rss-experiments
Use when designing or auditing the experimental campaign for an RSS (Robotics: Science and Systems) paper — hypothesis-shaped robot experiments, trial protocols and per-condition counts, mechanism-isolating ablations, simulation-versus-hardware evidence splits, and failure attribution that supports a scientific claim rather than a demo reel.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill rss-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.
# RSS Experiments Design experiments that *test the paper's claim*, not experiments that showcase the system. At RSS the evaluation section is where the scientific claim either becomes falsifiable or is exposed as marketing. ## Start from the claim, derive the conditions Write the claim, then derive what evidence its logical form demands: | Claim form | Evidence the form demands | |---|---| | "X c
What does the rss-experiments skill do?
Use when designing or auditing the experimental campaign for an RSS (Robotics: Science and Systems) paper — hypothesis-shaped robot experiments, trial protocols and per-condition counts, mechanism-isolating ablations, simulation-versus-hardware evidence splits, and failure attribution that supports a scientific claim rather than a demo reel.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill rss-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.