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-translation-text-py
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description: |
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Azure AI Text Translation SDK for real-time text translation, transliteration, language detection, and dictionary lookup. Use for translating text content in applications.
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Triggers: "text translation", "translator", "translate text", "transliterate", "TextTranslationClient".
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package: azure-ai-translation-text
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---
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# Azure AI Text Translation SDK for Python
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Client library for Azure AI Translator text translation service for real-time text translation, transliteration, and language operations.
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## Installation
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```bash
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pip install azure-ai-translation-text
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```
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## Environment Variables
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```bash
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AZURE_TRANSLATOR_KEY=<your-api-key>
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AZURE_TRANSLATOR_REGION=<your-region> # e.g., eastus, westus2
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# Or use custom endpoint
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AZURE_TRANSLATOR_ENDPOINT=https://<resource>.cognitiveservices.azure.com
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```
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## Authentication
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### API Key with Region
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```python
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import os
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from azure.ai.translation.text import TextTranslationClient
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from azure.core.credentials import AzureKeyCredential
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key = os.environ["AZURE_TRANSLATOR_KEY"]
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region = os.environ["AZURE_TRANSLATOR_REGION"]
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# Create credential with region
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credential = AzureKeyCredential(key)
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client = TextTranslationClient(credential=credential, region=region)
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```
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### API Key with Custom Endpoint
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```python
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endpoint = os.environ["AZURE_TRANSLATOR_ENDPOINT"]
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client = TextTranslationClient(
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credential=AzureKeyCredential(key),
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endpoint=endpoint
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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.translation.text import TextTranslationClient
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from azure.identity import DefaultAzureCredential
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client = TextTranslationClient(
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credential=DefaultAzureCredential(),
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endpoint=os.environ["AZURE_TRANSLATOR_ENDPOINT"]
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)
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```
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## Basic Translation
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```python
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# Translate to a single language
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result = client.translate(
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body=["Hello, how are you?", "Welcome to Azure!"],
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to=["es"] # Spanish
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)
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for item in result:
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for translation in item.translations:
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print(f"Translated: {translation.text}")
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print(f"Target language: {translation.to}")
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```
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## Translate to Multiple Languages
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```python
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result = client.translate(
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body=["Hello, world!"],
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to=["es", "fr", "de", "ja"] # Spanish, French, German, Japanese
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)
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for item in result:
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print(f"Source: {item.detected_language.language if item.detected_language else 'unknown'}")
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for translation in item.translations:
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print(f" {translation.to}: {translation.text}")
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```
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## Specify Source Language
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```python
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result = client.translate(
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body=["Bonjour le monde"],
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from_parameter="fr", # Source is French
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to=["en", "es"]
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)
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```
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## Language Detection
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```python
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result = client.translate(
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body=["Hola, como estas?"],
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to=["en"]
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)
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for item in result:
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if item.detected_language:
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print(f"Detected language: {item.detected_language.language}")
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print(f"Confidence: {item.detected_language.score:.2f}")
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```
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## Transliteration
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Convert text from one script to another:
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```python
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result = client.transliterate(
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body=["konnichiwa"],
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language="ja",
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from_script="Latn", # From Latin script
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to_script="Jpan" # To Japanese script
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)
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for item in result:
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print(f"Transliterated: {item.text}")
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print(f"Script: {item.script}")
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```
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## Dictionary Lookup
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Find alternate translations and definitions:
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```python
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result = client.lookup_dictionary_entries(
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body=["fly"],
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from_parameter="en",
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to="es"
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)
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for item in result:
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print(f"Source: {item.normalized_source} ({item.display_source})")
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for translation in item.translations:
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print(f" Translation: {translation.normalized_target}")
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print(f" Part of speech: {translation.pos_tag}")
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print(f" Confidence: {translation.confidence:.2f}")
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```
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## Dictionary Examples
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Get usage examples for translations:
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```python
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from azure.ai.translation.text.models import DictionaryExampleTextItem
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result = client.lookup_dictionary_examples(
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body=[DictionaryExampleTextItem(text="fly", translation="volar")],
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from_parameter="en",
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to="es"
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)
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for item in result:
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for example in item.examples:
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print(f"Source: {example.source_prefix}{example.source_term}{example.source_suffix}")
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print(f"Target: {example.target_prefix}{example.target_term}{example.target_suffix}")
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```
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## Get Supported Languages
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```python
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# Get all supported languages
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languages = client.get_supported_languages()
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# Translation languages
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print("Translation languages:")
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for code, lang in languages.translation.items():
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print(f" {code}: {lang.name} ({lang.native_name})")
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# Transliteration languages
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print("\nTransliteration languages:")
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for code, lang in languages.transliteration.items():
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print(f" {code}: {lang.name}")
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for script in lang.scripts:
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print(f" {script.code} -> {[t.code for t in script.to_scripts]}")
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# Dictionary languages
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print("\nDictionary languages:")
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for code, lang in languages.dictionary.items():
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print(f" {code}: {lang.name}")
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```
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## Break Sentence
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Identify sentence boundaries:
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```python
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result = client.find_sentence_boundaries(
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body=["Hello! How are you? I hope you are well."],
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language="en"
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)
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for item in result:
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print(f"Sentence lengths: {item.sent_len}")
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```
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## Translation Options
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```python
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result = client.translate(
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body=["Hello, world!"],
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to=["de"],
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text_type="html", # "plain" or "html"
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profanity_action="Marked", # "NoAction", "Deleted", "Marked"
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profanity_marker="Asterisk", # "Asterisk", "Tag"
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include_alignment=True, # Include word alignment
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include_sentence_length=True # Include sentence boundaries
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)
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for item in result:
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translation = item.translations[0]
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print(f"Translated: {translation.text}")
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if translation.alignment:
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print(f"Alignment: {translation.alignment.proj}")
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if translation.sent_len:
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print(f"Sentence lengths: {translation.sent_len.src_sent_len}")
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```
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## Async Client
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```python
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from azure.ai.translation.text.aio import TextTranslationClient
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from azure.core.credentials import AzureKeyCredential
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async def translate_text():
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async with TextTranslationClient(
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credential=AzureKeyCredential(key),
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region=region
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) as client:
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result = await client.translate(
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body=["Hello, world!"],
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to=["es"]
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)
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print(result[0].translations[0].text)
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```
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## Client Methods
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| Method | Description |
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|--------|-------------|
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| `translate` | Translate text to one or more languages |
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| `transliterate` | Convert text between scripts |
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| `detect` | Detect language of text |
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| `find_sentence_boundaries` | Identify sentence boundaries |
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| `lookup_dictionary_entries` | Dictionary lookup for translations |
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| `lookup_dictionary_examples` | Get usage examples |
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| `get_supported_languages` | List supported languages |
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## Best Practices
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1. **Batch translations** — Send multiple texts in one request (up to 100)
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2. **Specify source language** when known to improve accuracy
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3. **Use async client** for high-throughput scenarios
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4. **Cache language list** — Supported languages don't change frequently
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5. **Handle profanity** appropriately for your application
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6. **Use html text_type** when translating HTML content
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7. **Include alignment** for applications needing word mapping
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