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 satnamrsm/https-github.com-sickn33-antigravity-awesome-skills --skill agent-framework-azure-ai-pygit clone --depth 1 https://github.com/satnamrsm/https-github.com-sickn33-antigravity-awesome-skillsWrote 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/satnamrsm/https-github.com-sickn33-antigravity-awesome-skills/agent-framework-azure-ai-py)<a href="https://agentmods.dev/skills/satnamrsm/https-github.com-sickn33-antigravity-awesome-skills/agent-framework-azure-ai-py"><img src="https://agentmods.dev/badge/skills/satnamrsm/https-github.com-sickn33-antigravity-awesome-skills/agent-framework-azure-ai-py.svg" alt="Measured on agentmods" 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 | $0.00048 | $0.02126 |
| Opus 5 | $0.00024 | $0.01063 |
| Sonnet 5 | $0.00010 | $0.00425 |
| Haiku 4.5 | $0.00005 | $0.00213 |
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 8d 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
77% identical to agent-framework-azure-ai-py — 9 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 — 339 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.
- 8d ago First seen · 339 lines · 48 tokens per session scan A a035fc806be2
agent-framework-azure-ai-py is a skill published in the GitHub repository satnamrsm/https-github.com-sickn33-antigravity-awesome-skills (5 stars, last pushed today), licensed MIT. It adds 48 tokens to every session and 2,126 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 77% identical to agent-framework-azure-ai-py, differing in 9 lines, and is treated as a copy.
Other skills, from other repositories
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
pennylane
Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with…
dd-code-generation
Use pup CLI for immediate Datadog operations or generate code for integration into applications.
rocm-kernels
Provides guidance for writing and benchmarking optimized Triton kernels for AMD GPUs (MI355X, R9700) on ROCm, targeting HuggingFace diffusers (LTX-Video, SD3, FLUX) and transformers. Core kernels: RMSNorm, RoPE 3D, GEGLU, AdaLN. Includes XCD swizzle, autotune, diffusers integration patterns, and LTX-Video pipeline…
holoscan-install-wheel
Install Holoscan SDK Python wheel via pip into a venv. Use for Python installs; not for native C++/apt or Conda installs.
typing-exclusion-worker
Python typing exclusion worker: remove assigned mypy exclusion modules in small scoped batches, fix typing issues, run validation, and produce a structured completion summary. Use when running parallel typing-debt workers or when asked to remove modules from pyproject mypy exclusion overrides.