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Get Started Free →Use when writing the related-work and positioning of an ACM CoNEXT paper — covering the networking literature lanes (SIGCOMM, NSDI, IMC, SIGMETRICS, MobiCom, CCR), delta-first positioning against the venue's own recent programs, and keeping self-citations double-anonymous.
.claude/skills/brycewang-stanford-conext-related-work/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -9% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -2% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 0% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 56% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 101% | 0% |
Position the paper for a networking reviewer who knows the field's flagships cold. At CoNEXT, related work is not a literature dump; it is the delta argument — what your contribution adds over the closest prior systems and measurements — written so a double-anonymous reviewer can locate your paper in the networking landscape in one read.
A CoNEXT reviewer will mentally check whether you engage the right bodies of work:
now PACMNET itself.
Missing the lane your paper lives in — e.g., a measurement paper that ignores IMC methodology, or a data-plane paper that ignores the recent SIGCOMM/NSDI switch-hardware line — reads as unfamiliarity and invites a change-list item in a one-shot revision.
Write each comparison as a delta sentence, not a summary:
textPrior work X [SIGCOMM'YY] measures/builds <what> but <the gap on the real target>; we <the specific advance> and show it on <the real platform/trace>.
platform prior work did not evaluate on, a scale prior work did not reach.
a measurement reviewer will know a counterexample.
CoNEXT is double-anonymous, so your own prior work is a leak surface:
previous system."
describe it without owner-identifying detail.
Because CoNEXT runs two cycles a year and publishes in PACMNET, the venue's own recent output moves fast:
paper that ignores a directly relevant CoNEXT/PACMNET paper from last cycle looks careless.
paper's authors.
tolerates both, but the delta must be visible early regardless.
a wall of one-line summaries wastes body pages you need for evidence.
| Failure | Why it hurts at CoNEXT | Fix | |---|---|---| | Literature list with no delta | Reviewer cannot place your contribution | Rewrite each entry as a delta sentence | | Missing the closest prior system/measurement | Reads as unfamiliarity; revision risk | Lead with the nearest work and its gap | | First-person self-citation | Breaks double-anonymity | Third-person, no lineage reveal | | Ignoring the right lane (IMC/NSDI/SIGMETRICS) | Signals you are a visitor | Engage the venue where your method lives | | Over-claiming "first" | A reviewer knows a counterexample | Scope the novelty to the checkable delta |
text[Lanes covered] SIGCOMM / NSDI / IMC / SIGMETRICS / MobiCom / ToN-CCR-PACMNET as relevant [Closest prior work] <papers + the specific gap each leaves on the real target> [Delta sentences] <one per closest work> [Anonymity] third-person self-citation; no system-lineage leak [Recency] last two CoNEXT programs + recent PACMNET issues checked? yes/no
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 29,713 | 21,339 | -28% | 1 | 1 | 0% | 4,029 | 3,648 | -9% | 0 | 0 | — |
case-02 | fail→pass | 25,867 | 16,334 | -37% | 1 | 1 | 0% | 2,861 | 2,804 | -2% | 0 | 0 | — |
case-03 | fail→fail | 33,600 | 15,736 | -53% | 1 | 1 | 0% | 4,538 | 2,573 | -43% | 0 | 0 | — |
case-04 | fail→fail | 20,099 | 18,870 | -6% | 1 | 1 | 0% | 2,267 | 3,038 | +34% | 0 | 0 | — |
case-05 | pass→pass | 23,486 | 18,172 | -23% | 1 | 1 | 0% | 2,880 | 2,976 | +3% | 0 | 0 | — |
case-06 | pass→pass | 21,309 | 18,610 | -13% | 1 | 1 | 0% | 3,290 | 2,931 | -11% | 0 | 0 | — |
case-07 | fail→pass | 23,539 | 16,961 | -28% | 1 | 1 | 0% | 3,007 | 3,003 | -0% | 0 | 0 | — |
case-08 | pass→pass | 25,719 | 20,127 | -22% | 1 | 1 | 0% | 2,730 | 3,283 | +20% | 0 | 0 | — |
case-09 | pass→fail | 18,723 | 20,040 | +7% | 1 | 1 | 0% | 2,919 | 3,316 | +14% | 0 | 0 | — |
case-10 | pass→pass | 16,196 | 7,865 | -51% | 1 | 1 | 0% | 1,918 | 2,314 | +21% | 0 | 0 | — |
case-11 | fail→pass | 33,121 | 31,047 | -6% | 1 | 1 | 0% | 2,267 | 3,531 | +56% | 0 | 0 | — |
case-12 | fail→pass | 11,945 | 18,456 | +55% | 1 | 1 | 0% | 1,551 | 3,117 | +101% | 0 | 0 | — |
case-13 | fail→fail | 18,758 | 15,002 | -20% | 1 | 1 | 0% | 2,204 | 2,628 | +19% | 0 | 0 | — |
case-14 | fail→pass | 15,172 | 16,298 | +7% | 1 | 1 | 0% | 2,211 | 2,582 | +17% | 0 | 0 | — |
case-15 | pass→pass | 17,631 | 18,269 | +4% | 1 | 1 | 0% | 1,956 | 2,565 | +31% | 0 | 0 | — |
case-16 | pass→pass | 18,964 | 26,509 | +40% | 1 | 1 | 0% | 2,187 | 2,700 | +23% | 0 | 0 | — |
case-17 | pass→pass | 16,361 | 15,664 | -4% | 1 | 1 | 0% | 1,843 | 2,361 | +28% | 0 | 0 | — |
case-18 | pass→pass | 18,481 | 10,135 | -45% | 1 | 1 | 0% | 1,683 | 2,463 | +46% | 0 | 0 | — |
case-19 | pass→pass | 22,876 | 18,586 | -19% | 1 | 1 | 0% | 2,143 | 2,872 | +34% | 0 | 0 | — |
case-20 | fail→pass | 14,875 | 13,774 | -7% | 1 | 1 | 0% | 2,076 | 2,378 | +15% | 0 | 0 | — |
case-21 | pass→pass | 25,407 | 20,210 | -20% | 1 | 1 | 0% | 2,656 | 3,295 | +24% | 0 | 0 | — |
case-22 | pass→pass | 20,295 | 25,437 | +25% | 1 | 1 | 0% | 3,103 | 3,456 | +11% | 0 | 0 | — |
case-23 | pass→pass | 23,426 | 21,750 | -7% | 1 | 1 | 0% | 3,230 | 4,008 | +24% | 0 | 0 | — |
case-24 | pass→pass | 20,516 | 21,826 | +6% | 1 | 1 | 0% | 2,673 | 3,059 | +14% | 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 +25 percentage points is the difference between those two pass rates over the 24 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.