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Agent skill

Emnlp Related Work

by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills

A skill your agent uses when positioning an EMNLP submission inside NLP's fast-moving literature — covering ACL Anthology lineages and arXiv concurrency, citing Findings and workshop papers…

MITAuto-check passedResearch & Science

Install Emnlp Related Work

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

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

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills emnlp-related-work --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-related-work .claude/skills/emnlp-related-work && 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-related-work
GitHub stars
1.2k
Token cost
~1.7k tokens
SKILL.md length
856 words
Files
1
Skills in repo
2,387
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when positioning an EMNLP submission inside NLP's fast-moving literature — covering ACL Anthology lineages and arXiv concurrency, citing Findings and workshop papers…

  • Positioning an EMNLP submission inside NLPs fast-moving literature — covering ACL Anthology lineages and arXiv concurrency
  • SKILL.md covers The lineage obligations, Anthology-status literacy, Concurrency etiquette and The hallucination gate, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Citing Findings and workshop papers correctly

What it does

Emnlp Related Work is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when positioning an EMNLP submission inside NLP's fast-moving literature — covering ACL Anthology lineages and arXiv concurrency, citing Findings and workshop papers correctly, tracing dataset and benchmark ancestry, verifying every reference resolves under the hallucinated-citation policy, and keeping self-citation double-blind.

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science, covering Literature review, Academic paper search and Citation management. It works with arXiv. 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

  • Positioning an EMNLP submission inside NLPs fast-moving literature — covering ACL Anthology lineages and arXiv concurrency
  • Citing Findings and workshop papers correctly
  • Tracing dataset and benchmark ancestry
  • Verifying every reference resolves under the hallucinated-citation policy

Example prompts

  • “/emnlp-related-work”

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 (its code samples are bash).

    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 Related Work loads about 1.7k tokens when it runs. Until then it costs about 89 tokens; SKILL.md has 856 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~89
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k

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). 856 words, ~1,741 tokens.

Download SKILL.mdSave it as .claude/skills/emnlp-related-work/SKILL.md (or your agent's skills folder).
name
emnlp-related-work
description
Use when positioning an EMNLP submission inside NLP's fast-moving literature — covering ACL Anthology lineages and arXiv concurrency, citing Findings and workshop papers correctly, tracing dataset and benchmark ancestry, verifying every reference resolves under the hallucinated-citation policy, and keeping self-citation double-blind.

Use this to audit positioning and citation hygiene. NLP's literature moves faster than any reviewing cycle: between your ARR submission and the meta-review, the arXiv neighborhood of your paper will visibly change. EMNLP reviewers know this and calibrate — what they do not forgive is missing the lineage that existed before you submitted, or citing papers that do not exist.

The lineage obligations

An EMNLP related-work section owes coverage on distinct axes:

AxisThe reviewer's questionWhere to check
Task lineageWho defined and reshaped this task?ACL Anthology back through renamings
Dataset ancestryWhat data does your benchmark descend from or contain?Dataset papers + their own provenance sections
Method neighborsWhich current approaches would contest your comparison table?Last 2-3 years of ACL-family venues + arXiv
Evaluation critiqueHas this metric or protocol been critiqued?The methodology literature this venue itself publishes
Adjacent fieldsDoes IR / speech / ML own part of this idea?Cross-community search before claiming firsts

Dataset ancestry is the EMNLP-specific trap: benchmarks are frequently derived, filtered, or re-annotated versions of earlier corpora, and claiming novelty over a dataset whose ancestor solved the same problem is a reviewer catch that costs credibility beyond the one paragraph.

Anthology-status literacy

Citations at an ACL-family venue signal whether you know how your own field publishes:

  • Findings papers are real publications — cite them as "Findings of the Association for Computational Linguistics: EMNLP 20XX," not as the main proceedings and not as preprints.
  • Workshop papers are archival in the Anthology but represent a different review bar; leaning a central comparison on one deserves a sentence of context.
  • Prefer the Anthology version over arXiv whenever both exist — citing arXiv for a paper with a published version suggests reference lists assembled by search engine.
  • TACL and Computational Linguistics are journals inside the same ecosystem; their results are not "concurrent work" excuses, they are prior art.

Concurrency etiquette

For genuinely simultaneous arXiv work: cite it, mark it as concurrent, and state the technical difference in one neutral sentence — no priority litigation reviewers cannot adjudicate. ACL policy abolished the anonymity embargo, so your own preprint may be public; handle it in the third person like any other paper and do not cite it in a way that completes the identity loop ("we extend Xu et al." where Xu is you, plus a matching acknowledgements slip, is the classic double-blind failure).

The hallucination gate

The 2026 EMNLP call names hallucinated citations as a sanctionable integrity problem — a direct response to LLM-assisted writing. If any tool touched your bibliography, verify mechanically:

bash
# Every entry must resolve to a real, checkable identifier
grep -Eo 'doi\.org/[^ ,}]+|aclanthology\.org/[^ ,}]+|arxiv\.org/abs/[0-9.]+' refs.bib \
  | sort -u > ids.txt   # then spot-resolve each; zero tolerance for near-miss titles
# Red flags: plausible title + plausible authors + no findable venue = fabrication

Author-year pairs that "sound right," merged titles of two real papers, and wrong-venue attributions are the common fabrication shapes. One hallucinated reference now risks the whole submission, not a bibliography correction.

