rebuttaldraft

name	rebuttaldraft
description	Draft grounded, paste-ready responses to academic peer reviews. Use when the user asks for help writing a rebuttal, responding to individual reviewers, analyzing reviewer concerns, finding analogous rebuttal precedents from ICLR, ICML, NeurIPS, or COLM reviews, or running additional experiments needed to support the response.

RebuttalDraft

Turn a paper plus its reviews into per-reviewer, paste-ready rebuttal drafts. You (the host agent) play every stage with your own model — no API key. You also run the experiments a rebuttal needs, in the user's workspace, so the draft has real results instead of blanks.

Guarantees (state these to the user up front)

• Confidentiality: the paper and the review text never leave this machine. Only short, abstract search queries you write reach the ReviewSearch API. Paper content is never sent.
• No fabrication: never invent an experiment result, a number, or a precedent citation. Un-run experiments are handled honestly, never with a made-up outcome.

The workflow — stages 0 to 5

Read each stage's reference file (under references/, next to this SKILL.md) when you reach that stage, and follow it. Run Stages 1, 2, 4 and 5 once per reviewer; Stage 3 is shared across reviewers.

Paths in this skill are relative to this skill's own directory, the one this SKILL.md was loaded from. references/ sits beside this file, and the search script lives in the sibling reviewsearch skill at ../reviewsearch/scripts/search_reviews.py. Both skills ship together, so that path always resolves, in any host that discovers skills.

Stage 0 — Intake & detection

Find the paper (PDF / .tex / .md) and the review(s) (.md / .txt) in the workspace.

• Both present → confirm with the user which files to use.
• Review missing → ask the user to paste the reviews or save them as files. Multiple reviewers may be one file each or one combined file; split them into R1/R2/R3.
• Paper missing → ask for the PDF or source path.
• You cannot read the PDF (some environments) → ask for .tex / .md / plain text instead.

Stage 1 — Concern extraction (per reviewer)

Follow references/stage1-concerns.md. Produce, for each reviewer, a concern list (each concern: id, one-sentence text, type, severity, search_needed, abstract search_query) plus the reviewer_score.

Stage 2 — Grounding (per concern)

Follow references/stage2-grounding.md. For each concern with search_needed=true, run ../reviewsearch/scripts/search_reviews.py "<search_query>" --top-k 10 (full corpus, no --accepted-only) ONCE, then walk the results in rank order and stop at the first genuinely analogous case. Keep that ONE case whole, with the full past review and the full author rebuttal: it is the exemplar Stage 4 learns from, and its layout (strong rebuttals answer quantitative concerns with a table) is part of what it teaches. Keep none if nothing matches. Only the abstract query leaves the machine. Ground one concern per pass and gate immediately; the raw payload is large. Write each gated case to rebuttal/experiments/grounding.md as you go: Stage 4 reads it from there, not from your context.

Stage 3 — Experiments: plan, get consent, run (the headline step)

Follow references/stage3-experiments.md. This is what the web app cannot do, and it runs once, shared across reviewers: inventory what already exists (inside AND outside the repo), design a concrete experiment per concern that needs one, dedup overlapping experiments, and present ONE consolidated plan table as the only consent gate. Run every approved experiment, then write rebuttal/experiments/RESULTS.md, the single source of numbers for Stage 4, marking each section with its provenance. Anything rejected, failed, or infeasible is handled honestly in Stage 4 (no fabrication, no placeholders). With no compute at all, follow the dry-run rules: quarantine to rebuttal_DRYRUN/, mark synthetic numbers fabricated: true, and banner every draft.

Stage 4 — Rebuttal Draft + Guide (per reviewer)

Follow references/stage4-draft.md. Read rebuttal/experiments/grounding.md, rebuttal/experiments/RESULTS.md and the paper itself. Write the paste-ready draft filled with the REAL Stage 3 results, plus the per-concern guide (with real precedent References) and the overall plan.

Stage 5 — Critique + revise (per reviewer)

Follow references/stage5-critic.md. Score the draft against the rubric and revise (one pass; one more only for a CRITICAL issue). Only the final draft is written out.

Output

Create a rebuttal/ directory in the workspace:

• rebuttal/R1.md, rebuttal/R2.md, … — per-reviewer, paste-ready (Overview → Draft → Guide).
• rebuttal/experiments/grounding.md — the Stage 2 exemplars, kept whole.
• rebuttal/experiments/RESULTS.md — the Stage 3 results pack, every section marked with its provenance.
• rebuttal/experiments/ — Stage 3 run logs and artifacts.

In a dry run (no compute, synthetic numbers) the drafts go to rebuttal_DRYRUN/ instead, never to rebuttal/.

Then summarize inline: concern counts per reviewer, which experiments ran, which concerns rest on numbers that were NOT measured, and the draft file paths.

Notes

• The search API is a free demo and may cold-start (~1 min) after idle; if a call times out, wait ~1 minute and retry once. Point at a different backend with the RS_API_URL env var.

Attribution

Data: peer reviews from OpenReview (ICLR / ICML / NeurIPS / COLM), licensed CC-BY-4.0.