icdt-reproducibility
Use when strengthening the verifiability of an ICDT (International Conference on Database Theory) paper — the theory-venue analogue of reproducibility — covering complete and self-contained proofs, exact models and assumptions, claim-to-proof mapping, matching upper and lower bounds, consistency between the LIPIcs paper and the arXiv full version, and honest labeling of what is proved versus conjectured.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icdt-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.
# ICDT Reproducibility At a theory venue, "reproducibility" means **verifiability**: a competent reader, given your paper, can reconstruct every proof and independently confirm every theorem. There is no dataset to re-run and no benchmark to re-execute — the standard is that nothing is asserted without a checkable argument, and nothing in the published record contradicts the full version. Use this
What does the icdt-reproducibility skill do?
Use when strengthening the verifiability of an ICDT (International Conference on Database Theory) paper — the theory-venue analogue of reproducibility — covering complete and self-contained proofs, exact models and assumptions, claim-to-proof mapping, matching upper and lower bounds, consistency between the LIPIcs paper and the arXiv full version, and honest labeling of what is proved versus conjectured.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icdt-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.