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load-testing

Use when working on load tests for APIs, web services, queues, and critical user flows. Focus on realistic traffic models, throughput, latency percentiles, and bottleneck evidence.

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Repositório
Mr-Q526/TeamCC-Platform
Última atividade na origem
15 de abril de 2026 às 03:16
Idioma detectado do SKILL.md
inglês
Estrelas
8
Forks
1

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SKILL.md
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schemaVersion
2026-04-11T00:00:00.000Z
skillId
infra/load-testing
name
load-testing
displayName
Load Testing
description
Use when working on load tests for APIs, web services, queues, and critical user flows. Focus on realistic traffic models, throughput, latency percentiles, and bottleneck evidence.
aliases
["load-testing","Load Testing","loadtesting","负载测试","load test","压测","测试策略","load","性能","性能优化","性能分析","验证"]
version
0.1.0
sourceHash
sha256:92339c3f173c803a904cc2ae7d27040734c9ee9f9b141d8b996325a01ba5d2c9
domain
infra
departmentTags
["infra-platform"]
sceneTags
["performance","test"]
# Load Testing Use this skill when the task involves load tests for APIs, web services, queues, and critical user flows. Goal: produce reliable engineering guidance and implementation steps focused on realistic traffic models, throughput, latency percentiles, and bottleneck evidence. ## Working model 1. Identify the affected system, data, users, and failure modes. 2. Define invariants, inputs, outputs, ownership, and rollback needs. 3. Prefer small, auditable changes with explicit validation. 4. Call out security, performance, concurrency, and data-loss risks when relevant. 5. Finish with concrete verification steps and residual risks. ## Rules - Ground recommendations in the current codebase or runtime evidence. - Prefer explicit contracts, typed boundaries, and defensive validation. - Do not hide operational concerns behind generic best practices. - Include negative cases, edge cases, and failure behavior. - For review tasks, list findings first with file and line references when possible. - For test or performance tasks, define the workload, success criteria, and measurement method. ## Checklist - Are assumptions and ownership boundaries explicit? - Are risky changes reversible or safely deployable? - Are observability and diagnostics sufficient for production issues? - Are tests or validation steps targeted to the actual risk? - Are security and data-integrity concerns addressed?
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