Peer Review
K-Dense-AI/claude-scientific-writer
Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.
A skill your agent uses when strengthening ACM FAccT transparency and reproducibility — releasing code, data, and analysis for quantitative audits; documenting datasets and models with datasheets…
Install Facct Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill facct-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills facct-reproducibility --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-reproducibility .claude/skills/facct-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAccT-Skills/skills/facct-reproducibility into .claude/skills/facct-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "facct-reproducibility", 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-reproducibilityType 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-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills facct-reproducibility --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-reproducibility .agents/skills/facct-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAccT-Skills/skills/facct-reproducibility into .agents/skills/facct-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "facct-reproducibility", 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-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills facct-reproducibility --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-reproducibility .cursor/skills/facct-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAccT-Skills/skills/facct-reproducibility into .cursor/skills/facct-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "facct-reproducibility", 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-reproducibility--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-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills facct-reproducibility --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-reproducibility .gemini/skills/facct-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAccT-Skills/skills/facct-reproducibility into .gemini/skills/facct-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "facct-reproducibility", 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-reproducibilityInstalls 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-reproducibility -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-reproducibility .github/skills/facct-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAccT-Skills/skills/facct-reproducibility into .github/skills/facct-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "facct-reproducibility", 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-reproducibility -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-reproducibility --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-reproducibility .opencode/skills/facct-reproducibility && 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-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAccT-Skills/skills/facct-reproducibility into .opencode/skills/facct-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "facct-reproducibility", 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-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.6k tokens
- SKILL.md length
- 630 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when strengthening ACM FAccT transparency and reproducibility — releasing code, data, and analysis for quantitative audits; documenting datasets and models with datasheets…
- Strengthening ACM FAccT transparency and reproducibility — releasing code
- SKILL.md covers Transparency map, Documentation-and-availability…, Provenance pinning and Degrees of reproducibility…, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Analysis for quantitative audits
What it does
Facct Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening ACM FAccT transparency and reproducibility — releasing code, data, and analysis for quantitative audits; documenting datasets and models with datasheets, model cards, and data statements; making qualitative and participatory work auditable without breaking confidentiality; pinning provenance for scraped and model-generated data; and keeping the paper, the supplementary material, and any released artifact consistent.
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 Research & Science, covering Reproducible research and 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
- Strengthening ACM FAccT transparency and reproducibility — releasing code
- Analysis for quantitative audits
- Documenting datasets and models with datasheets
- Data statements
Example prompts
- “/facct-reproducibility”
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 Reproducibility loads about 1.6k tokens when it runs. Until then it costs about 116 tokens; SKILL.md has 630 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). 630 words, ~1,582 tokens.
Download SKILL.mdSave it as
.claude/skills/facct-reproducibility/SKILL.md (or your agent's skills folder).- name
- facct-reproducibility
- description
- Use when strengthening ACM FAccT transparency and reproducibility — releasing code, data, and analysis for quantitative audits; documenting datasets and models with datasheets, model cards, and data statements; making qualitative and participatory work auditable without breaking confidentiality; pinning provenance for scraped and model-generated data; and keeping the paper, the supplementary material, and any released artifact consistent.
FAccT Reproducibility
Use this before submission and again before camera-ready. At FAccT, transparency is not only the subject of the field — it is a norm the community holds its own papers to. But FAccT reproducibility is broader than "does the code run": it spans releasing and documenting the data and models behind an audit, making a qualitative study auditable without exposing participants, and being honest where confidentiality or proprietary access genuinely bars release. The goal is that a competent reader could trace how you got from evidence to conclusion — and judge whether the harm you claim is real.
Transparency map
- Map each finding to a verifiable location — a paper section, a table generated from released analysis, a codebook, or a documented case record.
- For quantitative audits: release the analysis code, the dataset (or documented access), the exact metrics and subgroup definitions, and the seeds/versions — enough that a reader could re-run the disaggregation and reach your gaps.
- For qualitative/participatory work: release what can be shared safely — the interview protocol, the codebook, aggregate coded results, consent materials — and state clearly what cannot be shared and why (participant confidentiality, community agreement).
- Document datasets and models, not just release them. A datasheet for a dataset, a model card for a model, and a data statement for a language corpus are the FAccT-native documentation genres; use them to record provenance, composition, intended use, and known limits.
- Keep the paper and artifact consistent. A disparity in the PDF that no released analysis reproduces is the contradiction reviewers read as carelessness — or worse, as an unfalsifiable harm claim.
Documentation-and-availability audit
| Claim in the paper | Weak availability answer | FAccT-ready answer |
|---|---|---|
| "We audit N deployed systems" | "Data available on request" | Released dataset (or documented access) + analysis code + subgroup definitions |
| "Our dataset is representative" | Raw files with no context | A datasheet: how collected, who is in it, gaps, intended and off-label uses |
| "Our model behaves fairly" | Weights only | A model card: evaluation disaggregated by group, intended use, known failure groups |
| "We interviewed P affected people" | Nothing (privacy cited vaguely) | Protocol + codebook + aggregate results + a clear, specific confidentiality boundary |
| "The LLM produced these outputs" | "We used a chatbot" | Model IDs and dates, prompts, cached raw outputs, sampling settings |
"Available on request" reads as not available; convert every such line into a concrete release, proper documentation, or an explicit, justified exception.
