sensys-experiments
Use when designing or auditing a SenSys evaluation — energy and low-power measurement with a named instrument, real-testbed and deployment realism, honest sensor ground truth, on-device latency and memory, and same-hardware baselines, so the evidence meets SenSys's built-and-measured bar rather than a simulation or offline-benchmark one.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-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.
# SenSys Experiments At SenSys the evaluation *is* the contribution's proof. A mechanism is only as strong as the measurements that show it behaving on real hardware, under real energy budgets, against honest ground truth. This skill audits an evaluation for the failure modes SenSys reviewers flag first: unmeasured energy, simulation standing in for deployment, and accuracy scored against an unsta
What does the sensys-experiments skill do?
Use when designing or auditing a SenSys evaluation — energy and low-power measurement with a named instrument, real-testbed and deployment realism, honest sensor ground truth, on-device latency and memory, and same-hardware baselines, so the evidence meets SenSys's built-and-measured bar rather than a simulation or offline-benchmark one.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-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.