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Latency Critical Systems

ASecurity

Use when use for latency-sensitive systems such as realtime dashboards, market data, streaming agents, execution gateways, queues, caches, or HFT-like infrastructure where freshness and p95 latency matter. Triggers on \"latency-critical-systems\", \"latency critical systems\", \"systems\".

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  • Added September 19, 2026
ai-agentsbashapi

Works with

  • cli
  • api

Security analysis

A100/100

Scanned September 19, 2026

npx -y skills add majinmagros/magros.ai-skills --skill latency-critical-systems --agent claude-code

Installs into .claude/skills of the current project.

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SKILL.md
---
name: latency-critical-systems
description: "Use when use for latency-sensitive systems such as realtime dashboards, market data, streaming agents, execution gateways, queues, caches, or HFT-like infrastructure where freshness and p95 latency matter. Triggers on \"latency-critical-systems\", \"latency critical systems\", \"systems\"."
license: MIT
metadata:
  origin: ECC
tools: Read, Write, Edit, Bash, Grep, Glob
---

# Latency Critical Systems

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.

## Quando usar

- "o dashboard tá stale", "p95 estourou", "fila crescendo sem parar"
- Hot path com p50/p95/p99, freshness, queue depth e cache hit rate
- Streaming, gateway de execução, cache/edge com verificação live
- Não use para: otimizar sem baseline; aconselhar trade (nunca)

## Split The Metrics

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.

## Map The Hot Path

Write the path from user/event to final visible state:

```text
source event -> provider API -> ingest worker -> queue -> cache -> edge route
-> client stream -> browser render -> user-visible state
```

Then measure each segment separately.

## Optimization Order

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.

## Verification

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 market-data or execution-adjacent paths, also verify orderbook age, VWAP
assumptions, provider status, and kill-switch behavior before calling the path

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