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
Acl Artifact Evaluation
by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when packaging code, datasets, prompts, model outputs, or annotation materials for an ACL submission under ACL Rolling Review, covering anonymized supplement archives…
Install Acl Artifact Evaluation
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acl-artifact-evaluation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills acl-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/ACL-Skills/skills/acl-artifact-evaluation .claude/skills/acl-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 "acl-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-artifact-evaluation into .claude/skills/acl-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "acl-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/ACL-Skills/skills/acl-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 acl-artifact-evaluation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills acl-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/ACL-Skills/skills/acl-artifact-evaluation .agents/skills/acl-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 "acl-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-artifact-evaluation into .agents/skills/acl-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "acl-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 acl-artifact-evaluation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills acl-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/ACL-Skills/skills/acl-artifact-evaluation .cursor/skills/acl-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 "acl-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-artifact-evaluation into .cursor/skills/acl-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "acl-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 ACL-Skills/skills/acl-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 acl-artifact-evaluation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills acl-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/ACL-Skills/skills/acl-artifact-evaluation .gemini/skills/acl-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 "acl-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-artifact-evaluation into .gemini/skills/acl-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "acl-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 acl-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 acl-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/ACL-Skills/skills/acl-artifact-evaluation .github/skills/acl-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 "acl-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-artifact-evaluation into .github/skills/acl-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "acl-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 acl-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 acl-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/ACL-Skills/skills/acl-artifact-evaluation .opencode/skills/acl-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 "acl-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACL-Skills/skills/acl-artifact-evaluation into .opencode/skills/acl-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "acl-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
acl-artifact-evaluation- GitHub stars
- 1.2k
- Token cost
- ~1.5k tokens
- SKILL.md length
- 617 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, datasets, prompts, model outputs, or annotation materials for an ACL submission under ACL Rolling Review, covering anonymized supplement archives…
- Works in 4 steps: The README — it has roughly one minute… → Prompt files and evaluation scripts, for… → Annotation guidelines, for any dataset… → …
- Annotation materials for an ACL submission under ACL Rolling Review
- SKILL.md covers What counts as an artifact here, Submission-time packaging rules, Checklist items your artifact… and What an ACL reviewer opens first, plus 5 more sections
- Calls jupyter
What it does
Acl Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging code, datasets, prompts, model outputs, or annotation materials for an ACL submission under ACL Rolling Review, covering anonymized supplement archives, scientific-artifact items of the Responsible NLP checklist, licensing and intended-use documentation, data statements, and post-acceptance public release.
Its SKILL.md is about 1.5k 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 ACL submission under ACL Rolling Review
- Covering anonymized supplement archives
- Scientific-artifact items of the Responsible NLP checklist
- Licensing and intended-use documentation
Example prompts
- “/acl-artifact-evaluation”
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- The README — it has roughly one minute to orient them.
- Prompt files and evaluation scripts, for any LLM claim: exact prompts,
- Annotation guidelines, for any dataset or human-eval claim: reviewers judge
- A sample of the data itself — quality problems visible in twenty random
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:
jupyter
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 Artifact Evaluation loads about 1.5k tokens when it runs. Until then it costs about 88 tokens; SKILL.md has 617 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). 617 words, ~1,452 tokens.
Download SKILL.mdSave it as
.claude/skills/acl-artifact-evaluation/SKILL.md (or your agent's skills folder).- name
- acl-artifact-evaluation
- description
- Use when packaging code, datasets, prompts, model outputs, or annotation materials for an ACL submission under ACL Rolling Review, covering anonymized supplement archives, scientific-artifact items of the Responsible NLP checklist, licensing and intended-use documentation, data statements, and post-acceptance public release.
ACL Artifact Evaluation
Use this to plan the evidence package around an ACL paper. ACL has no separate artifact-badge track; instead, artifact scrutiny is folded into review through the supplement archive and Section B ("scientific artifacts") of the Responsible NLP checklist, which reviewers cross-check against the PDF.
What counts as an artifact here
- Code: training/inference scripts, evaluation harnesses, prompt templates.
- Data: new corpora, annotations, filtered subsets of existing corpora, test suites, adversarial sets.
- Model outputs: generations, ranked lists, logits used in analysis — often the cheapest way to make an LLM paper checkable without GPUs.
- Human-subject materials: annotation guidelines, interface screenshots, consent text, compensation description.
Submission-time packaging rules
- Supplements upload as .tgz/.zip through the OpenReview form; links to tracked cloud storage are not acceptable, and any linked page must be anonymous.
- Scrub identity everywhere reviewers can look: file paths, git metadata, notebook author fields, license headers, dataset hosting pages, README contact lines.
- Reviewers are not required to open supplements. The paper plus checklist must stand alone; the archive is for verification, not for essential content.
Checklist items your artifact must satisfy
| Responsible NLP item (Section B) | Artifact implication |
|---|---|
| Cited creators + versions of used artifacts | Pin dataset/model versions in the README and bibliography |
| License / terms of use stated | Include the license you release under and those you consumed under |
| Use consistent with intended use | Justify research use of scraped or user-generated data |
| PII and offensive content handled | Describe scanning/anonymization steps actually performed |
| Documentation of domains, languages, demographics | Ship a data statement or datasheet, not just row counts |
| Statistics on splits reported | Train/dev/test sizes in both paper and README |
Checklist answers contradicted by the archive read as misleading information — grounds for desk rejection under ARR policy, and a credibility wound even when not enforced.
