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.
Agent skill
Eacl Artifact Evaluation
by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when packaging code, data, prompts, model outputs, and annotation materials for an EACL submission, first as an anonymized ACL Rolling Review supplement aligned with the…
Install Eacl Artifact Evaluation
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eacl-artifact-evaluation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills eacl-artifact-evaluation --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/EACL-Skills/skills/eacl-artifact-evaluation .claude/skills/eacl-artifact-evaluation && 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 "eacl-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EACL-Skills/skills/eacl-artifact-evaluation into .claude/skills/eacl-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eacl-artifact-evaluation", 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/EACL-Skills/skills/eacl-artifact-evaluationType 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 eacl-artifact-evaluation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills eacl-artifact-evaluation --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/EACL-Skills/skills/eacl-artifact-evaluation .agents/skills/eacl-artifact-evaluation && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "eacl-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EACL-Skills/skills/eacl-artifact-evaluation into .agents/skills/eacl-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eacl-artifact-evaluation", 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 eacl-artifact-evaluation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills eacl-artifact-evaluation --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/EACL-Skills/skills/eacl-artifact-evaluation .cursor/skills/eacl-artifact-evaluation && 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 "eacl-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EACL-Skills/skills/eacl-artifact-evaluation into .cursor/skills/eacl-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eacl-artifact-evaluation", 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 EACL-Skills/skills/eacl-artifact-evaluation--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 eacl-artifact-evaluation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills eacl-artifact-evaluation --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/EACL-Skills/skills/eacl-artifact-evaluation .gemini/skills/eacl-artifact-evaluation && 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 "eacl-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EACL-Skills/skills/eacl-artifact-evaluation into .gemini/skills/eacl-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eacl-artifact-evaluation", 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 eacl-artifact-evaluationInstalls 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 eacl-artifact-evaluation -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/EACL-Skills/skills/eacl-artifact-evaluation .github/skills/eacl-artifact-evaluation && 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 "eacl-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EACL-Skills/skills/eacl-artifact-evaluation into .github/skills/eacl-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eacl-artifact-evaluation", 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 eacl-artifact-evaluation -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 eacl-artifact-evaluation --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/EACL-Skills/skills/eacl-artifact-evaluation .opencode/skills/eacl-artifact-evaluation && 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 "eacl-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EACL-Skills/skills/eacl-artifact-evaluation into .opencode/skills/eacl-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eacl-artifact-evaluation", 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
eacl-artifact-evaluation- GitHub stars
- 1.2k
- Token cost
- ~936 tokens
- SKILL.md length
- 325 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when packaging code, data, prompts, model outputs, and annotation materials for an EACL submission, first as an anonymized ACL Rolling Review supplement aligned with the…
- Annotation materials for an EACL submission
- SKILL.md covers The two lives of an EACL…, What belongs in an EACL artifact, Anonymized-supplement checklist and Licensing and documentation…, plus 2 more sections
- Calls python3
- First as an anonymized ACL Rolling Review supplement aligned with the Responsible NLP checklist
What it does
Eacl Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging code, data, prompts, model outputs, and annotation materials for an EACL submission, first as an anonymized ACL Rolling Review supplement aligned with the Responsible NLP checklist, then as a public post-acceptance ACL Anthology release, with attention to licensing, dataset documentation, and reproduction instructions.
Its SKILL.md is about 940 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
- Annotation materials for an EACL submission
- First as an anonymized ACL Rolling Review supplement aligned with the Responsible NLP checklist
- Then as a public post-acceptance ACL Anthology release
- With attention to licensing
Example prompts
- “/eacl-artifact-evaluation”
Requirements
- Python 3
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
Shell commands in SKILL.md call:
python3
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
Eacl Artifact Evaluation loads about 936 tokens when it runs. Until then it costs about 91 tokens; SKILL.md has 325 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). 325 words, ~936 tokens.
Download SKILL.mdSave it as
.claude/skills/eacl-artifact-evaluation/SKILL.md (or your agent's skills folder).- name
- eacl-artifact-evaluation
- description
- Use when packaging code, data, prompts, model outputs, and annotation materials for an EACL submission, first as an anonymized ACL Rolling Review supplement aligned with the Responsible NLP checklist, then as a public post-acceptance ACL Anthology release, with attention to licensing, dataset documentation, and reproduction instructions.
EACL Artifact Evaluation
Use this to turn a paper's evidence into artifacts that survive review and become a public release. EACL runs through ACL Rolling Review, so the artifact lives two lives: an anonymized supplement attached at ARR submission, and a public release after commitment acceptance. Both are audited against the Responsible NLP checklist. Reopen the current checklist before packaging.
The two lives of an EACL artifact
| Stage | Form | Must be | Owner |
|---|---|---|---|
| ARR submission | Anonymized .zip/.tgz supplement | Fully de-identified, self-contained | Authors |
| Commitment acceptance | Public repo + Anthology link | Licensed, versioned, reproducible | Authors |
Do not conflate them: the review supplement must contain no author-identifying strings, while the public release must contain exactly the identifying and licensing information the supplement omitted.
What belongs in an EACL artifact
- Code to reproduce the headline tables, with a top-level entry point.
- Data: the dataset or a loader plus a documented path to it; if redistribution is restricted, document access precisely rather than implying release.
- Prompts and decoding settings verbatim for any LLM-based result — these are part of the method, not an afterthought.
- Model outputs retained so scores can be re-computed without re-running expensive models.
- Annotation materials: guidelines, interface, pay information, and inter-annotator agreement.
