LLM Benchmarking with lm-evaluation-harness
Orchestra-Research/AI-Research-SKILLs
Runs lm-evaluation-harness to benchmark language models on academic suites such as MMLU, GSM8K and HumanEval, compare models and track training checkpoints.
Agent skill
Facct Artifact Evaluation
by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when preparing the accountability artifacts that accompany an ACM FAccT paper — datasheets for datasets, model cards, data statements, audit and impact-assessment…
Install Facct Artifact Evaluation
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill facct-artifact-evaluation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills facct-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/FAccT-Skills/skills/facct-artifact-evaluation .claude/skills/facct-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 "facct-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAccT-Skills/skills/facct-artifact-evaluation into .claude/skills/facct-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "facct-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/FAccT-Skills/skills/facct-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 facct-artifact-evaluation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills facct-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/FAccT-Skills/skills/facct-artifact-evaluation .agents/skills/facct-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 "facct-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAccT-Skills/skills/facct-artifact-evaluation into .agents/skills/facct-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "facct-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 facct-artifact-evaluation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills facct-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/FAccT-Skills/skills/facct-artifact-evaluation .cursor/skills/facct-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 "facct-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAccT-Skills/skills/facct-artifact-evaluation into .cursor/skills/facct-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "facct-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 FAccT-Skills/skills/facct-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 facct-artifact-evaluation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills facct-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/FAccT-Skills/skills/facct-artifact-evaluation .gemini/skills/facct-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 "facct-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAccT-Skills/skills/facct-artifact-evaluation into .gemini/skills/facct-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "facct-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 facct-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 facct-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/FAccT-Skills/skills/facct-artifact-evaluation .github/skills/facct-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 "facct-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAccT-Skills/skills/facct-artifact-evaluation into .github/skills/facct-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "facct-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 facct-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 facct-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/FAccT-Skills/skills/facct-artifact-evaluation .opencode/skills/facct-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 "facct-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAccT-Skills/skills/facct-artifact-evaluation into .opencode/skills/facct-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "facct-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
facct-artifact-evaluation- GitHub stars
- 1.2k
- Token cost
- ~1.5k tokens
- SKILL.md length
- 605 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when preparing the accountability artifacts that accompany an ACM FAccT paper — datasheets for datasets, model cards, data statements, audit and impact-assessment…
- Preparing the accountability artifacts that accompany an ACM FAccT paper — datasheets for datasets
- SKILL.md covers The FAccT documentation genres…, What a credible documentation…, Released code and data (the… and Anonymized review version vs.…, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Data statements
What it does
Facct Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when preparing the accountability artifacts that accompany an ACM FAccT paper — datasheets for datasets, model cards, data statements, audit and impact-assessment documentation, and released code/data — since FAccT's culture centers documentation and accountability infrastructure rather than a formal ACM artifact-badging track; covers what makes each genre credible, anonymized-review versus public-release versions, and consistency with the paper's harm claims.
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 Model hubs and datasets. 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
- Preparing the accountability artifacts that accompany an ACM FAccT paper — datasheets for datasets
- Data statements
- Audit and impact-assessment documentation
- Covers what makes each genre credible
Example prompts
- “/facct-artifact-evaluation”
Requirements
- Docker
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
Facct Artifact Evaluation loads about 1.5k tokens when it runs. Until then it costs about 124 tokens; SKILL.md has 605 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). 605 words, ~1,490 tokens.
Download SKILL.mdSave it as
.claude/skills/facct-artifact-evaluation/SKILL.md (or your agent's skills folder).- name
- facct-artifact-evaluation
- description
- Use when preparing the accountability artifacts that accompany an ACM FAccT paper — datasheets for datasets, model cards, data statements, audit and impact-assessment documentation, and released code/data — since FAccT's culture centers documentation and accountability infrastructure rather than a formal ACM artifact-badging track; covers what makes each genre credible, anonymized-review versus public-release versions, and consistency with the paper's harm claims.
