chi-experiments
Use when designing or auditing the studies behind an ACM CHI paper — matching evidence shape to contribution type, powering quantitative experiments, making qualitative work rigorous and auditable, reporting participants and ethics properly, and avoiding the ADR-Data and ADR-Method screening grounds.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill chi-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.
# CHI Experiments and Studies "Experiments" at CHI means human evidence: controlled lab studies, field deployments, interview and diary studies, surveys, log analyses, and mixtures of these. Two of the four assisted desk-reject rubric grounds CHI now screens with — **ADR-Data** (grossly insufficient data for the claims) and **ADR-Method** (grossly insufficient methodological detail or transparency) — are study-design judgments made *before full review*. Evidence design is therefore survival, not polish. ## Match the evidence to the claim, not to habit | Claim shape | Evidence that convinces CHI reviewers | Chronic mismatch seen in reviews | |---|---|---| | "Technique X outperforms Y" | Controlled comparison, counterbalanced, powered, effect sizes | Underpowered n=12 with p-values only | | "Users experience/need Z" | Interviews or diary study to saturation, systematic analysis | Cherry-picked quotes, no analysis method stated | | "System S is usable/useful in practice" | Field deployment with real tasks over time | One-hour lab walkthrough of a demo | | "Population P interacts differently" | Sampling strategy that can reach P, comparative design | Convenience sample of students stan
- Match the evidence to the claim, not to habit
- Quantitative discipline
- Qualitative discipline
- Participants and ethics are results-page material
- Deployment and AI-system studies
- Pre-submission evidence audit
- Output format
What does the chi-experiments skill do?
Use when designing or auditing the studies behind an ACM CHI paper — matching evidence shape to contribution type, powering quantitative experiments, making qualitative work rigorous and auditable, reporting participants and ethics properly, and avoiding the ADR-Data and ADR-Method screening grounds.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill chi-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.