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Skills agent-framework-azure-ai-py
📦
agent-framework-azure-ai-py
Content revision r2 Safe ⚡ Contains scripts⚙️ External commands🌐 Network access
Build Persistent Azure AI Agents with Python
Persistent Azure AI agents require careful setup across authentication, tools, threads, and response handling. This skill provides focused Python patterns for each workflow.
Supports: Claude Codex Code(CC)
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Agent request
Review the Skillstore skill "agent-framework-azure-ai-py" from https://skillstore.io/skills/sickn33-agent-framework-azure-ai-py.md and its manifest at https://skillstore.io/api/skills/sickn33-agent-framework-azure-ai-py/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "agent-framework-azure-ai-py". Create a basic weather agent with Azure CLI authentication.
Expected outcome:
A concise implementation outline covering asynchronous credential setup, provider creation, agent instructions, one weather request, and resource cleanup.
Using "agent-framework-azure-ai-py". Add streaming and retain context between two support questions.
Expected outcome:
- Create one persistent conversation thread.
- Run the first support question on that thread.
- Stream the follow-up response while reusing the same thread.
- Persist the conversation identifier for later resumption.
Using "agent-framework-azure-ai-py". Return validated research findings from a tool-enabled agent.
Expected outcome:
A structured response design with a summary, key findings, and confidence value validated through a strict Pydantic model.
Security Audit
Safev5 • 7/23/2026 Open versioned report
Most static alerts are false positives caused by Markdown fences, inline code, Python imports, and Python async context managers. The installation example presents one medium supply-chain risk because it installs unpinned prerelease packages.
1
Files scanned
344
Lines analyzed
1
Review items
0
False positives ignored
Capability review items (1)
These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.
Medium
Ruby/shell backtick execution
```bash
The fenced bash example installs unpinned prerelease packages with pip. Executing it downloads and runs package installation code without a reviewed version lock.
Risk Factors
⚡ Contains scripts (9)
⚙️ External commands (35)
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🌐 Network access (2)
Audited by: codex View Audit History →
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Copy report link
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APA citation
sickn33. (2026). agent-framework-azure-ai-py security audit report (audit version 5) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-agent-framework-azure-ai-py/audits/5BibTeX citation
@techreport{sickn33-sickn33-agent-framework-azure-ai-py-2026,
author = {sickn33},
title = {agent-framework-azure-ai-py security audit report (audit version 5)},
institution = {Skillstore},
year = {2026},
number = {5},
url = {https://skillstore.io/skills/sickn33-agent-framework-azure-ai-py/audits/5},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "agent-framework-azure-ai-py security audit report (audit version 5)"
version: "unspecified"
type: report
authors:
- name: "sickn33"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/sickn33-agent-framework-azure-ai-py/audits/5"
identifiers:
- type: other
value: "skillstore:sickn33-agent-framework-azure-ai-py:audit:5"
description: "Skillstore immutable audit report identifier"
Skillstore Score
Why this score Evidence Confidence: High 55
Architecture
85
Maintainability
87
Content
69
Community
83
Spec Compliance
What You Can Build
Prototype an Azure Agent
Create a basic persistent agent with secure Azure authentication and a deployed model.
Add Hosted and Function Tools
Combine typed Python functions with Azure-hosted code, search, and web capabilities.
Design Stateful Research Workflows
Use threads, streaming, MCP, and structured outputs in a multi-turn research agent.
Try These Prompts
Create a Basic Agent
Create a minimal Azure AI agent in Python. Use AzureCliCredential, AzureAIAgentsProvider, an async context manager, and one user request.
Add Typed Function Tools
Extend my Azure AI agent with these Python functions: [FUNCTIONS]. Add Annotated parameter descriptions and show one request that invokes a tool.
Implement Persistent Streaming
Build a multi-turn agent for [WORKFLOW]. Reuse one conversation thread, stream the second response, and explain where to persist the conversation identifier.
Design a Governed Tool-Enabled Agent
Design an Azure AI agent for [USE CASE] using [TOOLS]. Include least-privilege guidance, MCP trust boundaries, structured outputs, error handling, and lifecycle cleanup.
Best Practices
- Use asynchronous context managers to close credentials, providers, and MCP clients.
- Grant each agent only the tools and Azure permissions required for its task.
- Validate structured responses and test tool behavior before production deployment.
Avoid
- Do not install unpinned prerelease dependencies in production environments.
- Do not expose code, web, and MCP tools without explicit trust boundaries.
- Do not create a new conversation thread for every turn in one interaction.
Frequently Asked Questions
Which Azure credential should I use?
Use AzureCliCredential for local development. Use DefaultAzureCredential with managed identity or another approved identity in production.
Does the skill create persistent agents?
Yes. It shows provider methods that create Azure-hosted agents and conversation threads that retain multi-turn context.
Can I add my own Python functions?
Yes. Pass typed functions through the tools parameter and describe parameters with Annotated and Pydantic Field metadata.
Which hosted tools are covered?
The examples cover code interpretation, file search references, web search, hosted MCP, and client-managed MCP.
How are structured outputs validated?
Define a Pydantic model, request that response format, and validate the returned text against the model.
Is the example ready for production?
No. Pin dependencies, review permissions, validate endpoints, handle failures, monitor costs, and test every enabled tool before deployment.
Developer Details
Author
sickn33License
MIT
Skillstore revision
r2
Version notice
The author did not declare a version.
Repository
https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/agent-framework-azure-ai-pyRef
ebdfe608f5de2b66ff37ab4af12af8ac4f5e8006
Maintenance freshness
7/26/2026
Usage
6 downloads · 147 views
File structure
📄 SKILL.md
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