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 fits ACL versus EMNLP, NAACL, EACL, TACL, Computational Linguistics, COLM, or an ML venue, covering contribution typing for NLP work…
Install Acl Topic Selection
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acl-topic-selection -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills acl-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/ACL-Skills/skills/acl-topic-selection .claude/skills/acl-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 "acl-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-topic-selection into .claude/skills/acl-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "acl-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/ACL-Skills/skills/acl-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 acl-topic-selection -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills acl-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/ACL-Skills/skills/acl-topic-selection .agents/skills/acl-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 "acl-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-topic-selection into .agents/skills/acl-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "acl-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 acl-topic-selection -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills acl-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/ACL-Skills/skills/acl-topic-selection .cursor/skills/acl-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 "acl-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-topic-selection into .cursor/skills/acl-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "acl-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 ACL-Skills/skills/acl-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 acl-topic-selection -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills acl-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/ACL-Skills/skills/acl-topic-selection .gemini/skills/acl-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 "acl-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-topic-selection into .gemini/skills/acl-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "acl-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 acl-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 acl-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/ACL-Skills/skills/acl-topic-selection .github/skills/acl-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 "acl-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-topic-selection into .github/skills/acl-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "acl-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 acl-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 acl-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/ACL-Skills/skills/acl-topic-selection .opencode/skills/acl-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 "acl-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-topic-selection into .opencode/skills/acl-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "acl-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
acl-topic-selection- GitHub stars
- 1.2k
- Token cost
- ~1.6k tokens
- SKILL.md length
- 816 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 fits ACL versus EMNLP, NAACL, EACL, TACL, Computational Linguistics, COLM, or an ML venue, covering contribution typing for NLP work…
- Works in 5 steps: Write the one-sentence claim naming the… → Name the reviewer community: who at ACL… → Check the theme track: a solid paper… → …
- Deciding whether a project fits ACL versus EMNLP
- SKILL.md covers What ACL rewards, Family routing, Long or short and Fit sharpening before writing, plus 5 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Acl Topic Selection is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deciding whether a project fits ACL versus EMNLP, NAACL, EACL, TACL, Computational Linguistics, COLM, or an ML venue, covering contribution typing for NLP work, long-versus-short paper choice, the annual theme track, Findings-tier expectations, and sharpening the computational-linguistics framing before writing starts.
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.
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 fits ACL versus EMNLP
- Computational Linguistics
- Covering contribution typing for NLP work
- Long-versus-short paper choice
Example prompts
- “/acl-topic-selection”
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Write the one-sentence claim naming the linguistic object: task,
- Name the reviewer community: who at ACL wants this answer? If the honest
- Check the theme track: a solid paper matching the year's theme gains a
- Stress-test the Findings scenario: would a Findings acceptance satisfy
- Verify novelty against the last two *ACL rounds specifically
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
Acl Topic Selection loads about 1.6k tokens when it runs. Until then it costs about 87 tokens; SKILL.md has 816 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). 816 words, ~1,631 tokens.
Download SKILL.mdSave it as
.claude/skills/acl-topic-selection/SKILL.md (or your agent's skills folder).- name
- acl-topic-selection
- description
- Use when deciding whether a project fits ACL versus EMNLP, NAACL, EACL, TACL, Computational Linguistics, COLM, or an ML venue, covering contribution typing for NLP work, long-versus-short paper choice, the annual theme track, Findings-tier expectations, and sharpening the computational-linguistics framing before writing starts.
ACL Topic Selection
Use this before the first draft. ACL is the flagship of the *ACL family: broadest scope across computational linguistics and NLP, the most competitive main-program bar, and — under ACL Rolling Review — a venue choice you finalize at commitment time, which gives topic strategy an unusual second chance.
What ACL rewards
- A contribution about language: modeling it, measuring it, resourcing it, or explaining how systems process it — with the linguistic question visible, not incidental.
- Typed contributions reviewers can classify fast: method, resource, evaluation/metric, analysis, theory, or position. Papers that are half method and half unvalidated resource read as neither.
- Evidence proportional to breadth (see
acl-experiments) and an error analysis that says something about language, not just scores. - Work engaging the current field conversation — for ACL 2026, the special theme was explainability of NLP models, with a dedicated Thematic Paper Award; each edition names its own theme.
Family routing
| Signal | Better home |
|---|---|
| Core NLP contribution, broad audience, strongest possible reviews wanted | ACL (or whichever *ACL your ARR package is eligible to commit to) |
| Empirical, engineering-forward NLP; dense experimental papers | EMNLP — historically the empirical sibling, same ARR pipeline |
| Regional relevance, or timing fits its cycle windows | NAACL / EACL / AACL |
| Needs >9 pages, revision-based journal reviewing, no conference clock | TACL (journal, also Anthology-published) |
| Survey-scale or theoretical linguistics depth | Computational Linguistics (journal) |
| LLM-centric work thin on language questions | COLM or an ML venue (NeurIPS/ICML/ICLR) |
| Deployed-system lessons, product constraints | ACL industry track — separate CFP and deadlines |
| Early-stage, student-led | ACL Student Research Workshop |
Because commitment is decoupled, "ACL vs EMNLP" is often not a submission-time decision: submit to ARR when ready, then commit to the conference whose window and bar the finished package fits.
Long or short
- Long (8 pages): a complete arc — method or resource, evaluation, analysis.
- Short (4 pages): one falsifiable point with one decisive experiment; a negative result, a focused analysis, an evaluation flaw demonstrated. Short papers are judged as short papers — reviewers reject compressed long papers but reward genuinely small, sharp claims.
