Hugging Face Tokenizers
Orchestra-Research/AI-Research-SKILLs
Shows how to load, train and use fast Hugging Face tokenizers, with BPE, WordPiece and Unigram models, padding, truncation and alignment tracking.
A skill your agent uses when deciding whether a project belongs at EMNLP, weighing its empirical-NLP identity against ACL, NAACL, EACL, AACL, CoNLL, LREC-COLING, TACL, and ML venues, matching the…
Install Emnlp Topic Selection
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill emnlp-topic-selection -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills emnlp-topic-selection --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-topic-selection .claude/skills/emnlp-topic-selection && 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-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EMNLP-Skills/skills/emnlp-topic-selection into .claude/skills/emnlp-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "emnlp-topic-selection", 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-topic-selectionType 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-topic-selection -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills emnlp-topic-selection --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-topic-selection .agents/skills/emnlp-topic-selection && 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-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EMNLP-Skills/skills/emnlp-topic-selection into .agents/skills/emnlp-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "emnlp-topic-selection", 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-topic-selection -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills emnlp-topic-selection --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-topic-selection .cursor/skills/emnlp-topic-selection && 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-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EMNLP-Skills/skills/emnlp-topic-selection into .cursor/skills/emnlp-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "emnlp-topic-selection", 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-topic-selection--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-topic-selection -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills emnlp-topic-selection --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-topic-selection .gemini/skills/emnlp-topic-selection && 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-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EMNLP-Skills/skills/emnlp-topic-selection into .gemini/skills/emnlp-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "emnlp-topic-selection", 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-topic-selectionInstalls 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-topic-selection -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-topic-selection .github/skills/emnlp-topic-selection && 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-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EMNLP-Skills/skills/emnlp-topic-selection into .github/skills/emnlp-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "emnlp-topic-selection", 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-topic-selection -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-topic-selection --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-topic-selection .opencode/skills/emnlp-topic-selection && 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-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EMNLP-Skills/skills/emnlp-topic-selection into .opencode/skills/emnlp-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "emnlp-topic-selection", 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-topic-selection- GitHub stars
- 1.2k
- Token cost
- ~1.8k tokens
- SKILL.md length
- 827 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when deciding whether a project belongs at EMNLP, weighing its empirical-NLP identity against ACL, NAACL, EACL, AACL, CoNLL, LREC-COLING, TACL, and ML venues, matching the…
- Works in 3 steps: Is there a language phenomenon or task… → Does the evidence plan include analysis,… → Would the paper survive its own…
- Deciding whether a project belongs at EMNLP
- SKILL.md covers The empirical-identity test, Welcomed contribution types, Routing among the siblings and One project, three honest…, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Emnlp Topic Selection is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deciding whether a project belongs at EMNLP, weighing its empirical-NLP identity against ACL, NAACL, EACL, AACL, CoNLL, LREC-COLING, TACL, and ML venues, matching the contribution to EMNLP's welcomed paper types including negative results and reproductions, and choosing between main conference, Findings, industry, and demo pipelines.
Its SKILL.md is about 1.8k 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 AI & LLM Engineering, covering Natural language processing. 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
- Deciding whether a project belongs at EMNLP
- Weighing its empirical-NLP identity against ACL
- Matching the contribution to EMNLPs welcomed paper types including negative results and reproductions
- Choosing between main conference
Example prompts
- “/emnlp-topic-selection”
Workflow steps
3 steps, taken from the first numbered list in SKILL.md.
- Is there a language phenomenon or task behavior at the center? EMNLP papers are
- Does the evidence plan include analysis, not just scores? The venue's reviewing
- Would the paper survive its own evaluation section being adversarial? Benchmarks
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 Topic Selection loads about 1.8k tokens when it runs. Until then it costs about 92 tokens; SKILL.md has 827 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). 827 words, ~1,836 tokens.
Download SKILL.mdSave it as
.claude/skills/emnlp-topic-selection/SKILL.md (or your agent's skills folder).- name
- emnlp-topic-selection
- description
- Use when deciding whether a project belongs at EMNLP, weighing its empirical-NLP identity against ACL, NAACL, EACL, AACL, CoNLL, LREC-COLING, TACL, and ML venues, matching the contribution to EMNLP's welcomed paper types including negative results and reproductions, and choosing between main conference, Findings, industry, and demo pipelines.
