latency-critical-systems

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Actualizado17 de julio de 2026 a las 21:13

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.

Instalación

Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.

Fuente

affaan-m

affaan-m/ECC

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SKILL.md

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name	latency-critical-systems
description	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.
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.

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:

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

Then measure each segment separately.

Optimization Order

Remove unnecessary round trips.
Cache stable reads with freshness metadata.
Batch small calls and writes.
Move compute closer to the data or the user.
Split hot and cold paths.
Apply backpressure before queues grow unbounded.
Use streaming only when it improves freshness or user experience.
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 ready.

Guardrails

Do not optimize latency by dropping required validation.
Do not hide stale data behind fast cache hits.
Do not claim millisecond behavior from client labels without measurement.
Do not run live orders, destructive migrations, or customer-impacting deploys without an explicit approval gate.
Keep secrets and private payloads out of logs and benchmark artifacts.

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Latency Critical Systems

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:

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

Then measure each segment separately.

Optimization Order

Remove unnecessary round trips.

Cache stable reads with freshness metadata.

Batch small calls and writes.

Move compute closer to the data or the user.

Split hot and cold paths.

Apply backpressure before queues grow unbounded.

Use streaming only when it improves freshness or user experience.

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 ready.

Guardrails

Do not optimize latency by dropping required validation.

Do not hide stale data behind fast cache hits.

Do not claim millisecond behavior from client labels without measurement.

Do not run live orders, destructive migrations, or customer-impacting deploys without an explicit approval gate.

Keep secrets and private payloads out of logs and benchmark artifacts.