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/java/foundry/anomalydetector/SKILL.md
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skills/official/microsoft/java/foundry/anomalydetector/SKILL.md
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---
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name: azure-ai-anomalydetector-java
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description: Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.
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package: com.azure:azure-ai-anomalydetector
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---
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# Azure AI Anomaly Detector SDK for Java
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Build anomaly detection applications using the Azure AI Anomaly Detector 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-anomalydetector</artifactId>
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<version>3.0.0-beta.6</version>
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</dependency>
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```
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## Client Creation
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### Sync and Async Clients
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```java
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import com.azure.ai.anomalydetector.AnomalyDetectorClientBuilder;
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import com.azure.ai.anomalydetector.MultivariateClient;
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import com.azure.ai.anomalydetector.UnivariateClient;
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import com.azure.core.credential.AzureKeyCredential;
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String endpoint = System.getenv("AZURE_ANOMALY_DETECTOR_ENDPOINT");
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String key = System.getenv("AZURE_ANOMALY_DETECTOR_API_KEY");
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// Multivariate client for multiple correlated signals
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MultivariateClient multivariateClient = new AnomalyDetectorClientBuilder()
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.credential(new AzureKeyCredential(key))
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.endpoint(endpoint)
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.buildMultivariateClient();
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// Univariate client for single variable analysis
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UnivariateClient univariateClient = new AnomalyDetectorClientBuilder()
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.credential(new AzureKeyCredential(key))
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.endpoint(endpoint)
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.buildUnivariateClient();
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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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MultivariateClient client = new AnomalyDetectorClientBuilder()
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.credential(new DefaultAzureCredentialBuilder().build())
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.endpoint(endpoint)
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.buildMultivariateClient();
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```
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## Key Concepts
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### Univariate Anomaly Detection
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- **Batch Detection**: Analyze entire time series at once
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- **Streaming Detection**: Real-time detection on latest data point
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- **Change Point Detection**: Detect trend changes in time series
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### Multivariate Anomaly Detection
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- Detect anomalies across 300+ correlated signals
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- Uses Graph Attention Network for inter-correlations
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- Three-step process: Train → Inference → Results
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## Core Patterns
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### Univariate Batch Detection
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```java
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import com.azure.ai.anomalydetector.models.*;
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import java.time.OffsetDateTime;
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import java.util.List;
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List<TimeSeriesPoint> series = List.of(
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new TimeSeriesPoint(OffsetDateTime.parse("2023-01-01T00:00:00Z"), 1.0),
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new TimeSeriesPoint(OffsetDateTime.parse("2023-01-02T00:00:00Z"), 2.5),
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// ... more data points (minimum 12 points required)
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);
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UnivariateDetectionOptions options = new UnivariateDetectionOptions(series)
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.setGranularity(TimeGranularity.DAILY)
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.setSensitivity(95);
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UnivariateEntireDetectionResult result = univariateClient.detectUnivariateEntireSeries(options);
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// Check for anomalies
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for (int i = 0; i < result.getIsAnomaly().size(); i++) {
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if (result.getIsAnomaly().get(i)) {
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System.out.printf("Anomaly detected at index %d with value %.2f%n",
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i, series.get(i).getValue());
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}
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}
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```
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### Univariate Last Point Detection (Streaming)
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```java
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UnivariateLastDetectionResult lastResult = univariateClient.detectUnivariateLastPoint(options);
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if (lastResult.isAnomaly()) {
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System.out.println("Latest point is an anomaly!");
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System.out.printf("Expected: %.2f, Upper: %.2f, Lower: %.2f%n",
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lastResult.getExpectedValue(),
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lastResult.getUpperMargin(),
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lastResult.getLowerMargin());
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}
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```
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### Change Point Detection
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```java
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UnivariateChangePointDetectionOptions changeOptions =
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new UnivariateChangePointDetectionOptions(series, TimeGranularity.DAILY);
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UnivariateChangePointDetectionResult changeResult =
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univariateClient.detectUnivariateChangePoint(changeOptions);
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for (int i = 0; i < changeResult.getIsChangePoint().size(); i++) {
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if (changeResult.getIsChangePoint().get(i)) {
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System.out.printf("Change point at index %d with confidence %.2f%n",
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i, changeResult.getConfidenceScores().get(i));
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}
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}
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```
