Agent skill · Data & Analytics

emnlp-supplementary

Use when dividing an EMNLP paper between content pages, uncounted Limitations and ethics sections, appendices, and ACL Rolling Review supplementary uploads — deciding what reviewers are owed in the body, what belongs in appendix prompts-and-tables territory, and what ships as anonymous code and data archives.

brycew6m878★ · +32/wk · 1 repos on radarProfile →
claude-codeMIT
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill emnlp-supplementary --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 7 KB
Bundled scripts: none
Path: EMNLP-Skills/skills/emnlp-supplementary/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 909 · +31 this week
Language: Stata
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# EMNLP Supplementary Use this when the paper is being carved into layers. Under ARR, an EMNLP submission is a stack of surfaces with different review guarantees: content pages that reviewers must read, uncounted mandatory sections they will read, appendices they may read, and supplementary uploads they are explicitly not obliged to open. Misplacing one artifact across these boundaries is a quiet way to lose a review. ## The four surfaces and their contracts | Surface | Counted? | Review contract | Belongs here | |---|---|---|---| | Content pages (8 long / 4 short) | Yes | Must-read | Claims, method, headline results, error analysis core | | Limitations (+ optional ethics) | No | Read and audited | Real boundaries, risks, cultural scope of annotations | | Appendices | No | Discretionary | Verbatim prompts, full result grids, guidelines, proofs of detail | | Supplementary upload | No | Explicitly optional | Code, data samples, model outputs, extended logs | The design rule: **a reviewer who stops at the Limitations section must already be convinced.** Anything decision-critical placed lower in the stack is a gamble on reviewer generosity that the venue's own rules tell you not to ma

What's inside
Steps it walks through
  1. The four surfaces and their contracts
  2. What EMNLP appendices are actually for
  3. Anonymity holds at every layer
  4. Splitting under the cap: a worked division
  5. What does not belong in an appendix
  6. Size, format, and cycle volatility
  7. Short papers: the compressed stack
  8. Output format
Commands it runs
Sweep the supplementary archive before upload
unzip -l supp.zip | grep -Ei '\.git/|\.ipynb_checkpoints|DS_Store'
unzip -p supp.zip '*.py' '*.md' '*.json' 2>/dev/null | \
grep -nEi 'github\.com/|/home/[a-z]+|@[a-z]+\.(edu|com)|wandb\.ai/[a-z]' | head
Notebook metadata carries usernames; strip outputs and metadata before packaging
More from Awesome-Journal-Skills
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About this skill
What does the emnlp-supplementary skill do?

Use when dividing an EMNLP paper between content pages, uncounted Limitations and ethics sections, appendices, and ACL Rolling Review supplementary uploads — deciding what reviewers are owed in the body, what belongs in appendix prompts-and-tables territory, and what ships as anonymous code and data archives.

How do I install it?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill emnlp-supplementary --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going