webgpt-todo-response
Use after receiving WebGPT or another LLM review on a Formax todo and before sending the todo back for another pass. Produce a concise handoff response that says what we adopted, what we reject or question, and what the reviewer should specifically re-evaluate in `docs/todolist.md`.
适合你,如果需要在收到代码审查后快速整理采纳与拒绝意见并回复。
npx oh-my-skill add yusifeng/formax/webgpt-todo-responsecurl -fsSL https://oh-my-skill.com/install.sh | bash -s -- yusifeng/formax/webgpt-todo-responsenpx oh-my-skill verify yusifeng/formax/webgpt-todo-response怎么用
商店整理自技能原文 · 版本 5a48573 · 表述以原文为准收到 WebGPT 或其他 LLM 对 Formax 待办事项的审查后,Claude 会在将待办发回再审前,生成一份简洁的交接回复,说明采纳了什么、拒绝了什么或有何疑问,并指出评审员应重新评估的具体内容。
当用户收到 WebGPT 审查结果,要求准备待办事项的下一步审核回复时触发;例如用户询问是否有反驳意见、要求生成回复请 WebGPT 评估待办,或对比建议与实现范围。
技能原文 SKILL.md
WebGPT Todo Response
Purpose
Generate the message to send back to WebGPT after we have read its previous analysis and drafted or updated docs/todolist.md.
Use this skill when the user asks:
- whether we have rebuttals or questions for WebGPT
- what to include when sending our todo back to WebGPT for another pass
- to prepare a response asking WebGPT to evaluate, improve, or challenge our todo
- to compare WebGPT's recommendations against our chosen implementation scope
Inputs To Inspect
Read only the files needed for the current handoff:
- WebGPT response, usually under
repomix-output/ - current todo, usually
docs/todolist.md - relevant canonical docs under
docs/contracts/*,docs/frontend/*, or other explicitly governing docs when the todo depends on them - optional other LLM replies if the user asks for a multi-model synthesis
Do not re-run broad repository analysis unless the todo or WebGPT response depends on code facts that are unclear.
Workflow
- Identify WebGPT's strongest recommendations.
- Mark which ones are adopted in the todo.
- Mark which ones are intentionally deferred.
- Mark which ones are rejected or still need clarification.
- Check the todo against Formax boundaries.
- Canonical semantics belong in
docs/contracts/*and canonical runtime layers, not only UI. - Web reference UI should reflect runtime/platform truth, not invent it.
- Do not move thread/runtime state ownership into ad hoc component-local logic when the task is structurally runtime-driven.
- Preserve parity-sensitive behavior when relevant: transcript surface semantics, URL/thread sync, prompt/tool exposure boundaries, permissions flow, and active-thread canonical gating.
- Avoid turning a focused task into a broad cleanup or cross-subsystem redesign unless explicitly requested.
- Find weak spots in the todo.
- Missing canonical-doc step
- Missing data/type/interface step before UI
- Runtime state ownership drift
- Welcome/draft/thread semantics being mixed together
- Scope creep into unrelated app-server, terminal, diff, approval, or desktop integration work
- Missing tests or review gates
- Missing statement of protocol constraints or non-atomic failure boundaries
- Write a concise message for WebGPT.
- Assume WebGPT has no hidden context beyond the attached todo and bundle.
- Be explicit about decisions already made.
- Ask targeted questions instead of open-ended “any thoughts?”
- Request concrete todo edits or challenges, not generic feedback.
Output Shape
Produce a copy-ready Markdown response with these sections:
# Response To WebGPT ## What We Adopted - ... ## Where We Differ / Pushback - ... ## My Current Leaning 1. ... ## Highest-Value Review Points 1. ... ## Specific Questions For You 1. ... ## Please Review The Todo For - ... ## Constraints To Preserve - ...
Keep it short enough to paste into WebGPT with the todo. Prefer 5-10 specific questions/checks over a long essay.
Use My Current Leaning to distinguish default decisions from genuinely open questions. WebGPT may challenge these, but should not treat them as blank slate.
Use Highest-Value Review Points to focus WebGPT on the few risks most likely to improve the todo. These should be sharper than the broader checklist.
Good Question Patterns
- “Does this todo still hide new semantics inside
!activeThreadId, or is the draft state truly first-class?” - “Are we separating
selectedCwdfromdraftCwdcleanly enough to avoid left-rail/runtime state drift?” - “Is the proposed first-send flow realistic given
thread/startandturn/startare non-atomic?” - “Are we over-expanding the task into unrelated desktop/add-project behavior instead of keeping the mainline on new-thread draft semantics?”
- “Do the loops lock runtime ownership first, then UI, then tests, or is there still UI-first drift?”
Avoid
- Do not ask WebGPT to implement patches unless the user explicitly wants that.
- Do not ask WebGPT to run commands.
- Do not include local absolute paths.
- Do not send vague requests like “please improve this.”
- Do not restate the whole todo; reference it and ask for specific audit points.