AI提案响应工具 - 自动化RFP/提案回复，基于RAG记忆与迭代策略
abd-proposal-respond by agilebydesign/agilebydesign-skills
1 周安装量
GitHub安装命令
npx skills add https://github.com/agilebydesign/agilebydesign-skills --skill abd-proposal-respond🇨🇳中文介绍
Ace-Proposal-Respond
通过将材料转换为记忆、创建响应策略并分小批回答问题来回复客户提案。使用 abd-context-to-memory 进行 RAG。采用与 abd-shaping 相同的迭代策略模式。_
适用场景
- 用户需要回复 RFP、Q&A 或提案要求
- 根据提案文档创建响应计划
- 使用记忆（RAG）回答客户问题
- 根据修正迭代策略（DO/DO NOT）
依赖项：abd-context-to-memory
在回答问题之前，请运行 index_memory.py --path <proposal_source>。在起草答案时使用 search_memory.py "<query>"。
流程
- 设置 — 将提案转换为记忆；创建响应文件夹；建立符号链接
- 策略优先 — 分析文档；提出响应计划；保存到
response/strategy.md - 仅回答少量问题 — 每批 3–5 个；使用 RAG；获取批准
- 加速器 — 当答案引用
*参见附录 X（名称）*时，在加速器表中定义并累积（添加/更新行：幻灯片文件、数字、URL）。完成后，运行build_appendix_deck.py来组装附录幻灯片。
🇺🇸English
Ace-Proposal-Respond
Respond to client proposals by converting materials to memory, creating a response strategy, and answering questions in small batches. Uses abd-context-to-memory for RAG. Same iterate-on-strategy pattern as abd-shaping. _
When to Apply
- User wants to respond to an RFP, Q&A, or proposal requirements
- Creating a response plan from proposal documents
- Answering client questions using memory (RAG)
- Iterating on strategy with corrections (DO/DO NOT)
Dependency: abd-context-to-memory
Run index_memory.py --path <proposal_source> before answering questions. Use search_memory.py "<query>" when drafting answers.
Process
- Setup — Convert proposal to memory; create response folder; symlink
- Strategy first — Analyze documents; propose response plan; save to
response/strategy.md - Answer a few questions ONLY — 3–5 per batch; use RAG; get approval
- Accelerators — When answers reference
*See Appendix X (Name)*, define and accumulate in the (add/update row: slide file, numbers, URL). When done, run to assemble the appendix deck.
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