EMNLP Topic Selection
Use this before writing. EMNLP's name is its thesis: empirical methods. The venue's center of gravity is work whose contribution is established by measurement — new evaluations, datasets, analyses of model behavior, and methods whose value is demonstrated rather than argued. Sibling venues share the reviewer pool through ARR, so the routing question is less "will it be reviewed differently" than "which program will want it."
The empirical-identity test
Ask three questions of the project:
- Is there a language phenomenon or task behavior at the center? EMNLP papers are about something a system does with language, not about an architecture that happens to be evaluated on text.
- Does the evidence plan include analysis, not just scores? The venue's reviewing culture expects error analysis, ablations that isolate the mechanism, and claims scoped to the languages and domains actually tested.
- Would the paper survive its own evaluation section being adversarial? Benchmarks chosen to flatter, contaminated test sets, and single-run comparisons are the fingerprints reviewers here are trained to find.
Three yeses: EMNLP-shaped. A no on (1) suggests an ML venue; a no on (2) or (3) is a project problem no venue choice fixes.
Welcomed contribution types
The 2026 call is unusually explicit that a diverse program is a goal. These genres are all first-class, and mislabeling yours is a self-inflicted wound:
| Genre | What convinces here | Common self-sabotage |
|---|---|---|
| Empirical method | Controlled comparisons + mechanism ablations | Selling a small delta as a paradigm |
| Resource / dataset | Documented collection, agreement stats, human ceiling | Size without characterization |
| Negative result | Rigorous design that would have detected the effect | Underpowered study dressed as disproof |
| Reproduction | Faithful reimplementation, divergences explained | Gotcha framing instead of diagnosis |
| Survey | Organizing insight the field lacked | Annotated bibliography |
| Position | Falsifiable stakes, engagement with counterevidence | Opinion without an empirical anchor |
| Linguistic insight | Existing methods revealing something about language | Insight about the model misread as insight about language |
Routing among the siblings
- ACL / NAACL / EACL / AACL — scope overlap with EMNLP is near-total; in the ARR era the practical differences are calendar, location, and program-committee taste. If your paper's strength is analytical and evaluative rather than conceptual framing, EMNLP's program has historically been the natural home.
- CoNLL — computational learning of language, cognitive and linguistic modeling angle; a better fit when the object of study is the learning process itself.
- LREC-COLING lineage — resource-centric work whose contribution is the artifact and its documentation more than a finding built on it.
- TACL / Computational Linguistics — journal pacing, revision cycles, no page fight; right when the work needs 20 pages or authoritative permanence more than a fall deadline.
- ML venues (NeurIPS/ICML/ICLR) — the contribution is the learning method and language is one testbed among several; EMNLP reviewers will ask "what did we learn about language?" and dislike silence.
- EMNLP Industry Track — deployed systems under production constraints (latency, cost, drift, no clean test sets); direct submission, its own deadline (June 16, 2026), its own reviewers. Demo track — working systems worth showing; verify the 2026 demo call before planning on it (dates were unposted at check time).
Show full SKILL.md (321 more words)Show less
One project, three honest pitches
Project: retrieval-augmented QA fails on temporally-shifted questions.
EMNLP main pitch: "We characterize *when* and *why* RAG answers decay with temporal
shift (4 datasets, 3 retrieval stacks), and show index-refresh
policies fix only the retrieval half of the failure."
ML-venue pitch: "A new time-conditioned retrieval scoring function with gains on
one QA benchmark." (EMNLP would ask for the analysis above.)
Industry pitch: "Operating a news QA system through 12 months of index drift:
what broke, what monitoring caught it, what it cost."
Pick the pitch whose evidence you actually have — not the venue with the next deadline.Timing as a fit dimension
Venue choice at an ARR conference is also a calendar choice, and the calendar can overrule taste. Questions to settle before committing the project to EMNLP:
- Will the evidence be complete by the aligned ARR deadline (May 25 in 2026)? A dataset whose annotation finishes in June fits the next cycle and therefore a different conference — submitting the incomplete version to make the EMNLP date is how sound projects earn reject-shaped reviews.
- Does anyone on the paper need the proceedings date rather than the acceptance date? Findings and main papers publish on the same conference schedule; a graduation or visa timeline that needs certainty in early summer is arguing for an earlier cycle, whatever venue it feeds.
- Is the topic volatile? In fast-moving subfields (LLM evaluation especially), a
paper reviewed in June and presented in late October will be read against an
October literature; build the response-window and camera-ready plan expecting
"how does this relate to
<July preprint>?" questions.
