_default()
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print(f"Default: {default_ds.name}")
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```
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## MLClient Operations
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| Property | Operations |
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|----------|------------|
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| `workspaces` | create, get, list, delete |
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| `jobs` | create_or_update, get, list, stream, cancel |
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| `models` | create_or_update, get, list, archive |
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| `data` | create_or_update, get, list |
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| `compute` | begin_create_or_update, get, list, delete |
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| `environments` | create_or_update, get, list |
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| `datastores` | create_or_update, get, list, get_default |
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| `components` | create_or_update, get, list |
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## Best Practices
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1. **Use versioning** for data, models, and environments
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2. **Configure idle scale-down** to reduce compute costs
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3. **Use environments** for reproducible training
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4. **Stream job logs** to monitor progress
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5. **Register models** after successful training jobs
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6. **Use pipelines** for multi-step workflows
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7. **Tag resources** for organization and cost tracking
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