A skill your agent uses when drafting an EMNLP author response in the ACL Rolling Review discussion window — triaging replies by soundness versus excitement, deciding what evidence can be added…

MITAuto-check passed

Install Emnlp Author Response

skills CLI
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill emnlp-author-response -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills emnlp-author-response --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/EMNLP-Skills/skills/emnlp-author-response .claude/skills/emnlp-author-response && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
emnlp-author-response
GitHub stars
1.2k
Token cost
~1.6k tokens
SKILL.md length
799 words
Files
1
Skills in repo
2,387
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when drafting an EMNLP author response in the ACL Rolling Review discussion window — triaging replies by soundness versus excitement, deciding what evidence can be added…

  • Drafting an EMNLP author response in the ACL Rolling Review discussion window — triaging replies by soundness versus excitement
  • SKILL.md covers Triage by score axis, What can and cannot be added, Anatomy of a reply that moves… and EMNLP-typical objections and…, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Deciding what evidence can be added mid-review

What it does

Emnlp Author Response is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when drafting an EMNLP author response in the ACL Rolling Review discussion window — triaging replies by soundness versus excitement, deciding what evidence can be added mid-review, staying anonymous, and writing for the area chair's meta-review and the post-commitment Senior Area Chairs, not the reviewers alone.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. | 覆盖主流期刊的… The licence is MIT.

When your agent uses it

  • Drafting an EMNLP author response in the ACL Rolling Review discussion window — triaging replies by soundness versus excitement
  • Deciding what evidence can be added mid-review
  • Staying anonymous
  • Writing for the area chairs meta-review and the post-commitment Senior Area Chairs

Example prompts

  • “/emnlp-author-response”

What it can do on your machine

Read from SKILL.md and the folder at commit 932eb23. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Emnlp Author Response loads about 1.6k tokens when it runs. Until then it costs about 85 tokens; SKILL.md has 799 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~85
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 799 words, ~1,649 tokens.

Download SKILL.mdSave it as .claude/skills/emnlp-author-response/SKILL.md (or your agent's skills folder).
name
emnlp-author-response
description
Use when drafting an EMNLP author response in the ACL Rolling Review discussion window — triaging replies by soundness versus excitement, deciding what evidence can be added mid-review, staying anonymous, and writing for the area chair's meta-review and the post-commitment Senior Area Chairs, not the reviewers alone.

EMNLP Author Response

Use this when ARR reviews land. In the May 2026 cycle the author response and author-reviewer discussion ran July 7-13, with meta-reviews released July 30 and EMNLP commitment due August 2 — a pipeline where the response is written once but read three times: by reviewers who may update, by the AC composing the meta-review, and by the conference SACs who see the whole record after commitment. Confirm the current window mechanics on the live ARR pages before drafting.

Triage by score axis

Sort every reviewer point by which score it threatens:

  • Soundness attacks ("the baseline is mistuned", "no significance test", "the claim exceeds the tested languages") — answer with evidence or a scope concession. These are the points that move outcomes, because soundness anchors publishability.
  • Excitement discounts ("incremental", "unsurprising") — answerable only by reframing what the finding changes for the field, briefly, once. Arguing taste at length reads as not having evidence.
  • Misreadings — correct with a quote and a section number, tone-free. A factual correction the AC can verify in ten seconds is your highest-value paragraph.

What can and cannot be added

The response box takes text and, in ARR practice, a revised PDF may accompany a resubmission rather than a same-cycle response — do not build the plan around uploading new material unless the current cycle's instructions explicitly allow it. Within text:

AdditionSafe?Why
Numbers from runs completed during the windowUsuallyReviewers can weigh them; label as new
A significance test on already-reported resultsYesIt re-analyzes existing evidence
A promised camera-ready rewriteYes, if scopedCommit to wording, not to new experiments
A brand-new experimental directionNoUnreviewable in-window; save it for revise-and-resubmit
Links to external results pagesNoAnonymity and auditability both break

Anatomy of a reply that moves a meta-review

text
R2.1 (baseline tuning, soundness): Correct that §5.2 did not state the search budget.
Both systems received identical 32-trial random search over the grid in App. C; we
will state this in §5.2. Under matched budgets the gap is 2.1 F1 (±0.4 over 5 seeds,
paired bootstrap p=0.003, already in Table 3).

R2.4 (only high-resource languages, scope): We agree and will retitle the claim to
the six tested languages. We note Swahili and Tamil results in App. E show the same
direction at lower magnitude — we will surface this in §7 rather than claim
generality we did not test.

The pattern: number every reply to a numbered concern; concede early where the reviewer is right; convert each concession into a specific, checkable edit; anchor every number to a location in the submitted record.

EMNLP-typical objections and their strongest answers

  • "Results may reflect data contamination." Point to the contamination audit (overlap statistics, cutoff dates) in the submission. If none exists, run the overlap analysis during the window and report it — this objection does not age away.
  • "No error analysis." If the paper has one, the reviewer missed it — cite the section. If it doesn't, a compact categorization of 50-100 sampled failures, added as in-window text, is feasible and persuasive.
  • "Human evaluation lacks detail." Report annotator count, guidelines location, agreement statistic, and pay — items the Responsible NLP checklist already made you record.
  • "Why not compare against <system>?" Either the comparison exists under another name (say so), or explain the confound that makes it uninformative, or run it if the window permits. "Out of scope" without a reason is read as "we would lose."
Show full SKILL.md (331 more words)Show less

When reviewers contradict each other

Three-reviewer packets regularly split: R1 wants more languages, R3 thinks the language set is already too broad for the method's claims. Do not answer each in isolation — the AC will read both replies side by side and see you agreeing with everyone. Instead:

  • Name the disagreement once, neutrally: "R1 and R3 read the language coverage in opposite directions."
  • State your position with its evidence, and accept the cost with the other reviewer explicitly.
  • Give the AC the synthesis you want the meta-review to adopt — resolving reviewer conflict is the AC's job, and a response that does the work credibly usually gets adopted wholesale.

