用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/BEKO2210/Firstbrain --skill azure-ai-projects-py命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
正在显示 SKILL.md
| name | azure-ai-projects-py |
| description | Build AI applications on Microsoft Foundry using the azure-ai-projects SDK. |
| type | skill |
| created | 2026-02-27T00:00:00.000Z |
| domain | cloud-infrastructure |
| category | azure |
| risk | unknown |
| source | community |
| tags | ["skill","cloud-infrastructure","azure","projects"] |
Build AI applications on Microsoft Foundry using the azure-ai-projects SDK.
pip install azure-ai-projects azure-identity
AZURE_AI_PROJECT_ENDPOINT="https://<resource>.services.ai.azure.com/api/projects/<project>"
AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
import os
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
credential = DefaultAzureCredential()
client = AIProjectClient(
endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
credential=credential,
)
| Operation | Access | Purpose |
|---|---|---|
client.agents | .agents.* | Agent CRUD, versions, threads, runs |
client.connections | .connections.* | List/get project connections |
client.deployments | .deployments.* | List model deployments |
client.datasets | .datasets.* | Dataset management |
client.indexes | .indexes.* | Index management |
client.evaluations | .evaluations.* | Run evaluations |
client.red_teams | .red_teams.* | Red team operations |
from azure.ai.projects import AIProjectClient
client = AIProjectClient(
endpoint=os.environ[],
credential=DefaultAzureCredential(),
)
agent = client.agents.create_agent(
model=os.environ[],
name=,
instructions=,
)
# Get OpenAI-compatible client from project
openai_client = client.get_openai_client()
# Use standard OpenAI API
response = openai_client.chat.completions.create(
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
messages=[{"role": "user", "content": "Hello!"}],
)
agent = client.agents.create_agent(
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
name="my-agent",
instructions="You are a helpful assistant.",
)
from azure.ai.agents import CodeInterpreterTool, FileSearchTool
agent = client.agents.create_agent(
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
name="tool-agent",
instructions="You can execute code and search files.",
tools=[CodeInterpreterTool(), FileSearchTool()],
)
from azure.ai.projects.models import PromptAgentDefinition
# Create a versioned agent
agent_version = client.agents.create_version(
agent_name="customer-support-agent",
definition=PromptAgentDefinition(
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
instructions="You are a customer support specialist.",
tools=[], # Add tools as needed
),
version_label="v1.0",
)
See references/agents.md for detailed agent patterns.
| Tool | Class | Use Case |
|---|---|---|
| Code Interpreter | CodeInterpreterTool | Execute Python, generate files |
| File Search | FileSearchTool | RAG over uploaded documents |
| Bing Grounding | BingGroundingTool | Web search (requires connection) |
| Azure AI Search | AzureAISearchTool | Search your indexes |
| Function Calling | FunctionTool | Call your Python functions |
| OpenAPI | OpenApiTool | Call REST APIs |
| MCP | McpTool | Model Context Protocol servers |
| Memory Search | MemorySearchTool | Search agent memory stores |
| SharePoint | SharepointGroundingTool | Search SharePoint content |
See references/tools.md for all tool patterns.
# 1. Create thread
thread = client.agents.threads.create()
# 2. Add message
client.agents.messages.create(
thread_id=thread.id,
role="user",
content="What's the weather like?",
)
# 3. Create and process run
run = client.agents.runs.create_and_process(
thread_id=thread.id,
agent_id=agent.id,
)
# 4. Get response
if run.status == "completed":
messages = client.agents.messages.list(thread_id=thread.id)
for msg in messages:
if msg.role == "assistant":
print(msg.content[0].text.value)