Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Analyze network latency and optimize request patterns for faster communication. Use when diagnosing slow network performance or optimizing API calls. Trigger with phrases like "analyze network latency", "optimize API calls", or "reduce network delays".
.claude/skills/jeremylongshore-analyzing-network-latency/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 20% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 45% | 0% |
| case-08 | ✓→✗ | ▼ Worse | 27% | 0% |
| case-04 | ✓→✓ | = Same ✓ | 26% | 0% |
| case-05 | ✓→✓ | = Same ✓ | 25% | 0% |
Diagnose network latency issues and optimize request patterns through parallelization, batching, connection pooling, and timeout tuning.
This skill empowers Claude to diagnose network latency issues and propose optimizations to improve application performance. It analyzes request patterns, identifies potential bottlenecks, and recommends solutions for faster and more efficient network communication.
This skill activates when you need to:
User request: "Analyze network latency and suggest improvements for our API calls."
The skill will:
User request: "Optimize network request patterns to reduce page load time."
The skill will:
This skill can be used in conjunction with other plugins that manage infrastructure or application code, allowing for automated implementation of the suggested optimizations. For instance, it can work with a code modification plugin to automatically apply connection pooling or adjust timeout values.
If latency analysis fails:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-04 | pass→pass | 14,199 | 14,539 | +2% | 1 | 1 | 0% | 2,683 | 3,385 | +26% | 0 | 0 | — |
case-05 | pass→pass | 15,079 | 14,953 | -1% | 1 | 1 | 0% | 2,753 | 3,444 | +25% | 0 | 0 | — |
case-20 | pass→pass | 14,123 | 14,404 | +2% | 1 | 1 | 0% | 2,436 | 3,239 | +33% | 0 | 0 | — |
case-21 | pass→pass | 10,619 | 11,552 | +9% | 1 | 1 | 0% | 1,787 | 2,777 | +55% | 0 | 0 | — |
case-02 | pass→pass | 6,433 | 9,001 | +40% | 1 | 1 | 0% | 1,062 | 2,248 | +112% | 0 | 0 | — |
case-03 | pass→pass | 11,528 | 9,882 | -14% | 1 | 1 | 0% | 2,400 | 2,762 | +15% | 0 | 0 | — |
case-01 | fail→pass | 20,612 | 21,573 | +5% | 1 | 1 | 0% | 3,869 | 4,628 | +20% | 0 | 0 | — |
case-06 | pass→pass | 11,974 | 14,037 | +17% | 1 | 1 | 0% | 1,937 | 3,267 | +69% | 0 | 0 | — |
case-07 | pass→pass | 12,263 | 13,411 | +9% | 1 | 1 | 0% | 2,155 | 3,231 | +50% | 0 | 0 | — |
case-08 | pass→fail | 16,600 | 15,349 | -8% | 1 | 1 | 0% | 2,859 | 3,628 | +27% | 0 | 0 | — |
case-09 | pass→pass | 14,279 | 13,477 | -6% | 1 | 1 | 0% | 2,533 | 3,060 | +21% | 0 | 0 | — |
case-10 | pass→pass | 15,032 | 15,297 | +2% | 1 | 1 | 0% | 2,643 | 3,192 | +21% | 0 | 0 | — |
case-22 | pass→pass | 17,425 | 13,890 | -20% | 1 | 1 | 0% | 2,854 | 3,245 | +14% | 0 | 0 | — |
case-11 | pass→pass | 15,222 | 12,620 | -17% | 1 | 1 | 0% | 2,719 | 3,154 | +16% | 0 | 0 | — |
case-12 | pass→pass | 14,105 | 9,109 | -35% | 1 | 1 | 0% | 2,470 | 2,191 | -11% | 0 | 0 | — |
case-13 | fail→pass | 15,915 | 17,959 | +13% | 1 | 1 | 0% | 2,656 | 3,850 | +45% | 0 | 0 | — |
case-14 | pass→pass | 13,956 | 14,203 | +2% | 1 | 1 | 0% | 2,459 | 3,385 | +38% | 0 | 0 | — |
case-15 | pass→pass | 8,570 | 9,275 | +8% | 1 | 1 | 0% | 1,520 | 2,411 | +59% | 0 | 0 | — |
case-16 | pass→pass | 10,110 | 10,601 | +5% | 1 | 1 | 0% | 1,955 | 2,683 | +37% | 0 | 0 | — |
case-17 | pass→pass | 16,675 | 19,239 | +15% | 1 | 1 | 0% | 2,741 | 4,026 | +47% | 0 | 0 | — |
case-18 | pass→pass | 6,994 | 6,589 | -6% | 1 | 1 | 0% | 1,290 | 1,840 | +43% | 0 | 0 | — |
case-19 | pass→pass | 13,650 | 14,076 | +3% | 1 | 1 | 0% | 2,384 | 3,206 | +34% | 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 +5 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.