What an ACL reviewer opens first
- The README — it has roughly one minute to orient them.
- Prompt files and evaluation scripts, for any LLM claim: exact prompts, decoding parameters, and scoring code are the reproduction spine.
- Annotation guidelines, for any dataset or human-eval claim: reviewers judge whether the labels could possibly mean what the paper says.
- A sample of the data itself — quality problems visible in twenty random examples have sunk otherwise strong resource papers.
Show full SKILL.md (247 more words)Show less
Vignette: packaging a multilingual benchmark submission
A hypothetical paper releases a 7-language reading-comprehension test suite built from news text plus a baseline evaluation of five LLMs.
- Ship per-language provenance: source, license, collection window, and the filtering pipeline as runnable code, since "web text" alone fails checklist item B on documentation.
- Include annotator guidelines, pay, recruitment channel, and agreement statistics; multilingual annotation quality is the first attack surface.
- Provide the exact prompts and outputs for all five models so reviewers can re-score without API keys.
- Keep a versioned, hash-stamped test file so post-publication contamination can be audited later.
Release ladder after acceptance
anonymous supplement -> public repo + dataset page -> archived, versioned release
(review-time) (camera-ready links) (DOI/hub artifact, cited version)Post-acceptance, register the artifact where your community actually looks (model/dataset hubs, a maintained repo), state the license explicitly, and put the citation-of-record (the Anthology entry) in the README.
Anonymization sweep, concretely
Run these before zipping, on a copy:
# authorship trails in code and docs
grep -ri "yourname\|yourlab\|university" . --include="*.py" --include="*.md"
# git history and remotes leak owners
rm -rf .git; # or re-init a fresh repo for the archive copy
# notebook metadata carries usernames and kernel paths
jupyter nbconvert --clear-output --inplace *.ipynb
# absolute paths in configs and logs
grep -r "/home/\|/Users/" . | headThen check the parts tools miss: license headers naming the lab, dataset hosting pages with institutional branding, model cards listing maintainers, and README badges pointing at owner-named CI.
Sizing and format sanity
- Keep the archive lean: strip checkpoints reviewers cannot load anyway, cached datasets, and virtualenvs; describe big assets and provide them at camera-ready instead.
- One top-level README, one environment file, one entry point per claimed result — reviewers grant roughly a minute before giving up.
- Verify the .zip/.tgz opens on a machine that has never seen the project; OpenReview upload limits and accepted fields vary by cycle, so check the live form rather than last cycle's.
Output format
[Artifact role] anonymous supplement / camera-ready release / public benchmark
[Contents] <code/data/prompts/outputs/guidelines>
[Checklist alignment] <Section B items satisfied vs missing>
[Anonymity findings] <paths/metadata/hosting leaks>
[Release plan] <post-acceptance registry, license, versioning>© 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-artifact-evaluation of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Acl 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 |
|---|---|---|---|---|---|---|
| Acl Artifact Evaluation this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.5k | 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 Acl Artifact Evaluation
What does Acl Artifact Evaluation do?
A skill your agent uses when packaging code, datasets, prompts, model outputs, or annotation materials for an ACL submission under ACL Rolling Review, covering anonymized supplement archives…. Acl Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging code, datasets, prompts, model outputs, or annotation materials for an ACL submission under ACL Rolling Review, covering anonymized supplement archives, scientific-artifact items of the Responsible NLP checklist, licensing and intended-use documentation, data statements, and post-acceptance public release.
When should I use Acl Artifact Evaluation?
Acl Artifact Evaluation fits situations like: annotation materials for an ACL submission under ACL Rolling Review; covering anonymized supplement archives; scientific-artifact items of the Responsible NLP checklist; licensing and intended-use documentation.
How do I install Acl Artifact Evaluation in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acl-artifact-evaluation -a claude-code`. Or copy the skill folder (ACL-Skills/skills/acl-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/acl-artifact-evaluation in your project. Claude Code loads it when a task matches its description.
How do I install Acl Artifact Evaluation in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acl-artifact-evaluation -a codex`. Or copy the skill folder (ACL-Skills/skills/acl-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/acl-artifact-evaluation in your project. Codex loads it when a task matches its description.
Can I use Acl 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 acl-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/acl-artifact-evaluation, .gemini/skills/acl-artifact-evaluation, .github/skills/acl-artifact-evaluation and .opencode/skills/acl-artifact-evaluation in your project.
What does Acl Artifact Evaluation need to run?
Going by SKILL.md and its folder, Acl Artifact Evaluation needs the command-line tools its instructions call (jupyter).
Does Acl 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 Acl 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 Acl Artifact Evaluation use?
Acl 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 Acl Artifact Evaluation use?
About 1.5k tokens (SKILL.md is roughly 5.8k 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 Artifact Evaluation?
Skills that share tags, products or a category with Acl 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 Acl Artifact Evaluation?
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.