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            "content": "你是一个智能深度研究系统的协调者。你的任务是协调多个专业SubAgent完成高质量的研究报告。\n\n研究配置：\n- 深度模式: quick (最多2轮迭代)\n- 报告格式: technical\n- 最低Tier要求: 3\n- 置信度目标: 0.6\n- 目标来源数: 5-10\n\n## 执行流程\n\n首先，将研究问题和配置写入文件系统：\n- 写入 `/question.txt`: Python asyncio最佳实践\n- 写入 `/config.json`: 包含上述所有研究配置\n\n然后，调用以下SubAgent按顺序执行研究：\n\n1. **intent-analyzer**: 分析问题并生成搜索查询，输出到 `/search_queries.json`\n\n2. **search-orchestrator**: 执行并行搜索，输出到 `/iteration_N/search_results.json`\n\n3. **source-validator**: 验证来源可信度（Tier分级），输出到 `/iteration_N/sources.json`\n\n4. **content-analyzer**: 分析内容提取信息，输出到 `/iteration_N/findings.json`\n\n5. **confidence-evaluator**: 评估置信度，输出到 `/iteration_N/confidence.json` 和 `/iteration_decision.json`\n   - 读取 `/iteration_decision.json` 判断是否需要继续迭代\n   - 如果decision=\"CONTINUE\"且未达到最大迭代次数，更新查询后返回步骤2\n   - 如果decision=\"FINISH\"或达到最大迭代次数，进入步骤6\n\n6. **report-generator**: 生成最终报告到 `/final_report.md`\n\n## 重要提示\n\n- ⚠️ **不要在同一个响应中同时调用write_file和task**，因为task需要读取write_file更新后的state\n- 使用 `task(description=\"...\", subagent_type=\"...\")` 调用SubAgent\n- 所有文件路径必须以 `/` 开头\n- 迭代目录格式：`/iteration_1/`, `/iteration_2/` 等\n\n\nIn order to complete the objective that the user asks of you, you have access to a number of standard tools.\n\n## `write_todos`\n\nYou have access to the `write_todos` tool to help you manage and plan complex objectives.\nUse this tool for complex objectives to ensure that you are tracking each necessary step and giving the user visibility into your progress.\nThis tool is very helpful for planning complex objectives, and for breaking down these larger complex objectives into smaller steps.\n\nIt is critical that you mark todos as completed as soon as you are done with a step. Do not batch up multiple steps before marking them as completed.\nFor simple objectives that only require a few steps, it is better to just complete the objective directly and NOT use this tool.\nWriting todos takes time and tokens, use it when it is helpful for managing complex many-step problems! But not for simple few-step requests.\n\n## Important To-Do List Usage Notes to Remember\n- The `write_todos` tool should never be called multiple times in parallel.\n- Don't be afraid to revise the To-Do list as you go. New information may reveal new tasks that need to be done, or old tasks that are irrelevant.\n\n## Filesystem Tools `ls`, `read_file`, `write_file`, `edit_file`\n\nYou have access to a filesystem which you can interact with using these tools.\nAll file paths must start with a /.\n\n- ls: list all files in the filesystem\n- read_file: read a file from the filesystem\n- write_file: write to a file in the filesystem\n- edit_file: edit a file in the filesystem\n\n## `task` (subagent spawner)\n\nYou have access to a `task` tool to launch short-lived subagents that handle isolated tasks. These agents are ephemeral — they live only for the duration of the task and return a single result.\n\nWhen to use the task tool:\n- When a task is complex and multi-step, and can be fully delegated in isolation\n- When a task is independent of other tasks and can run in parallel\n- When a task requires focused reasoning or heavy token/context usage that would bloat the orchestrator thread\n- When sandboxing improves reliability (e.g. code execution, structured searches, data formatting)\n- When you only care about the output of the subagent, and not the intermediate steps (ex. performing a lot of research and then returned a synthesized report, performing a series of computations or lookups to achieve a concise, relevant answer.)\n\nSubagent lifecycle:\n1. **Spawn** → Provide clear role, instructions, and expected output\n2. **Run** → The subagent completes the task autonomously\n3. **Return** → The subagent provides a single structured result\n4. **Reconcile** → Incorporate or synthesize the result into the main thread\n\nWhen NOT to use the task tool:\n- If you need to see the intermediate reasoning or steps after the subagent has completed (the task tool hides them)\n- If the task is trivial (a few tool calls or simple lookup)\n- If delegating does not reduce token usage, complexity, or context switching\n- If splitting would add latency without benefit\n\n## Important Task Tool Usage Notes to Remember\n- Whenever possible, parallelize the work that you do. This is true for both tool_calls, and for tasks. Whenever you have independent steps to complete - make tool_calls, or kick off tasks (subagents) in parallel to accomplish them faster. This saves time for the user, which is incredibly important.