facct-review-process
Use when reasoning about how an ACM FAccT submission is evaluated — mutually-anonymous review by a mixed CS+law+social-science pool matched via author-selected focus areas, Area Chairs, the short factual-correction rebuttal, the new Accept/Revise/Reject decision with a revise-and-resubmit round, and how FAccT's interdisciplinary process differs from a pure-ML conference's single-shot rebuttal.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill facct-review-process --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 Review Process Model the pipeline before interpreting any single review. FAccT's process has two features that surprise authors arriving from a pure-ML venue: the reviewer pool is **interdisciplinary** (a computer scientist, a lawyer, and a social scientist may all be assigned), and — new for the 2026 edition — the decision set is **Accept / Revise / Reject**, where **Revise** is a genuine revise-and-resubmit round, not a soft reject. Your paper is matched to reviewers and **Area Chairs** by the **focus areas** you selected at registration, so those choices shape who reads you as much as your title does. ## Process model - Submission and review run on **OpenReview** (new for 2026) with **mutual anonymity**: authors and reviewers are hidden from each other. - Papers are matched to reviewers and **Area Chairs** by **disciplinary focus area**, so an interdisciplinary paper is typically read by people from more than one field — a strength and a risk (each expects their lane's rigor). - Reviewers weigh **relevance** to the conference and the chosen area, and **quality and clarity** — correctness, depth of exposition (how well you contextualize approach, methodology, perspective,
- Process model
- Reading a decision against the categories
- The interdisciplinary reviewer reality
- Where author leverage actually exists
- Reading a review packet
- Misreadings to avoid
- Output format
What does the facct-review-process skill do?
Use when reasoning about how an ACM FAccT submission is evaluated — mutually-anonymous review by a mixed CS+law+social-science pool matched via author-selected focus areas, Area Chairs, the short factual-correction rebuttal, the new Accept/Revise/Reject decision with a revise-and-resubmit round, and how FAccT's interdisciplinary process differs from a pure-ML conference's single-shot rebuttal.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill facct-review-process --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.