Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Ghosteken/agent-harness --skill agent-framework-azure-ai-pygit clone --depth 1 https://github.com/Ghosteken/agent-harnessWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ghosteken/agent-harness/agent-framework-azure-ai-py)<a href="https://agentmods.dev/skills/ghosteken/agent-harness/agent-framework-azure-ai-py"><img src="https://agentmods.dev/badge/skills/ghosteken/agent-harness/agent-framework-azure-ai-py/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ghosteken/agent-harness/agent-framework-azure-ai-py"><img src="https://agentmods.dev/badge/skills/ghosteken/agent-harness/agent-framework-azure-ai-py.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00024 | $0.02162 |
| Opus 5 | $0.00012 | $0.01081 |
| Sonnet 5 | $0.00005 | $0.00432 |
| Haiku 4.5 | $0.00002 | $0.00216 |
Grade A, and why
agent-framework-azure-ai-py scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
95% identical to agent-framework-azure-ai-py — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 344 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Framework Azure Hosted Agents
Build persistent agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK.
Architecture
User Query → AzureAIAgentsProvider → Azure AI Agent Service (Persistent)
↓
Agent.run() / Agent.run_stream()
↓
Tools: Functions | Hosted (Code/Search/Web) | MCP
↓
AgentThread (conversation persistence)
Installation
# Full framework (recommended)
pip install agent-framework --pre
# Or Azure-specific package only
pip install agent-framework-azure-ai --pre
Environment Variables
export AZURE_AI_PROJECT_ENDPOINT="https://<project>.services.ai.azure.com/api/projects/<project-id>"
export AZURE_AI_MODEL_DEPLOYMENT_NAME="gpt-4o-mini"
export BING_CONNECTION_ID="your-bing-connection-id" # For web search
Authentication
from azure.identity.aio import AzureCliCredential, DefaultAzureCredential
# Development
credential = AzureCliCredential()
# Production
credential = DefaultAzureCredential()
Core Workflow
Basic Agent
import asyncio
from agent_framework.azure import AzureAIAgentsProvider
from azure.identity.aio import AzureCliCredential
async def main():
async with (
AzureCliCredential() as credential,
AzureAIAgentsProvider(credential=credential) as provider,
):
agent = await provider.create_agent(
name="MyAgent",
instructions="You are a helpful assistant.",
)
result = await agent.run("Hello!")
print(result.text)
asyncio.run(main())
Agent with Function Tools
from typing import Annotated
from pydantic import Field
from agent_framework.azure import AzureAIAgentsProvider
from azure.identity.aio import AzureCliCredential
def get_weather(
location: Annotated[str, Field(description="City name to get weather for")],
) -> str:
"""Get the current weather for a location."""
return f"Weather in {location}: 72°F, sunny"
def get_current_time() -> str:
"""Get the current UTC time."""
from datetime import datetime, timezone
return datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S UTC")
async def main():
async with (
AzureCliCredential() as credential,
AzureAIAgentsProvider(credential=credential) as provider,
):
agent = await provider.create_agent(
name="WeatherAgent",
instructions="You help with weather and time queries.",
tools=[get_weather, get_current_time], # Pass functions directly
)
result = await agent.run("What's the weather in Seattle?")
print(result.text)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 344 lines · 24 tokens per session scan A 28348541cfd8
agent-framework-azure-ai-py is a skill published in the GitHub repository Ghosteken/agent-harness (2 stars, last pushed 4d ago), licensed MIT. It adds 24 tokens to every session and 2,162 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to agent-framework-azure-ai-py, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
python-conventions
Python style and structure conventions based on PEP 8. Load before implementing or reviewing any pyproject.toml/requirements.txt-based feature.
statistical-analysis
Structured pipeline for statistical analysis deliverables — SPSS, R, Python. Covers reliability, chi-square, correlation, regression, assumption checking, and client-ready reporting.
makefile-generation
Generates Makefiles with testing, linting, formatting, and automation targets. Use when starting a project or standardizing build automation.
python-testing
A Python testing guide covering pytest, test coverage, and test-driven development (TDD), a method of writing a failing test before the code that makes it pass.
python-patterns
A guide to common Python coding patterns, style rules, type hints, and practices for writing code that is readable, robust, efficient, and easier to maintain.
plankton-code-quality
Write-time code quality enforcement using Plankton — auto-formatting, linting, and Claude-powered fixes on every file edit via hooks.