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Acl Author Response

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brycewang-stanford
acl-author-response

Use when drafting an ACL author response inside an ACL Rolling Review cycle on OpenReview, covering the response window before meta-review, reviewer discussion dynamics, score-change strategy, flagging review issues to the area chair, anonymity rules, and deciding between responding now versus revising for a later ARR cycle.

Overview

Publisherbrycewang-stanford
RepositoryAwesome-Journal-Skills
Skill nameacl-author-response
Stars
1.2K
Forks
159
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by brycewang-stanford on GitHub. Read the source before you install it.

Installation

Install the Acl Author Response 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/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-author-response
  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/brycewang-stanford/Awesome-Journal-Skills.git /tmp/Awesome-Journal-Skills
mkdir -p .claude/skills
cp -r /tmp/Awesome-Journal-Skills/ACL-Skills/skills/acl-author-response .claude/skills/acl-author-response
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Acl Author Response 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 Acl Author Response 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 Acl Author Response 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.

ACL Author Response

Use this when ARR reviews land. The response is written inside the review cycle, before the meta-review is finalized — its primary reader is the area chair who will summarize your paper for whatever conference you later commit to. Reopen aclrollingreview.org/reviewing for the current cycle's exact window and rules.

What the response can actually move

  • ARR assigns at least three reviewers; after the author response there is a reviewer-discussion phase in which reviewers are expected to update reviews. A response that gives a reviewer a concrete reason to revise is worth more than one that argues.
  • The meta-review travels with the paper to commitment. Senior area chairs at the venue read the meta-review first, so your real goal is to shape what the AC writes, not to win a debate transcript.
  • Scores are not the only currency: a meta-review that says "the main weakness was resolved in discussion" can rescue a committed paper that scores alone would sink.

Triage order

  1. Factual errors in reviews (wrong dataset, misread table, missed section).
  2. Misunderstandings a two-sentence clarification can dissolve.
  3. Requests satisfiable from submitted material — appendix tables, checklist answers, the supplement archive.
  4. Requests needing new experiments: promise only what is genuinely small, or explicitly defer to a revision cycle (ARR is built for resubmission).
  5. Taste disagreements: answer once, briefly, without heat.

NLP-reviewer objection patterns

ObjectionUnderlying worryResponse that works at ACL
"Only evaluated on English"Overclaimed generalityScope the claim explicitly; cite the multilingual appendix run if one exists
"Gains may be contamination"Test data in pretrainingPoint to the decontamination check or concede and describe the planned audit
"No error analysis"Numbers without understandingQuote the analysis section or summarize 2-3 systematic error classes from logged outputs
"Human eval lacks agreement stats"Unreliable judgmentsReport the IAA already computed; never invent a number in the response box
"Missing recent baseline X"Stale comparison setDistinguish X technically, or accept it as revision work with a stated plan

Rules of the response box

  • Stay anonymous: no names, lab hints, grant numbers, or identifying links.
  • Never claim results that are not in the submitted PDF, supplement, or checklist; ARR reviewers can and do check.
  • Keep each reviewer reply skimmable — lead with the single sentence you want quoted in the meta-review.
  • If a review is deficient (template text, wrong paper, LLM-generated, hostile), use the current cycle's review-issue flag to the AC rather than fighting it in the reply thread.

Respond now or resubmit later

  • Respond in-cycle when the objections are misreadings or scoping problems — these are exactly what discussion can fix before the meta-review.
  • Prefer withdrawing the commitment plan and revising for a later cycle when reviews identify real missing experiments; a resubmission must carry a point-by-point revision note anyway, so bank the work.
  • Remember the asymmetry: reviews and meta-review persist and follow a resubmission. An abrasive response is permanent record.

Skeleton reply

text
We thank Reviewer X. Main point first: [one-sentence resolution].

1. [Factual correction] The model is trained only on the training split
   (Section 3.2, line refs); Table 2 reports held-out results.
2. [Clarification] The claim is scoped to the five languages tested
   (Section 5); we will tighten the abstract wording.
3. [Evidence pointer] Seeds, prompts, and compute are in Appendix C and
   checklist items C1-C3.
4. [Deferred work] A comparison to X requires retraining; we commit to it
   in a revision and note why it should not change the ranking.

Tone calibration for the ARR record

  • Write for three audiences at once: the reviewer being answered, the AC composing the meta-review, and the venue chairs who will skim the thread months later at commitment.
  • Concede fast and specifically; a clean concession plus a scoped fix reads as competence, while minimizing a real weakness reads as evasion.
  • Quantify wherever a reviewer hedged: replace "we believe the effect is robust" with the interval, the seed count, or the appendix table number.
  • Do not thank effusively or argue emotionally; both waste the reader's attention budget in a thread they are legally required to skim.
  • If two reviewers contradict each other, say so neutrally and let the AC arbitrate — pointing out the contradiction is legitimate and effective.

A workable 5-day response window

  1. Day 1: read all reviews twice; extract every distinct objection into a sheet with owner and evidence anchor; no drafting yet.
  2. Day 2: run any genuinely small verification (a re-scoring, a missing statistic already computable from logged outputs).
  3. Day 3: draft all replies; lead each with its one-sentence resolution.
  4. Day 4: internal cold read by a co-author who did not draft; cut 30%.
  5. Day 5: post early, not at the buzzer — reviewers who will engage in discussion tend to do it once, soon after your reply appears.

If the real answer is "withdraw and revise"

  • Skipping the response entirely is a signal too — reviewers note silence in discussion, and the package records it. A short, gracious reply that acknowledges the substantive points costs little and keeps the record clean for the resubmission's mandatory revision note.
  • Harvest the reviews immediately into the revision plan while the team still has context; the next cycle's point-by-point response is easiest to write as a running document, not a deadline reconstruction.

Output format

text
[Response stance] clarify-in-cycle / concede-and-revise / mixed
[Meta-review target] <sentence you want the AC to write>
[Per-reviewer plan] <R1/R2/R3: main point + evidence anchor>
[Review-issue flags] <deficient-review reports to AC, if any>
[Deferred to resubmission] <experiments promised for a later cycle>

Frequently asked questions

What does the Acl Author Response AI skill do?

Use when drafting an ACL author response inside an ACL Rolling Review cycle on OpenReview, covering the response window before meta-review, reviewer discussion dynamics, score-change strategy, flagging review issues to the area chair, anonymity rules, and deciding between responding now versus revising for a later ARR cycle.

Why use Acl Author Response on TypingMind?

Because you install it once and use it with any model. Acl Author Response 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 Acl Author Response in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-author-response. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Acl Author Response?

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 Acl Author Response?

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

Is the Acl Author Response AI skill free?

Yes. It is published on GitHub by brycewang-stanford under the MIT 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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