Once initial questions are answered, encourage the user to dump all the context they have. Request information such as:
- Background on the project/problem
- Related team discussions or shared documents
- Why alternative solutions aren't being used
- Organizational context (team dynamics, past incidents, politics)
- Timeline pressures or constraints
- Technical architecture or dependencies
- Stakeholder concerns
Advise them not to worry about organizing it - just get it all out. Offer multiple ways to provide context:
- Info dump stream-of-consciousness
- Point to team channels or threads to read
- Link to shared documents
If integrations are available (e.g., Slack, Teams, Google Drive, SharePoint, or other MCP servers), mention that these can be used to pull in context directly.
If no integrations are detected and in Claude.ai or Claude app: Suggest they can enable connectors in their Claude settings to allow pulling context from messaging apps and document storage directly.
Inform them clarifying questions will be asked once they've done their initial dump.
During context gathering:
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If user mentions team channels or shared documents:
- If integrations available: Inform them the content will be read now, then use the appropriate integration
- If integrations not available: Explain lack of access. Suggest they enable connectors in Claude settings, or paste the relevant content directly.
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If user mentions entities/projects that are unknown:
- Ask if connected tools should be searched to learn more
- Wait for user confirmation before searching
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As user provides context, track what's being learned and what's still unclear
Asking clarifying questions:
When user signals they've done their initial dump (or after substantial context provided), ask clarifying questions to ensure understanding:
Generate 5-10 numbered questions based on gaps in the context.
Inform them they can use shorthand to answer (e.g., "1: yes, 2: see #channel, 3: no because backwards compat"), link to more docs, point to channels to read, or just keep info-dumping. Whatever's most efficient for them.
Exit condition:
Sufficient context has been gathered when questions show understanding - when edge cases and trade-offs can be asked about without needing basics explained.
Transition:
Ask if there's any more context they want to provide at this stage, or if it's time to move on to drafting the document.
If user wants to add more, let them. When ready, proceed to Stage 2.
在初始问题得到解答后，鼓励用户汇总所有相关上下文。请求提供以下信息：
- 项目/问题的背景
- 相关的团队讨论或共享文档
- 为何不采用其他解决方案
- 组织上下文（团队动态、过往事件、内部情况）
- 时间线压力或约束条件
- 技术架构或依赖关系
- 利益相关者的关注点
建议用户不必担心组织方式，只需将所有信息提供出来。提供多种上下文提交方式：
- 自由式信息汇总
- 指向团队频道或讨论线程
- 链接到共享文档
如果有可用集成（例如Slack、Teams、Google Drive、SharePoint或其他MCP服务器），提及可直接通过这些集成获取上下文。
如果未检测到集成且在Claude.ai或Claude应用中： 建议用户在Claude设置中启用连接器，以便直接从消息应用和文档存储中获取上下文。
告知用户在初始信息汇总后，Claude会提出澄清问题。
上下文收集期间：
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如果用户提及团队频道或共享文档：
- 如果有可用集成：告知用户将立即读取内容，然后使用相应集成
- 如果无可用集成：说明无法访问，建议用户在Claude设置中启用连接器，或直接粘贴相关内容。
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如果用户提及未知的实体/项目：
- 询问是否应通过连接的工具搜索相关信息
- 等待用户确认后再进行搜索
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在用户提供上下文时，记录已了解的信息和仍不明确的内容
提出澄清问题：
当用户表示完成初始信息汇总（或提供了大量上下文后），提出澄清问题以确保理解到位：
根据上下文缺口生成5-10个编号问题。
告知用户可以用简写回答（例如：“1: 是，2: 查看#频道，3: 不行，因为要向后兼容”），也可以链接到更多文档、指向要读取的频道，或继续汇总信息。选择最高效的方式即可。
退出条件：
当问题能体现对内容的理解——能够询问边缘情况和权衡问题，无需再解释基础知识时，说明已收集到足够的上下文。
过渡：
询问用户是否还有其他上下文要补充，还是可以进入文档起草阶段。
如果用户想要补充更多信息，允许其继续。准备就绪后，进入第二阶段。