Adjust router latency-versus-quality weights and warm-pool settings from configuration files and logs.
npx skills add https://github.com/AesopScott/mojo --skill route-tuner
Please help me install this Agent Skill. Skill: route-tuner Source: https://github.com/AesopScott/mojo/tree/main/harnesses/skills/route-tuner Command: npx skills add https://github.com/AesopScott/mojo --skill route-tuner
This Skill addresses performance bottlenecks in the Model Router harness by providing a structured framework to tune latency-vs-quality trade-offs and manage warm-pool resources.
Use the route-tuner skill to analyze the current Model Router performance logs and propose a configuration change to reduce latency for the primary dispatch path.
High-intent search queries and answers about installing and using this skill.
To optimize model routing and reduce high latency, you must adjust route scoring weights to prioritize speed over quality. This involves analyzing performance logs and trace data to validate route weight adjustments within the model router harness.
Warm-pool availability ensures dispatch paths are ready for immediate use by managing keepalive settings. Configuring these resources prevents cold-start delays, maintaining throughput and stabilizing overall model router performance.
Balancing latency versus quality trade-offs requires tuning route scoring weights based on real-time requirements. By adjusting these parameters, the model router dynamically prioritizes either faster response times or higher quality outputs.
To refine routing policy and mitigate bottlenecks, you need access to router configuration files, performance logs, and trace data. This data validates route weight adjustments and identifies high-latency paths for speculative routing.
Yes, you can implement speculative routing to maintain throughput when a specific model provider path experiences high latency. Rebalancing route weights alongside this approach ensures continuous dispatch without waiting for slow responses.
You should adjust route scoring weights to prioritize speed over quality during real-time requirements or when a specific model provider path experiences high latency. Rebalancing these weights mitigates bottlenecks and maintains necessary throughput.
sQVeDiagnose bugs by tracing evidence and generating a BR issue template.
binfen1Monitor live apps for console errors, performance regressions, and page failures.
h4vzzMonitor cloud infrastructure with metrics, logs, traces, and alerting.
ConsiliencyCoordinate task execution across multiple AI providers with routing and fallback chains.
