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Giles Odigwe
7c2dae8855
* Fix sample bugs: incorrect API params, wrong client types, and invalid options - typed_options.py: Fix AnthropicClient model->model_id, wrap raw strings in Message objects for get_response(), fix reasoning_effort->reasoning dict, fix budget_tokens minimum (1024), use OpenAIChatClient not FoundryChatClient, remove unused import - client_reasoning.py: Fix deprecated model_id to model param - client_with_hosted_mcp.py: Remove invalid store=True kwarg from Agent.run() - code_defined_skill.py: Fix precision kwarg to use function_invocation_kwargs - Various other samples: Fix deprecated API usage and incorrect params Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Address PR review comments - client_with_hosted_mcp.py: Fix remaining store=True kwarg on line 68 to use options dict - client_with_session.py: Change store=True to store=False to match in-memory persistence demo intent - typed_options.py: Remove non-existent import and model key from docstring example Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * new sample fixes --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
7c2dae8855
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2026-03-31 16:58:51 +00:00
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Code-Defined Agent Skills
This sample demonstrates how to create Agent Skills in Python code, without needing SKILL.md files on disk. A unit-converter skill shows three approaches:
What's Demonstrated
- Static Resources — Pass inline content via the
resourcesparameter when constructing aSkill - Dynamic Resources — Attach callable functions via the
@skill.resourcedecorator that return content computed at runtime - Dynamic Scripts — Attach callable scripts via the
@skill.scriptdecorator (unit conversion via a single factor parameter)
All three can be combined with file-based skills in a single SkillsProvider.
Project Structure
code_defined_skill/
├── code_defined_skill.py
└── README.md
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/code_defined_skill/code_defined_skill.py