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Get Started Free →Use when assembling supplementary materials for a CSCW submission — appendices, codebooks, interview guides, survey instruments, analysis code, and data extracts — deciding what accompanies a journal-model paper, anonymized end to end and consistent across R&R rounds.
.claude/skills/brycewang-stanford-cscw-supplementary/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-08 | ✗→✓ | ▲ Improved | 35% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 61% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 49% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 15% | 0% |
| case-23 | ✗→✓ | ▲ Improved | -46% | 0% |
Supplements at CSCW serve a journal-style review: they let reviewers verify the methods section without bloating a paper already policed by the contribution-vs- length standard. Because Revise and Resubmit sends the paper back to the same reviewers, the supplement is also a versioned record — round-two reviewers can and do compare it against round one.
| Material | Main paper | Appendix | Separate supplement file | | --- | --- | --- | --- | | Method summary + key instrument excerpts | ✓ | | | | Full interview guide / survey instrument | | ✓ | ✓ if long | | Codebook (definitions + paraphrased exemplars) | | ✓ | ✓ | | Trace-pipeline ledger and attrition table | summary | ✓ | code as archive | | Analysis scripts | | | ✓ (archive) | | Additional tables/robustness checks | | ✓ | ✓ | | Consent forms / recruitment text | | | ✓ | | Raw transcripts, identifiable data | never | never | never |
Decision rule: anything a reviewer needs to evaluate a claim belongs in the paper or appendix; anything a reviewer needs to trust the method belongs in the supplement; anything participants did not consent to share belongs nowhere.
The supplement is the classic identity leak at this venue, because it aggregates exactly the artifacts teams forget to scrub:
numbers tied to a university; recruitment emails carry names. Redact, and check PDF/Office metadata (author fields, tracked changes, comments).
/home/username/), git history, packagelockfiles with private registry URLs, notebook execution metadata. Export clean archives, never a cloned .git.
reverse-search, URL slugs. For small communities, the combination of quoted text and date can identify the community even with names removed.
locale details.
Mechanical sweep before upload:
bash# strings that must not appear anywhere in the supplement tree grep -ri -l -E "(surname|firstname|university-name|lab-name|grant-no|protocol-no)" supplement/ # metadata in every PDF for f in supplement/**/*.pdf; do exiftool "$f" | grep -iE "author|creator|company"; done # accidental git history or user paths in code archives find supplement/ -name ".git" -o -path "*Users*" -o -path "*home*" | head
Adapt the string list per project; run it on the packed archive, since packing often reintroduces metadata.
(supplement-r2.zip), never overwrite.
..."), the cited file must exist under that name — reviewers check.
silently appearing files read as scope creep or, worse, as identity leaks made under revision pressure.
text[Inventory] every supplement file listed in the paper or response letter? y/n [Consent] each artifact within participants' consent scope? y/n [Sweep] mechanical anonymity sweep run on the packed archive? y/n [Size/format] platform limits confirmed on the live submission system? 待核实 per regime (PCS legacy vs Manuscript Central) — do not assume [Round label] files named by round; prior rounds preserved? y/n
Platform-specific size and format caps were not verifiable at 2026-07-08; check the live Manuscript Central instructions for the rolling pathway before packaging.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-14 | pass→pass | 12,811 | 14,578 | +14% | 1 | 1 | 0% | 1,996 | 2,586 | +30% | 0 | 0 | — |
case-01 | fail→fail | 21,458 | 17,007 | -21% | 1 | 1 | 0% | 2,636 | 2,991 | +13% | 0 | 0 | — |
case-02 | pass→pass | 24,624 | 30,816 | +25% | 1 | 1 | 0% | 3,417 | 5,547 | +62% | 0 | 0 | — |
case-03 | fail→fail | 23,495 | 19,245 | -18% | 1 | 1 | 0% | 2,930 | 3,583 | +22% | 0 | 0 | — |
case-04 | pass→pass | 20,926 | 19,546 | -7% | 1 | 1 | 0% | 1,748 | 3,413 | +95% | 0 | 0 | — |
case-05 | pass→pass | 24,227 | 19,510 | -19% | 1 | 1 | 0% | 2,609 | 3,411 | +31% | 0 | 0 | — |
case-06 | pass→pass | 15,564 | 20,282 | +30% | 1 | 1 | 0% | 2,549 | 3,476 | +36% | 0 | 0 | — |
case-07 | pass→pass | 19,802 | 12,895 | -35% | 1 | 1 | 0% | 2,205 | 2,646 | +20% | 0 | 0 | — |
case-08 | fail→pass | 17,489 | 19,807 | +13% | 1 | 1 | 0% | 2,114 | 2,864 | +35% | 0 | 0 | — |
case-09 | pass→pass | 18,525 | 17,296 | -7% | 1 | 1 | 0% | 1,983 | 2,846 | +44% | 0 | 0 | — |
case-10 | fail→pass | 14,579 | 15,598 | +7% | 1 | 1 | 0% | 1,550 | 2,489 | +61% | 0 | 0 | — |
case-11 | pass→pass | 18,059 | 20,274 | +12% | 1 | 1 | 0% | 2,003 | 3,085 | +54% | 0 | 0 | — |
case-12 | pass→pass | 19,301 | 17,070 | -12% | 1 | 1 | 0% | 2,205 | 2,710 | +23% | 0 | 0 | — |
case-13 | fail→pass | 14,649 | 8,612 | -41% | 1 | 1 | 0% | 1,541 | 2,291 | +49% | 0 | 0 | — |
case-15 | pass→pass | 10,939 | 14,801 | +35% | 1 | 1 | 0% | 1,726 | 2,464 | +43% | 0 | 0 | — |
case-16 | pass→pass | 16,313 | 11,807 | -28% | 1 | 1 | 0% | 1,782 | 2,682 | +51% | 0 | 0 | — |
case-17 | fail→pass | 10,765 | 8,574 | -20% | 1 | 1 | 0% | 1,804 | 2,076 | +15% | 0 | 0 | — |
case-18 | pass→pass | 16,726 | 12,860 | -23% | 1 | 1 | 0% | 1,670 | 2,567 | +54% | 0 | 0 | — |
case-19 | pass→pass | 14,734 | 11,502 | -22% | 1 | 1 | 0% | 1,704 | 1,799 | +6% | 0 | 0 | — |
case-20 | fail→fail | 18,269 | 13,148 | -28% | 1 | 1 | 0% | 2,037 | 2,951 | +45% | 0 | 0 | — |
case-21 | pass→pass | 17,713 | 17,030 | -4% | 1 | 1 | 0% | 2,029 | 2,296 | +13% | 0 | 0 | — |
case-22 | pass→pass | 17,689 | 21,596 | +22% | 1 | 1 | 0% | 2,967 | 3,604 | +21% | 0 | 0 | — |
case-23 | fail→pass | 23,412 | 8,571 | -63% | 1 | 1 | 0% | 2,520 | 1,352 | -46% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +22 percentage points is the difference between those two pass rates over the 23 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.