Findings in the fit calculus
Committing to EMNLP offers two acceptance surfaces: the main conference and Findings of EMNLP — real, Anthology-indexed peer-reviewed publication, historically without a guaranteed talk or poster. If the project's purpose requires a stage (job market, community-building around a resource), plan for the possibility that a sound-but-not- exciting verdict lands in Findings, and decide in advance whether that outcome ends the project or triggers a revise-and-resubmit instead.
The re-route that saves a cycle
If the empirical-identity test half-fails — real phenomenon, thin analysis — the cheapest repair is usually not a venue change but an evidence change: adding the error taxonomy, the contamination audit, and one deliberate stress test often converts an "ML paper evaluated on text" into an EMNLP paper in four weeks of work. Change venues when the audience is wrong; change the evidence when the audience is right but the paper isn't speaking its language yet.
Output format
[Fit verdict] EMNLP-shaped / sibling-better / ML-venue-better / journal-better
[Genre] method / resource / negative / reproduction / survey / position / insight
[Empirical identity] <the phenomenon or behavior at the center, one sentence>
[Weakest fit signal] <which of the three test questions is shakiest>
[Pipeline] main via ARR / industry / demo / next-cycle target© 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-topic-selection of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Emnlp Topic Selection 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 Topic Selection this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.8k | Automated safety check: Pass | MIT | |
| Hugging Face TokenizersOrchestra-Research/AI-Research-SKILLs | 13k | 6 repos | ~3.4k | Automated safety check: Pass | MIT | |
| OpenMed Model Card Writermaziyarpanahi/openmed | 5.5k | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| Gptqmodel Tokenizer NormalizationModelCloud/GPTQModel | 1.3k | — | ~1.1k | Automated safety check: Pass | Custom licence | |
| Andrej KarpathyK-Dense-AI/mimeo | 282 | — | ~1.9k | Automated safety check: Pass | MIT | |
| Comparetaishi-i/awesome-japanese-nlp-resources | 1k | — | ~4.1k | Automated safety check: Notes | CC0-1.0 |
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Questions about Emnlp Topic Selection
What does Emnlp Topic Selection do?
A skill your agent uses when deciding whether a project belongs at EMNLP, weighing its empirical-NLP identity against ACL, NAACL, EACL, AACL, CoNLL, LREC-COLING, TACL, and ML venues, matching the…. Emnlp Topic Selection is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deciding whether a project belongs at EMNLP, weighing its empirical-NLP identity against ACL, NAACL, EACL, AACL, CoNLL, LREC-COLING, TACL, and ML venues, matching the contribution to EMNLP's welcomed paper types including negative results and reproductions, and choosing between main conference, Findings, industry, and demo pipelines.
When should I use Emnlp Topic Selection?
Emnlp Topic Selection fits situations like: deciding whether a project belongs at EMNLP; weighing its empirical-NLP identity against ACL; matching the contribution to EMNLPs welcomed paper types including negative results and reproductions; choosing between main conference.
How do I install Emnlp Topic Selection in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill emnlp-topic-selection -a claude-code`. Or copy the skill folder (EMNLP-Skills/skills/emnlp-topic-selection in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/emnlp-topic-selection in your project. Claude Code loads it when a task matches its description.
How do I install Emnlp Topic Selection in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill emnlp-topic-selection -a codex`. Or copy the skill folder (EMNLP-Skills/skills/emnlp-topic-selection in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/emnlp-topic-selection in your project. Codex loads it when a task matches its description.
Can I use Emnlp Topic Selection 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-topic-selection -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-topic-selection, .gemini/skills/emnlp-topic-selection, .github/skills/emnlp-topic-selection and .opencode/skills/emnlp-topic-selection in your project.
What does Emnlp Topic Selection need to run?
SKILL.md names no scripts, command-line tools or credentials: Emnlp Topic Selection is instructions for the agent only.
Does Emnlp Topic Selection 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 Topic Selection 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 Topic Selection use?
Emnlp Topic Selection 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 Topic Selection use?
About 1.8k tokens (SKILL.md is roughly 7.3k 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 Topic Selection?
Skills that share tags, products or a category with Emnlp Topic Selection: Hugging Face Tokenizers (Orchestra-Research/AI-Research-SKILLs, 13k stars), OpenMed Model Card Writer (maziyarpanahi/openmed, 5.5k stars), Gptqmodel Tokenizer Normalization (ModelCloud/GPTQModel, 1.3k stars) and Andrej Karpathy (K-Dense-AI/mimeo, 282 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Emnlp Topic Selection?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,228 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.