percom-reproducibility
Use when strengthening IEEE PerCom reproducibility and open-data evidence for human-subjects sensing, covering the dataset-availability statement, de-identified datasets with IRB/consent handling, sensing provenance (devices, sampling, labeling), cross-subject reproducibility, honest degrees of reproducibility, and consistency between what the paper says and what the dataset contains.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill percom-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.
# PerCom Reproducibility Use this before submission and again before camera-ready. In pervasive computing, reproducibility turns on **the sensing data**: a PerCom result is only as trustworthy as the dataset behind it, how it was collected, and whether it generalizes across people. The goal is that a competent reader could rebuild your pipeline and reach your conclusions — and, where ethics permit, on your actual data. ## Evidence map - Map each recognition/system claim and reported number to a **verifiable location** — a paper section, a table generated from logged data, or a script in the artifact. - For recognizers, give enough of the features, model, hyperparameters, and **evaluation split** (leave-one-subject-out / leave-one-session-out) that a reader could re-run it. - For datasets, report subjects and their selection, sensors and placement, sampling rates, labeling protocol and inter-annotator agreement, and preprocessing (filtering, windowing, normalization). - Keep the **dataset-availability statement** truthful and specific: what is shared, where it will live after acceptance, and — if something cannot be shared — exactly why (privacy, IRB, consent). - Keep the paper and
- Evidence map
- Dataset-availability statement audit
- Sensing provenance floor
- Degrees of reproducibility (state the one you achieved)
- Vignette: a wearable HAR study
- Consistency and camera-ready pass
- Output format
What does the percom-reproducibility skill do?
Use when strengthening IEEE PerCom reproducibility and open-data evidence for human-subjects sensing, covering the dataset-availability statement, de-identified datasets with IRB/consent handling, sensing provenance (devices, sampling, labeling), cross-subject reproducibility, honest degrees of reproducibility, and consistency between what the paper says and what the dataset contains.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill percom-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.