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• 由于缺少实时演示或示例输出，用户在不下载并运行代码的情况下难以评估该技能的功能，而许多人又不愿意去执行这些步骤。

• Skills 是以结构化的 Markdown 文件形式存在的，描述了如何处理特定任务，允许代理根据上下文加载并执行它们，类似于工具调用，但表现为指令而非函数。

• 有争议的 Skill 本质上是一个在代码提交后触发学习练习的提示，一些人认为它过于复杂，背离了本应简单的初衷。

• Skills 是描述可重复工作流的标准，通过逐步披露和提示共享上下文，从而减少重复信息开销，可以被视为一种增量式的软件，而不是万能灵丹。

• 如果 Skills 的适用范围较窄，它们具有可迁移性，类似封装好的抽象库，能够被用来构建个人化工具（例如借助 Claude 之类的工具）。

• "适应性动态教科书方法"指的是一种人类技能培养方式，通过基于证据的学习科学技术（如预测练习与检索练习）提供学习机会。

• 目前缺乏基准和评估，尚不清楚这些技能是否比更简单的替代方法带来更好的结果，粗糙的测试并不能让人对其有效性放心。

• 该 Skill 的目标是帮助理解 AI 生成的代码，并通过在重要里程碑后向用户提问来降低技能退化的风险。

• 这种 Skill 可以扩展到更广泛的学习领域，但当前实现主要面向代码领域，用户也有兴趣将其应用到其他主题，例如 Java Spring 。

• 通过构建 Spring 应用进行动手学习是可行的，但有些用户更偏好 AI 辅助的学习方式，以获得上下文相关的指导和个性化学习计划。

• 虽然 Spring 框架相对容易上手，但由于其灵活性，浏览 Spring 代码库时可能会迷失，很难迅速定位 Bean 的定义和核心业务逻辑。

• 像 IntelliJ 这样的 IDE 可以帮助导航 Spring 代码，但由于框架并非强制性规范，理解陌生代码库的结构仍然具有挑战性。

讨论表明，围绕"Skills"作为构建 AI 辅助工作流的概念，用户既持怀疑态度又充满期待：有人质疑其复杂性和缺乏明确基准，另一些人则看到它在个性化学习与自动化方面的潜力。适应性动态教科书方法被视为学习科学中有希望的应用，但在缺乏合适评估的情况下，其有效性仍未得到验证。讨论还涉及学习 Spring 框架的困难，比较了动手实践与 AI 辅助指导的优劣，并强调了 IDE 在导航复杂代码库时的重要性。总体而言，大家一致认为需要更清晰的评估方法和更具体的示例来展示这些 Skills 与 AI 辅助学习方法的价值。 • The absence of a live demo or sample output makes it difficult to evaluate the skill's functionality without downloading and running it, which many users are unwilling to do.

• Skills are structured markdown files that describe how to handle specific tasks, allowing agents to load and execute them based on context, similar to tool calls but as instructions rather than functions.

• The skill in question is essentially a prompt that triggers a learning exercise after code commits, which some find overly complex for its simple purpose.

• Skills are a standard for describing repeatable workflows, saving context through progressive disclosure and prompt sharing, and can be treated as incremental software rather than magic solutions.

• Skills are transferable if narrowly tailored, similar to libraries that encapsulate abstractions, and can be built using tools like Claude to create similar tools for personal use.

• The "adaptive dynamic textbook approach" refers to human skill development, offering learning opportunities through exercises grounded in evidence-based learning science techniques like prediction and retrieval practice.

• The lack of benchmarks and evals makes it unclear if the skill produces better results than simpler alternatives, and naive testing doesn't provide confidence in its effectiveness.

• The skill is intended to understand AI-generated code and potentially reduce skill atrophy by asking users questions about their work after important milestones.

• The skill can be extended to more generalized areas of learning, but its current implementation is domain-specific to code, and users are interested in applying it to other subjects like Java Spring.

• Hands-on learning through building a Spring app is feasible, but some users prefer AI-assisted learning for context-specific guidance and personalized learning plans.

• Spring is reasonably easy to learn, but navigating a Spring codebase can be confusing due to the framework's flexibility, making it difficult to locate bean definitions and business logic.

• IDEs like IntelliJ can help navigate Spring code, but the framework's non-opinionated nature can make it challenging to understand the structure of unfamiliar codebases.

The discussion reveals a mix of skepticism and enthusiasm around the concept of "skills" as a way to structure AI-assisted workflows, with some users questioning the complexity and lack of clear benchmarks, while others see value in their potential for personalized learning and automation. The adaptive dynamic textbook approach is seen as a promising application of evidence-based learning science, but its effectiveness is uncertain without proper evaluation. The conversation also touches on the challenges of learning Spring framework, with users debating the merits of hands-on building versus AI-assisted guidance, and the importance of IDE tools for navigating complex codebases. Overall, the discussion highlights the need for clearer evaluation methods and more practical examples to demonstrate the value of these skills and AI-assisted learning approaches.