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-java
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description: Build image analysis applications with Azure AI Vision SDK for Java. Use when implementing image captioning, OCR text extraction, object detection, tagging, or smart cropping.
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package: com.azure:azure-ai-vision-imageanalysis
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---
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# Azure AI Vision Image Analysis SDK for Java
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Build image analysis applications using the Azure AI Vision Image Analysis SDK for Java.
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## Installation
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```xml
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<dependency>
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<groupId>com.azure</groupId>
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<artifactId>azure-ai-vision-imageanalysis</artifactId>
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<version>1.1.0-beta.1</version>
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</dependency>
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```
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## Client Creation
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### With API Key
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```java
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import com.azure.ai.vision.imageanalysis.ImageAnalysisClient;
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import com.azure.ai.vision.imageanalysis.ImageAnalysisClientBuilder;
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import com.azure.core.credential.KeyCredential;
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String endpoint = System.getenv("VISION_ENDPOINT");
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String key = System.getenv("VISION_KEY");
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ImageAnalysisClient client = new ImageAnalysisClientBuilder()
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.endpoint(endpoint)
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.credential(new KeyCredential(key))
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.buildClient();
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```
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### Async Client
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```java
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import com.azure.ai.vision.imageanalysis.ImageAnalysisAsyncClient;
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ImageAnalysisAsyncClient asyncClient = new ImageAnalysisClientBuilder()
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.endpoint(endpoint)
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.credential(new KeyCredential(key))
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.buildAsyncClient();
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```
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### With DefaultAzureCredential
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```java
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import com.azure.identity.DefaultAzureCredentialBuilder;
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ImageAnalysisClient client = new ImageAnalysisClientBuilder()
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.endpoint(endpoint)
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.credential(new DefaultAzureCredentialBuilder().build())
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.buildClient();
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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` | Generate human-readable image description |
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| `DENSE_CAPTIONS` | Captions for up to 10 regions |
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| `READ` | OCR - Extract text from images |
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| `TAGS` | Content tags for objects, scenes, actions |
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| `OBJECTS` | Detect objects with bounding boxes |
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| `SMART_CROPS` | Smart thumbnail regions |
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| `PEOPLE` | Detect people with locations |
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## Core Patterns
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### Generate Caption
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```java
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import com.azure.ai.vision.imageanalysis.models.*;
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import com.azure.core.util.BinaryData;
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import java.io.File;
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import java.util.Arrays;
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// From file
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BinaryData imageData = BinaryData.fromFile(new File("image.jpg").toPath());
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ImageAnalysisResult result = client.analyze(
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imageData,
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Arrays.asList(VisualFeatures.CAPTION),
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new ImageAnalysisOptions().setGenderNeutralCaption(true));
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System.out.printf("Caption: \"%s\" (confidence: %.4f)%n",
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result.getCaption().getText(),
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result.getCaption().getConfidence());
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```
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### Generate Caption from URL
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```java
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ImageAnalysisResult result = client.analyzeFromUrl(
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"https://example.com/image.jpg",
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Arrays.asList(VisualFeatures.CAPTION),
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new ImageAnalysisOptions().setGenderNeutralCaption(true));
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System.out.printf("Caption: \"%s\"%n", result.getCaption().getText());
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```
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### Extract Text (OCR)
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```java
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ImageAnalysisResult result = client.analyze(
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BinaryData.fromFile(new File("document.jpg").toPath()),
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Arrays.asList(VisualFeatures.READ),
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null);
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for (DetectedTextBlock block : result.getRead().getBlocks()) {
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for (DetectedTextLine line : block.getLines()) {
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System.out.printf("Line: '%s'%n", line.getText());
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System.out.printf(" Bounding polygon: %s%n", line.getBoundingPolygon());
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for (DetectedTextWord word : line.getWords()) {
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System.out.printf(" Word: '%s' (confidence: %.4f)%n",
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word.getText(),
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word.getConfidence());
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}
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}
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}
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```
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### Detect Objects
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```java
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ImageAnalysisResult result = client.analyzeFromUrl(
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imageUrl,
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Arrays.asList(VisualFeatures.OBJECTS),
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null);
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for (DetectedObject obj : result.getObjects()) {
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System.out.printf("Object: %s (confidence: %.4f)%n",
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obj.getTags().get(0).getName(),
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obj.getTags().get(0).getConfidence());
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ImageBoundingBox box = obj.getBoundingBox();
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System.out.printf(" Location: x=%d, y=%d, w=%d, h=%d%n",