Fit sharpening before writing
- Write the one-sentence claim naming the linguistic object: task, phenomenon, language set, or evaluation practice.
- Name the reviewer community: who at ACL wants this answer? If the honest answer is "ML engineers," reconsider the venue or reframe toward the language question.
- Check the theme track: a solid paper matching the year's theme gains a natural reviewer pool and an award lane.
- Stress-test the Findings scenario: would a Findings acceptance satisfy the project's goals? If not, ask what would push it into the main program — usually analysis depth or evaluation breadth — and plan that now.
- Verify novelty against the last two *ACL rounds specifically
(see
acl-related-work); ACL's most common fit failure is a project scooped between conception and cycle deadline.
Show full SKILL.md (356 more words)Show less
Vignette: routing an LLM evaluation project
A team measures whether chat models track discourse referents across long dialogues. Framed as "LLM long-context benchmark #47," it drifts toward COLM. Framed with the linguistic object first — anaphora resolution under distance, with typologically varied test languages and a coreference-aware error taxonomy — it becomes an ACL analysis paper, and the benchmark becomes a resource contribution with a data statement. Same experiments; the venue fit is decided by which question the paper asks.
Anti-fit signals worth trusting
- The paper's interest evaporates if a specific commercial model updates — a snapshot artifact, not a finding about language or method.
- No error analysis is imaginable because outputs are only scores — the project measured something but cannot yet explain anything.
- The "multilingual" plan is English plus machine-translated test sets with no native-speaker validation — reviewers treat this as English squared.
- The contribution is a wrapper around an API with prompt engineering as the method — workshops and system demos exist for exactly this.
- The dataset section cannot answer license and consent questions — fix
the resource before choosing any venue (see
acl-artifact-evaluation).
Questions that settle borderline calls
- Which existing ACL paper would cite this one first, and in which section — methods, data, or related work? No answer means no audience.
- Does the claim survive being scoped to the tested languages and models? If the honest scoped version sounds trivial, the work is not done.
- Is the evaluation itself a contribution? If yes, consider leading with it — evaluation and analysis papers are a strong current at ACL.
- Could the short-paper version carry the whole point? If yes, submitting long dilutes it across pages reviewers will judge as padding.
Theme-track fine print
- Theme submissions ride the same ARR pipeline and format rules; the theme is a reviewing lane and award category, not a separate venue.
- Fit is judged on whether the paper answers the theme question, not on keyword overlap — retrofitting a theme paragraph onto an unrelated paper is transparent to theme-track reviewers.
- Themes change annually and are announced in each edition's call; never assume last year's theme (or its reviewer pool) carries over.
Output format
[Fit] strong ACL / possible ACL / sibling venue / non-*ACL venue
[Contribution type] method / resource / evaluation / analysis / theory / position
[Format] long / short / industry / SRW / theme-track
[Claim sentence] <one sentence with the linguistic object named>
[Scoop check] <nearest recent work + standing delta>
[Route decision] <submit cycle X, commit target Y, fallback Z>© 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 ACL-Skills/skills/acl-topic-selection of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Acl 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 |
|---|---|---|---|---|---|---|
| Acl Topic Selection this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.6k | Automated safety check: Pass | MIT | |
| Hugging Face TokenizersOrchestra-Research/AI-Research-SKILLs | 13k | 7 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 | |
| Comparetaishi-i/awesome-japanese-nlp-resources | 1k | 1 repos | ~4.1k | Automated safety check: Notes | CC0-1.0 | |
| Researchtaishi-i/awesome-japanese-nlp-resources | 1k | 1 repos | ~3.5k | Automated safety check: Notes | CC0-1.0 |
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Questions about Acl Topic Selection
What does Acl Topic Selection do?
A skill your agent uses when deciding whether a project fits ACL versus EMNLP, NAACL, EACL, TACL, Computational Linguistics, COLM, or an ML venue, covering contribution typing for NLP work…. Acl Topic Selection is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deciding whether a project fits ACL versus EMNLP, NAACL, EACL, TACL, Computational Linguistics, COLM, or an ML venue, covering contribution typing for NLP work, long-versus-short paper choice, the annual theme track, Findings-tier expectations, and sharpening the computational-linguistics framing before writing starts.
When should I use Acl Topic Selection?
Acl Topic Selection fits situations like: deciding whether a project fits ACL versus EMNLP; computational Linguistics; covering contribution typing for NLP work; long-versus-short paper choice.
How do I install Acl Topic Selection in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acl-topic-selection -a claude-code`. Or copy the skill folder (ACL-Skills/skills/acl-topic-selection in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/acl-topic-selection in your project. Claude Code loads it when a task matches its description.
How do I install Acl Topic Selection in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acl-topic-selection -a codex`. Or copy the skill folder (ACL-Skills/skills/acl-topic-selection in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/acl-topic-selection in your project. Codex loads it when a task matches its description.
Can I use Acl 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 acl-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/acl-topic-selection, .gemini/skills/acl-topic-selection, .github/skills/acl-topic-selection and .opencode/skills/acl-topic-selection in your project.
What does Acl Topic Selection need to run?
SKILL.md names no scripts, command-line tools or credentials: Acl Topic Selection is instructions for the agent only.
Does Acl 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 Acl 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 Acl Topic Selection use?
Acl 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 Acl Topic Selection use?
About 1.6k tokens (SKILL.md is roughly 6.5k 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 Acl Topic Selection?
Skills that share tags, products or a category with Acl 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 Compare (taishi-i/awesome-japanese-nlp-resources, 1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Acl Topic Selection?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,219 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.