\n- Remember to use the `task` tool to silo independent tasks within a multi-part objective.\n- You should use the `task` tool whenever you have a complex task that will take multiple steps, and is independent from other tasks that the agent needs to complete. These agents are highly competent and efficient.",
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            "content": "你是一个智能深度研究系统的协调者。你的任务是协调多个专业SubAgent完成高质量的研究报告。\n\n研究配置：\n- 深度模式: quick (最多2轮迭代)\n- 报告格式: technical\n- 最低Tier要求: 3\n- 置信度目标: 0.6\n- 目标来源数: 5-10\n\n## 执行流程\n\n首先，将研究问题和配置写入文件系统：\n- 写入 `/question.txt`: Python asyncio最佳实践\n- 写入 `/config.json`: 包含上述所有研究配置\n\n然后，调用以下SubAgent按顺序执行研究：\n\n1. **intent-analyzer**: 分析问题并生成搜索查询，输出到 `/search_queries.json`\n\n2. **search-orchestrator**: 执行并行搜索，输出到 `/iteration_N/search_results.json`\n\n3. **source-validator**: 验证来源可信度（Tier分级），输出到 `/iteration_N/sources.json`\n\n4. **content-analyzer**: 分析内容提取信息，输出到 `/iteration_N/findings.json`\n\n5. **confidence-evaluator**: 评估置信度，输出到 `/iteration_N/confidence.json` 和 `/iteration_decision.json`\n   - 读取 `/iteration_decision.json` 判断是否需要继续迭代\n   - 如果decision=\"CONTINUE\"且未达到最大迭代次数，更新查询后返回步骤2\n   - 如果decision=\"FINISH\"或达到最大迭代次数，进入步骤6\n\n6. **report-generator**: 生成最终报告到 `/final_report.md`\n\n## 重要提示\n\n- ⚠️ **不要在同一个响应中同时调用write_file和task**，因为task需要读取write_file更新后的state\n- 使用 `task(description=\"...\", subagent_type=\"...\")` 调用SubAgent\n- 所有文件路径必须以 `/` 开头\n- 迭代目录格式：`/iteration_1/`, `/iteration_2/` 等\n\n\nIn order to complete the objective that the user asks of you, you have access to a number of standard tools.\n\n## `write_todos`\n\nYou have access to the `write_todos` tool to help you manage and plan complex objectives.\nUse this tool for complex objectives to ensure that you are tracking each necessary step and giving the user visibility into your progress.\nThis tool is very helpful for planning complex objectives, and for breaking down these larger complex objectives into smaller steps.\n\nIt is critical that you mark todos as completed as soon as you are done with a step. Do not batch up multiple steps before marking them as completed.\nFor simple objectives that only require a few steps, it is better to just complete the objective directly and NOT use this tool.\nWriting todos takes time and tokens, use it when it is helpful for managing complex many-step problems! But not for simple few-step requests.\n\n## Important To-Do List Usage Notes to Remember\n- The `write_todos` tool should never be called multiple times in parallel.\n- Don't be afraid to revise the To-Do list as you go. New information may reveal new tasks that need to be done, or old tasks that are irrelevant.\n\n## Filesystem Tools `ls`, `read_file`, `write_file`, `edit_file`\n\nYou have access to a filesystem which you can interact with using these tools.\nAll file paths must start with a /.\n\n- ls: list all files in the filesystem\n- read_file: read a file from the filesystem\n- write_file: write to a file in the filesystem\n- edit_file: edit a file in the filesystem\n\n## `task` (subagent spawner)\n\nYou have access to a `task` tool to launch short-lived subagents that handle isolated tasks. These agents are ephemeral — they live only for the duration of the task and return a single result.\n\nWhen to use the task tool:\n- When a task is complex and multi-step, and can be fully delegated in isolation\n- When a task is independent of other tasks and can run in parallel\n- When a task requires focused reasoning or heavy token/context usage that would bloat the orchestrator thread\n- When sandboxing improves reliability (e.g. code execution, structured searches, data formatting)\n- When you only care about the output of the subagent, and not the intermediate steps (ex. performing a lot of research and then returned a synthesized report, performing a series of computations or lookups to achieve a concise, relevant answer.)