Python: Support skill scripts execution (#4558)

* 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>
This commit is contained in:
SergeyMenshykh
2026-03-11 18:28:30 +00:00
committed by GitHub
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parent 2f8fd5f82f
commit 23ebfbc937
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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
1. **Static Resources** — Pass inline content via the `resources` parameter when constructing a `Skill`
2. **Dynamic Resources** — Attach callable functions via the `@skill.resource` decorator that return content computed at runtime
3. **Dynamic Scripts** — Attach callable scripts via the `@skill.script` decorator (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](https://ai.azure.com/) 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 endpoint
- `AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME`: 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
```bash
cd python
uv run samples/02-agents/skills/code_defined_skill/code_defined_skill.py
```
## Learn More
- [Agent Skills Specification](https://agentskills.io/)
- [File-Based Skills Sample](../file_based_skill/)
- [Mixed Skills Sample](../mixed_skills/)
- [Microsoft Agent Framework Documentation](../../../../../docs/)