percom-experiments
Use when designing or auditing IEEE PerCom empirical evaluations, covering real human subjects, leave-one-subject-out / cross-subject evaluation, F1 and event-level metrics on imbalanced activity classes, deployment realism (free-living vs. lab), fair baselines, contamination-aware model ablations, and matching evidence to the shape of each pervasive-computing claim.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill percom-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.
# PerCom Experiments Use this before submission when the evaluation is not yet locked. PerCom reviewers are ubicomp empiricists; the evaluation is where a sensing idea is won or lost, and — because the review is a single round with a bounded rebuttal — **the evaluation must be complete at submission** (you cannot add experiments in the rebuttal). The organizing principle is **evidence proportional to the claim**, tested on **people and conditions a skeptic would accept**. ## Evaluation audit - **Evaluate cross-subject by default.** A recognition claim about *users* needs **leave-one-subject-out** (or leave-one-session-out) results, not a pooled split that lets the same person appear in train and test. Within-subject numbers are a supporting detail, never the headline. - **Use real human subjects,** described by count and relevant characteristics, with the collection protocol stated. Report how many, doing what, wearing/placed where. - **Report the right metric.** Human activity is imbalanced (most of a day is "null"), so **F1 (macro and per-class)**, precision/recall, or **event-level** metrics tell the truth where raw accuracy flatters. Say whether metrics are frame-level or event
- Evaluation audit
- Claim-to-evidence design table
- Contamination- and leakage-aware evaluation
- Human-subjects provenance floor
- Vignette: evaluating a HAR recognizer
- Reporting floor
- Output format
What does the percom-experiments skill do?
Use when designing or auditing IEEE PerCom empirical evaluations, covering real human subjects, leave-one-subject-out / cross-subject evaluation, F1 and event-level metrics on imbalanced activity classes, deployment realism (free-living vs. lab), fair baselines, contamination-aware model ablations, and matching evidence to the shape of each pervasive-computing claim.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill percom-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.