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Get Started Free →Use when deciding whether a networking project belongs at ACM CoNEXT or should be routed to SIGCOMM, NSDI, IMC, SIGMETRICS, MobiCom, or HotNets, and when using CoNEXT's two-cycles-per-year calendar and PACMNET journal-style fit to choose the venue and the cycle by contribution shape and evidence maturity.
.claude/skills/brycewang-stanford-conext-topic-selection/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 49% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 25% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 39% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 36% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 12% | 0% |
Decide the venue and the cycle before drafting. CoNEXT — the ACM International Conference on emerging Networking EXperiments and Technologies, sponsored by ACM SIGCOMM — is a broad systems-and-measurement networking venue whose research papers are PACMNET journal articles. Reviewers read for a durable networking contribution evaluated on the real target (a testbed, deployment, or trace), not a one-conference result. A technically strong paper whose real lesson is about pure theory, pure ML, or a non-networking system is respected and then rejected as out of scope.
Networking has several strong flagships with overlapping scope, so ask two things:
IMC/SIGMETRICS/MobiCom.)
(December vs. June — a real lever unique to CoNEXT.)
A strong paper is often publishable at more than one venue; the nearer honest deadline and the best community fit usually win.
| Signal in your project | Better home | Why | |---|---|---| | Broad networking contribution — systems, measurement, or architecture — evaluated on a real platform, ready for the nearer CoNEXT cycle | CoNEXT | Two-cycle PACMNET track; broad systems-networking scope | | Top-tier, polished result aimed at the field's most selective single-deadline stage | SIGCOMM | The flagship; single annual deadline, its own bar and calendar | | Systems paper centered on a built-and-deployed networked system with a strong implementation story | NSDI | Networked-systems design and implementation focus; USENIX proceedings | | The whole contribution is a measurement study of the Internet or a large system | IMC | Purpose-built internet-measurement venue | | Core is performance modeling, analysis, or evaluation methodology | SIGMETRICS | Measurement-and-modeling home (also a PACMNET-family stream) | | Wireless/mobile systems are the heart | MobiCom / MobiSys | Mobile/wireless-systems scope | | Early idea or position with an argument but little evaluation | HotNets (or CoNEXT Student Workshop) | Venue for provocative early work |
transport, routing, or the network stack, built and run on real hardware or a faithful testbed (the ASIC-switch-insertions lineage).
networks or protocols behave in the wild (the QUIC/TLS-performance lineage).
scaling, not merely proposed (the inter-domain multi-path lineage).
carrier/operator scale (the hyper-giants lineage).
and is validated on a testbed (the ultra-low-power broadcast lineage).
Two quick tests sharpen a borderline verdict:
about (switch, NIC, kernel, testbed, deployment, or real trace), or does a simulation stand in for hardware? CoNEXT rewards evidence on the real platform; a simulation-only systems claim is a re-route or a revision risk.
or a theory venue unchanged and read as native there? If its heart is one of those, route accordingly; CoNEXT rewards the broad networking framing with real-platform evidence.
If your paper leans on a learner or LLM, ask whether the networking lesson survives swapping the model for another. If not, the model is the contribution and an ML venue fits better; if the lesson is about the network (a measured phenomenon, a deployable mechanism), CoNEXT fits — evaluate it on the real target and add a contamination-aware ablation (see conext-experiments).
Fit is necessary but not sufficient: the same idea sits at different doors depending on how far the evidence has come. An idea with an argument but little evaluation is a HotNets or Student-Workshop paper; a mechanism evaluated only in simulation needs a testbed before the research track; a measurement too preliminary for the bar belongs in a workshop first. Submitting one step early earns a "promising, but..." — but CoNEXT's two cycles soften this: the next honest deadline is months away, not a year.