FAccT Artifact Evaluation
Use this for the material that backs a FAccT paper's transparency and accountability claims. Note the venue difference up front: FAccT does not run the SIGSOFT-style ACM Artifact Review and Badging track that software-engineering venues use, and it does not hand out Available/Functional/ Reusable/Reproduced badges. 待核实: confirm on the current Author Guide whether any optional artifact/reproducibility appendix or badge scheme has been added for your cycle. What FAccT does have is a strong norm of accountability documentation — datasheets, model cards, data statements, audit trails, and impact assessments — plus released code and data. Treat those genres as your artifact and make each one credible on its own.
The FAccT documentation genres (know which your paper needs)
| Genre | What it documents | When your paper needs it |
|---|---|---|
| Datasheet for a dataset | Motivation, composition, collection, preprocessing, uses, distribution, maintenance | You release or rely on a dataset |
| Model card | Intended use, training data, evaluation disaggregated by group, ethical considerations, limits | You release or audit a model |
| Data statement (for language data) | Speaker/annotator demographics, curation rationale, language variety | You build or study a text/NLP corpus |
| Audit / evaluation report | Method, subgroup metrics, thresholds, what was and was not tested | Your contribution is an audit |
| Impact / risk assessment | Foreseeable harms, affected populations, mitigations, residual risk | Deployment or dual-use is plausible |
Pick the genres your claims actually require; a model audit with no model card, or a dataset paper with no datasheet, reads as incomplete to this community.
What a credible documentation artifact contains
[Provenance] where the data/model came from, when, under what terms and consent
[Composition] who/what is in it, who is absent, and the resulting blind spots
[Disaggregation] evaluation broken out by protected/affected subgroup, with uncertainty
[Intended use] what it is for — and an explicit "off-label" / do-not-use list
[Limits & harms] known failure groups and foreseeable adverse impacts, not just accuracy
[Maintenance] who updates it, how issues are reported, how long it persists
[License] a clear license for released code/data so others can lawfully reuse itReleased code and data (the reproducibility half)
- Ship the analysis that turns data into the paper's disaggregated findings, with pinned data versions and seeds, so a reader can re-run the harm claim.
- Deposit released data or a public archive in a persistent location (e.g. a DOI-issuing repository) for the camera-ready; keep it consistent with the datasheet.
- For model-generated or scraped inputs, cache raw outputs and record model IDs, dates, and terms — a study that needs a live API or a since-changed website re-samples rather than reproduces.
Show full SKILL.md (271 more words)Show less
Anonymized review version vs. public release
- At submission: any documentation or code shipped for reviewers must be anonymized — no author names, institution paths, cluster URLs, or identity-revealing repositories, and the Positionality statement stays out entirely (it is not anonymous).
- After acceptance: replace anonymized placeholders with the public, licensed, persistently archived versions the camera-ready cites, and finalize the datasheet/model card so it matches the released artifact exactly.
Consistency with the paper's harm claims
The artifact's job at FAccT is to make the paper's accountability claims checkable. Every disparity, harm, or transparency benefit the paper asserts should be traceable into the documentation or released analysis. A model card whose disaggregated numbers disagree with the paper's table, or an impact assessment that omits the harm a reviewer can foresee, undercuts the paper more than having no artifact at all.
Vignette: an audit paper's artifact set
A paper auditing a commercial classifier ships: a datasheet for the evaluation dataset (how assembled, subgroup composition, consent basis); a model card-style report for the audited system as the authors understand it (intended use, disaggregated error, failure groups); the audit code with pinned data and seeds regenerating each subgroup table; and a short impact assessment naming who is harmed by both the system and by publishing the audit, with mitigations. All anonymized for review, all public and licensed at camera-ready, all consistent with the paper's tables.