Anonymized-supplement checklist
[ ] No author names in paths, file headers, LICENSE, or notebook metadata
[ ] Git history stripped or repo re-initialized
[ ] No personal hosting URLs (Drive/Dropbox) that identify authors
[ ] Prompts + decoding params included verbatim
[ ] Model outputs included for re-scoring
[ ] A README that reproduces at least one reported table
[ ] Smoke-checked (see resources/code/README.md)Run the shared smoke checker before upload:
python3 ../../../shared-resources/ml-conference-methods/code/check_repro_package.py /path/to/anonymous-supplementLicensing and documentation for the public release
- Choose a license appropriate to code (e.g. permissive) and data (respecting upstream dataset terms); the paper text should state it.
- Document intended use and known limitations of any released dataset — required by the checklist and expected by the European community's data-governance norms.
- Version the release with a tag that matches the camera-ready, so the Anthology PDF and the repo cannot drift.
Multilingual and lower-resource specifics
- If the artifact covers lower-resourced languages, document provenance and speaker/annotator context carefully; thin documentation of a low-resource dataset is a common EACL reviewer concern.
- Keep language codes and scripts explicit (ISO codes, script variants) so the artifact is usable by others working on those languages.
Output format
[Artifact stage] Anonymized supplement / Public release
[Contents] <code/data/prompts/outputs/annotation coverage>
[Anonymization] <pass/fail with specific leaks>
[Reproduces] <which reported table the README regenerates>
[Licensing + docs] <license, dataset terms, intended-use note>
[Gaps] <what a reviewer could still not reproduce>© 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 EACL-Skills/skills/eacl-artifact-evaluation of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Eacl Artifact Evaluation 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 |
|---|---|---|---|---|---|---|
| Eacl Artifact Evaluation this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~936 | 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 |
Similar skills
OpenMed Model Card Writer
maziyarpanahi/openmed
Fills in a model card for an OpenMed clinical NER or de-identification model from its evaluation reports: intended use, metrics, subgroups and limitations.
Gptqmodel Tokenizer Normalization
ModelCloud/GPTQModel
Diagnose and correct GPT-QModel tokenizer initialization, tokenization normalization, special-token handling, prompt rendering, and chat-template problems.
Andrej Karpathy
K-Dense-AI/mimeo
Applies the mental models and frameworks of Andrej Karpathy (deep learning, former Director of AI at Tesla, founding member of OpenAI, Eureka Labs).
Compare
taishi-i/awesome-japanese-nlp-resources
Compare several Japanese NLP libraries, models, or datasets for a keyword (a specific tool name, or a function/task like '形態素解析') across a handful of criteria chosen for that comparison, rendered as…
Research
taishi-i/awesome-japanese-nlp-resources
Analyze current trends and challenges in Japanese NLP for a topic.
More from brycewang-stanford/Awesome-Journal-Skills
All 2,387 skills in this repoAaag Data Analysis
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when running and reporting the analysis for an Annals of the American Association of Geographers manuscript — spatial statistics and modeling, remote-sensing accuracy, or…
Aaag Literature Positioning
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when positioning an Annals of the American Association of Geographers manuscript in the literature — engaging geographic scholarship across the relevant area and the…
Aaag Rebuttal
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when responding to an Annals of the American Association of Geographers decision letter (major/minor revision) — building a point-by-point response to the subject editor and…
Aaag Research Design
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when defending the research design of an Annals of the American Association of Geographers manuscript — spatial/quantitative analysis and GIScience, remote-sensing and…
Aaag Review Process
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when you need to understand how the Annals of the American Association of Geographers evaluates a manuscript — double-anonymous review routed through a subject editor by…
Aaag Submission
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when running the final pre-submission preflight for the Annals of the American Association of Geographers via ScholarOne Manuscripts — area/article-type selection…
Related
Categories
Questions about Eacl Artifact Evaluation
What does Eacl Artifact Evaluation do?
A skill your agent uses when packaging code, data, prompts, model outputs, and annotation materials for an EACL submission, first as an anonymized ACL Rolling Review supplement aligned with the…. Eacl Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging code, data, prompts, model outputs, and annotation materials for an EACL submission, first as an anonymized ACL Rolling Review supplement aligned with the Responsible NLP checklist, then as a public post-acceptance ACL Anthology release, with attention to licensing, dataset documentation, and reproduction instructions.
When should I use Eacl Artifact Evaluation?
Eacl Artifact Evaluation fits situations like: annotation materials for an EACL submission; first as an anonymized ACL Rolling Review supplement aligned with the Responsible NLP checklist; then as a public post-acceptance ACL Anthology release; with attention to licensing.
How do I install Eacl Artifact Evaluation in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eacl-artifact-evaluation -a claude-code`. Or copy the skill folder (EACL-Skills/skills/eacl-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/eacl-artifact-evaluation in your project. Claude Code loads it when a task matches its description.
How do I install Eacl Artifact Evaluation in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eacl-artifact-evaluation -a codex`. Or copy the skill folder (EACL-Skills/skills/eacl-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/eacl-artifact-evaluation in your project. Codex loads it when a task matches its description.
Can I use Eacl Artifact Evaluation 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 eacl-artifact-evaluation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/eacl-artifact-evaluation, .gemini/skills/eacl-artifact-evaluation, .github/skills/eacl-artifact-evaluation and .opencode/skills/eacl-artifact-evaluation in your project.
What does Eacl Artifact Evaluation need to run?
Going by SKILL.md and its folder, Eacl Artifact Evaluation needs the command-line tools its instructions call (python3). Our summary lists: Python 3.
Does Eacl Artifact Evaluation 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 Eacl Artifact Evaluation 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 Eacl Artifact Evaluation use?
Eacl Artifact Evaluation 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 Eacl Artifact Evaluation use?
About 936 tokens (SKILL.md is roughly 3.7k 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 Eacl Artifact Evaluation?
Skills that share tags, products or a category with Eacl Artifact Evaluation: 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 Eacl Artifact Evaluation?
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.