chi-reproducibility
Use when strengthening research transparency for an ACM CHI paper — protocols, instruments, codebooks, analysis scripts, preregistration, and data availability under human-subjects constraints — so methods survive the ADR-Method screening and others can actually build on the work.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill chi-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.
# CHI Reproducibility Reproducibility at CHI is not "same script, same numbers." Human-subjects research reproduces at the level of *protocol and analysis*: could a competent lab run your study again, and could a skeptic re-derive your findings from your materials? CHI's screening now names "research transparency" explicitly inside the **ADR-Method** assisted desk-reject ground, so opacity is a pre-review rejection risk. The working principle for data: **as open as consent allows, as documented as possible where it does not.** ## Three layers, three different obligations | Layer | What must be true | Typical artifacts | |---|---|---| | Protocol | Another lab could run the study | Task descriptions, scripts read to participants, stimuli, apparatus specs, recruitment text, screening criteria, compensation | | Analysis | A skeptic could re-derive results from your data | Analysis code, codebook + coding decisions, exclusion rules, model specifications, software versions | | Data | Shared where consent permits; described honestly where not | De-identified quantitative data, aggregate tables, transcript excerpts, or a documented reason why not | The protocol layer is the cheapest and th
- Three layers, three different obligations
- Quantitative transparency
- Qualitative transparency
- Data sharing under human-subjects constraints
- The availability statement
- Verification before the claim
- Output format
The availability statement is a claim; test it like one. ls protocol/ instruments/ codebook/ data/ analysis/ # inventory vs statement grep -rEin 'available (upon|on) request' paper/ && echo "WEAK: replace or justify" python3 -m venv /tmp/repro && /tmp/repro/bin/pip install -r analysis/requirements.txt \ grep -rEil 'participant|P[0-9]+_(name|email)' data/ | head # de-identification sweep
What does the chi-reproducibility skill do?
Use when strengthening research transparency for an ACM CHI paper — protocols, instruments, codebooks, analysis scripts, preregistration, and data availability under human-subjects constraints — so methods survive the ADR-Method screening and others can actually build on the work.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill chi-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.