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Get Started Free →Use when shaping the prose and structure of an ACM CoNEXT paper — leading with the networking problem and deployment context, tying every claim to a measurement on the real target platform, arguing limitations rather than reciting them, and holding the acmart page budget for long (≤16) and short (≤10) papers.
.claude/skills/brycewang-stanford-conext-writing-style/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -13% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 2% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 21% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 47% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 9% | 0% |
Write to the CoNEXT reader: a networking systems/measurement expert who wants the networking contribution and the evaluation platform on the first page, evidence proportional to the claim and drawn from the real target, and a limitations posture visible from the start. Because CoNEXT papers are PACMNET journal articles judged in a two-round, one-shot-revision process, the draft must read like a manuscript whose claims are already backed, not a promising sketch.
Lead with the arc the worked example demonstrates (../../resources/worked-examples/01-introduction.md):
designer, or systems builder recognizes, stated in the first breath.
gap matters on real paths or hardware.
model or a score.
the measurement that backs it.
threat named up front rather than deferred.
throttled short flows drops"), not as an offline metric ("accuracy is high").
points, which baseline. A claim with no matching measurement is the fastest path to a one-shot-revision change-list item.
single bar with no variance reads as a lab artifact to a measurement reviewer.
CoNEXT reviewers reward a paper that names its own central threat and bounds it. Put the limitation where the result lives, not only in a closing section:
subset.
A limitations section that only lists generic caveats ("results may not generalize") wastes the one place you can pre-empt the reviewer's strongest objection.
papers ≤10 pages (+ unlimited references + ≤2 appendix pages). Verify the current numbers.
redundant tables instead.
to accept the paper should live only there (see conext-supplementary).
acmart template to reclaim space; editorial compression is the only safe lever.what scale before the evaluation.
soundness flag.
in one glance is worth more than a paragraph; a decorative architecture box is not.
not name testbeds, operators, or repositories (double-anonymous review).
| Failure | Why it hurts at CoNEXT | Fix | |---|---|---| | Simulation stands in for the real platform | Platform-realism is a core CoNEXT expectation | Add a testbed/deployment run, or scope the claim | | Claim outruns the measurement | The two-round review catches it | Pair each claim with evidence, or soften the claim | | Limitations deferred to one closing paragraph | Misses the chance to pre-empt objections | Argue the central threat where the result lives | | Model/leaderboard framing | Reads as an ML paper wearing a networking title | Reframe around the networking lesson (topic-selection) | | Over-signposted roadmap | Substitutes structure for argument | One-line roadmap; let the contributions carry it |
text[First-page arc] problem -> inadequacy -> contribution -> real-target evidence -> what changes + limitations [Claim-evidence pairs] <claim -> measurement + platform + baseline> [Limitations posture] <central threat named where the result lives? yes/no> [Budget] pages used (body/appendix), acmart compliant? refs unlimited [Anonymity] third-person self-reference; no testbed/operator/repo names
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 45,129 | 33,011 | -27% | 1 | 1 | 0% | 6,674 | 5,775 | -13% | 0 | 0 | — |
case-02 | fail→pass | 13,227 | 8,881 | -33% | 1 | 1 | 0% | 1,771 | 1,806 | +2% | 0 | 0 | — |
case-03 | fail→fail | 40,322 | 34,986 | -13% | 1 | 1 | 0% | 5,364 | 2,552 | -52% | 0 | 0 | — |
case-04 | pass→pass | 14,547 | 12,400 | -15% | 1 | 1 | 0% | 2,357 | 3,070 | +30% | 0 | 0 | — |
case-05 | pass→pass | 19,655 | 14,706 | -25% | 1 | 1 | 0% | 2,264 | 3,064 | +35% | 0 | 0 | — |
case-06 | pass→pass | 27,202 | 34,922 | +28% | 1 | 1 | 0% | 3,756 | 5,342 | +42% | 0 | 0 | — |
case-07 | fail→pass | 23,117 | 14,469 | -37% | 1 | 1 | 0% | 2,814 | 3,394 | +21% | 0 | 0 | — |
case-08 | fail→pass | 18,956 | 17,913 | -6% | 1 | 1 | 0% | 2,152 | 3,170 | +47% | 0 | 0 | — |
case-09 | pass→pass | 14,003 | 15,224 | +9% | 1 | 1 | 0% | 2,112 | 2,736 | +30% | 0 | 0 | — |
case-10 | fail→pass | 22,788 | 11,653 | -49% | 1 | 1 | 0% | 2,667 | 2,899 | +9% | 0 | 0 | — |
case-11 | fail→pass | 13,148 | 16,570 | +26% | 1 | 1 | 0% | 2,062 | 2,876 | +39% | 0 | 0 | — |
case-12 | pass→fail | 14,311 | 16,609 | +16% | 1 | 1 | 0% | 2,176 | 3,073 | +41% | 0 | 0 | — |
case-13 | pass→pass | 33,627 | 11,466 | -66% | 1 | 1 | 0% | 2,826 | 2,996 | +6% | 0 | 0 | — |
case-14 | fail→pass | 18,207 | 13,535 | -26% | 1 | 1 | 0% | 2,070 | 3,106 | +50% | 0 | 0 | — |
case-15 | pass→pass | 15,384 | 26,433 | +72% | 1 | 1 | 0% | 2,374 | 2,810 | +18% | 0 | 0 | — |
case-16 | fail→pass | 16,870 | 17,077 | +1% | 1 | 1 | 0% | 2,197 | 2,710 | +23% | 0 | 0 | — |
case-17 | fail→pass | 17,700 | 14,199 | -20% | 1 | 1 | 0% | 1,637 | 2,517 | +54% | 0 | 0 | — |
case-18 | fail→pass | 12,347 | 9,492 | -23% | 1 | 1 | 0% | 1,488 | 1,617 | +9% | 0 | 0 | — |
case-19 | fail→pass | 10,618 | 8,355 | -21% | 1 | 1 | 0% | 1,679 | 1,803 | +7% | 0 | 0 | — |
case-20 | fail→pass | 14,490 | 7,087 | -51% | 1 | 1 | 0% | 1,568 | 1,478 | -6% | 0 | 0 | — |
case-21 | pass→pass | 19,908 | 16,409 | -18% | 1 | 1 | 0% | 2,453 | 2,644 | +8% | 0 | 0 | — |
case-22 | fail→pass | 19,787 | 21,292 | +8% | 1 | 1 | 0% | 2,252 | 2,972 | +32% | 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 +55 percentage points is the difference between those two pass rates over the 22 comparable cases. 1 case got worse with the skill loaded, and it is 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.