microsoft--agent-framework/python/samples/02-agents/skills/skills_auto_approval

Skills Auto-Approval — Configure Auto-Approval Rules for Skill Tools

This sample demonstrates how to configure auto-approval rules for skill tools using ToolApprovalMiddleware. Every tool exposed by SkillsProvider (load_skill, read_skill_resource, and run_skill_script) requires host approval by default. Auto-approval rules let you selectively bypass the approval prompt for safe operations.

How It Works

1. A code-defined unit-converter skill (with a resource and a script) is registered via SkillsProvider.
2. The agent installs ToolApprovalMiddleware with SkillsProvider.read_only_tools_auto_approval_rule.
3. The read-only tools (load_skill, read_skill_resource) are approved automatically.
4. run_skill_script still requires explicit approval and is handled with the standard result.user_input_requests loop.

Auto-Approval Rules

SkillsProvider exposes two static rules to pass to ToolApprovalMiddleware(auto_approval_rules=[...]):

• SkillsProvider.read_only_tools_auto_approval_rule — approves only the read-only tools (load_skill, read_skill_resource), while still prompting for run_skill_script.
• SkillsProvider.all_tools_auto_approval_rule — approves every skill tool, including run_skill_script (no manual approval loop needed).

Both rules reject any call carrying a server_label, so they stay scoped to this provider's local tools and never auto-approve a same-named hosted tool.

Note: To use auto-approval rules, the agent must have ToolApprovalMiddleware in its middleware stack.

Key Components

• ToolApprovalMiddleware(auto_approval_rules=[...]) — Drives the approval handshake and applies the rules
• SkillsProvider.read_only_tools_auto_approval_rule — Auto-approves read-only skill tools
• SkillsProvider.all_tools_auto_approval_rule — Auto-approves all skill tools
• SkillsProvider.LOAD_SKILL_TOOL_NAME / READ_SKILL_RESOURCE_TOOL_NAME / RUN_SKILL_SCRIPT_TOOL_NAME — Tool-name constants for building custom rules

Running the Sample

Prerequisites

• An Azure AI Foundry project with a deployed model (e.g. gpt-4o-mini)

Environment Variables

Set the required environment variables in a .env file (see python/.env.example):

• FOUNDRY_PROJECT_ENDPOINT: Your Azure AI Foundry project endpoint
• FOUNDRY_MODEL: The name of your model deployment (defaults to gpt-4o-mini)

Authentication

This sample uses AzureCliCredential for authentication. Run az login in your terminal before running the sample.

Run

cd python
uv run samples/02-agents/skills/skills_auto_approval/skills_auto_approval.py

Learn More

• Skill Tool Approval Sample — manual human-in-the-loop approval
• Code-Defined Skills Sample
• File-Based Skills Sample
• Agent Skills Specification