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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---
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name: azure-ai-vision-imageanalysis-py
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description: |
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Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks.
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Triggers: "image analysis", "computer vision", "OCR", "object detection", "ImageAnalysisClient", "image caption".
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package: azure-ai-vision-imageanalysis
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---
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# Azure AI Vision Image Analysis SDK for Python
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Client library for Azure AI Vision 4.0 image analysis including captions, tags, objects, OCR, and more.
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## Installation
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```bash
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pip install azure-ai-vision-imageanalysis
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```
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## Environment Variables
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```bash
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VISION_ENDPOINT=https://<resource>.cognitiveservices.azure.com
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VISION_KEY=<your-api-key> # If using API key
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```
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## Authentication
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### API Key
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```python
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import os
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from azure.ai.vision.imageanalysis import ImageAnalysisClient
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from azure.core.credentials import AzureKeyCredential
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endpoint = os.environ["VISION_ENDPOINT"]
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key = os.environ["VISION_KEY"]
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client = ImageAnalysisClient(
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endpoint=endpoint,
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credential=AzureKeyCredential(key)
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)
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```
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### Entra ID (Recommended)
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```python
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from azure.ai.vision.imageanalysis import ImageAnalysisClient
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from azure.identity import DefaultAzureCredential
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client = ImageAnalysisClient(
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endpoint=os.environ["VISION_ENDPOINT"],
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credential=DefaultAzureCredential()
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)
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```
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## Analyze Image from URL
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```python
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from azure.ai.vision.imageanalysis.models import VisualFeatures
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image_url = "https://example.com/image.jpg"
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result = client.analyze_from_url(
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image_url=image_url,
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visual_features=[
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VisualFeatures.CAPTION,
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VisualFeatures.TAGS,
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VisualFeatures.OBJECTS,
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VisualFeatures.READ,
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VisualFeatures.PEOPLE,
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VisualFeatures.SMART_CROPS,
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VisualFeatures.DENSE_CAPTIONS
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],
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gender_neutral_caption=True,
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language="en"
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)
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```
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## Analyze Image from File
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```python
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with open("image.jpg", "rb") as f:
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image_data = f.read()
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result = client.analyze(
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image_data=image_data,
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visual_features=[VisualFeatures.CAPTION, VisualFeatures.TAGS]
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)
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```
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## Image Caption
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```python
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result = client.analyze_from_url(
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image_url=image_url,
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visual_features=[VisualFeatures.CAPTION],
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gender_neutral_caption=True
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)
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if result.caption:
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print(f"Caption: {result.caption.text}")
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print(f"Confidence: {result.caption.confidence:.2f}")
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```
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## Dense Captions (Multiple Regions)
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```python
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result = client.analyze_from_url(
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image_url=image_url,
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visual_features=[VisualFeatures.DENSE_CAPTIONS]
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)
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if result.dense_captions:
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for caption in result.dense_captions.list:
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print(f"Caption: {caption.text}")
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print(f" Confidence: {caption.confidence:.2f}")
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print(f" Bounding box: {caption.bounding_box}")
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```
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## Tags
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```python
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result = client.analyze_from_url(
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image_url=image_url,
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visual_features=[VisualFeatures.TAGS]
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)
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if result.tags:
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for tag in result.tags.list:
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print(f"Tag: {tag.name} (confidence: {tag.confidence:.2f})")
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```
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## Object Detection
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```python
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result = client.analyze_from_url(
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image_url=image_url,
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visual_features=[VisualFeatures.OBJECTS]
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)
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if result.objects:
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for obj in result.objects.list:
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print(f"Object: {obj.tags[0].name}")
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print(f" Confidence: {obj.tags[0].confidence:.2f}")
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box = obj.bounding_box
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print(f" Bounding box: x={box.x}, y={box.y}, w={box.width}, h={box.height}")
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```
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## OCR (Text Extraction)
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```python
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result = client.analyze_from_url(
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image_url=image_url,
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visual_features=[VisualFeatures.READ]
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)
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if result.read:
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for block in result.read.blocks:
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for line in block.lines:
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print(f"Line: {line.text}")
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print(f" Bounding polygon: {line.bounding_polygon}")
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# Word-level details
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for word in line.words:
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print(f" Word: {word.text} (confidence: {word.confidence:.2f})")
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```
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## People Detection
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```python
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result = client.analyze_from_url(
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image_url=image_url,
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visual_features=[VisualFeatures.PEOPLE]
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)
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if result.people:
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for person in result.people.list:
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print(f"Person detected:")
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print(f" Confidence: {person.confidence:.2f}")
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box = person.bounding_box
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print(f" Bounding box: x={box.x}, y={box.y}, w={box.width}, h={box.height}")
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```
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## Smart Cropping
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```python
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result = client.analyze_from_url(
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image_url=image_url,
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visual_features=[VisualFeatures.SMART_CROPS],
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smart_crops_aspect_ratios=[0.9, 1.33, 1.78] # Portrait, 4:3, 16:9
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)
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if result.smart_crops:
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for crop in result.smart_crops.list:
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print(f"Aspect ratio: {crop.aspect_ratio}")
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box = crop.bounding_box
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print(f" Crop region: x={box.x}, y={box.y}, w={box.width}, h={box.height}")
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```
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## Async Client
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```python
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from azure.ai.vision.imageanalysis.aio import ImageAnalysisClient
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from azure.identity.aio import DefaultAzureCredential
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async def analyze_image():
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async with ImageAnalysisClient(
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endpoint=endpoint,
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credential=DefaultAzureCredential()
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) as client:
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result = await client.analyze_from_url(
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image_url=image_url,
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visual_features=[VisualFeatures.CAPTION]
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)
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print(result.caption.text)
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```
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## Visual Features
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| Feature | Description |
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|---------|-------------|
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| `CAPTION` | Single sentence describing the image |
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| `DENSE_CAPTIONS` | Captions for multiple regions |
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| `TAGS` | Content tags (objects, scenes, actions) |
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| `OBJECTS` | Object detection with bounding boxes |
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| `READ` | OCR text extraction |
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| `PEOPLE` | People detection with bounding boxes |
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| `SMART_CROPS` | Suggested crop regions for thumbnails |
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## Error Handling
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```python
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from azure.core.exceptions import HttpResponseError
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try:
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result = client.analyze_from_url(
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image_url=image_url,
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visual_features=[VisualFeatures.CAPTION]
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)
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except HttpResponseError as e:
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print(f"Status code: {e.status_code}")
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print(f"Reason: {e.reason}")
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print(f"Message: {e.error.message}")
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```
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## Image Requirements
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- Formats: JPEG, PNG, GIF, BMP, WEBP, ICO, TIFF, MPO
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- Max size: 20 MB
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- Dimensions: 50x50 to 16000x16000 pixels
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## Best Practices
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1. **Select only needed features** to optimize latency and cost
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2. **Use async client** for high-throughput scenarios
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3. **Handle HttpResponseError** for invalid images or auth issues
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4. **Enable gender_neutral_caption** for inclusive descriptions
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5. **Specify language** for localized captions
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6. **Use smart_crops_aspect_ratios** matching your thumbnail requirements
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7. **Cache results** when analyzing the same image multiple times
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