icdt-review-process
Use when reasoning about how an ICDT (International Conference on Database Theory) submission is evaluated, covering the two-submission-cycle model, the first-cycle revision (Accept / Revise / Reject) decision, anonymous review since 2024, the cross-cycle resubmission restriction, proof-correctness scrutiny by a database-theory PC, and how ICDT's process differs from PODS and from the co-located EDBT systems track.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icdt-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.
# ICDT Review Process Model the pipeline before interpreting any single review. ICDT's process is built around **two submission cycles per year** and a **first-cycle revision option**, and the reviewing bar is **proof correctness**: the referees are database theorists who will check the argument, not just weigh the idea. The most consequential mental shift for authors arriving from a systems venue
What does the icdt-review-process skill do?
Use when reasoning about how an ICDT (International Conference on Database Theory) submission is evaluated, covering the two-submission-cycle model, the first-cycle revision (Accept / Revise / Reject) decision, anonymous review since 2024, the cross-cycle resubmission restriction, proof-correctness scrutiny by a database-theory PC, and how ICDT's process differs from PODS and from the co-located EDBT systems track.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icdt-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.