facct-reproducibility
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.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill facct-reproducibility --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 — an
- Transparency map
- Documentation-and-availability audit
- Provenance pinning
- Degrees of reproducibility (state the one you achieved)
- Vignette: a mixed-methods accountability study
- Consistency and camera-ready pass
- Output format
What does the facct-reproducibility skill do?
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.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill facct-reproducibility --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.