Strategic Compact
affaan-m/ECC
任意の自動コンパクションではなく、タスクフェーズを通じてコンテキストを保持するための論理的な間隔での手動コンパクションを提案します。
A skill your agent uses when explaining or strategizing around EMNLP's two-stage pipeline — ACL Rolling Review scoring with soundness, excitement, and overall recommendation, area-chair…
MITAuto-check passed
Install Emnlp Review Process
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill emnlp-review-process -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills emnlp-review-process --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-review-process .claude/skills/emnlp-review-process && rm -rf skills-srcUse ~/.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/
Install the "emnlp-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EMNLP-Skills/skills/emnlp-review-process into .claude/skills/emnlp-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "emnlp-review-process", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EMNLP-Skills/skills/emnlp-review-processType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill emnlp-review-process -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills emnlp-review-process --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/EMNLP-Skills/skills/emnlp-review-process .agents/skills/emnlp-review-process && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "emnlp-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EMNLP-Skills/skills/emnlp-review-process into .agents/skills/emnlp-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "emnlp-review-process", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill emnlp-review-process -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills emnlp-review-process --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/EMNLP-Skills/skills/emnlp-review-process .cursor/skills/emnlp-review-process && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "emnlp-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EMNLP-Skills/skills/emnlp-review-process into .cursor/skills/emnlp-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "emnlp-review-process", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path EMNLP-Skills/skills/emnlp-review-process--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill emnlp-review-process -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills emnlp-review-process --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/EMNLP-Skills/skills/emnlp-review-process .gemini/skills/emnlp-review-process && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "emnlp-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EMNLP-Skills/skills/emnlp-review-process into .gemini/skills/emnlp-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "emnlp-review-process", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills emnlp-review-processInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill emnlp-review-process -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/EMNLP-Skills/skills/emnlp-review-process .github/skills/emnlp-review-process && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "emnlp-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EMNLP-Skills/skills/emnlp-review-process into .github/skills/emnlp-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "emnlp-review-process", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill emnlp-review-process -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills emnlp-review-process --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/EMNLP-Skills/skills/emnlp-review-process .opencode/skills/emnlp-review-process && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "emnlp-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EMNLP-Skills/skills/emnlp-review-process into .opencode/skills/emnlp-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "emnlp-review-process", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
emnlp-review-process- GitHub stars
- 1.2k
- Token cost
- ~1.6k tokens
- SKILL.md length
- 723 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when explaining or strategizing around EMNLP's two-stage pipeline — ACL Rolling Review scoring with soundness, excitement, and overall recommendation, area-chair…
- Works in 4 steps: Extract the three scores per reviewer;… → Classify each substantive point: factual… → Anything in class two becomes response… → …
- Strategizing around EMNLPs two-stage pipeline — ACL Rolling Review scoring with soundness
- SKILL.md covers Pipeline anatomy, What the scores mean…, The meta-review is the product and Resubmission dynamics, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Emnlp Review Process is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when explaining or strategizing around EMNLP's two-stage pipeline — ACL Rolling Review scoring with soundness, excitement, and overall recommendation, area-chair meta-reviews, the commitment step, and conference-side decisions by Senior Area Chairs into Main, Findings, or rejection — including resubmission dynamics and ethics flags.
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
- Strategizing around EMNLPs two-stage pipeline — ACL Rolling Review scoring with soundness
- Overall recommendation
- Area-chair meta-reviews
- The commitment step
Example prompts
- “/emnlp-review-process”
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Extract the three scores per reviewer; plot soundness against excitement mentally.
- Classify each substantive point: factual error by reviewer / missing-but-runnable
- Anything in class two becomes response content; class three drives the
- Draft the sentence you want the meta-review to contain, then write the 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 Review Process loads about 1.6k tokens when it runs. Until then it costs about 90 tokens; SKILL.md has 723 words of instructions outside code blocks.
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). 723 words, ~1,570 tokens.