Positioning prose that works here

The strong EMNLP positioning move is evaluative contrast, not adjacency listing: state what the nearest work measured, what it could not see with that measurement, and which experiment in your paper closes the gap. Three sentences of that beat two paragraphs of "X did A. Y did B. Z did C." — and it survives the reviewer who knows X, Y, and Z personally. Reserve explicit tables-of-differences for when reviewers of a previous cycle demanded one; otherwise the contrast belongs woven into the experiment motivation.

Show full SKILL.md (312 more words)Show less

Prior versions of your own work

ARR's continuity makes self-lineage a form field, not just a citation question:

  • A resubmission across cycles declares itself, and the revision note is effectively related-work prose about your own previous version — write it with the same precision you demand of external comparisons.
  • A published workshop version of the same idea must be cited and differentiated like anyone else's paper, in the third person, with the delta stated; the overlap question on the form exists to be answered honestly.
  • An earlier paper from your group that shares infrastructure (the same corpus, the same codebase) is a disclosure judgment call: cite it neutrally if published, and avoid constructions that only make sense if the reader knows the two papers share authors.
  • Never split one contribution across two concurrent ARR submissions and cite them at each other — reviewers drawn from the same pool see both, and the 2026 integrity language about thin slicing is aimed at exactly this shape.

Freshness without churn-chasing

Cover everything Anthology-published in your niche through the submission cycle; treat the newest arXiv layer with judgment — cite what genuinely shapes your claims, skip the weekly leaderboard shuffle. A response-phase reviewer pointing at a paper newer than your submission is asking about robustness of your conclusions, not accusing you of omission; answer that question, not an imagined one.

Two vocabularies, one section

Empirical NLP sits between linguistics and machine learning, and reviewers arrive from both directions. A related-work section that cites only the ML lineage of a phenomenon ("hallucination," "faithfulness") while ignoring decades of linguistic work on the underlying construct (presupposition, veridicality, entailment) will draw the one reviewer who knows the older literature — and that review writes itself. The inverse failure exists too. One paragraph acknowledging the other community's framing, with two accurate citations, is cheap insurance and often genuinely improves the paper's construct definitions.

Output format

text
[Axis coverage] <task / dataset / method / evaluation / adjacent — gaps per axis>
[Anthology hygiene] <Findings-vs-main errors, arXiv-vs-published swaps>
[Concurrency handling] <concurrent items + one-line distinctions>
[Citation verification] <resolved N/N; unresolvable entries listed>
[Positioning sentence] <the evaluative contrast, drafted>

© 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-related-work of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

Emnlp Related Work 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 Related Work compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Emnlp Related Work this skillbrycewang-stanford/Awesome-Journal-Skills1.2k—~1.7kAutomated safety check: PassMIT
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Systematic Literature Review Builderbytedance/deer-flow84k2 repos~4.3kAutomated safety check: PassMIT
Paper Research on arXivXiaomiMiMo/MiMo-Code14k—~1.5kAutomated safety check: PassMIT
Literature Review AgentAr9av/PaperOrchestra6791 repos~5.2kAutomated safety check: PassCustom licence
Arxiv MCP Serverblazickjp/arxiv-mcp-server3.2k—~353Automated safety check: PassApache-2.0

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Works with

Categories

Questions about Emnlp Related Work

What does Emnlp Related Work do?

A skill your agent uses when positioning an EMNLP submission inside NLP's fast-moving literature — covering ACL Anthology lineages and arXiv concurrency, citing Findings and workshop papers…. Emnlp Related Work is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when positioning an EMNLP submission inside NLP's fast-moving literature — covering ACL Anthology lineages and arXiv concurrency, citing Findings and workshop papers correctly, tracing dataset and benchmark ancestry, verifying every reference resolves under the hallucinated-citation policy, and keeping self-citation double-blind.

When should I use Emnlp Related Work?

Emnlp Related Work fits situations like: positioning an EMNLP submission inside NLPs fast-moving literature — covering ACL Anthology lineages and arXiv concurrency; citing Findings and workshop papers correctly; tracing dataset and benchmark ancestry; verifying every reference resolves under the hallucinated-citation policy.

How do I install Emnlp Related Work in Claude Code?

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

How do I install Emnlp Related Work in Codex?

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

Can I use Emnlp Related Work 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-related-work -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-related-work, .gemini/skills/emnlp-related-work, .github/skills/emnlp-related-work and .opencode/skills/emnlp-related-work in your project.

What does Emnlp Related Work need to run?

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

Does Emnlp Related Work 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 Related Work 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 Related Work use?

Emnlp Related Work 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 Related Work use?

About 1.7k tokens (SKILL.md is roughly 7k 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 Related Work?

Skills that share tags, products or a category with Emnlp Related Work: Literature Review (neflibata-feng/MyArxiv-Agent, 126 stars), Systematic Literature Review Builder (bytedance/deer-flow, 84k stars), Paper Research on arXiv (XiaomiMiMo/MiMo-Code, 14k stars) and Literature Review Agent (Ar9av/PaperOrchestra, 679 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Emnlp Related Work?

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.