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Get Started Free →Use when targeting ACM SIGMOD International Conference on Management of Data (SIGMOD) or deciding whether a computer-science manuscript fits this venue. Encodes conference fit, framing, evidence bar, submission-cycle checks, rebuttal posture, and desk-reject risks for database flagship.
.claude/skills/brycewang-stanford-acm-sigmod-international-conference-on-management-of-data/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 48% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 86% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 16% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 57% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 55% | 0% |
ACM SIGMOD International Conference on Management of Data (SIGMOD) is a top computer-science conference venue for database systems, query processing, data management, transactions, data lakes, and ML for data systems. It rewards a data-management paper with strong systems or algorithmic contribution and realistic workloads. Treat this skill as a fit / venue-selection / re-framing tool for conference submission strategy, not as a substitute for the current year's CFP, author kit, ethics policy, or submission portal.
Because CS conferences change deadlines, templates, page limits, review workflow, artifact rules, AI-use policy, and rebuttal formats every cycle, always verify the live official instructions before making a submission-ready recommendation. Start from the official source anchor recorded for this venue in ../../resources/conference-roster.md and ../../resources/official-source-map.md.
transactions, indexing, or large-scale data infrastructure with systems evidence.
with its own submission mechanics and ICDE for data-engineering scope.
If the paper misses SIGMOD's bar, compare against international-conference-on-very-large-data-bases / ieee-international-conference-on-data-engineering / acm-symposium-on-theory-of-computing / ieee-symposium-on-foundations-of-computer-science. Re-route based on contribution type, not prestige: theory to a theory venue, systems to a systems venue, application-heavy work to a domain venue, and early ideas to workshops or shorter tracks when the official CFP supports them.
text[Fit] High / Medium / Low (one-line reason) [Target] ACM SIGMOD International Conference on Management of Data (SIGMOD) [Contribution type] algorithm / theory / system / dataset / benchmark / empirical / design / security / other [Main evidence gap] <single most important missing proof, experiment, study, artifact, or policy check> [Official items to re-check] CFP / author kit / deadline / format / anonymity / ethics / AI-use / artifact / rebuttal / camera-ready [Top rejection risk] <venue-specific risk> [Re-route suggestion] <better-matched conference or journal if not a fit>
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 13,865 | 15,572 | +12% | 1 | 1 | 0% | 2,181 | 3,233 | +48% | 0 | 0 | — |
case-02 | fail→pass | 14,823 | 10,617 | -28% | 1 | 1 | 0% | 1,551 | 2,885 | +86% | 0 | 0 | — |
case-03 | fail→pass | 22,247 | 13,547 | -39% | 1 | 1 | 0% | 2,861 | 3,312 | +16% | 0 | 0 | — |
case-04 | pass→pass | 12,951 | 13,109 | +1% | 1 | 1 | 0% | 1,065 | 3,088 | +190% | 0 | 0 | — |
case-05 | pass→pass | 21,105 | 13,370 | -37% | 1 | 1 | 0% | 2,386 | 3,252 | +36% | 0 | 0 | — |
case-06 | pass→pass | 23,074 | 17,874 | -23% | 1 | 1 | 0% | 2,451 | 4,300 | +75% | 0 | 0 | — |
case-07 | pass→pass | 13,896 | 15,386 | +11% | 1 | 1 | 0% | 2,132 | 3,130 | +47% | 0 | 0 | — |
case-08 | pass→pass | 25,149 | 14,876 | -41% | 1 | 1 | 0% | 2,532 | 3,178 | +26% | 0 | 0 | — |
case-09 | fail→pass | 16,311 | 6,725 | -59% | 1 | 1 | 0% | 1,779 | 2,790 | +57% | 0 | 0 | — |
case-10 | fail→pass | 21,887 | 16,163 | -26% | 1 | 1 | 0% | 2,154 | 3,345 | +55% | 0 | 0 | — |
case-11 | pass→pass | 18,826 | 12,960 | -31% | 1 | 1 | 0% | 2,491 | 2,947 | +18% | 0 | 0 | — |
case-12 | pass→fail | 17,913 | 15,568 | -13% | 1 | 1 | 0% | 2,655 | 3,252 | +22% | 0 | 0 | — |
case-13 | pass→pass | 13,194 | 7,829 | -41% | 1 | 1 | 0% | 1,896 | 2,921 | +54% | 0 | 0 | — |
case-14 | fail→fail | 14,653 | 17,670 | +21% | 1 | 1 | 0% | 2,203 | 3,630 | +65% | 0 | 0 | — |
case-15 | fail→pass | 23,240 | 14,085 | -39% | 1 | 1 | 0% | 2,373 | 3,159 | +33% | 0 | 0 | — |
case-16 | pass→pass | 21,373 | 11,931 | -44% | 1 | 1 | 0% | 2,268 | 2,919 | +29% | 0 | 0 | — |
case-17 | pass→pass | 12,640 | 7,147 | -43% | 1 | 1 | 0% | 1,577 | 2,843 | +80% | 0 | 0 | — |
case-18 | fail→fail | 15,172 | 7,884 | -48% | 1 | 1 | 0% | 2,151 | 3,180 | +48% | 0 | 0 | — |
case-19 | fail→fail | 14,128 | 7,807 | -45% | 1 | 1 | 0% | 2,261 | 3,198 | +41% | 0 | 0 | — |
case-20 | pass→pass | 15,837 | 13,722 | -13% | 1 | 1 | 0% | 1,626 | 3,163 | +95% | 0 | 0 | — |
case-21 | fail→fail | 18,672 | 11,058 | -41% | 1 | 1 | 0% | 2,014 | 3,526 | +75% | 0 | 0 | — |
case-22 | pass→pass | 16,954 | 11,835 | -30% | 1 | 1 | 0% | 2,272 | 2,915 | +28% | 0 | 0 | — |
case-23 | fail→pass | 19,475 | 8,820 | -55% | 1 | 1 | 0% | 2,865 | 3,235 | +13% | 0 | 0 | — |
case-24 | fail→pass | 20,244 | 7,245 | -64% | 1 | 1 | 0% | 2,270 | 3,048 | +34% | 0 | 0 | — |
case-25 | fail→pass | 17,995 | 10,534 | -41% | 1 | 1 | 0% | 2,582 | 3,303 | +28% | 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. 25 cases were attempted. The headline lift of +32 percentage points is the difference between those two pass rates over the 25 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.