Calibration
- FAccT's artifact expectations are documentation- and release-centered, not badge-centered; do not import a Docker-image/badge checklist as if it were the bar.
- Whether any optional artifact appendix, reproducibility checklist, or badge exists is cycle-volatile — confirm on the current Author Guide (待核实).
Output format
[Genres needed] <datasheet / model card / data statement / audit report / impact assessment>
[Artifact role] anonymized review version / public release
[Contents] <provenance / disaggregation / intended-use / limits / license>
[Claim mapping] <paper harm claim -> where in the documentation/analysis it is checkable? yes/no>
[Consistency] <artifact numbers match the paper's tables? yes/no>
[Fixes before upload] <ordered list, kept anonymous for review>© 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 FAccT-Skills/skills/facct-artifact-evaluation of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Facct 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 |
|---|---|---|---|---|---|---|
| Facct Artifact Evaluation this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.5k | Automated safety check: Pass | MIT | |
| LLM Benchmarking with lm-evaluation-harnessOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~3k | Automated safety check: Pass | MIT | |
| SageMaker Serving Image Selectionhuggingface/skills | 11k | 1 repos | ~4.6k | Automated safety check: Pass | Apache-2.0 | |
| Hugging Face Local Model Evalshuggingface/skills | 11k | 2 repos | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| Upload Post Imagehuggingface/blog | 3.5k | — | ~1.1k | Automated safety check: Pass | None | |
| Esmfold2JimLiu/science-skills | 228 | 4 repos | ~2.5k | Automated safety check: Pass | Apache-2.0 |
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Categories
Questions about Facct Artifact Evaluation
What does Facct Artifact Evaluation do?
A skill your agent uses when preparing the accountability artifacts that accompany an ACM FAccT paper — datasheets for datasets, model cards, data statements, audit and impact-assessment…. Facct Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when preparing the accountability artifacts that accompany an ACM FAccT paper — datasheets for datasets, model cards, data statements, audit and impact-assessment documentation, and released code/data — since FAccT's culture centers documentation and accountability infrastructure rather than a formal ACM artifact-badging track; covers what makes each genre credible, anonymized-review versus public-release versions, and consistency with the paper's harm claims.
When should I use Facct Artifact Evaluation?
Facct Artifact Evaluation fits situations like: preparing the accountability artifacts that accompany an ACM FAccT paper — datasheets for datasets; data statements; audit and impact-assessment documentation; covers what makes each genre credible.
How do I install Facct Artifact Evaluation in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill facct-artifact-evaluation -a claude-code`. Or copy the skill folder (FAccT-Skills/skills/facct-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/facct-artifact-evaluation in your project. Claude Code loads it when a task matches its description.
How do I install Facct Artifact Evaluation in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill facct-artifact-evaluation -a codex`. Or copy the skill folder (FAccT-Skills/skills/facct-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/facct-artifact-evaluation in your project. Codex loads it when a task matches its description.
Can I use Facct 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 facct-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/facct-artifact-evaluation, .gemini/skills/facct-artifact-evaluation, .github/skills/facct-artifact-evaluation and .opencode/skills/facct-artifact-evaluation in your project.
What does Facct Artifact Evaluation need to run?
SKILL.md names no scripts, command-line tools or credentials: Facct Artifact Evaluation is instructions for the agent only. Our summary lists: Docker.
Does Facct 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 Facct 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 Facct Artifact Evaluation use?
Facct 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 Facct Artifact Evaluation use?
About 1.5k tokens (SKILL.md is roughly 6k 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 Facct Artifact Evaluation?
Skills that share tags, products or a category with Facct Artifact Evaluation: LLM Benchmarking with lm-evaluation-harness (Orchestra-Research/AI-Research-SKILLs, 13k stars), SageMaker Serving Image Selection (huggingface/skills, 11k stars), Hugging Face Local Model Evals (huggingface/skills, 11k stars) and Upload Post Image (huggingface/blog, 3.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Facct 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.