AgentSkillsCN

low-latency-systems

适用于量化研究、系统实施及生产管控的低延迟系统工作流。当任务涉及尾部延迟压缩与确定性路径行为时，可选用此类工作流。

SKILL.md
--- frontmatter
name: low-latency-systems
description: "Low Latency Systems workflows for quantitative research, implementation, and production controls. use when tasks involve tail-latency compression and deterministic path behavior."

Low Latency Systems

objective

Execute low latency systems work with reproducible research, explicit controls, and deployable outputs.

workflow

  1. •define end-to-end latency budget and deterministic performance targets.
  2. •instrument each stage from feed ingress to order egress.
  3. •optimize kernel, memory, and network path for tail-latency reduction.
  4. •stress packet bursts, failovers, and capacity saturation scenarios.
  5. •promote only after reproducible latency and recovery behavior is verified.

required diagnostics

  • •stage-level p50, p99, and p999 latency decomposition.
  • •jitter and throughput stability under sustained burst load.
  • •packet-loss recovery time and replay correctness.
  • •resource saturation signals before service-level breach.
  • •tail-latency regressions during peak message bursts

risk controls

  • •enforce hard latency and packet-loss service objectives.
  • •enforce automatic failover and load-shedding thresholds.
  • •enforce runbooks for exchange-connectivity incidents.

outputs

  • •run python scripts/low_latency_systems_diagnostics.py input.csv --output diagnostics.json and keep the json artifact.
  • •write an implementation memo using references/low-latency-systems-playbook.md with assumptions, tests, limits, and rollout plan.

resources

  • •use scripts/low_latency_systems_diagnostics.py for deterministic diagnostics.
  • •use references/low-latency-systems-playbook.md for the domain-specific checklist and delivery structure.