Webgpt Todo Response

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 repomix-output/webgpt.md or repomix-output/llm/webgpt.md
• current todo, usually docs/todolist.md
• relevant source-of-truth docs under docs/source-of-truth/
• 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 need clarification.

2. Check the todo against project boundaries.

   • Shared semantics should live in packages/* or source-of-truth docs.
   • Playground UI should validate platform capabilities, not define them.
   • /chat should not become a quality-management surface.
   • Trace/timeline data must not be treated as deterministic replay logs.
   • Do not introduce shared user/org/tenant/billing models unless the task explicitly requires them.

3. Find weak spots in the todo.

   • Missing source-of-truth step
   • Missing data/type/interface step before UI
   • Metadata overwrite risk
   • Auth/visibility ambiguity
   • Scope creep into eval runner, experiment, exporter, prompt hub, or LLM judge
   • Missing tests or review gates
   • UI route boundary drift

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, 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 accidentally move product semantics into playground code?”
• “Is metadataJson.review sufficient for v1, or is there a concrete reason to add top-level columns now?”
• “Are we missing any app-layer merge guards that prevent UI from overwriting capture, feedback, or host metadata?”
• “Do the loops defer eval runner cleanly, or is eval scope leaking into review?”
• “Are the proposed tests enough to prove auth, dataset visibility, and source lineage boundaries?”

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.