Measure per-call and end-to-end latency for MEV infrastructure workflows.
npx skills add https://github.com/BarisSozen/claude --skill latency-tracker
Please help me install this Agent Skill. Skill: latency-tracker Source: https://github.com/BarisSozen/claude/tree/main/.claude/skills/latency-tracker Command: npx skills add https://github.com/BarisSozen/claude --skill latency-tracker
Latency tracking is essential for MEV infrastructure to monitor per-call and end-to-end timing, enabling rapid debugging and SLA compliance.
Start latency tracking by creating an end-to-end trace with tracker.startE2E('liq'), wrap important sections with tracker.span(), and finalize with e2e.complete({ success: true }).
High-intent search queries and answers about installing and using this skill.
Measure end-to-end latency in MEV infrastructure by initializing an e2e trace with tracker.startE2E, wrapping critical code paths using tracker.span(), and finalizing the trace to capture aggregated RPC and transaction submission timing.
Span-based instrumentation for Rust hotpath calls works by wrapping specific code sections with tracker.span(), which captures per-call latency to enable granular performance debugging within critical MEV transaction workflows.
Yes, you can configure alert thresholds for RPC interaction latency anomalies by setting specific timing limits within the tracker, which surfaces latency deviations across end-to-end MEV transaction submission workflows.
The best way to track transaction submission latency for MEV bots is using span-based timing across end-to-end traces, wrapping critical Rust hotpath calls and RPC interactions to monitor per-call performance and ensure SLA compliance.
Yes, latency tracing requires manually wrapping code paths with tracker.span() and using tracker.startE2E for end-to-end tracing, enabling precise per-call and aggregated performance debugging across MEV infrastructure components.
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