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### Multivariate Model Training
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```java
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import com.azure.ai.anomalydetector.models.*;
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import com.azure.core.util.polling.SyncPoller;
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// Prepare training request with blob storage data
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ModelInfo modelInfo = new ModelInfo()
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.setDataSource("https://storage.blob.core.windows.net/container/data.zip?sasToken")
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.setStartTime(OffsetDateTime.parse("2023-01-01T00:00:00Z"))
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.setEndTime(OffsetDateTime.parse("2023-06-01T00:00:00Z"))
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.setSlidingWindow(200)
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.setDisplayName("MyMultivariateModel");
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// Train model (long-running operation)
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AnomalyDetectionModel trainedModel = multivariateClient.trainMultivariateModel(modelInfo);
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String modelId = trainedModel.getModelId();
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System.out.println("Model ID: " + modelId);
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// Check training status
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AnomalyDetectionModel model = multivariateClient.getMultivariateModel(modelId);
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System.out.println("Status: " + model.getModelInfo().getStatus());
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```
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### Multivariate Batch Inference
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```java
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MultivariateBatchDetectionOptions detectionOptions = new MultivariateBatchDetectionOptions()
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.setDataSource("https://storage.blob.core.windows.net/container/inference-data.zip?sasToken")
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.setStartTime(OffsetDateTime.parse("2023-07-01T00:00:00Z"))
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.setEndTime(OffsetDateTime.parse("2023-07-31T00:00:00Z"))
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.setTopContributorCount(10);
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MultivariateDetectionResult detectionResult =
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multivariateClient.detectMultivariateBatchAnomaly(modelId, detectionOptions);
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String resultId = detectionResult.getResultId();
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// Poll for results
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MultivariateDetectionResult result = multivariateClient.getBatchDetectionResult(resultId);
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for (AnomalyState state : result.getResults()) {
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if (state.getValue().isAnomaly()) {
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System.out.printf("Anomaly at %s, severity: %.2f%n",
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state.getTimestamp(),
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state.getValue().getSeverity());
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}
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}
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```
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### Multivariate Last Point Detection
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```java
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MultivariateLastDetectionOptions lastOptions = new MultivariateLastDetectionOptions()
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.setVariables(List.of(
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new VariableValues("variable1", List.of("timestamp1"), List.of(1.0f)),
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new VariableValues("variable2", List.of("timestamp1"), List.of(2.5f))
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))
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.setTopContributorCount(5);
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MultivariateLastDetectionResult lastResult =
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multivariateClient.detectMultivariateLastAnomaly(modelId, lastOptions);
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if (lastResult.getValue().isAnomaly()) {
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System.out.println("Anomaly detected!");
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// Check contributing variables
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for (AnomalyContributor contributor : lastResult.getValue().getInterpretation()) {
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System.out.printf("Variable: %s, Contribution: %.2f%n",
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contributor.getVariable(),
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contributor.getContributionScore());
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}
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}
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```
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### Model Management
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```java
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// List all models
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PagedIterable<AnomalyDetectionModel> models = multivariateClient.listMultivariateModels();
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for (AnomalyDetectionModel m : models) {
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System.out.printf("Model: %s, Status: %s%n",
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m.getModelId(),
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m.getModelInfo().getStatus());
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}
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// Delete a model
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multivariateClient.deleteMultivariateModel(modelId);
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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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univariateClient.detectUnivariateEntireSeries(options);
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} catch (HttpResponseException e) {
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System.out.println("Status code: " + 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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AZURE_ANOMALY_DETECTOR_ENDPOINT=https://<resource>.cognitiveservices.azure.com/
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AZURE_ANOMALY_DETECTOR_API_KEY=<your-api-key>
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```
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## Best Practices
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1. **Minimum Data Points**: Univariate requires at least 12 points; more data improves accuracy
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2. **Granularity Alignment**: Match `TimeGranularity` to your actual data frequency
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3. **Sensitivity Tuning**: Higher values (0-99) detect more anomalies
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4. **Multivariate Training**: Use 200-1000 sliding window based on pattern complexity
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5. **Error Handling**: Always handle `HttpResponseException` for API errors
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## Trigger Phrases
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- "anomaly detection Java"
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- "detect anomalies time series"
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- "multivariate anomaly Java"
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- "univariate anomaly detection"
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- "streaming anomaly detection"
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- "change point detection"
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- "Azure AI Anomaly Detector"
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