Download SKILL.mdSave it as
.claude/skills/emnlp-review-process/SKILL.md (or your agent's skills folder).- name
- emnlp-review-process
- description
- Use when explaining or strategizing around EMNLP's two-stage pipeline — ACL Rolling Review scoring with soundness, excitement, and overall recommendation, area-chair meta-reviews, the commitment step, and conference-side decisions by Senior Area Chairs into Main, Findings, or rejection — including resubmission dynamics and ethics flags.
EMNLP Review Process
Use this to reason about what happens to the paper after upload. EMNLP outsources
reviewing to ACL Rolling Review and keeps only the decision: understanding which body
controls which lever is most of review-stage strategy. Mechanics below are the May 2026
cycle configuration (checked 2026-07-08); ARR revises its own rules midyear, so reconfirm
on aclrollingreview.org before relying on any of it.
Pipeline anatomy
ARR side (owns reviewing) Conference side (owns deciding)
───────────────────────── ───────────────────────────────
submit PDF + checklist (May 25)
→ AC assigned, reviewers assigned
→ 3(ish) reviews, scored commit reviewed paper (by Aug 2)
→ author response + discussion → SACs rank committed papers
(Jul 7-13) → PCs set boundaries
→ AC meta-review (Jul 30) → Main / Findings / reject (Aug 20)
→ OR: skip commitment, revise,
resubmit to a later cycleNothing on the left is a decision; nothing on the right adds new reviews. A paper is therefore argued twice — once in front of reviewers, once (silently, via the record) in front of SACs — and the response you write in July serves both audiences.
What the scores mean strategically
Since February 2025, ARR reviewers file three scores plus text:
| Score | What it measures | Who consumes it hardest |
|---|---|---|
| Soundness | Is the evidence valid for the claims as scoped? | The Findings bar — sound work is publishable |
| Excitement | Would the community care? | The Main-vs-Findings boundary |
| Overall recommendation | The reviewer's synthesis | SAC triage ordering |
The operational asymmetry: soundness is repairable in a response; excitement rarely is. A soundness objection usually names a missing control, test, or scope edit that text can address. Low excitement with high soundness signals the likely outcome is Findings, and the levers are framing and venue choice, not rebuttal volume.
The meta-review is the product
The AC's meta-review is what SACs actually read at scale. Everything an author does in the review phase should be optimized to make the meta-review say: the reviewers' substantive concerns were addressed or shown mistaken. Practical consequences:
- Answer the concern the AC is likely to quote, not the reviewer's most annoying sentence.
- If reviewers disagree with each other, say so explicitly and give the AC the resolution — ACs synthesize, and unresolved contradictions default to caution.
- The discussion window (July 7-13 in 2026) is short and reviewers may engage once; front-load your strongest material rather than staging it.
Resubmission dynamics
Skipping commitment and revising for a later cycle is a first-class outcome, with sharp edges:
- Resubmissions return to the same AC and reviewers where possible. New reviewers form an independent opinion before seeing the old reviews — so a genuinely improved paper gets a fresh read, but a cosmetically edited one faces reviewers holding the diff.
- ARR asks for a revision note explaining what changed; write it as an audit trail keyed to the previous reviews.
- Cycle arithmetic matters: each ARR cycle aligns to specific conferences. Revising out of the May cycle meant targeting whatever the next cycle fed, not EMNLP 2026.
Show full SKILL.md (302 more words)Show less
Ethics and integrity lanes
Reviewers can flag submissions for ethics review, which runs parallel to technical review and can alter or override outcomes. Separately, the 2026 conference call names thinly sliced submissions, hallucinated citations, and entirely AI-generated papers as integrity violations. Neither lane is appealable through a better rebuttal — the defense is a submission that never trips them: documented data provenance, honest checklist answers, and verified references.
Scale effects you should price in
The May 2026 cycle carried 17,087 submissions across a pool of roughly 10,600 reviewers and 1,400 area chairs. Scale has predictable consequences for any single paper:
- Reviewer expertise is matched statistically, not curated; expect one close expert, one adjacent reader, and occasionally one genuine mismatch. Write the paper so the adjacent reader can follow the argument — they are the median vote.
- Late, short, or template-flavored reviews happen; the remedy is the discussion window and, where a review is substantively deficient, a calm note to the AC — not a public quarrel in the thread.
