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: agent-framework-azure-ai-py
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description: Build Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.
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package: agent-framework-azure-ai
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---
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# Agent Framework Azure Hosted Agents
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Build persistent agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK.
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## Architecture
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```
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User Query → AzureAIAgentsProvider → Azure AI Agent Service (Persistent)
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↓
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Agent.run() / Agent.run_stream()
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↓
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Tools: Functions | Hosted (Code/Search/Web) | MCP
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↓
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AgentThread (conversation persistence)
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```
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## Installation
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```bash
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# Full framework (recommended)
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pip install agent-framework --pre
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# Or Azure-specific package only
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pip install agent-framework-azure-ai --pre
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```
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## Environment Variables
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```bash
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export AZURE_AI_PROJECT_ENDPOINT="https://<project>.services.ai.azure.com/api/projects/<project-id>"
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export AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
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export BING_CONNECTION_ID="your-bing-connection-id" # For web search
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```
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## Authentication
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```python
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from azure.identity.aio import AzureCliCredential, DefaultAzureCredential
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# Development
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credential = AzureCliCredential()
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# Production
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credential = DefaultAzureCredential()
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```
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## Core Workflow
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### Basic Agent
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```python
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import asyncio
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from agent_framework.azure import AzureAIAgentsProvider
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from azure.identity.aio import AzureCliCredential
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async def main():
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async with (
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AzureCliCredential() as credential,
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AzureAIAgentsProvider(credential=credential) as provider,
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):
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agent = await provider.create_agent(
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name="MyAgent",
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instructions="You are a helpful assistant.",
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)
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result = await agent.run("Hello!")
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print(result.text)
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asyncio.run(main())
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```
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### Agent with Function Tools
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```python
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from typing import Annotated
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from pydantic import Field
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from agent_framework.azure import AzureAIAgentsProvider
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from azure.identity.aio import AzureCliCredential
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def get_weather(
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location: Annotated[str, Field(description="City name to get weather for")],
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) -> str:
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"""Get the current weather for a location."""
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return f"Weather in {location}: 72°F, sunny"
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def get_current_time() -> str:
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"""Get the current UTC time."""
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from datetime import datetime, timezone
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return datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S UTC")
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async def main():
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async with (
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AzureCliCredential() as credential,
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AzureAIAgentsProvider(credential=credential) as provider,
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):
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agent = await provider.create_agent(
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name="WeatherAgent",
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instructions="You help with weather and time queries.",
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tools=[get_weather, get_current_time], # Pass functions directly
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)
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result = await agent.run("What's the weather in Seattle?")
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print(result.text)
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```
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### Agent with Hosted Tools
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```python
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from agent_framework import (
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HostedCodeInterpreterTool,
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HostedFileSearchTool,
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HostedWebSearchTool,
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)
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from agent_framework.azure import AzureAIAgentsProvider
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from azure.identity.aio import AzureCliCredential
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async def main():
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async with (
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AzureCliCredential() as credential,
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AzureAIAgentsProvider(credential=credential) as provider,
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):
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agent = await provider.create_agent(
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name="MultiToolAgent",
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instructions="You can execute code, search files, and search the web.",
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tools=[
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HostedCodeInterpreterTool(),
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HostedWebSearchTool(name="Bing"),
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],
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)
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result = await agent.run("Calculate the factorial of 20 in Python")
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print(result.text)
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```
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### Streaming Responses
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```python
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async def main():
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async with (
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AzureCliCredential() as credential,
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AzureAIAgentsProvider(credential=credential) as provider,
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):
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agent = await provider.create_agent(
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name="StreamingAgent",
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instructions="You are a helpful assistant.",
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)
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print("Agent: ", end="", flush=True)
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async for chunk in agent.run_stream("Tell me a short story"):
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if chunk.text:
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print(chunk.text, end="", flush=True)
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print()
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```
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### Conversation Threads
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```python
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from agent_framework.azure import AzureAIAgentsProvider
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from azure.identity.aio import AzureCliCredential
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async def main():
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async with (
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AzureCliCredential() as credential,
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AzureAIAgentsProvider(credential=credential) as provider,
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):
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agent = await provider.create_agent(
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name="ChatAgent",
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instructions="You are a helpful assistant.",
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tools=[get_weather],
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)
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# Create thread for conversation persistence
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thread = agent.get_new_thread()
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# First turn
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result1 = await agent.run("What's the weather in Seattle?", thread=thread)
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print(f"Agent: {result1.text}")
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# Second turn - context is maintained
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result2 = await agent.run("What about Portland?", thread=thread)
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print(f"Agent: {result2.text}")
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# Save thread ID for later resumption
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print(f"Conversation ID: {thread.conversation_id}")
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```
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### Structured Outputs
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```python
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from pydantic import BaseModel, ConfigDict
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from agent_framework.azure import AzureAIAgentsProvider
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from azure.identity.aio import AzureCliCredential
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class WeatherResponse(BaseModel):
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model_config = ConfigDict(extra="forbid")
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location: str
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temperature: float
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unit: str
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conditions: str
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async def main():
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async with (
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AzureCliCredential() as credential,
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AzureAIAgentsProvider(credential=credential) as provider,
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):
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agent = await provider.create_agent(
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name="StructuredAgent",
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instructions="Provide weather information in structured format.",
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response_format=WeatherResponse,
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)
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result = await agent.run("Weather in Seattle?")
