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/textanalytics/SKILL.md
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skills/official/microsoft/python/foundry/textanalytics/SKILL.md
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---
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name: azure-ai-textanalytics-py
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description: |
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Azure AI Text Analytics SDK for sentiment analysis, entity recognition, key phrases, language detection, PII, and healthcare NLP. Use for natural language processing on text.
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Triggers: "text analytics", "sentiment analysis", "entity recognition", "key phrase", "PII detection", "TextAnalyticsClient".
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package: azure-ai-textanalytics
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---
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# Azure AI Text Analytics SDK for Python
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Client library for Azure AI Language service NLP capabilities including sentiment, entities, key phrases, and more.
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## Installation
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```bash
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pip install azure-ai-textanalytics
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```
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## Environment Variables
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```bash
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AZURE_LANGUAGE_ENDPOINT=https://<resource>.cognitiveservices.azure.com
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AZURE_LANGUAGE_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.core.credentials import AzureKeyCredential
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from azure.ai.textanalytics import TextAnalyticsClient
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endpoint = os.environ["AZURE_LANGUAGE_ENDPOINT"]
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key = os.environ["AZURE_LANGUAGE_KEY"]
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client = TextAnalyticsClient(endpoint, AzureKeyCredential(key))
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```
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### Entra ID (Recommended)
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```python
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from azure.ai.textanalytics import TextAnalyticsClient
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from azure.identity import DefaultAzureCredential
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client = TextAnalyticsClient(
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endpoint=os.environ["AZURE_LANGUAGE_ENDPOINT"],
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credential=DefaultAzureCredential()
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)
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```
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## Sentiment Analysis
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```python
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documents = [
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"I had a wonderful trip to Seattle last week!",
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"The food was terrible and the service was slow."
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]
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result = client.analyze_sentiment(documents, show_opinion_mining=True)
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for doc in result:
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if not doc.is_error:
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print(f"Sentiment: {doc.sentiment}")
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print(f"Scores: pos={doc.confidence_scores.positive:.2f}, "
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f"neg={doc.confidence_scores.negative:.2f}, "
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f"neu={doc.confidence_scores.neutral:.2f}")
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# Opinion mining (aspect-based sentiment)
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for sentence in doc.sentences:
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for opinion in sentence.mined_opinions:
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target = opinion.target
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print(f" Target: '{target.text}' - {target.sentiment}")
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for assessment in opinion.assessments:
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print(f" Assessment: '{assessment.text}' - {assessment.sentiment}")
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```
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## Entity Recognition
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```python
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documents = ["Microsoft was founded by Bill Gates and Paul Allen in Albuquerque."]
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result = client.recognize_entities(documents)
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for doc in result:
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if not doc.is_error:
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for entity in doc.entities:
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print(f"Entity: {entity.text}")
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print(f" Category: {entity.category}")
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print(f" Subcategory: {entity.subcategory}")
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print(f" Confidence: {entity.confidence_score:.2f}")
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```
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## PII Detection
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```python
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documents = ["My SSN is 123-45-6789 and my email is john@example.com"]
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result = client.recognize_pii_entities(documents)
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for doc in result:
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if not doc.is_error:
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print(f"Redacted: {doc.redacted_text}")
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for entity in doc.entities:
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print(f"PII: {entity.text} ({entity.category})")
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```
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## Key Phrase Extraction
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```python
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documents = ["Azure AI provides powerful machine learning capabilities for developers."]
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result = client.extract_key_phrases(documents)
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for doc in result:
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if not doc.is_error:
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print(f"Key phrases: {doc.key_phrases}")
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```
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## Language Detection
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```python
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documents = ["Ce document est en francais.", "This is written in English."]
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result = client.detect_language(documents)
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for doc in result:
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if not doc.is_error:
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print(f"Language: {doc.primary_language.name} ({doc.primary_language.iso6391_name})")
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print(f"Confidence: {doc.primary_language.confidence_score:.2f}")
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```
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## Healthcare Text Analytics
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```python
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documents = ["Patient has diabetes and was prescribed metformin 500mg twice daily."]
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poller = client.begin_analyze_healthcare_entities(documents)
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result = poller.result()
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for doc in result:
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if not doc.is_error:
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for entity in doc.entities:
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print(f"Entity: {entity.text}")
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print(f" Category: {entity.category}")
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print(f" Normalized: {entity.normalized_text}")
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# Entity links (UMLS, etc.)
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for link in entity.data_sources:
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print(f" Link: {link.name} - {link.entity_id}")
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```
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## Multiple Analysis (Batch)
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```python
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from azure.ai.textanalytics import (
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RecognizeEntitiesAction,
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ExtractKeyPhrasesAction,
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AnalyzeSentimentAction
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)
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documents = ["Microsoft announced new Azure AI features at Build conference."]
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poller = client.begin_analyze_actions(
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documents,
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actions=[
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RecognizeEntitiesAction(),
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ExtractKeyPhrasesAction(),
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AnalyzeSentimentAction()
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]
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)
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results = poller.result()
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for doc_results in results:
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for result in doc_results:
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if result.kind == "EntityRecognition":
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print(f"Entities: {[e.text for e in result.entities]}")
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elif result.kind == "KeyPhraseExtraction":
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print(f"Key phrases: {result.key_phrases}")
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elif result.kind == "SentimentAnalysis":
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print(f"Sentiment: {result.sentiment}")
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```
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## Async Client
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```python
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from azure.ai.textanalytics.aio import TextAnalyticsClient
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from azure.identity.aio import DefaultAzureCredential
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async def analyze():
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async with TextAnalyticsClient(
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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_sentiment(documents)
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# Process results...
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```
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## Client Types
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| Client | Purpose |
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|--------|---------|
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| `TextAnalyticsClient` | All text analytics operations |
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| `TextAnalyticsClient` (aio) | Async version |
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## Available Operations
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| Method | Description |
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|--------|-------------|
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| `analyze_sentiment` | Sentiment analysis with opinion mining |
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| `recognize_entities` | Named entity recognition |
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| `recognize_pii_entities` | PII detection and redaction |
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| `recognize_linked_entities` | Entity linking to Wikipedia |
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| `extract_key_phrases` | Key phrase extraction |
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| `detect_language` | Language detection |
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| `begin_analyze_healthcare_entities` | Healthcare NLP (long-running) |
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| `begin_analyze_actions` | Multiple analyses in batch |
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## Best Practices
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1. **Use batch operations** for multiple documents (up to 10 per request)
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2. **Enable opinion mining** for detailed aspect-based sentiment
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3. **Use async client** for high-throughput scenarios
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4. **Handle document errors** — results list may contain errors for some docs
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5. **Specify language** when known to improve accuracy
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6. **Use context manager** or close client explicitly
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