About skillsets for Copilot Extensions

Learn what Github Copilot 技能集 are and how they simplify integrating third-party tools and functions into your Copilot experience.

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A skill within GitHub Copilot is a tool that the model calls to perform a specific task in response to a user query. A skillset is a collection of these skills (up to five per skillset). Github Copilot 技能集 provide a streamlined way to extend Copilot’s functionality, allowing builders to integrate external services or custom API endpoints into their Copilot workflow. With skillsets, builders can enable Copilot to perform tasks���such as retrieving data or executing actions in third-party services—without needing to manage complex workflows or architecture.

For a quickstart example of a skillset, see the skillset-example repository. For information on building a skillset, see 构建 Copilot 技能集.

How skillsets and agents differ

技能集和代理是通过 Copilot 扩展性平台 扩展 Copilot 的功能和上下文的两种方式。 它们支持将外部服务和 API 集成到 Copilot Chat 中，但每一种都适用于不同的用例，并提供不同程度的控制和复杂度：

  • 技能集轻巧且精简，是为那些需要使用 Copilot 执行特定任务（例如，数据检索或简单操作）且希望以最少的设置来完成的开发人员而设计的。**** 它们会自动处理路由、提示构建、函数评估以及响应生成，这使得它们非常适合进行快速且直接的集成。
  • 代理适用于那些需要完全控制请求处理方式以及响应生成方式的复杂集成****。 它们支持实现自定义逻辑，与其他 LLM 和/或 Copilot API 集成，管理对话上下文，并处理用户交互的各个方面。 虽然代理需要更多的工程和维护工作，但它们为复杂的工作流提供了最大的灵活性。 有关代理的详细信息，请参阅“About agents for Copilot Extensions”。

The extensibility platform

Skillsets and agents both operate on the GitHub Copilot 扩展性平台, which manages the flow of user requests and function evaluations. With Copilot 技能集, the platform handles routing, prompt crafting, function calls and prompt generation.

Workflow overview

The extensibility platform follows a structured workflow to process user requests and generate responses:

  1. User request
    A user issues a request in the Copilot Chat interface, such as asking for data or executing a specific action.

  2. Routing
    The request is routed to the appropriate extension. For skillsets, this means the platform agent identifies and invokes the corresponding skillset based on the user’s intent. Each skill’s inference description helps the platform determine which skill to call.

  3. Dynamic Prompt Crafting
    GitHub Copilot generates a prompt using:

    • The user’s query.
    • Relevant thread history.
    • Available functions within the skillset.
    • Results from any prior function calls.

  4. LLM Completion
    The language model (LLM) processes the prompt and determines:

    • Whether the user’s intent matches a skillset function.
    • Which function(s) to call and with what arguments.
    • If required, the LLM may send additional function calls to gather more context.

  5. Function Evaluation
    The extension invokes the selected function(s), which may involve:

    • Gathering relevant context, such as Copilot 技能集 repository or user metadata.
    • Making an API call to an external service to retrieve data or execute an action.

  6. Response generation The platform iteratively refines the output, looping through prompt crafting, LLM completion, and function evaluation as needed. Once the process is complete, Copilot streams a final response back to the user in the chat interface.