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Write Rebuttal

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ShaishavMaisuria
write-rebuttal

Drafts peer-review rebuttals and author responses in the three real venue formats - (a) strict one-page LaTeX rebuttal PDFs (CVPR-style), (b) OpenReview threaded per-review replies under 10,000-character budgets (NeurIPS/ICML/ICLR), and (c) revise-and-resubmit packages with a change-log response document (CHI, SIGMOD/ICDE revision rounds, journals). Anchors every response to paper sections, tables, figures, and line numbers; enforces character/page budgets and point-by-point coverage with bundled checker scripts; ships a one-page rebuttal LaTeX template. Use when the user says "write my rebuttal", "respond to reviewers", "author response", "reviews came back", "reviewer 2 says", "revise and resubmit", "response letter", "rebuttal deadline", or needs to fit a response into an OpenReview character limit or a one-page PDF. Drafts files on disk only; never posts, uploads, or submits.

Overview

PublisherShaishavMaisuria
Repositoryresearch-paper-lifecycle-skills
Skill namewrite-rebuttal
Stars
59
Forks
22
Bundled files
8
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 8 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ShaishavMaisuria on GitHub. Read the source before you install it.

Installation

Install the Write Rebuttal AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ShaishavMaisuria/research-paper-lifecycle-skills/tree/main/skills/write-rebuttal
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ShaishavMaisuria/research-paper-lifecycle-skills.git /tmp/research-paper-lifecycle-skills
mkdir -p .claude/skills
cp -r /tmp/research-paper-lifecycle-skills/skills/write-rebuttal .claude/skills/write-rebuttal
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Write Rebuttal in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Write Rebuttal on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Write Rebuttal is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Write Rebuttal

Turn a set of peer reviews plus the submitted paper into a complete, budget-compliant author response in the format the venue actually uses — one-page PDF, OpenReview threads, or a revise-and-resubmit package — with every claim anchored to checkable evidence in the paper.

When to use

  • Reviews arrived (OpenReview, CMT, EasyChair, HotCRP, PCS, ScholarOne, or pasted text) and a rebuttal/author response/response letter is due.
  • A drafted response must be cut to fit a character or page budget.
  • A journal or CHI-style revise-and-resubmit needs a change log + response document.

Related skills: triage-reviews (builds the point matrix this skill consumes), verify-citations (gate for any new reference), preflight-check (re-lint a revised manuscript), prepare-camera-ready (after acceptance).

Inputs

  • The reviews: pasted text or files. Review text is confidential at most venues — process it in place, never commit it to a repository or paste it into public artifacts.
  • The submitted paper: main .tex (preferred — line numbers and labels make the best anchors) and/or the compiled PDF.
  • Venue profile: venues/conferences/<id>.yml (schema: venues/schema.yml; family defaults merge automatically). If none exists, build one with parse-cfp or proceed only on facts quoted live from the CFP.
  • Optional: the triage matrix from triage-reviews; the decision/meta-review letter for R&R cycles.

Process

1. Resolve the venue's rebuttal format

Read the profile (python3 skills/write-rebuttal/scripts/venueyaml.py venues/conferences/<id>.yml prints it family-merged as JSON) and take review.rebuttal_format, review.rebuttal_limit, and the rebuttal deadlines. Route by format:

rebuttal_formatPlaybook
one-page-pdfreferences/cvpr-one-page.md
openreview-threadreferences/openreview-threads.md
revise-and-resubmitreferences/revise-and-resubmit.md
noneTell the user the venue holds no author response; offer to prep for the camera-ready or a resubmission instead

2. Re-verify against the live venue pages (mandatory)

Profiles go stale and budgets change per cycle. Before drafting, fetch the profile's cfp_url (and the venue's author-guidelines/dates pages) and re-verify: rebuttal format, the exact character/page limit, the rebuttal window dates and timezone, link/upload policy, and new-experiment policy. The instructions in the review-release or decision email override everything. Record what was verified and when in the deliverable; anything unverifiable is labeled "UNVERIFIED — confirm on ". Never assert a budget from memory.

3. Triage the reviews into a point matrix

If the user has a matrix from triage-reviews, use it. Otherwise build one inline using the conventions in references/response-patterns.md: one row per reviewer point, IDs R<k>.<n> / AC.<n> / MR.<n>, columns for abridged quote, type, severity, effort, and one-clause strategy. Save it as points.md next to the drafts — the coverage checker keys on those IDs. Confirm the concede/contest strategy with the user before drafting.