text[Scope] scan the last two CoNEXT programs (dblp, conferences.sigcomm.org) for your subarea -> 3+ recent papers = a reviewer pool exists; 0 = opening or mismatch [Citations] is your bibliography majority networking venues (CoNEXT/SIGCOMM/NSDI/IMC/SIGMETRICS)? -> majority non-networking => reviewers read you as a visitor; naturalize the intro first [Calendar] which CoNEXT cycle (Dec/Jun) is next, vs. SIGCOMM/NSDI/IMC dates -> route to the nearest honest fit rather than waiting for a marginal preference
text[Audience] who acts differently if the claim holds? -> operators / protocol designers / systems builders / researchers? [Claim type] systems mechanism / measurement / architecture / operational / wireless [Platform] is it evaluated on the real target, or only simulated? -> real => CoNEXT-ready [CoNEXT vs siblings] pure measurement -> IMC; built system with deep impl -> NSDI; top polish -> SIGCOMM [Cycle] December vs June -> nearest honest deadline the evidence is ready for [Verdict] CoNEXT <cycle> / sibling venue / workshop, with a one-line reason
Run this before the writing skills; a wrong venue or cycle decision wastes every later step. When the verdict is CoNEXT, continue with conext-workflow for the calendar and conext-writing-style for the paper shape.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 27,346 | 23,144 | -15% | 1 | 1 | 0% | 3,186 | 4,011 | +26% | 0 | 0 | — |
case-02 | fail→pass | 28,378 | 18,691 | -34% | 1 | 1 | 0% | 2,355 | 3,507 | +49% | 0 | 0 | — |
case-03 | fail→fail | 23,841 | 18,680 | -22% | 1 | 1 | 0% | 2,762 | 3,564 | +29% | 0 | 0 | — |
case-04 | fail→pass | 24,616 | 20,534 | -17% | 1 | 1 | 0% | 3,088 | 3,849 | +25% | 0 | 0 | — |
case-05 | fail→pass | 22,700 | 19,150 | -16% | 1 | 1 | 0% | 2,622 | 3,654 | +39% | 0 | 0 | — |
case-06 | pass→pass | 25,292 | 16,858 | -33% | 1 | 1 | 0% | 2,812 | 3,898 | +39% | 0 | 0 | — |
case-07 | pass→pass | 24,306 | 17,219 | -29% | 1 | 1 | 0% | 2,398 | 3,394 | +42% | 0 | 0 | — |
case-08 | pass→pass | 18,495 | 7,945 | -57% | 1 | 1 | 0% | 1,922 | 2,712 | +41% | 0 | 0 | — |
case-09 | pass→pass | 18,766 | 16,479 | -12% | 1 | 1 | 0% | 2,206 | 3,284 | +49% | 0 | 0 | — |
case-10 | fail→pass | 19,691 | 17,064 | -13% | 1 | 1 | 0% | 2,459 | 3,342 | +36% | 0 | 0 | — |
case-11 | fail→pass | 17,981 | 11,762 | -35% | 1 | 1 | 0% | 2,086 | 2,337 | +12% | 0 | 0 | — |
case-12 | fail→fail | 35,045 | 20,605 | -41% | 1 | 1 | 0% | 3,714 | 3,519 | -5% | 0 | 0 | — |
case-13 | fail→pass | 20,234 | 11,743 | -42% | 1 | 1 | 0% | 2,146 | 2,494 | +16% | 0 | 0 | — |
case-14 | pass→pass | 19,362 | 18,465 | -5% | 1 | 1 | 0% | 2,768 | 3,337 | +21% | 0 | 0 | — |
case-15 | pass→pass | 24,277 | 13,079 | -46% | 1 | 1 | 0% | 2,453 | 3,518 | +43% | 0 | 0 | — |
case-21 | pass→pass | 36,840 | 18,867 | -49% | 1 | 1 | 0% | 4,035 | 5,280 | +31% | 0 | 0 | — |
case-16 | fail→fail | 17,367 | 20,606 | +19% | 1 | 1 | 0% | 2,426 | 3,703 | +53% | 0 | 0 | — |
case-17 | pass→pass | 21,609 | 15,866 | -27% | 1 | 1 | 0% | 1,940 | 3,096 | +60% | 0 | 0 | — |
case-18 | fail→pass | 24,100 | 15,533 | -36% | 1 | 1 | 0% | 2,379 | 3,348 | +41% | 0 | 0 | — |
case-19 | pass→pass | 14,705 | 10,482 | -29% | 1 | 1 | 0% | 1,469 | 2,214 | +51% | 0 | 0 | — |
case-20 | pass→pass | 19,891 | 10,502 | -47% | 1 | 1 | 0% | 2,166 | 3,334 | +54% | 0 | 0 | — |
case-22 | pass→pass | 21,850 | 24,653 | +13% | 1 | 1 | 0% | 2,932 | 4,271 | +46% | 0 | 0 | — |
case-23 | fail→fail | 23,783 | 16,753 | -30% | 1 | 1 | 0% | 2,414 | 3,027 | +25% | 0 | 0 | — |
case-24 | fail→pass | 19,621 | 9,161 | -53% | 1 | 1 | 0% | 1,948 | 2,046 | +5% | 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. 24 cases were attempted. The headline lift of +33 percentage points is the difference between those two pass rates over the 24 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.