Individual skill
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.
PerCom-Skills/skills/percom-experiments/SKILL.md
Source commit 932eb23331721b16e967c59f093781be1928b498
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Not checked yet
- Last checked
- Nov 9, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 1,409
- Repository last updated
- Sep 27, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- academic-writing Repository topic
- agent-skills Repository topic
- journal Repository topic
- academic-research Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- awesome-list Repository topic
- claude Repository topic
- claude-code Repository topic
- economics Repository topic
- devops Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Read excerpt
# 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.