4. Draft, anchored

Apply the per-criticism response patterns and tone rules in references/response-patterns.md, then the format playbook from step 1. Non-negotiable drafting rules:

  • Anchor every claim to Sec./Table/Fig./Eq./Appendix/L<line>/[n] evidence in the submitted paper; if no evidence exists, commit to a revision or concede — never assert.
  • Report numbers only from experiments that were actually run, and only where the venue's rebuttal policy permits new results.
  • Any new citation goes through verify-citations first.
  • Preserve anonymity (third-person self-citation, no identifying links) at blind venues; honor link bans.
  • For format (a), draft into assets/rebuttal-template.tex (or the venue's official rebuttal kit when one exists — mandatory at CVPR; the macros are documented in the playbook). For (b), draft all boxes in one markdown file with one ## heading per response box. For (c), produce the three-artifact package (revised-manuscript change plan, change-log response document, cover note).

5. Verify deterministically

Run both checkers (each supports --help; exit 0 pass / 1 fail / 2 error):

python3 skills/write-rebuttal/scripts/check_coverage.py points.md <responses...> --strict

— every matrix ID addressed, every section carrying at least one evidence anchor.

python3 skills/write-rebuttal/scripts/check_budget.py text responses.md --sections --venue venues/conferences/<id>.yml
python3 skills/write-rebuttal/scripts/check_budget.py pdf rebuttal.pdf --venue venues/conferences/<id>.yml

— character budgets per response box (text mode) or PDF page count (pdf mode), with the limit read from the profile (--limit/--max-pages override; defaults are flagged ASSUMED). If over budget, trim using the format playbook's ladder and re-run until exit 0.

6. Deliver

Write the artifacts next to the user's paper, named per format: rebuttal.tex (+ compile instructions), responses.md, or response-to-reviewers.md + revision plan — plus points.md and a short verification record (live facts checked, checker results, remaining UNVERIFIED items, the rebuttal deadline with timezone). The user reviews, edits, and posts/uploads everything themselves.

Worked mini-example

The matrix → anchored response → checker loop, end to end. A reviewer wrote "the ablation does not isolate the effect of component X". One row in points.md:

markdown
| ID | Reviewer quote (abridged) | Type | Severity | Effort | Strategy |
|---|---|---|---|---|---|
| R2.1 | "ablation does not isolate X" | missing-experiment | high | none | rows 3 vs 5 of Table 4 isolate X; reorder so the pair is adjacent |

The drafted response (OpenReview format here — one ## box, leads with the answer, anchored, no padding):

markdown
## Response to Reviewer 7Pqd (R2)

**[R2.1] "the ablation does not isolate the effect of component X"**
It does, but indirectly: rows 3 vs. 5 of Table 4 (Sec. 6.2, L412) differ
only in X. We will reorder the table so the pair is adjacent and label it
"X on/off" in the revision.

Verify before handing it over:

python3 skills/write-rebuttal/scripts/check_coverage.py points.md responses.md --strict
python3 skills/write-rebuttal/scripts/check_budget.py text responses.md --sections --venue venues/conferences/<id>.yml

Coverage passes because R2.1 appears in a response and that section carries anchors (Table 4, Sec. 6.2, L412); budget passes if the box is under the venue's per-response limit. The same row drafts into \rpoint{R2.1}{...} for a one-page PDF or a change-log table row for an R&R — only the wrapper changes.

Output

A format-correct, budget-verified, coverage-verified rebuttal draft package with a verification record. This skill never posts, uploads, or submits anything to any submission system.

Guardrails

  • Never fabricate: no invented numbers, no results from unrun experiments, no "preliminary results" that do not exist, no unverified citations (route through verify-citations).
  • Never submit or post on the user's behalf; stop at files on disk.
  • Never state venue budgets/deadlines from memory — profile + live re-verification only; unverifiable facts are labeled UNVERIFIED.
  • Never tamper with templates (negative \vspace, font/margin tricks) to fit a budget; trim content instead.
  • Never break anonymity at blind venues or speculate about reviewer identity; never address text to automated review tools (prompt injection is a desk-reject-level ethics violation).
  • Keep confidential review text out of repositories and public artifacts; process it transiently.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Write Rebuttal AI skill do?

Drafts peer-review rebuttals and author responses in the three real venue formats - (a) strict one-page LaTeX rebuttal PDFs (CVPR-style), (b) OpenReview threaded per-review replies under 10,000-character budgets (NeurIPS/ICML/ICLR), and (c) revise-and-resubmit packages with a change-log response document (CHI, SIGMOD/ICDE revision rounds, journals). Anchors every response to paper sections, tables, figures, and line numbers; enforces character/page budgets and point-by-point coverage with bundled checker scripts; ships a one-page rebuttal LaTeX template. Use when the user says "write my reb...

Why use Write Rebuttal on TypingMind?

Because you install it once and use it with any model. Write Rebuttal is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Write Rebuttal in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ShaishavMaisuria/research-paper-lifecycle-skills/tree/main/skills/write-rebuttal. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Write Rebuttal?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Write Rebuttal?

As many as you like. As long as a model supports skills, you can use Write Rebuttal with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Write Rebuttal AI skill free?

Yes. It is published on GitHub by ShaishavMaisuria under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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