agent-framework/python/samples/02-agents/skills/file_based_skill

File-Based Agent Skills

This sample demonstrates how to use file-based Agent Skills with a SkillsProvider in the Microsoft Agent Framework. File-based skills are discovered from SKILL.md files on disk and can include reference documents and executable scripts.

What are Agent Skills?

Agent Skills are modular packages of instructions and resources that enable AI agents to perform specialized tasks. They follow the Agent Skills specification and implement progressive disclosure:

1. Advertise: Skills are advertised with name + description (~100 tokens per skill)
2. Load: Full instructions are loaded on-demand via load_skill tool
3. Resources: References and other files loaded via read_skill_resource tool
4. Scripts: Executable scripts run via run_skill_script tool

Skills Included

unit-converter

Converts between common units (miles↔km, pounds↔kg) using a multiplication factor following agentskills.io guidelines.

• references/CONVERSION_TABLES.md — Supported conversions and their factors
• scripts/convert.py — Executable script with --value and --factor flags, JSON output, and --help support

Key Components

• SkillsProvider — Discovers skills from SKILL.md files in a directory and registers tools for the agent
• subprocess_script_runner — A SkillScriptRunner callback that runs scripts as local Python subprocesses, enabling the run_skill_script tool. Converts argument dicts to CLI flags (e.g. {"value": 26.2, "factor": 1.60934} → --value 26.2 --factor 1.60934). Shared across samples in ../subprocess_script_runner.py.

Project Structure

file_based_skill/
├── file_based_skill.py
├── README.md
└── skills/
    └── unit-converter/
        ├── SKILL.md
        ├── references/
        │   └── CONVERSION_TABLES.md
        └── scripts/
            └── convert.py

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
• AZURE_OPENAI_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

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

Learn More

• Agent Skills Specification
• Code-Defined Skills Sample
• Mixed Skills Sample
• Microsoft Agent Framework Documentation