icdt-experiments
Use when deciding what counts as evidence for an ICDT (International Conference on Database Theory) result — matching-bound complexity analysis as the primary evidence, worked examples and counterexamples that establish separations, and, only for papers with an algorithmic contribution, a proportional and honestly-scoped experimental evaluation that does not pretend to be the contribution.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icdt-experiments --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 Experiments At ICDT the primary "evidence" is a **proof**, not a benchmark. This skill is about matching your *form of evidence* to your *claim*: most ICDT papers are purely theoretical and need no experiments at all, while a minority with a genuine algorithmic contribution benefit from a small, honest evaluation. Bolting a systems-style experiment section onto a theorem paper does not rais
What does the icdt-experiments skill do?
Use when deciding what counts as evidence for an ICDT (International Conference on Database Theory) result — matching-bound complexity analysis as the primary evidence, worked examples and counterexamples that establish separations, and, only for papers with an algorithmic contribution, a proportional and honestly-scoped experimental evaluation that does not pretend to be the contribution.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icdt-experiments --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.