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Get Started Free →Use when targeting International Conference on Pattern Recognition (ICPR) 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 pattern recognition.
.claude/skills/brycewang-stanford-international-conference-on-pattern-recognition/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-08 | ✗→✓ | ▲ Improved | 85% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 64% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 101% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 88% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 7% | 0% |
International Conference on Pattern Recognition (ICPR) is a top computer-science conference venue for pattern recognition, document analysis, biometrics, computer vision, machine learning, and signal patterns. It rewards a pattern-recognition paper with broad method relevance and careful empirical validation. 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.
benchmarks, or applications across vision, signal, document, biometrics, or recognition tasks.
for flagship vision and KDD/ICDM/SDM for data mining.
If the paper misses ICPR's bar, compare against neural-information-processing-systems / international-conference-on-machine-learning / international-conference-on-learning-representations / aaai-conference-on-artificial-intelligence. 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] International Conference on Pattern Recognition (ICPR) [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-08 | fail→pass | 8,849 | 6,618 | -25% | 1 | 1 | 0% | 1,676 | 3,107 | +85% | 0 | 0 | — |
case-01 | fail→pass | 20,304 | 18,094 | -11% | 1 | 1 | 0% | 2,446 | 4,015 | +64% | 0 | 0 | — |
case-02 | fail→pass | 13,300 | 6,664 | -50% | 1 | 1 | 0% | 1,526 | 3,064 | +101% | 0 | 0 | — |
case-03 | fail→pass | 14,829 | 7,247 | -51% | 1 | 1 | 0% | 1,743 | 3,284 | +88% | 0 | 0 | — |
case-04 | fail→pass | 22,257 | 12,785 | -43% | 1 | 1 | 0% | 3,095 | 3,314 | +7% | 0 | 0 | — |
case-05 | fail→pass | 9,638 | 10,402 | +8% | 1 | 1 | 0% | 1,655 | 2,931 | +77% | 0 | 0 | — |
case-06 | pass→pass | 7,309 | 11,319 | +55% | 1 | 1 | 0% | 1,390 | 3,118 | +124% | 0 | 0 | — |
case-07 | pass→pass | 13,003 | 4,923 | -62% | 1 | 1 | 0% | 1,386 | 2,858 | +106% | 0 | 0 | — |
case-09 | pass→pass | 19,487 | 7,917 | -59% | 1 | 1 | 0% | 2,334 | 3,179 | +36% | 0 | 0 | — |
case-10 | fail→pass | 13,397 | 7,807 | -42% | 1 | 1 | 0% | 2,402 | 3,382 | +41% | 0 | 0 | — |
case-11 | pass→fail | 10,970 | 11,879 | +8% | 1 | 1 | 0% | 1,842 | 3,144 | +71% | 0 | 0 | — |
case-12 | pass→pass | 11,445 | 11,564 | +1% | 1 | 1 | 0% | 1,094 | 3,095 | +183% | 0 | 0 | — |
case-13 | fail→pass | 16,455 | 5,820 | -65% | 1 | 1 | 0% | 1,858 | 2,854 | +54% | 0 | 0 | — |
case-14 | pass→pass | 13,428 | 6,951 | -48% | 1 | 1 | 0% | 1,790 | 3,179 | +78% | 0 | 0 | — |
case-15 | pass→pass | 10,564 | 5,242 | -50% | 1 | 1 | 0% | 1,777 | 2,918 | +64% | 0 | 0 | — |
case-16 | fail→pass | 16,040 | 10,757 | -33% | 1 | 1 | 0% | 2,696 | 2,893 | +7% | 0 | 0 | — |
case-17 | pass→pass | 15,541 | 9,214 | -41% | 1 | 1 | 0% | 2,645 | 3,170 | +20% | 0 | 0 | — |
case-18 | pass→pass | 13,598 | 11,198 | -18% | 1 | 1 | 0% | 2,283 | 2,940 | +29% | 0 | 0 | — |
case-19 | pass→pass | 20,259 | 26,091 | +29% | 1 | 1 | 0% | 4,122 | 6,388 | +55% | 0 | 0 | — |
case-20 | pass→pass | 18,129 | 20,925 | +15% | 1 | 1 | 0% | 2,826 | 5,004 | +77% | 0 | 0 | — |
case-21 | pass→fail | 12,639 | 17,625 | +39% | 1 | 1 | 0% | 2,705 | 4,226 | +56% | 0 | 0 | — |
case-22 | pass→pass | 13,429 | 11,779 | -12% | 1 | 1 | 0% | 2,214 | 3,884 | +75% | 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. 22 cases were attempted. The headline lift of +32 percentage points is the difference between those two pass rates over the 22 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
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.