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-document-py
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description: |
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Azure AI Document Translation SDK for batch translation of documents with format preservation. Use for translating Word, PDF, Excel, PowerPoint, and other document formats at scale.
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Triggers: "document translation", "batch translation", "translate documents", "DocumentTranslationClient".
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package: azure-ai-translation-document
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---
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# Azure AI Document Translation SDK for Python
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Client library for Azure AI Translator document translation service for batch document translation with format preservation.
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## Installation
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```bash
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pip install azure-ai-translation-document
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```
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## Environment Variables
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```bash
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AZURE_DOCUMENT_TRANSLATION_ENDPOINT=https://<resource>.cognitiveservices.azure.com
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AZURE_DOCUMENT_TRANSLATION_KEY=<your-api-key> # If using API key
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# Storage for source and target documents
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AZURE_SOURCE_CONTAINER_URL=https://<storage>.blob.core.windows.net/<container>?<sas>
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AZURE_TARGET_CONTAINER_URL=https://<storage>.blob.core.windows.net/<container>?<sas>
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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.translation.document import DocumentTranslationClient
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from azure.core.credentials import AzureKeyCredential
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endpoint = os.environ["AZURE_DOCUMENT_TRANSLATION_ENDPOINT"]
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key = os.environ["AZURE_DOCUMENT_TRANSLATION_KEY"]
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client = DocumentTranslationClient(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.translation.document import DocumentTranslationClient
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from azure.identity import DefaultAzureCredential
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client = DocumentTranslationClient(
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endpoint=os.environ["AZURE_DOCUMENT_TRANSLATION_ENDPOINT"],
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credential=DefaultAzureCredential()
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)
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```
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## Basic Document Translation
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```python
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from azure.ai.translation.document import DocumentTranslationInput, TranslationTarget
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source_url = os.environ["AZURE_SOURCE_CONTAINER_URL"]
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target_url = os.environ["AZURE_TARGET_CONTAINER_URL"]
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# Start translation job
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poller = client.begin_translation(
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inputs=[
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DocumentTranslationInput(
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source_url=source_url,
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targets=[
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TranslationTarget(
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target_url=target_url,
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language="es" # Translate to Spanish
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)
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]
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)
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]
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)
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# Wait for completion
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result = poller.result()
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print(f"Status: {poller.status()}")
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print(f"Documents translated: {poller.details.documents_succeeded_count}")
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print(f"Documents failed: {poller.details.documents_failed_count}")
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```
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## Multiple Target Languages
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```python
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poller = client.begin_translation(
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inputs=[
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DocumentTranslationInput(
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source_url=source_url,
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targets=[
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TranslationTarget(target_url=target_url_es, language="es"),
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TranslationTarget(target_url=target_url_fr, language="fr"),
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TranslationTarget(target_url=target_url_de, language="de")
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]
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)
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]
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)
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```
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## Translate Single Document
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```python
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from azure.ai.translation.document import SingleDocumentTranslationClient
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single_client = SingleDocumentTranslationClient(endpoint, AzureKeyCredential(key))
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with open("document.docx", "rb") as f:
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document_content = f.read()
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result = single_client.translate(
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body=document_content,
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target_language="es",
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content_type="application/vnd.openxmlformats-officedocument.wordprocessingml.document"
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)
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# Save translated document
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with open("document_es.docx", "wb") as f:
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f.write(result)
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```
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## Check Translation Status
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```python
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# Get all translation operations
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operations = client.list_translation_statuses()
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for op in operations:
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print(f"Operation ID: {op.id}")
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print(f"Status: {op.status}")
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print(f"Created: {op.created_on}")
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print(f"Total documents: {op.documents_total_count}")
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print(f"Succeeded: {op.documents_succeeded_count}")
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print(f"Failed: {op.documents_failed_count}")
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```
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## List Document Statuses
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```python
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# Get status of individual documents in a job
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operation_id = poller.id
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document_statuses = client.list_document_statuses(operation_id)
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for doc in document_statuses:
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print(f"Document: {doc.source_document_url}")
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print(f" Status: {doc.status}")
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print(f" Translated to: {doc.translated_to}")
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if doc.error:
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print(f" Error: {doc.error.message}")
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```
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## Cancel Translation
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```python
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# Cancel a running translation
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client.cancel_translation(operation_id)
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```
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## Using Glossary
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```python
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from azure.ai.translation.document import TranslationGlossary
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poller = client.begin_translation(
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inputs=[
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DocumentTranslationInput(
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source_url=source_url,
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targets=[
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TranslationTarget(
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target_url=target_url,
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language="es",
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glossaries=[
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TranslationGlossary(
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glossary_url="https://<storage>.blob.core.windows.net/glossary/terms.csv?<sas>",
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file_format="csv"
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)
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]
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)
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]
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)
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]
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)
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```
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## Supported Document Formats
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```python
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# Get supported formats
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formats = client.get_supported_document_formats()
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for fmt in formats:
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print(f"Format: {fmt.format}")
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print(f" Extensions: {fmt.file_extensions}")
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print(f" Content types: {fmt.content_types}")
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```
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## Supported Languages
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```python
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# Get supported languages
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languages = client.get_supported_languages()
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for lang in languages:
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print(f"Language: {lang.name} ({lang.code})")
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```
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## Async Client
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```python
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from azure.ai.translation.document.aio import DocumentTranslationClient
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from azure.identity.aio import DefaultAzureCredential
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async def translate_documents():
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async with DocumentTranslationClient(
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endpoint=endpoint,
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credential=DefaultAzureCredential()
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) as client:
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poller = await client.begin_translation(inputs=[...])
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result = await poller.result()
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```
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## Supported Formats
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| Category | Formats |
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|----------|---------|
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| Documents | DOCX, PDF, PPTX, XLSX, HTML, TXT, RTF |
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| Structured | CSV, TSV, JSON, XML |
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| Localization | XLIFF, XLF, MHTML |
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## Storage Requirements
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- Source and target containers must be Azure Blob Storage
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- Use SAS tokens with appropriate permissions:
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- Source: Read, List
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- Target: Write, List
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## Best Practices
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1. **Use SAS tokens** with minimal required permissions
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2. **Monitor long-running operations** with `poller.status()`
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3. **Handle document-level errors** by iterating document statuses
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4. **Use glossaries** for domain-specific terminology
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5. **Separate target containers** for each language
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6. **Use async client** for multiple concurrent jobs
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7. **Check supported formats** before submitting documents
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