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weather = WeatherResponse.model_validate_json(result.text)
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print(f"{weather.location}: {weather.temperature}°{weather.unit}")
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```
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## Provider Methods
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| Method | Description |
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|--------|-------------|
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| `create_agent()` | Create new agent on Azure AI service |
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| `get_agent(agent_id)` | Retrieve existing agent by ID |
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| `as_agent(sdk_agent)` | Wrap SDK Agent object (no HTTP call) |
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## Hosted Tools Quick Reference
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| Tool | Import | Purpose |
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|------|--------|---------|
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| `HostedCodeInterpreterTool` | `from agent_framework import HostedCodeInterpreterTool` | Execute Python code |
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| `HostedFileSearchTool` | `from agent_framework import HostedFileSearchTool` | Search vector stores |
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| `HostedWebSearchTool` | `from agent_framework import HostedWebSearchTool` | Bing web search |
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| `HostedMCPTool` | `from agent_framework import HostedMCPTool` | Service-managed MCP |
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| `MCPStreamableHTTPTool` | `from agent_framework import MCPStreamableHTTPTool` | Client-managed MCP |
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## Complete Example
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```python
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import asyncio
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from typing import Annotated
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from pydantic import BaseModel, Field
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from agent_framework import (
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HostedCodeInterpreterTool,
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HostedWebSearchTool,
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MCPStreamableHTTPTool,
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)
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from agent_framework.azure import AzureAIAgentsProvider
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from azure.identity.aio import AzureCliCredential
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def get_weather(
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location: Annotated[str, Field(description="City name")],
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) -> str:
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"""Get weather for a location."""
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return f"Weather in {location}: 72°F, sunny"
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class AnalysisResult(BaseModel):
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summary: str
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key_findings: list[str]
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confidence: float
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async def main():
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async with (
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AzureCliCredential() as credential,
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MCPStreamableHTTPTool(
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name="Docs MCP",
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url="https://learn.microsoft.com/api/mcp",
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) as mcp_tool,
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AzureAIAgentsProvider(credential=credential) as provider,
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):
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agent = await provider.create_agent(
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name="ResearchAssistant",
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instructions="You are a research assistant with multiple capabilities.",
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tools=[
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get_weather,
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HostedCodeInterpreterTool(),
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HostedWebSearchTool(name="Bing"),
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mcp_tool,
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],
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)
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thread = agent.get_new_thread()
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# Non-streaming
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result = await agent.run(
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"Search for Python best practices and summarize",
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thread=thread,
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)
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print(f"Response: {result.text}")
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# Streaming
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print("\nStreaming: ", end="")
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async for chunk in agent.run_stream("Continue with examples", thread=thread):
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if chunk.text:
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print(chunk.text, end="", flush=True)
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print()
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# Structured output
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result = await agent.run(
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"Analyze findings",
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thread=thread,
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response_format=AnalysisResult,
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)
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analysis = AnalysisResult.model_validate_json(result.text)
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print(f"\nConfidence: {analysis.confidence}")
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if __name__ == "__main__":
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asyncio.run(main())
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```
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## Conventions
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- Always use async context managers: `async with provider:`
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- Pass functions directly to `tools=` parameter (auto-converted to AIFunction)
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- Use `Annotated[type, Field(description=...)]` for function parameters
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- Use `get_new_thread()` for multi-turn conversations
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- Prefer `HostedMCPTool` for service-managed MCP, `MCPStreamableHTTPTool` for client-managed
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## Reference Files
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- [references/tools.md](references/tools.md): Detailed hosted tool patterns
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- [references/mcp.md](references/mcp.md): MCP integration (hosted + local)
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- [references/threads.md](references/threads.md): Thread and conversation management
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- [references/advanced.md](references/advanced.md): OpenAPI, citations, structured outputs
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