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box.getX(), box.getY(), box.getWidth(), box.getHeight());
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}
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```
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### Get Tags
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```java
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ImageAnalysisResult result = client.analyzeFromUrl(
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imageUrl,
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Arrays.asList(VisualFeatures.TAGS),
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null);
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for (DetectedTag tag : result.getTags()) {
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System.out.printf("Tag: %s (confidence: %.4f)%n",
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tag.getName(),
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tag.getConfidence());
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}
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```
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### Detect People
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```java
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ImageAnalysisResult result = client.analyzeFromUrl(
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imageUrl,
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Arrays.asList(VisualFeatures.PEOPLE),
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null);
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for (DetectedPerson person : result.getPeople()) {
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ImageBoundingBox box = person.getBoundingBox();
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System.out.printf("Person at x=%d, y=%d (confidence: %.4f)%n",
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box.getX(), box.getY(), person.getConfidence());
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}
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```
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### Smart Cropping
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```java
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ImageAnalysisResult result = client.analyzeFromUrl(
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imageUrl,
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Arrays.asList(VisualFeatures.SMART_CROPS),
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new ImageAnalysisOptions().setSmartCropsAspectRatios(Arrays.asList(1.0, 1.5)));
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for (CropRegion crop : result.getSmartCrops()) {
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System.out.printf("Crop region: aspect=%.2f, x=%d, y=%d, w=%d, h=%d%n",
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crop.getAspectRatio(),
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crop.getBoundingBox().getX(),
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crop.getBoundingBox().getY(),
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crop.getBoundingBox().getWidth(),
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crop.getBoundingBox().getHeight());
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}
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```
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### Dense Captions
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```java
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ImageAnalysisResult result = client.analyzeFromUrl(
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imageUrl,
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Arrays.asList(VisualFeatures.DENSE_CAPTIONS),
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new ImageAnalysisOptions().setGenderNeutralCaption(true));
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for (DenseCaption caption : result.getDenseCaptions()) {
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System.out.printf("Caption: \"%s\" (confidence: %.4f)%n",
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caption.getText(),
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caption.getConfidence());
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System.out.printf(" Region: x=%d, y=%d, w=%d, h=%d%n",
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caption.getBoundingBox().getX(),
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caption.getBoundingBox().getY(),
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caption.getBoundingBox().getWidth(),
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caption.getBoundingBox().getHeight());
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}
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```
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### Multiple Features
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```java
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ImageAnalysisResult result = client.analyzeFromUrl(
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imageUrl,
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Arrays.asList(
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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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new ImageAnalysisOptions()
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.setGenderNeutralCaption(true)
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.setLanguage("en"));
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// Access all results
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System.out.println("Caption: " + result.getCaption().getText());
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System.out.println("Tags: " + result.getTags().size());
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System.out.println("Objects: " + result.getObjects().size());
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System.out.println("Text blocks: " + result.getRead().getBlocks().size());
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```
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### Async Analysis
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```java
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asyncClient.analyzeFromUrl(
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imageUrl,
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Arrays.asList(VisualFeatures.CAPTION),
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null)
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.subscribe(
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result -> System.out.println("Caption: " + result.getCaption().getText()),
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error -> System.err.println("Error: " + error.getMessage()),
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() -> System.out.println("Complete")
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);
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```
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## Error Handling
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```java
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import com.azure.core.exception.HttpResponseException;
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try {
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client.analyzeFromUrl(imageUrl, Arrays.asList(VisualFeatures.CAPTION), null);
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} catch (HttpResponseException e) {
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System.out.println("Status: " + e.getResponse().getStatusCode());
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System.out.println("Error: " + e.getMessage());
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}
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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>
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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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- Size: < 20 MB
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- Dimensions: 50x50 to 16000x16000 pixels
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## Regional Availability
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Caption and Dense Captions require GPU-supported regions. Check [supported regions](https://learn.microsoft.com/azure/ai-services/computer-vision/concept-describe-images-40) before deployment.
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## Trigger Phrases
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- "image analysis Java"
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- "Azure Vision SDK"
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- "image captioning"
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- "OCR image text extraction"
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- "object detection image"
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- "smart crop thumbnail"
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- "detect people image"
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