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Acl Review Process

CommunityPopular
brycewang-stanford
acl-review-process

Use when reasoning about how an ACL paper travels through ACL Rolling Review and conference commitment, covering reviewer and area-chair roles, meta-reviews, the commitment decision by senior area chairs and program chairs, Findings versus main-conference outcomes, ethics review escalation, and resubmission strategy across cycles.

Overview

Publisherbrycewang-stanford
RepositoryAwesome-Journal-Skills
Skill nameacl-review-process
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 Review Process 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-review-process
  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-review-process .claude/skills/acl-review-process
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Acl Review Process 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 Review Process 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 Review Process 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 Review Process

Use this to model where an ACL paper actually is and who decides what next. ACL decoupled reviewing from the conference: ARR produces the review package; the conference consumes it. Confirm current mechanics at aclrollingreview.org/reviewing, since cycle structure has changed repeatedly (most recently to 10-week cycles in 2025).

The pipeline, stage by stage

  1. ARR cycle intake: submission on OpenReview to a dated cycle (e.g., ARR January 2026); checks for format, Limitations, checklist, anonymity.
  2. Review: at least three reviewers score and write reviews.
  3. Author response + discussion: authors reply; reviewers discuss and may update reviews (see acl-author-response).
  4. Meta-review: the action editor / area chair synthesizes reviews into a meta-review with a recommendation. Notably, ARR senior area chairs do not make accept/reject recommendations — that authority sits with the venue.
  5. Commitment: authors choose whether and where to send the finished package — ACL, or another *ACL venue accepting that cycle.
  6. Venue decision: the conference's senior area chairs and program chairs read reviews + meta-review and decide: main conference, Findings, or reject.
  7. Ethics branch: papers flagged during review go to an ethics committee, which can require revisions or recommend rejection on ACL Code of Ethics grounds independent of technical merit.

Who decides what

ActorControlsCannot do
ReviewerReview text, scores, discussion updatesAccept or reject anything
AC / action editorMeta-review, review-issue handlingBind the conference's decision
AuthorResponse, revision, where/whether to commitChange reviews after the cycle closes
Venue SACs + PCsMain/Findings/reject from the ARR packageRe-run the reviewing itself
Ethics committeeEthics-based conditions or rejectionJudge technical contribution

Outcomes and what they mean

  • Main conference: full acceptance with a presentation slot.
  • Findings of ACL: peer-reviewed, Anthology-published acceptance for solid work that missed the main-program bar; typically without a main-stage oral. Citation-wise Findings papers perform respectably — treat it as a real publication decision, not a consolation to appeal.
  • Reject at commitment: the ARR reviews remain attached to the paper's history; a stronger resubmission through a later cycle is the designed path.

Reading your review package strategically

  • Weigh the meta-review over individual scores: it is the document venue chairs actually read.
  • A package with one detailed negative review and a meta-review that endorses the objection rarely survives commitment — fix the objection in a new cycle instead of shopping the package to another venue unchanged.
  • Conversely, mediocre scores with a meta-review noting resolved concerns can be worth committing: the venue decision is made on the whole record.
  • A meta-review score at the floor triggers the resubmission constraint: within six months, only a wholesale revision avoids desk rejection.

Venue-shopping within the ARR ecosystem

The same review package can be committed to exactly one venue at a time, but a rejected or withheld package can target a later conference (EMNLP, EACL, NAACL, AACL) whose commitment window accepts that cycle. Sequence deliberately: each conference publishes which ARR cycles it accepts, and reviews age — a package built on a two-cycle-old baseline set gets punished at commitment.

Anatomy of one 10-week cycle

text
week 0      submission deadline (AoE)
weeks 1-2   desk checks; reviewer + AC assignment
weeks 3-5   reviewing
week 5      reviews released -> author response (a few days)
week 6      reviewer discussion; reviews may be updated
weeks 7-8   meta-review written
~week 10    package complete -> commitment window for eligible venues

Offsets drift by cycle; the January 2026 cycle ran Jan 5 → reviews by early March → ACL 2026 commitment March 14. Always re-derive from the current dates page.

Reading reviewer language for what it predicts

  • "The contribution is unclear" from two reviewers predicts a meta-review hedge — fix framing in the response, not just individual complaints.
  • "Missing citation X, Y, Z" alone rarely sinks a paper; the same list attached to "novelty over X is marginal" usually does.
  • A confident low-score review with factual errors is the best target for a review-issue flag plus a surgical rebuttal.
  • Uniform middling scores with short reviews often mean poor reviewer fit; at commitment, venue chairs weigh the meta-review's own reading more — make sure the response arms the AC with the paper's best case.

Confidentiality and conduct boundaries

  • Review content is confidential to the cycle; quoting reviews publicly (even anonymized) before decisions violates ARR expectations.
  • Contacting reviewers or guessing identities is out of bounds; all communication flows through the OpenReview thread and the AC.
  • Authors are bound by the ACL code of ethics throughout — including in how the author response characterizes reviewers' competence.
  • Deadlines inside the cycle (response, discussion) are short and rarely extended; plan around them rather than requesting exceptions.

Output format

text
[Stage] cycle-intake / under review / response / meta-review / commit-window / venue decision / ethics review
[Package strength] <meta-review thrust + score pattern>
[Commit recommendation] commit to ACL / hold for revision / target sibling venue
[Ethics exposure] <flag risk and mitigation>
[Next actor to influence] <reviewer via discussion / AC via response / venue via commitment note>

Frequently asked questions

What does the Acl Review Process AI skill do?

Use when reasoning about how an ACL paper travels through ACL Rolling Review and conference commitment, covering reviewer and area-chair roles, meta-reviews, the commitment decision by senior area chairs and program chairs, Findings versus main-conference outcomes, ethics review escalation, and resubmission strategy across cycles.

Why use Acl Review Process on TypingMind?

Because you install it once and use it with any model. Acl Review Process 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 Review Process 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-review-process. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Acl Review Process?

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 Review Process?

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

Is the Acl Review Process 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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