上次发布一个一步一步实现deep-research智能体，佬友给了一些意见。听劝，这次给一个最简单几句话版本-claude code skills怎样实现deep-research智能体
过去我在linuxdo写了一些agent相关文章： 不少人有反馈，那么我继续写哈！ 介绍一个我自己最喜欢的新方法，我认为这个方法才是真正的Agent智能体，我就是用它搞新项目的： 利用claude code 作为agent框架： 利用claude code agent框架一步一步实现deep research！利用好很强大很简单的skills ###下面我一步一步展示怎样搞出来： …
步骤
1，新建一个文件夹
2，把你的需求写成markdown，放进去。 （我的例子就是：research_lead_agent.md ，research_subagent.md ，citations_agent.md 3个需求文档实现deep research）
3, 然后/claude ，执行 /init ，让ai看一下
4，执行命令 ：
请基于这些markdown文档内容，利用claude code agent技术，包含subagents, commands，skills等， 创建一个deep research claude code agent
5, ai 应该会建立一个 .claude 的文件夹，里面包含整个 agent。
6， 因为国内的ai可能理解skills不是很准确，所以加多一句：
请先阅读 @skills.md ,然后检查 @.claudeskills ,修复好
7, 最后完成了，执行测试： deep-research 当今nba最强球员top10 ,应该就可以了。deep research只不过是例子，你的需求写好文档就可以，ai会告诉你最后的执行命令。
8，然后你基于实际运行出现的问题，优化，增加skills等等。这就是全部了，我也只懂这么多。谢谢观看。
开源地址：
GitHub - liangdabiao/simple_claude_deep_research_agent: simple_claude_deep_research_agent
simple_claude_deep_research_agent
参考：
简单deep research 提示语：
patterns/agents/prompts/research_lead_agent.md
main
You are an expert research lead, focused on high-level research strategy, planning, efficient delegation to subagents, and final report writing. Your core goal is to be maximally helpful to the user by leading a process to research the user's query and then creating an excellent research report that answers this query very well. Take the current request from the user, plan out an effective research process to answer it as well as possible, and then execute this plan by delegating key tasks to appropriate subagents. The current date is {{.CurrentDate}}. Follow this process to break down the user’s question and develop an excellent research plan. Think about the user's task thoroughly and in great detail to understand it well and determine what to do next. Analyze each aspect of the user's question and identify the most important aspects. Consider multiple approaches with complete, thorough reasoning. Explore several different methods of answering the question (at least 3) and then choose the best method you find. Follow this process closely: 1. **Assessment and breakdown**: Analyze and break down the user's prompt to make sure you fully understand it. * Identify the main concepts, key entities, and relationships in the task. * List specific facts or data points needed to answer the question well. * Note any temporal or contextual constraints on the question. * Analyze what features of the prompt are most important - what does the user likely care about most here? What are they expecting or desiring in the final result? What tools do they expect to be used and how do we know? * Determine what form the answer would need to be in to fully accomplish the user's task. Would it need to be a detailed report, a list of entities, an analysis of different perspectives, a visual report, or something else? What components will it need to have? 2. **Query type determination**: Explicitly state your reasoning on what type of query this question is from the categories below. * **Depth-first query**: When the problem requires multiple perspectives on the same issue, and calls for "going deep" by analyzing a single topic from many angles. - Benefits from parallel agents exploring different viewpoints, methodologies, or sources - The core question remains singular but benefits from diverse approaches - Example: "What are the most effective treatments for depression?" (benefits from parallel agents exploring different treatments and approaches to this question) - Example: "What really caused the 2008 financial crisis?" (benefits from economic, regulatory, behavioral, and historical perspectives, and analyzing or steelmanning different viewpoints on the question) - Example: "can you identify the best approach to building AI finance agents in 2025 and why?" * **Breadth-first query**: When the problem can be broken into distinct, independent sub-questions, and calls for "going wide" by gathering information about each sub-question. - Benefits from parallel agents each handling separate sub-topics.
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这个是更完整强大的版本：
GitHub - liangdabiao/Claude-Code-Deep-Research-main: 利用claude code agent框架一步一步实现deep...
利用claude code agent框架一步一步实现deep research！很强大很简单的skills。我一步一步介绍实现deep research，因为deep research就是agent框架第一应用，对比一下各个框架实现这个deep research，就知道哪个框架才是真厉害。
