ipsn-experiments
Use when designing or auditing IPSN-lineage evaluations, covering real testbeds and deployments, ground-truth instrumentation, energy/latency/footprint measurement on real hardware, on-device/TinyML profiling, estimation-theoretic baselines and bounds, and matching evidence to the shape of each sensing claim across the IP and SPOTS tracks.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ipsn-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.
# IPSN Experiments Use this before submission when the evaluation is not yet locked. IPSN reviewers are sensor-systems and information-processing specialists; the evaluation is where a good idea is won or lost. The organizing principle is **evidence measured on real hardware against real ground truth** — the evaluation must test the sensing claim the paper actually makes, on platforms and baselines a skeptic would accept. ## Evaluation audit - **Measure on real hardware, not just simulation.** Simulation can motivate or scale-test, but a sensing claim needs real sensors: an estimator run on real traces, a pipeline profiled on the actual MCU, a deployment in a real environment. "Simulation only" is IPSN's classic reject. - **Instrument ground truth.** Localization needs surveyed positions; detection needs hand-labeled events; a physical estimate needs a co-located reference instrument. Report the ground truth's own error — perfect ground truth is a red flag. - **Measure energy, latency, and footprint on the platform.** Report joules/µJ per operation from an instrumented power rail (name the shunt/instrument/sampling rate), end-to-end latency on the real SoC at a stated clock, and RA
- Evaluation audit
- Claim-to-evidence design table
- On-device / TinyML measurement floor
- Ground-truth and calibration floor
- Deployment reporting floor
- Vignette: evaluating a localization estimator (IP track)
- Output format
What does the ipsn-experiments skill do?
Use when designing or auditing IPSN-lineage evaluations, covering real testbeds and deployments, ground-truth instrumentation, energy/latency/footprint measurement on real hardware, on-device/TinyML profiling, estimation-theoretic baselines and bounds, and matching evidence to the shape of each sensing claim across the IP and SPOTS tracks.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ipsn-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 984 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.