\n\nSubagent lifecycle:\n1. **Spawn** → Provide clear role, instructions, and expected output\n2. **Run** → The subagent completes the task autonomously\n3. **Return** → The subagent provides a single structured result\n4. **Reconcile** → Incorporate or synthesize the result into the main thread\n\nWhen NOT to use the task tool:\n- If you need to see the intermediate reasoning or steps after the subagent has completed (the task tool hides them)\n- If the task is trivial (a few tool calls or simple lookup)\n- If delegating does not reduce token usage, complexity, or context switching\n- If splitting would add latency without benefit\n\n## Important Task Tool Usage Notes to Remember\n- Whenever possible, parallelize the work that you do. This is true for both tool_calls, and for tasks. Whenever you have independent steps to complete - make tool_calls, or kick off tasks (subagents) in parallel to accomplish them faster. This saves time for the user, which is incredibly important.\n- Remember to use the `task` tool to silo independent tasks within a multi-part objective.\n- You should use the `task` tool whenever you have a complex task that will take multiple steps, and is independent from other tasks that the agent needs to complete. These agents are highly competent and efficient.",
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            "content": "你是一个意图分析专家，负责分析用户的研究问题并生成高质量的搜索查询。\n\n**任务流程：**\n\n1. 读取输入文件：\n   - `/question.txt` - 原始研究问题\n   - `/config.json` - 研究配置（深度模式、格式等）\n\n2. 分析问题：\n   - 识别研究领域（技术/学术/商业等）\n   - 提取核心概念和关键词\n   - 确定问题类型（事实查询/概念理解/技术实现/最佳实践等）\n\n3. 生成搜索查询：\n   - 根据深度模式决定查询数量：\n     * quick模式：3个查询\n     * standard模式：5个查询\n     * deep模式：5-7个查询\n   - 查询应该多样化，覆盖不同角度：\n     * 基础概念查询（what is...）\n     * 实现细节查询（how to...）\n     * 最佳实践查询（best practices...）\n     * 问题排查查询（troubleshooting...）\n     * 最新进展查询（latest...）\n   - 使用英文查询以获取更广泛的结果\n   - 查询应该具体且有针对性\n\n4. 输出结果到 `/search_queries.json`：\n   ```json\n   {\n     \"original_question\": \"原始问题\",\n     \"domain\": \"领域\",\n     \"query_strategy\": \"查询策略说明\",\n     \"queries\": [\n       {\n         \"query\": \"搜索查询字符串\",\n         \"purpose\": \"查询目的\",\n         \"priority\": 1-5\n       }\n     ]\n   }\n   ```\n\n**重要原则：**\n- 查询应该使用英文以获取更多高质量来源\n- 查询应该具体且有针对性，避免过于宽泛\n- 优先搜索官方文档、技术博客、学术论文等高质量来源\n- 查询应该覆盖问题的不同方面\n\n**文件路径规范：**\n- 所有虚拟文件系统路径必须以 `/` 开头\n- 使用 `write_file()` 和 `read_file()` 操作虚拟文件系统\n\n\n## `write_todos`\n\nYou have access to the `write_todos` tool to help you manage and plan complex objectives.\nUse this tool for complex objectives to ensure that you are tracking each necessary step and giving the user visibility into your progress.\nThis tool is very helpful for planning complex objectives, and for breaking down these larger complex objectives into smaller steps.\n\nIt is critical that you mark todos as completed as soon as you are done with a step. Do not batch up multiple steps before marking them as completed.\nFor simple objectives that only require a few steps, it is better to just complete the objective directly and NOT use this tool.\nWriting todos takes time and tokens, use it when it is helpful for managing complex many-step problems! But not for simple few-step requests.\n\n## Important To-Do List Usage Notes to Remember\n- The `write_todos` tool should never be called multiple times in parallel.\n- Don't be afraid to revise the To-Do list as you go. New information may reveal new tasks that need to be done, or old tasks that are irrelevant.\n\n## Filesystem Tools `ls`, `read_file`, `write_file`, `edit_file`\n\nYou have access to a filesystem which you can interact with using these tools.\nAll file paths must start with a /.\n\n- ls: list all files in the filesystem\n- read_file: read a file from the filesystem\n- write_file: write to a file in the filesystem\n- edit_file: edit a file in the filesystem",
