webgpt-todo-response

name: webgpt-todo-response description: 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.

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

1. 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.

2. 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.

3. 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

4. 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 selectedCwd from draftCwd cleanly enough to avoid left-rail/runtime state drift?”
• “Is the proposed first-send flow realistic given thread/start and turn/start are 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.