Acl Camera Ready logo

Acl Camera Ready

CommunityPopular
brycewang-stanford
acl-camera-ready

Use when preparing an accepted ACL main-conference or Findings paper for camera-ready, covering the extra content page, de-anonymization and acknowledgements, AI-assistance disclosure, keeping the Limitations section, ACL Anthology metadata and CC BY 4.0 publication, meta-review-driven edits, and presentation-mode logistics.

Overview

Publisherbrycewang-stanford
RepositoryAwesome-Journal-Skills
Skill nameacl-camera-ready
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 Camera Ready 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-camera-ready
  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-camera-ready .claude/skills/acl-camera-ready
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Acl Camera Ready 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 Camera Ready 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 Camera Ready 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 Camera Ready

Use this after ACL (or Findings of ACL) acceptance. Camera-ready quality controls how the paper looks forever in the ACL Anthology, which is the permanent open-access record. For ACL 2026 the camera-ready deadline was April 19, 2026 — confirm the current edition's date in the acceptance email before planning anything.

The extra page and what it is for

  • Accepted *ACL papers traditionally receive one additional content page: long papers up to 9 pages, short papers up to 5. The page exists to address reviewer and meta-review comments, not to smuggle in a new contribution.
  • Spend it in priority order: fixes the meta-review asked for, clarifications reviewers requested, then de-anonymization overhead (author block, acknowledgements) which consumes real space.
  • References remain unlimited; the Limitations section remains mandatory in the camera-ready and still sits outside the page count.

De-anonymization pass

  • Restore authors, affiliations, and acknowledgements; convert third-person self-citations ("Smith showed") back to natural first person where clearer.
  • Replace anonymous supplement links with the permanent public repository, dataset page, or model card; test every URL logged out.
  • Add the funding and AI-assistance acknowledgements: ACL policy requires generative-AI use beyond polishing to be disclosed, with details in the Acknowledgements matching your Responsible NLP checklist answers.

Anthology-facing metadata

ItemWhy it matters at ACLCommon error
Title/abstract in the formBecomes the Anthology landing pageUnicode or LaTeX macros pasted raw
Author names + orderPermanent citation record, BibTeX for everyoneName spelled differently than prior papers
PDF fonts embeddedAnthology archival requirementMissing Type-1/TrueType embedding from figures
License acceptanceAnthology publishes under CC BY 4.0 (post-2016 policy)Assuming you can restrict reuse later
Video/poster uploadsLinked from the Anthology page when providedSkipped, losing visibility

Publication in the Anthology is open access with no author fee; ACL's cost model is conference registration, not APCs.

Integrating reviewer-mandated changes

Work from the meta-review, not memory:

  1. Extract every "should," "must," and "the authors agreed to" from the meta-review and discussion thread into a checklist.
  2. Map each to an edit: new sentence, moved table, scoped claim, added citation. The accepted contribution must not silently grow — a stronger claim than reviewers evaluated is an integrity problem, not a bonus.
  3. Keep section and table numbering stable where possible so the review record still points at the right objects.
  4. Re-run the checklist consistency pass: if you promised error bars or a contamination note in discussion, the camera-ready must contain it.

Main conference vs Findings logistics

  • Findings papers are full peer-reviewed publications in the Anthology but are typically not part of the main oral program; presentation options (Findings poster sessions, workshop presentation slots, videos) vary per edition — check the current conference's Findings instructions rather than assuming.
  • Registration and presentation expectations differ by track and edition; ACL 2026 ran as a hybrid conference in San Diego (July 2-7, 2026). Verify the current attendance requirement before booking or skipping travel.

Camera-ready week runbook

text
Day 1: meta-review edit checklist; claim-scope audit
Day 2: de-anonymize; acknowledgements incl. funding + AI disclosure
Day 3: public artifact links live; README + license on the repo
Day 4: rebuild with final style; font embedding + page-count check
Day 5: metadata form; abstract matches PDF; submit and archive everything

Common camera-ready rejections and rework requests

  • Unembedded fonts from matplotlib or Inkscape figures — regenerate with embedding forced rather than patching the PDF.
  • Title case mismatch between the form and the PDF, or LaTeX math left in the plain-text abstract field.
  • Author list drift: an author added or reordered after review needs chair approval, not a silent metadata edit.
  • Limitations section deleted "for space" — it is still required and still free; removing it can bounce the paper at publication checks.
  • Non-resolving artifact URLs: the Anthology page will outlive your lab server; prefer archived, versioned hosting for the link of record.

If the paper landed in Findings

  • The Anthology volume is "Findings of the Association for Computational Linguistics: ACL " — cite and list it that way; miscrediting your own venue tier propagates into others' bibliographies.
  • Camera-ready mechanics are the same as main-conference papers, extra page included; deadlines are usually shared — confirm in the decision letter.
  • Ask early which presentation options the edition offers Findings papers (poster session, affiliated workshop slot, video); slots can require separate sign-up shortly after notification.

Presentation assets riding the same deadline window

  • Poster/slide templates, video-upload requirements, and session-format details arrive with or shortly after camera-ready instructions; the video deadline is often earlier than authors expect.
  • Registration typically must be completed by a named author before the paper is scheduled — check the decision email for the binding rule.
  • Book the error budget: font, metadata, and form rejections bounce back days later, so submitting on the deadline hour forfeits the retry.

Output format

text
[Camera-ready status] Ready / Needs fixes / Blocked
[Meta-review edits] <requested change -> edit made>
[Extra-page budget] <what the added space was spent on>
[De-anonymization + disclosure] <authors/acks/AI-use/links status>
[Anthology package] <PDF/metadata/license/artifact links>

Frequently asked questions

What does the Acl Camera Ready AI skill do?

Use when preparing an accepted ACL main-conference or Findings paper for camera-ready, covering the extra content page, de-anonymization and acknowledgements, AI-assistance disclosure, keeping the Limitations section, ACL Anthology metadata and CC BY 4.0 publication, meta-review-driven edits, and presentation-mode logistics.

Why use Acl Camera Ready on TypingMind?

Because you install it once and use it with any model. Acl Camera Ready 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 Camera Ready 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-camera-ready. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Acl Camera Ready?

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 Camera Ready?

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

Is the Acl Camera Ready 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