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/contentsafety/SKILL.md
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skills/official/microsoft/python/foundry/contentsafety/SKILL.md
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---
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name: azure-ai-contentsafety-py
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description: |
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Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification.
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Triggers: "azure-ai-contentsafety", "ContentSafetyClient", "content moderation", "harmful content", "text analysis", "image analysis".
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package: azure-ai-contentsafety
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---
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# Azure AI Content Safety SDK for Python
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Detect harmful user-generated and AI-generated content in applications.
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## Installation
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```bash
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pip install azure-ai-contentsafety
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```
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## Environment Variables
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```bash
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CONTENT_SAFETY_ENDPOINT=https://<resource>.cognitiveservices.azure.com
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CONTENT_SAFETY_KEY=<your-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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from azure.ai.contentsafety import ContentSafetyClient
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from azure.core.credentials import AzureKeyCredential
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import os
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client = ContentSafetyClient(
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endpoint=os.environ["CONTENT_SAFETY_ENDPOINT"],
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credential=AzureKeyCredential(os.environ["CONTENT_SAFETY_KEY"])
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)
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```
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### Entra ID
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```python
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from azure.ai.contentsafety import ContentSafetyClient
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from azure.identity import DefaultAzureCredential
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client = ContentSafetyClient(
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endpoint=os.environ["CONTENT_SAFETY_ENDPOINT"],
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credential=DefaultAzureCredential()
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)
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```
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## Analyze Text
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```python
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from azure.ai.contentsafety import ContentSafetyClient
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from azure.ai.contentsafety.models import AnalyzeTextOptions, TextCategory
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from azure.core.credentials import AzureKeyCredential
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client = ContentSafetyClient(endpoint, AzureKeyCredential(key))
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request = AnalyzeTextOptions(text="Your text content to analyze")
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response = client.analyze_text(request)
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# Check each category
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for category in [TextCategory.HATE, TextCategory.SELF_HARM,
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TextCategory.SEXUAL, TextCategory.VIOLENCE]:
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result = next((r for r in response.categories_analysis
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if r.category == category), None)
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if result:
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print(f"{category}: severity {result.severity}")
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```
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## Analyze Image
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```python
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from azure.ai.contentsafety import ContentSafetyClient
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from azure.ai.contentsafety.models import AnalyzeImageOptions, ImageData
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from azure.core.credentials import AzureKeyCredential
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import base64
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client = ContentSafetyClient(endpoint, AzureKeyCredential(key))
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# From file
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with open("image.jpg", "rb") as f:
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image_data = base64.b64encode(f.read()).decode("utf-8")
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request = AnalyzeImageOptions(
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image=ImageData(content=image_data)
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)
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response = client.analyze_image(request)
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for result in response.categories_analysis:
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print(f"{result.category}: severity {result.severity}")
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```
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### Image from URL
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```python
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from azure.ai.contentsafety.models import AnalyzeImageOptions, ImageData
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request = AnalyzeImageOptions(
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image=ImageData(blob_url="https://example.com/image.jpg")
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)
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response = client.analyze_image(request)
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```
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## Text Blocklist Management
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### Create Blocklist
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```python
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from azure.ai.contentsafety import BlocklistClient
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from azure.ai.contentsafety.models import TextBlocklist
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from azure.core.credentials import AzureKeyCredential
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blocklist_client = BlocklistClient(endpoint, AzureKeyCredential(key))
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blocklist = TextBlocklist(
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blocklist_name="my-blocklist",
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description="Custom terms to block"
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)
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result = blocklist_client.create_or_update_text_blocklist(
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blocklist_name="my-blocklist",
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options=blocklist
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)
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```
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### Add Block Items
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```python
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from azure.ai.contentsafety.models import AddOrUpdateTextBlocklistItemsOptions, TextBlocklistItem
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items = AddOrUpdateTextBlocklistItemsOptions(
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blocklist_items=[
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TextBlocklistItem(text="blocked-term-1"),
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TextBlocklistItem(text="blocked-term-2")
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]
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)
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result = blocklist_client.add_or_update_blocklist_items(
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blocklist_name="my-blocklist",
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options=items
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)
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```
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### Analyze with Blocklist
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```python
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from azure.ai.contentsafety.models import AnalyzeTextOptions
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request = AnalyzeTextOptions(
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text="Text containing blocked-term-1",
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blocklist_names=["my-blocklist"],
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halt_on_blocklist_hit=True
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)
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response = client.analyze_text(request)
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if response.blocklists_match:
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for match in response.blocklists_match:
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print(f"Blocked: {match.blocklist_item_text}")
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```
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## Severity Levels
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Text analysis returns 4 severity levels (0, 2, 4, 6) by default. For 8 levels (0-7):
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```python
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from azure.ai.contentsafety.models import AnalyzeTextOptions, AnalyzeTextOutputType
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request = AnalyzeTextOptions(
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text="Your text",
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output_type=AnalyzeTextOutputType.EIGHT_SEVERITY_LEVELS
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)
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```
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## Harm Categories
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| Category | Description |
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|----------|-------------|
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| `Hate` | Attacks based on identity (race, religion, gender, etc.) |
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| `Sexual` | Sexual content, relationships, anatomy |
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| `Violence` | Physical harm, weapons, injury |
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| `SelfHarm` | Self-injury, suicide, eating disorders |
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## Severity Scale
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| Level | Text Range | Image Range | Meaning |
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|-------|------------|-------------|---------|
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| 0 | Safe | Safe | No harmful content |
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| 2 | Low | Low | Mild references |
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| 4 | Medium | Medium | Moderate content |
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| 6 | High | High | Severe content |
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## Client Types
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| Client | Purpose |
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|--------|---------|
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| `ContentSafetyClient` | Analyze text and images |
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| `BlocklistClient` | Manage custom blocklists |
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## Best Practices
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1. **Use blocklists** for domain-specific terms
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2. **Set severity thresholds** appropriate for your use case
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3. **Handle multiple categories** — content can be harmful in multiple ways
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4. **Use halt_on_blocklist_hit** for immediate rejection
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5. **Log analysis results** for audit and improvement
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6. **Consider 8-severity mode** for finer-grained control
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7. **Pre-moderate AI outputs** before showing to users
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