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* support skill scripts execution * fix mixed line endings * address comments and fix syntax issues * use few try/except instead of one * change samples * validate either script path or script resource is set not both * fix: separate LLM args from runtime kwargs in skill script execution * address pr review comments * address PR review comments * Update python/packages/core/agent_framework/_skills.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update python/packages/core/agent_framework/_skills.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update python/packages/core/agent_framework/_skills.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * 1. Fixing the caching bug where parameters_schema would re-inspect on every call when the result was None 2. Updating the arguments tool description to be more generic (not CLI-specific) * fix failing tests * address pr review comments * address pr review comments * allow resource function returning any instead of sting * address PR review comments --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
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Script Approval — Human-in-the-Loop for Skill Scripts
This sample demonstrates how to require human approval before executing skill scripts using the require_script_approval=True option on SkillsProvider.
How It Works
When require_script_approval=True is set, the agent pauses before executing any skill script and returns approval requests instead:
- The agent tries to call
run_skill_script— execution is paused result.user_input_requestscontains approval request(s) with function name and arguments- The application inspects each request and decides to approve or reject
request.to_function_approval_response(approved=True|False)creates the response- The response is sent back via
agent.run(approval_response, session=session) - If approved, the script executes; if rejected, the agent receives an error
Key Components
require_script_approval=True— Gates all script execution on human approvalresult.user_input_requests— Contains pending approval requests afteragent.run()request.to_function_approval_response()— Creates an approval or rejection response
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):
AZURE_AI_PROJECT_ENDPOINT: Your Azure AI Foundry project endpointAZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: The name of your model deployment (defaults togpt-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/script_approval/script_approval.py