latency-critical-systems
优化并验证对延迟敏感的系统——实时仪表盘、行情数据源、流式智能体、执行网关、队列和缓存——通过跟踪p50/p95/p99延迟、数据新鲜度时长和队列深度,梳理热点路径,并执行实时回读。适用于p95延迟、吞吐量或数据新鲜度至关重要的场景。
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Optimize and verify latency-sensitive systems — realtime dashboards, market data feeds, streaming agents, execution gateways, queues, and caches — by tracking p50/p95/p99 latency, freshness age, and queue depth, mapping hot paths, and running live readbacks. Use when p95 latency, throughput, or data freshness matters.
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从固定版本的 SKILL.md 提取,保留原文语言。以下内容属于作者说明,本站尚未验证安装、兼容或运行结果。
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原文涵盖的主题
- Latency Critical Systems
- Split The Metrics
- Map The Hot Path
- Optimization Order
- Verification
- Guardrails
Use this skill when the user cares about realtime behavior, hot paths, streaming freshness, or execution speed. This includes HFT-like infrastructure, but the skill is engineering-focused. It does not authorize live trading or financial advice. Do not collapse everything into "fast." Track: p50, p95, and p99 latency; throughput; freshness age; queue depth; cache hit rate; provider/API response time; browser render time; correctness under load; failure and retry behavior. Write the path from user/event to final visible state: Then measure each segment separately. 1. Remove unnecessary round trips. 2. Cache stable reads with freshness metadata. 3. Batch small calls and writes. 4. Move compute closer to the data or the user. 5. Split hot and cold paths. 6. Apply backpressure before queues grow unbounded. 7. Use streaming only when it improves freshness or user experience. 8. Add canaries for stale data, degraded providers, and bad cache state. Use live readbacks when a deployed surface exists: HTTP timing and response headers; provider freshness timestamp; queue or job state; edge/cache state; browser verification for actual UI freshness; logs around retries and degraded mode. For mar
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- affaan-m/ECC
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- pi/core/skills/latency-critical-systems/SKILL.md
- 固定版本
- d2ef2680594f9a17526a6e1a2e18b3d242c5d17b
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原作者:affaan-m/ECC · 原作者仓库元数据。文档核查:未核查。最近来源检查:2026-11-09T15:01:43.094Z。
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