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* Python: Add ClassSkill for class-based skill definitions Add ClassSkill abstract base class with decorator-based resource and script discovery, porting .NET's AgentClassSkill (PRs #5027 and #5183) to Python. - Add ClassSkill(Skill, ABC) with instructions abstract property, cached content/resources/scripts properties - Add @ClassSkill.resource and @ClassSkill.script static method decorators for auto-discovery of methods and properties - Extract _build_skill_content() and _create_resource_element() shared helpers from InlineSkill for reuse - Add _discover_marked_members() for scanning class hierarchies - Add _make_method_name() for Python-to-skill name conversion - Add class_based_skill sample (UnitConverterSkill) - Update mixed_skills sample with TemperatureConverterSkill - Add 58 new tests covering ClassSkill, decorator discovery, property resources, inheritance, kwargs forwarding, and duplicate detection - Export ClassSkill from agent_framework public API Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: replace try/except/continue with assignment to satisfy bandit B112 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * address PR review feedback - Walk cls.__mro__ in _discover_marked_members for inherited property resources - Use inspect.getattr_static for MRO-aware is_property check - Return defensive copies from resources/scripts properties - Raise TypeError on wrong decorator stacking order (@resource above @property) - Log warning instead of silently swallowing descriptor errors during discovery - Validate explicit name= at decoration time via _validate_member_name - Add tests for all of the above Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix temperature converter skill: make resource necessary for script Refactor TemperatureConverterSkill so the agent must read the formulas resource (factor/offset) before calling the script, aligning with the volume-converter pattern. - Resource: numeric factor/offset table instead of symbolic formulas - Script: generic linear transform (value * factor + offset) - Instructions: updated to reflect new workflow Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
64 lines
4.2 KiB
Markdown
64 lines
4.2 KiB
Markdown
# Agent Skills Samples
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These samples demonstrate how to use **Agent Skills** — modular packages of instructions, resources, and scripts that extend an agent's capabilities. Skills follow the [Agent Skills specification](https://agentskills.io/) and use progressive disclosure to optimize token usage.
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## Learning Path
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Start with file-based or code-defined skills, then explore combining them and adding approval workflows.
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| Sample | Description |
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|--------|-------------|
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| [**file_based_skill**](file_based_skill/) | Define skills as `SKILL.md` files on disk with reference documents and executable scripts. Uses the unit-converter skill. |
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| [**code_defined_skill**](code_defined_skill/) | Define skills entirely in Python code using `Skill`, `@skill.resource`, and `@skill.script` decorators. Uses a code-defined unit-converter skill. |
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| [**class_based_skill**](class_based_skill/) | Define skills as Python classes using `ClassSkill` with `@ClassSkill.resource` and `@ClassSkill.script` decorators for auto-discovery. Uses a class-based unit-converter skill. |
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| [**mixed_skills**](mixed_skills/) | Combine code-defined, class-based, and file-based skills in a single agent. Uses a code-defined volume-converter, a class-based temperature-converter, and a file-based unit-converter. |
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| [**script_approval**](script_approval/) | Require human-in-the-loop approval before executing skill scripts |
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## Key Concepts
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### Progressive Disclosure
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Skills use a three-step interaction model to minimize token usage:
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1. **Advertise** — Skill names and descriptions (~100 tokens each) are injected into the system prompt
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2. **Load** — Full instructions are loaded on-demand via the `load_skill` tool
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3. **Access** — Resources are read via `read_skill_resource`; scripts are executed via `run_skill_script`
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### File-Based vs Code-Defined vs Class-Based Skills
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| Aspect | File-Based | Code-Defined | Class-Based |
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|--------|-----------|--------------|-------------|
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| Definition | `SKILL.md` files on disk | `Skill` instances in Python | Classes extending `ClassSkill` |
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| Resources | Static files in `references/` and `assets/` directories | Callable functions via `@skill.resource` decorator | `@ClassSkill.resource` decorator (auto-discovered) |
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| Scripts | Python files in `scripts/` directory (executed via subprocess) | Callable functions via `@skill.script` decorator (executed in-process) | `@ClassSkill.script` decorator (executed in-process) |
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| Discovery | Automatic via `skill_paths` parameter | Explicit via `skills` parameter | Explicit via `skills` parameter |
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| Dynamic content | No (static files only) | Yes (functions can generate content at runtime) | Yes (functions can generate content at runtime) |
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| Sharing pattern | Copy skill directory | Inline or shared instances | Package in shared libraries/PyPI |
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All three types can be combined in a single `SkillsProvider` — see the [mixed_skills](mixed_skills/) sample.
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### Script Execution
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Skills can include executable scripts. How a script runs depends on how it was defined:
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| | Code-Defined Scripts | File-Based Scripts |
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|---|---|---|
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| **Defined via** | `@skill.script` decorator | `.py` files in `scripts/` directory |
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| **Execution** | In-process (direct function call) | Delegated to a `script_runner` |
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| **`script_runner` needed?** | No — runs in-process automatically | **Yes** — required |
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The `script_runner` parameter on `SkillsProvider` is only applicable to **file-based** scripts. Code-defined scripts are always executed in-process regardless of this setting. See [file_based_skill](file_based_skill/) for an example using a `SkillScriptRunner` callable with a subprocess runner, and [code_defined_skill](code_defined_skill/) for in-process scripts that need no runner.
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## Prerequisites
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All samples require:
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- An [Azure AI Foundry](https://ai.azure.com/) project with a deployed model (e.g. `gpt-4o-mini`)
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- Azure CLI authentication (`az login`)
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- Environment variables set in a `.env` file (see `python/.env.example`)
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## Suppressing the experimental warning
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The Agent Skills APIs in these samples are still experimental. Each sample includes
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a short commented `warnings.filterwarnings(...)` snippet near the imports. Uncomment
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it if you want to suppress the Skills warning before using the experimental APIs.
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