Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Communications-domain literature review and related-work search with database-aware source control. Use when the task is about communications, wireless, networking, satellite/NTN, Wi-Fi, cellular, transport protocols, congestion control, routing, scheduling, MAC/PHY, rate adaptation, channel estimation, beamforming, or communication-system research and the user wants papers, prior art, a survey, related work, or a landscape summary. Prioritize IEEE Xplore and ScienceDirect, prefer formal publica
.claude/skills/brycewang-stanford-comm-lit-review/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 14% | 0% |
| case-12 | ✗→✓ | ▲ Improved | -5% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 52% | 0% |
| case-14 | ✓→✗ | ▼ Worse | -42% | 0% |
| case-21 | ✓→✗ | ▼ Worse | -77% | 0% |
Run communications-focused paper search with tighter source policy than a generic literature review. Default to formal publications, prioritize IEEE Xplore and ScienceDirect, then ACM Digital Library, and output a review that is structured for research use rather than casual browsing.
Read references/source-policy.md before searching. Use references/domain-taxonomy.md to classify the topic, references/venue-tiering.md to rank venues, and references/output-template.md to format the final answer.
Decide whether the request is primarily about:
If the request is not clearly in communications systems research, fall back to a more general literature skill.
Apply these defaults unless the user overrides them:
IEEE Xplore, ScienceDirect, then ACM Digital Library, then broader webIf the user explicitly narrows scope, obey the narrower scope:
Use a layered search strategy. For communications topics, do not build the review from random blog posts or derivative summaries.
Search in this order by default:
ieeexplore.ieee.orgsciencedirect.comdl.acm.orgOnly move to the next database tier when one of these is true:
Within each database tier, search venue tiers in this order:
Follow the concrete tier lists in references/venue-tiering.md.
By default this venue tiering is a soft priority, not a hard whitelist.
only top venues, top journals only, top conferences only, or equivalent, switch to hard constraint mode and do not auto-expand beyond Tier A unless the user later relaxes the constraintUse preprints only when:
When a preprint is used, label it clearly as preprint.
For each relevant paper, capture:
Favor numbers, assumptions, and actual problem statements over generic summaries.
Group papers by technical axis, not by search order. Common groupings:
Explicitly separate:
Follow the templates in references/output-template.md.
The default output should include:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 33,917 | 31,159 | -8% | 1 | 1 | 0% | 5,270 | 6,019 | +14% | 0 | 0 | — |
case-02 | fail→fail | 49,821 | 12,389 | -75% | 1 | 1 | 0% | 8,259 | 1,749 | -79% | 0 | 0 | — |
case-03 | fail→fail | 51,447 | 49,469 | -4% | 1 | 1 | 0% | 8,255 | 9,321 | +13% | 0 | 0 | — |
case-04 | pass→pass | 31,789 | 36,489 | +15% | 1 | 1 | 0% | 5,040 | 8,164 | +62% | 0 | 0 | — |
case-05 | fail→fail | 39,547 | 37,951 | -4% | 1 | 1 | 0% | 8,246 | 9,432 | +14% | 0 | 0 | — |
case-06 | pass→pass | 10,494 | 13,325 | +27% | 1 | 1 | 0% | 1,690 | 3,443 | +104% | 0 | 0 | — |
case-12 | fail→pass | 46,197 | 37,218 | -19% | 1 | 1 | 0% | 6,843 | 6,526 | -5% | 0 | 0 | — |
case-07 | pass→pass | 27,561 | 50,184 | +82% | 1 | 1 | 0% | 4,575 | 9,428 | +106% | 0 | 0 | — |
case-08 | pass→pass | 20,492 | 35,396 | +73% | 1 | 1 | 0% | 3,474 | 7,148 | +106% | 0 | 0 | — |
case-09 | fail→fail | 28,300 | 8,953 | -68% | 1 | 1 | 0% | 4,334 | 1,831 | -58% | 0 | 0 | — |
case-10 | pass→pass | 27,755 | 33,794 | +22% | 1 | 1 | 0% | 4,188 | 6,464 | +54% | 0 | 0 | — |
case-11 | pass→pass | 28,360 | 43,423 | +53% | 1 | 1 | 0% | 4,169 | 7,957 | +91% | 0 | 0 | — |
case-13 | fail→pass | 36,502 | 44,678 | +22% | 1 | 1 | 0% | 5,760 | 8,749 | +52% | 0 | 0 | — |
case-14 | pass→fail | 25,573 | 57,464 | +125% | 1 | 1 | 0% | 3,931 | 2,278 | -42% | 0 | 0 | — |
case-15 | pass→pass | 38,000 | 40,672 | +7% | 1 | 1 | 0% | 5,959 | 6,696 | +12% | 0 | 0 | — |
case-16 | pass→pass | 59,077 | 57,650 | -2% | 1 | 1 | 0% | 8,232 | 9,418 | +14% | 0 | 0 | — |
case-17 | fail→fail | 27,650 | 9,973 | -64% | 1 | 1 | 0% | 4,202 | 1,771 | -58% | 0 | 0 | — |
case-18 | fail→fail | 27,567 | 56,923 | +106% | 1 | 1 | 0% | 4,178 | 8,496 | +103% | 0 | 0 | — |
case-19 | fail→fail | 37,315 | 9,250 | -75% | 1 | 1 | 0% | 6,275 | 1,800 | -71% | 0 | 0 | — |
case-20 | fail→fail | 22,095 | 61,063 | +176% | 1 | 1 | 0% | 3,205 | 10,113 | +216% | 0 | 0 | — |
case-21 | pass→fail | 51,077 | 10,267 | -80% | 1 | 1 | 0% | 8,219 | 1,876 | -77% | 0 | 0 | — |
case-22 | pass→fail | 24,407 | 8,432 | -65% | 1 | 1 | 0% | 4,394 | 1,741 | -60% | 0 | 0 | — |
case-23 | pass→fail | 31,810 | 9,779 | -69% | 1 | 1 | 0% | 4,997 | 1,718 | -66% | 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. 23 cases were attempted, and 14 counted toward the lift figure. The other 9 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of -4 percentage points is the difference between those two pass rates over the 14 comparable cases. 8 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.