A special case is the score-text mismatch: a review whose text is mild but whose overall recommendation is harsh (or vice versa). Respond to the text — it is the only part you can engage — and let the mismatch itself be visible to the AC without commenting on it.

Deciding not to fight

Some packets should not be argued with. If all three reviewers converge on a missing piece of evidence that cannot be produced in the window — a new annotation effort, a second domain, a human study — the strongest move is a short, gracious response that corrects factual errors only, followed by revise-and-resubmit into a later cycle where the same reviewers will see the gap actually closed. A maximal rebuttal that concedes nothing, against a unanimous packet, damages the record that the SACs will later read at commitment time.

Discussion-window conduct

  • Reply early; a July 7 response can get a reviewer follow-up, a July 12 response cannot.
  • Keep the whole response skimmable — ACs in a 17,000-submission cycle read dozens of these; a one-screen summary block on top ("three concerns, three fixes") earns goodwill.
  • Stay anonymous end to end: no identity hints, no "as our prior work" phrasing, no personal repositories.
  • Never write toward the SACs explicitly ("we urge acceptance") — they read the record for resolved substance, and advocacy is not substance.

Output format

text
[Concern map] <Rx.y -> soundness / excitement / misreading / taste>
[Reply drafts] <numbered, evidence-anchored, concession-explicit>
[New-in-window evidence] <what was computed, labeled as new>
[Camera-ready commitments] <specific edits promised>
[Residual risk after response] <what remains unresolved and why>

© brycewang-stanford, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in EMNLP-Skills/skills/emnlp-author-response of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

Emnlp Author Response next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Emnlp Author Response compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Emnlp Author Response this skillbrycewang-stanford/Awesome-Journal-Skills1.2k—~1.6kAutomated safety check: PassMIT
Responsive Unitsthedaviddias/Front-End-Checklist74k—~472Automated safety check: PassMIT
Hermes Agent Skill AuthoringNousResearch/hermes-agent252k—~3.6kAutomated safety check: PassMIT
Acl Author Responsebrycewang-stanford/Awesome-Journal-Skills1.2k—~1.6kAutomated safety check: PassMIT
Eacl Author Responsebrycewang-stanford/Awesome-Journal-Skills1.2k—~1kAutomated safety check: PassMIT
Icsme Author Responsebrycewang-stanford/Awesome-Journal-Skills1.2k—~1.3kAutomated safety check: PassMIT

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Questions about Emnlp Author Response

What does Emnlp Author Response do?

A skill your agent uses when drafting an EMNLP author response in the ACL Rolling Review discussion window — triaging replies by soundness versus excitement, deciding what evidence can be added…. Emnlp Author Response is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when drafting an EMNLP author response in the ACL Rolling Review discussion window — triaging replies by soundness versus excitement, deciding what evidence can be added mid-review, staying anonymous, and writing for the area chair's meta-review and the post-commitment Senior Area Chairs, not the reviewers alone.

When should I use Emnlp Author Response?

Emnlp Author Response fits situations like: drafting an EMNLP author response in the ACL Rolling Review discussion window — triaging replies by soundness versus excitement; deciding what evidence can be added mid-review; staying anonymous; writing for the area chairs meta-review and the post-commitment Senior Area Chairs.

How do I install Emnlp Author Response in Claude Code?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill emnlp-author-response -a claude-code`. Or copy the skill folder (EMNLP-Skills/skills/emnlp-author-response in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/emnlp-author-response in your project. Claude Code loads it when a task matches its description.

How do I install Emnlp Author Response in Codex?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill emnlp-author-response -a codex`. Or copy the skill folder (EMNLP-Skills/skills/emnlp-author-response in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/emnlp-author-response in your project. Codex loads it when a task matches its description.

Can I use Emnlp Author Response in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill emnlp-author-response -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/emnlp-author-response, .gemini/skills/emnlp-author-response, .github/skills/emnlp-author-response and .opencode/skills/emnlp-author-response in your project.

What does Emnlp Author Response need to run?

SKILL.md names no scripts, command-line tools or credentials: Emnlp Author Response is instructions for the agent only.

Does Emnlp Author Response access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Emnlp Author Response safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Emnlp Author Response use?

Emnlp Author Response is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Emnlp Author Response use?

About 1.6k tokens (SKILL.md is roughly 6.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Emnlp Author Response?

Skills that share tags, products or a category with Emnlp Author Response: Responsive Units (thedaviddias/Front-End-Checklist, 74k stars), Hermes Agent Skill Authoring (NousResearch/hermes-agent, 252k stars), Acl Author Response (brycewang-stanford/Awesome-Journal-Skills, 1.2k stars) and Eacl Author Response (brycewang-stanford/Awesome-Journal-Skills, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Emnlp Author Response?

brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,231 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.

Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.