- Meta-review quality varies with AC load. A response that pre-writes the synthesis (short summary block, numbered resolutions) is partly self-defense against a rushed meta-review.
- Score inflation and compression are real at scale: the difference between "accept" and "Findings" often lives in the meta-review's confidence language rather than in the numeric scores, which is one more reason the response should target prose, not arithmetic.
Reading a review packet in ten minutes
- Extract the three scores per reviewer; plot soundness against excitement mentally.
- Classify each substantive point: factual error by reviewer / missing-but-runnable evidence / missing-and-unrunnable evidence / taste.
- Anything in class two becomes response content; class three drives the commit-vs-revise decision; class four gets one polite paragraph, maximum.
- Draft the sentence you want the meta-review to contain, then write the response backward from it.
Output format
[Stage] under review / response window / meta-review out / committed / decided
[Score read] soundness <low/mid/high> × excitement <low/mid/high> per reviewer
[Likely trajectory] Main / Findings / revise-resubmit / withdraw
[Meta-review target sentence] <what the AC should conclude>
[Ethics or integrity exposure] none / flagged / at-risk items© 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-review-process of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Emnlp Review Process 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Emnlp Review Process this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.6k | Automated safety check: Pass | MIT | |
| Strategic Compactaffaan-m/ECC | 277k | 2 repos | ~434 | Automated safety check: Pass | MIT | |
| Strategic Compactaffaan-m/ECC | 277k | 2 repos | ~724 | Automated safety check: Pass | MIT | |
| Strategic Compactaffaan-m/ECC | 277k | 1 repos | ~2.1k | Automated safety check: Pass | MIT | |
| Strategic Compactaffaan-m/ECC | 277k | 3 repos | ~943 | Automated safety check: Pass | MIT | |
| Logic Explainsickn33/agentic-awesome-skills | 47k | 1 repos | ~894 | Automated safety check: Pass | MIT |
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Questions about Emnlp Review Process
What does Emnlp Review Process do?
A skill your agent uses when explaining or strategizing around EMNLP's two-stage pipeline — ACL Rolling Review scoring with soundness, excitement, and overall recommendation, area-chair…. Emnlp Review Process is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when explaining or strategizing around EMNLP's two-stage pipeline — ACL Rolling Review scoring with soundness, excitement, and overall recommendation, area-chair meta-reviews, the commitment step, and conference-side decisions by Senior Area Chairs into Main, Findings, or rejection — including resubmission dynamics and ethics flags.
When should I use Emnlp Review Process?
Emnlp Review Process fits situations like: strategizing around EMNLPs two-stage pipeline — ACL Rolling Review scoring with soundness; overall recommendation; area-chair meta-reviews; the commitment step.
How do I install Emnlp Review Process in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill emnlp-review-process -a claude-code`. Or copy the skill folder (EMNLP-Skills/skills/emnlp-review-process in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/emnlp-review-process in your project. Claude Code loads it when a task matches its description.
How do I install Emnlp Review Process in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill emnlp-review-process -a codex`. Or copy the skill folder (EMNLP-Skills/skills/emnlp-review-process in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/emnlp-review-process in your project. Codex loads it when a task matches its description.
Can I use Emnlp Review Process 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-review-process -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-review-process, .gemini/skills/emnlp-review-process, .github/skills/emnlp-review-process and .opencode/skills/emnlp-review-process in your project.
What does Emnlp Review Process need to run?
SKILL.md names no scripts, command-line tools or credentials: Emnlp Review Process is instructions for the agent only.
Does Emnlp Review Process 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 Review Process 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 Review Process use?
Emnlp Review Process 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 Review Process use?
About 1.6k tokens (SKILL.md is roughly 6.3k 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 Review Process?
Skills that share tags, products or a category with Emnlp Review Process: Strategic Compact (affaan-m/ECC, 277k stars), Strategic Compact (affaan-m/ECC, 277k stars), Strategic Compact (affaan-m/ECC, 277k stars) and Strategic Compact (affaan-m/ECC, 277k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Emnlp Review Process?
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.