t1-performance-evidence

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Atualizado14 de julho de 2026 às 01:00

T1 measurement-driven performance optimization playbook. Use when a job claims latency, throughput, rendering, query, memory, or resource concerns.

Instalação

Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.

Fonte

lebobo88

lebobo88/orchestrator

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Ocupações relacionadasSOC

Baseado na classificação ocupacional SOC

Desenvolvedores de softwareInformática e Matemática·SOC 15-1252

SKILL.md

readonly

name	t1-performance-evidence
description	T1 measurement-driven performance optimization playbook. Use when a job claims latency, throughput, rendering, query, memory, or resource concerns.
user-invocable	false

Performance evidence

Define the user-visible or operational metric, workload, environment, and acceptable regression limit.
Capture a reproducible baseline before changing production code. Trace the likely bottleneck and distinguish CPU, memory, I/O, network, database, rendering, and algorithmic causes.
Form a bounded hypothesis; make one small change at a time under the normal TDD loop.
Measure before/after with the same method. Do not claim improvement without comparable evidence.
Check correctness, error behavior, cache invalidation, resource lifecycle, and worst-case/edge inputs. Report noise, limits, and any tradeoff honestly.

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name	t1-performance-evidence
description	T1 measurement-driven performance optimization playbook. Use when a job claims latency, throughput, rendering, query, memory, or resource concerns.
user-invocable	false

Performance evidence

Define the user-visible or operational metric, workload, environment, and acceptable regression limit.
Capture a reproducible baseline before changing production code. Trace the likely bottleneck and distinguish CPU, memory, I/O, network, database, rendering, and algorithmic causes.
Form a bounded hypothesis; make one small change at a time under the normal TDD loop.
Measure before/after with the same method. Do not claim improvement without comparable evidence.
Check correctness, error behavior, cache invalidation, resource lifecycle, and worst-case/edge inputs. Report noise, limits, and any tradeoff honestly.