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Get Started Free →brycewang-stanford/chi-artifact-evaluation
Use when packaging the artifacts behind an ACM CHI paper — prototypes, study instruments, codebooks, datasets, analysis code — for anonymous review scrutiny and for post-acceptance archival release, in a venue with no formal artifact-evaluation committee doing it for you.
Passed 19 of 22 test cases with the skill, and 14 of 22 without it.
and 4 more in the full report
7 fixed12 passed either way1 still failing2 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
I am preparing the supplementary package for my CHI paper submission, including our system prototype, study questionnaire, and anonymized user study logs. Please evaluate our artifact bundle and provide a report covering five sections: an inventory detailing disposition per item, a claims map audit for the README, cold-demo execution status, anonymity scrub results, and our post-acceptance release plan.
| Checked | Without skill | With skill |
|---|---|---|
| The inventory section assigns one of the three documented disposition statuses to each item. | Missed | Passed |
| The release plan section specifies hosting on a persistent repository with a DOI rather than a lab web page. | Passed | Passed |
The instructions your model reads when the skill loads.
CHI's Papers track posted no separate artifact-evaluation committee or badge track for the cycles checked (2026, 2027; confirm each year — 待核实 as a standing item). That absence cuts both ways: nobody certifies your artifacts, and nobody but you ensures a skeptical reviewer can inspect them. At CHI the "artifact" is rarely just code — it is the prototype, the study instruments, the codebook, the dataset, and the video evidence of the system working. Package for two audiences with opposite needs: the anonymous reviewer with five minutes, and the future researcher with a real use for your materials.
| Artifact | Reviewer's question | Archival value after acceptance |
|---|---|---|
| Prototype / system code | "Does the claimed interaction actually exist?" | Others extend or compare against it |
| Video figure of real use | "Does it work outside the authors' hands?" | Permanent DL evidence (chi-supplementary) |
| Study instruments (guides, questionnaires, tasks) | "Was the study what the paper says?" | Direct reuse in replications |
| Codebook / analysis audit trail | "Do the themes live in the data?" | Methods teaching material |
| Dataset (de-identified) | "Do the numbers re-derive?" | Secondary analysis |
| Design files (3D prints, schematics, figma) | "Could this be rebuilt?" | Fabrication replication |
| Prompts / model configs for AI conditions | "What system did participants face?" | The only record once the API moves |
Inventory first, then decide per artifact: reviewed now, released later, or honestly
withheld with a reason (chi-reproducibility has the data-sharing ladder).
The review-phase archive rides the single September deadline with everything else. Design it so the first five minutes land:
analysis/h1_test.R →
Table 2." Three such lines are worth thirty pages of appendix.requirements.txt, renv.lock) because a reviewer will not debug pip..git, no metadata, no named accounts, anonymized
platform views only, usernames scrubbed from notebook outputs and file paths.test cases passed, vs 14 / 22 without
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