What does this skill do?

The Critical Latency Systems Skill provides a structured framework for designing, measuring, and optimizing systems where latency and data freshness are critical: real-time dashboards, market feeds, streaming agents, queues, and caches. It maps the entire critical path from the source event to the visible state, breaks down metrics into p50/p95/p99, and applies prioritized improvements with live verification of the deployed system.

Critical Path Mapping
Trace the entire hot path from the source event to the user's visible state, measuring the latency of each segment separately.
Reduction in p95/p99
Identify bottlenecks on the critical path and apply prioritized optimizations: eliminate network round trips, cache reads, batch calls, and separate hot and cold paths.
Backpressure in Streaming
Control latency during load spikes by applying explicit backpressure on bounded queues and degraded modes, rather than allowing latency to accumulate indefinitely.
Live Verification
Validate optimizations using actual readings from the deployed system: HTTP timing, provider freshness timestamp, queue status, cache status, and retry logs.

Usage examples

🗺️ Dashboard mapping
Map the critical path of my dashboard in real time—from the market event to the DOM render—and tell me where the p95 bottleneck is.
📊 Reduce p99
My streaming system has a P99 of 500 ms. Analyze the critical path and suggest prioritized optimizations to bring it below 100 ms.
🔄 Backpressure in the tail
The latency of my Kafka queue keeps increasing indefinitely under load. Help me implement explicit backpressure and a degraded mode to keep the p95 under control.
✅ Check in real time
I've already implemented cache optimizations and call bundling. Use live readings to verify that data freshness hasn't deteriorated and that the p95 has improved.

Features

Separate metrics Independently tracks p50, p95, p99, throughput, freshness age, tail depth, cache hit rate, and vendor response time.
Segment-Based Hot Path Mapping Trace the path from the source event to the visible state, measuring the provider API, ingestion worker, queue, cache, edge, client stream, and browser rendering.
Optimizations in Strict Order Implement prioritized improvements: eliminate network trips, cache stable reads, batch calls, bring computation closer to the data, separate hot and cold paths, and add canaries.
Safety Guardrails It does not eliminate required validation, does not hide outdated data behind a fast cache, does not claim millisecond-level performance without measurement, and keeps secrets out of logs and benchmarks.
Market Route Support Additionally, verify the age of the order book, VWAP assumptions, the provider's status, and the behavior of the kill switch in financial data feeds.

Frequently asked questions

No. The skill is a methodological framework that is applied to your existing code and infrastructure. If you have a system deployed, the skill can guide you through live testing with real data.
Yes. The skill covers low-latency HFT-style infrastructure, including order book age verification, VWAP estimates, and kill-switch behavior. However, it does not authorize or execute live orders, nor does it provide financial advice.
The skill guides you through mapping the entire critical path and measuring each segment separately—provider API, ingestion worker, queue, cache, CDN edge, client WebSocket, and browser rendering—to calculate the total P95 latency.
Yes. You can use the skill to design the architecture, map out the theoretical critical path, and plan optimizations. Live verification applies when a deployment is available.
Critical Latency Systems — Optimizing Real-Time Systems with Claude AI

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