Show full SKILL.md (241 more words)Show less
Provenance pinning
[Scraped/mined data] record source, extraction date, and terms; archive the extracted dataset,
not just the scraper; document deduplication and filtering
[Protected attributes] document how group labels were obtained/inferred and their error
[Models] record exact model identifiers + access dates; cache raw prompts and outputs;
report sampling settings; a live-API-only study re-samples, it does not reproduce
[Qualitative] version the codebook; log coding decisions; keep an audit trail a second
reader could follow
[Consent] keep the consent/ethics record aligned with what you releaseDegrees of reproducibility (state the one you achieved)
- Turnkey: one documented command regenerates each disaggregated table/figure from released data.
- Scripted: analysis scripts exist but need documented manual steps or restricted-data access.
- Documented: for qualitative or confidential work, the protocol, codebook, and aggregate results let a reader audit the reasoning without re-running.
For FAccT, aim turnkey for anything a reviewer could rerun quickly (a fairness-metric recomputation, a plot from released results); confidential interview data or proprietary system access stays documented with the boundary stated. Stating the achieved level honestly beats promising turnkey behavior that fails.
Vignette: a mixed-methods accountability study
Consider a study combining a quantitative audit of a benefits system with interviews of claimants. Its transparency spine: the audit code with pinned data versions and subgroup definitions; the released (or access-documented) audit dataset with a datasheet; the interview protocol, codebook, and aggregate themes; the consent and ethics record; and one honest paragraph on what cannot be shared (claimant identities, the agency's internal data) and why — so the audit is falsifiable and the qualitative reasoning is auditable, without re-harming participants.
Consistency and camera-ready pass
- Before submission: every disparity/finding traces to released or documented evidence; datasheets and model cards drafted; the artifact is anonymized (no author names, institution paths, or identity-revealing repository).
- Before camera-ready: swap any anonymized link for a permanent one, finalize the datasheet/model card, and align the Ethical Considerations and Adverse Impacts statements with what you release.
Output format
[Finding inventory] <finding -> evidence location>
[Availability] concrete release / documented access / vague / missing
[Documentation] <datasheet / model card / data statement present where relevant? yes/no>
[Provenance gaps] <scrape terms / proxy labels / model caching / codebook>
[Reproducibility level] turnkey / scripted / documented, stated honestly
[Paper fixes] <must appear in the PDF>
[Artifact fixes] <additions before upload, kept anonymous>© 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-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Facct Reproducibility 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 Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.6k | Automated safety check: Pass | MIT | |
| Peer ReviewK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~3.1k | Automated safety check: Notes | MIT | |
| CHARLS Paper Reproduction Guidexjtulyc/MedgeClaw | 617 | 1 repos | ~1.8k | Automated safety check: Pass | None | |
| Compute Environment Setupaipoch/open-science | 5.5k | — | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Hugging Face Paper Publisherhuggingface/skills | 11k | 4 repos | ~4.2k | Automated safety check: Pass | Apache-2.0 | |
| Ideer Daily PaperAI45Lab/iDeer | 416 | — | ~2.3k | Automated safety check: Notes | AGPL-3.0 |
Similar skills
CHARLS Paper Reproduction Guide
xjtulyc/MedgeClaw
Guides an agent through reproducing papers built on the CHARLS health and retirement survey, from variable mapping to cognition, depression and isolation scores.
Compute Environment Setup
aipoch/open-science
Prepares setup instructions and a named activation file for a user-managed software environment on an Open-Science SSH or Slurm compute host.
- Official
Hugging Face Paper Publisher
huggingface/skills
Indexes research papers on the Hugging Face Hub from arXiv, links them to models and datasets, claims authorship and generates markdown research articles from templates.
Ideer Daily Paper
AI45Lab/iDeer
Daily paper/repo digest where YOU are the reader. An agent skill from AI45Lab/iDeer.
Figure Style
aipoch/open-science
Publication-grade correctness and legibility rules for final-deliverable scientific figures, not exploratory plots.
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Categories
Questions about Facct Reproducibility
What does Facct Reproducibility do?
A skill your agent uses when strengthening ACM FAccT transparency and reproducibility — releasing code, data, and analysis for quantitative audits; documenting datasets and models with datasheets…. Facct Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening ACM FAccT transparency and reproducibility — releasing code, data, and analysis for quantitative audits; documenting datasets and models with datasheets, model cards, and data statements; making qualitative and participatory work auditable without breaking confidentiality; pinning provenance for scraped and model-generated data; and keeping the paper, the supplementary material, and any released artifact consistent.
When should I use Facct Reproducibility?
Facct Reproducibility fits situations like: strengthening ACM FAccT transparency and reproducibility — releasing code; analysis for quantitative audits; documenting datasets and models with datasheets; data statements.
How do I install Facct Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill facct-reproducibility -a claude-code`. Or copy the skill folder (FAccT-Skills/skills/facct-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/facct-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Facct Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill facct-reproducibility -a codex`. Or copy the skill folder (FAccT-Skills/skills/facct-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/facct-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Facct Reproducibility 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-reproducibility -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-reproducibility, .gemini/skills/facct-reproducibility, .github/skills/facct-reproducibility and .opencode/skills/facct-reproducibility in your project.
What does Facct Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Facct Reproducibility is instructions for the agent only.
Does Facct Reproducibility 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 Reproducibility 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 Reproducibility use?
Facct Reproducibility 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 Reproducibility use?
About 1.6k tokens (SKILL.md is roughly 6.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Facct Reproducibility?
Skills that share tags, products or a category with Facct Reproducibility: Peer Review (K-Dense-AI/claude-scientific-writer, 2.4k stars), CHARLS Paper Reproduction Guide (xjtulyc/MedgeClaw, 617 stars), Compute Environment Setup (aipoch/open-science, 5.5k stars) and Hugging Face Paper Publisher (huggingface/skills, 11k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Facct Reproducibility?
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.