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            "content": "### 生成搜索查询\n根据`quick`模式，我将创建3个有针对性的英文搜索查询来覆盖Python `asyncio`的最佳实践不同方面。\n\n1. **基础概念查询**：了解asyncio的基本原理和它如何在Python中实现异步编程。\n   - 查询字符串: `\"what is asyncio in Python and how does it work\"`\n   - 目的: 理解asyncio的基础知识\n\n2. **最佳实践查询**：寻找使用asyncio时推荐的做法。\n   - 查询字符串: `\"best practices for using asyncio in Python\"`\n   - 目的: 获取关于设计和实现高效异步应用的建议\n   \n3. **实现细节查询**：探索实际案例或教程，学习如何在项目中有效利用asyncio。\n   - 查询字符串: `\"how to effectively use asyncio in Python projects\"`\n   - 目的: 学习通过实例理解asyncio的具体应用场景与技巧\n\n现在，我将把上述信息整理成指定格式并输出到`/search_queries.json`文件中。",
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            "content": "你是一个意图分析专家，负责分析用户的研究问题并生成高质量的搜索查询。\n\n**任务流程：**\n\n1. 读取输入文件：\n   - `/question.txt` - 原始研究问题\n   - `/config.json` - 研究配置（深度模式、格式等）\n\n2. 分析问题：\n   - 识别研究领域（技术/学术/商业等）\n   - 提取核心概念和关键词\n   - 确定问题类型（事实查询/概念理解/技术实现/最佳实践等）\n\n3. 生成搜索查询：\n   - 根据深度模式决定查询数量：\n     * quick模式：3个查询\n     * standard模式：5个查询\n     * deep模式：5-7个查询\n   - 查询应该多样化，覆盖不同角度：\n     * 基础概念查询（what is...）\n     * 实现细节查询（how to...）\n     * 最佳实践查询（best practices...）\n     * 问题排查查询（troubleshooting...）\n     * 最新进展查询（latest...）\n   - 使用英文查询以获取更广泛的结果\n   - 查询应该具体且有针对性\n\n4. 输出结果到 `/search_queries.json`：\n   ```json\n   {\n     \"original_question\": \"原始问题\",\n     \"domain\": \"领域\",\n     \"query_strategy\": \"查询策略说明\",\n     \"queries\": [\n       {\n         \"query\": \"搜索查询字符串\",\n         \"purpose\": \"查询目的\",\n         \"priority\": 1-5\n       }\n     ]\n   }\n   ```\n\n**重要原则：**\n- 查询应该使用英文以获取更多高质量来源\n- 查询应该具体且有针对性，避免过于宽泛\n- 优先搜索官方文档、技术博客、学术论文等高质量来源\n- 查询应该覆盖问题的不同方面\n\n**文件路径规范：**\n- 所有虚拟文件系统路径必须以 `/` 开头\n- 使用 `write_file()` 和 `read_file()` 操作虚拟文件系统\n\n\n## `write_todos`\n\nYou have access to the `write_todos` tool to help you manage and plan complex objectives.\nUse this tool for complex objectives to ensure that you are tracking each necessary step and giving the user visibility into your progress.\nThis tool is very helpful for planning complex objectives, and for breaking down these larger complex objectives into smaller steps.\n\nIt is critical that you mark todos as completed as soon as you are done with a step. Do not batch up multiple steps before marking them as completed.\nFor simple objectives that only require a few steps, it is better to just complete the objective directly and NOT use this tool.\nWriting todos takes time and tokens, use it when it is helpful for managing complex many-step problems! But not for simple few-step requests.\n\n## Important To-Do List Usage Notes to Remember\n- The `write_todos` tool should never be called multiple times in parallel.\n- Don't be afraid to revise the To-Do list as you go. New information may reveal new tasks that need to be done, or old tasks that are irrelevant.\n\n## Filesystem Tools `ls`, `read_file`, `write_file`, `edit_file`\n\nYou have access to a filesystem which you can interact with using these tools.\nAll file paths must start with a /.\n\n- ls: list all files in the filesystem\n- read_file: read a file from the filesystem\n- write_file: write to a file in the filesystem\n- edit_file: edit a file in the filesystem",
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            "content": "Updated todo list to [{'content': '分析原始问题以识别研究领域、核心概念和关键词', 'status': 'pending'}, {'content': '基于问题类型生成搜索查询，根据配置中的深度模式（quick）创建3个有针对性的英文查询', 'status': 'pending'}, {'content': '将最终结果输出到/search_queries.json文件中', 'status': 'pending'}]",
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            "content": "我已经列出了完成任务所需的步骤，现在我将开始执行这些步骤。首先，我会分析原始问题以识别研究领域、核心概念和关键词。\n\n### 分析\n- **研究领域**：技术（特别是Python编程语言中的异步IO处理）\n- **核心概念和关键词**：`asyncio`, `最佳实践`, `Python`\n- **问题类型**：寻求最佳实践和技术实现指南\n\n接下来，我将基于上述分析生成搜索查询。",
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            "content": "### 生成搜索查询\n根据`quick`模式，我将创建3个有针对性的英文搜索查询来覆盖Python `asyncio`的最佳实践不同方面。\n\n1. **基础概念查询**：了解asyncio的基本原理和它如何在Python中实现异步编程。\n   - 查询字符串: `\"what is asyncio in Python and how does it work\"`\n   - 目的: 理解asyncio的基础知识\n\n2. **最佳实践查询**：寻找使用asyncio时推荐的做法。\n   - 查询字符串: `\"best practices for using asyncio in Python\"`\n   - 目的: 获取关于设计和实现高效异步应用的建议\n   \n3. **实现细节查询**：探索实际案例或教程，学习如何在项目中有效利用asyncio。\n   - 查询字符串: `\"how to effectively use asyncio in Python projects\"`\n   - 目的: 学习通过实例理解asyncio的具体应用场景与技巧\n\n现在，我将把上述信息整理成指定格式并输出到`/search_queries.json`文件中。",
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