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shift-right-testing右移测试

Agent Skill

用于辅助测试设计、自动化测试、用例整理和回归验证。它适合让 Agent 编写单元测试、端到端测试、测试计划或根据失败日志定位问题。使用时需要确认项目测试框架、运行命令和夹具数据,避免为了通过测试而改坏真实逻辑;涉及浏览器或外部服务时,应区分本地模拟、测试环境和生产环境。

总安装

1,665

周安装

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下载量

533
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:shift-right-testing(右移测试)
来源仓库:https://github.com/proffesor-for-testing/agentic-qe
仓库路径:skills/shift-right-testing
安装命令:
npx skills add https://github.com/proffesor-for-testing/agentic-qe --skill shift-right-testing
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/proffesor-for-testing/agentic-qe --skill shift-right-testing

简介

辅助测试设计和自动化用例整理,提升代码后期质量。

  • 适合编写单元测试、端到端测试或生成测试计划。
  • 需确认项目测试框架、运行命令和夹具数据。shift-right-testing 属于开发类 Skill,可作为该场景下的辅助能力补充。
  • 避免为了通过测试而破坏真实逻辑,保持功能正确性。
  • 涉及浏览器或外部服务时,应使用本地模拟和测试环境。

SKILL.md

Shift-Right Testing

<default_to_action> When testing in production or implementing progressive delivery:

  1. IMPLEMENT feature flags for progressive rollout (1% → 10% → 50% → 100%)
  2. DEPLOY with canary releases (compare metrics before full rollout)
  3. MONITOR with synthetic tests (proactive) + RUM (reactive)
  4. INJECT failures with chaos engineering (build resilience)
  5. ANALYZE production data to improve pre-production testing

Quick Shift-Right Techniques:

  • Feature flags → Control who sees what, instant rollback
  • Canary deployment → 5% traffic, compare error rates
  • Synthetic monitoring → Simulate users 24/7, catch issues before users
  • Chaos engineering → Netflix-style failure injection
  • RUM (Real User Monitoring) → Actual user experience data

Critical Success Factors:

  • Production is the ultimate test environment
  • Ship fast with safety nets, not slow with certainty
  • Use production data to improve shift-left testing </default_to_action>

Quick Reference Card

When to Use

  • Progressive feature rollouts
  • Production reliability validation
  • Performance monitoring at scale
  • Learning from real user behavior

Shift-Right Techniques

TechniquePurposeWhen
Feature FlagsControlled rolloutEvery feature
CanaryCompare new vs oldEvery deployment
Synthetic MonitoringProactive detection24/7
RUMReal user metricsAlways on
Chaos EngineeringResilience validationRegularly
A/B TestingUser behavior validationFeature decisions

Progressive Rollout Pattern

1% → 10% → 25% → 50% → 100%
↓      ↓      ↓      ↓
Check  Check  Check  Monitor

Key Metrics to Monitor

MetricSLO TargetAlert Threshold
Error rate< 0.1%> 1%
p95 latency< 200ms> 500ms
Availability99.9%< 99.5%
Apdex> 0.95< 0.8

Feature Flags

// Progressive rollout with LaunchDarkly/Unleash pattern
const newCheckout = featureFlags.isEnabled('new-checkout', {
  userId: user.id,
  percentage: 10,  // 10% of users
  allowlist: ['beta-testers']
});

if (newCheckout) {
  return <NewCheckoutFlow />;
} else {
  return <LegacyCheckoutFlow />;
}

// Instant rollback on issues
await featureFlags.disable('new-checkout');

Canary Deployment

# Flagger canary config
apiVersion: flagger.app/v1beta1
kind: Canary
spec:
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: checkout-service
  progressDeadlineSeconds: 60
  analysis:
    interval: 1m
    threshold: 5      # Max failed checks
    maxWeight: 50     # Max traffic to canary
    stepWeight: 10    # Increment per interval
    metrics:
      - name: request-success-rate
        threshold: 99
      - name: request-duration
        threshold: 500

Synthetic Monitoring

// Continuous production validation
await Task("Synthetic Tests", {
  endpoints: [
    { path: '/health', expected: 200, interval: '30s' },
    { path: '/api/products', expected: 200, interval: '1m' },
    { path: '/checkout', flow: 'full-purchase', interval: '5m' }
  ],
  locations: ['us-east', 'eu-west', 'ap-south'],
  alertOn: {
    statusCode: '!= 200',
    latency: '> 500ms',
    contentMismatch: true
  }
}, "qe-production-intelligence");

Chaos Engineering

// Controlled failure injection
await Task("Chaos Experiment", {
  hypothesis: 'System handles database latency gracefully',
  steadyState: {
    metric: 'error_rate',
    expected: '< 0.1%'
  },
  experiment: {
    type: 'network-latency',
    target: 'database',
    delay: '500ms',
    duration: '5m'
  },
  rollback: {
    automatic: true,
    trigger: 'error_rate > 5%'
  }
}, "qe-chaos-engineer");

Production → Pre-Production Feedback Loop

// Convert production incidents to regression tests
await Task("Incident Replay", {
  incident: {
    id: 'INC-2024-001',
    type: 'performance-degradation',
    conditions: { concurrent_users: 500, cart_items: 10 }
  },
  generateTests: true,
  addToRegression: true
}, "qe-production-intelligence");

// Output: New test added to prevent recurrence

Agent Coordination Hints

Memory Namespace

aqe/shift-right/
├── canary-results/*      - Canary deployment metrics
├── synthetic-tests/*     - Monitoring configurations
├── chaos-experiments/*   - Experiment results
├── production-insights/* - Issues → test conversions
└── rum-analysis/*        - Real user data patterns

Fleet Coordination

const shiftRightFleet = await FleetManager.coordinate({
  strategy: 'shift-right-testing',
  agents: [
    'qe-production-intelligence',  // RUM, incident replay
    'qe-chaos-engineer',           // Resilience testing
    'qe-performance-tester',       // Synthetic monitoring
    'qe-quality-analyzer'          // Metrics analysis
  ],
  topology: 'mesh'
});

Related Skills


Remember

Production is the ultimate test environment. Feature flags enable instant rollback. Canary catches issues before 100% rollout. Synthetic monitoring detects problems before users. Chaos engineering builds resilience. RUM shows real user experience.

With Agents: Agents monitor production, replay incidents as tests, run chaos experiments, and convert production insights to pre-production tests. Use agents to maintain continuous production quality.

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用户想查找某类 Agent Skill 时

02

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能力 5

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平台分布

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13.09%
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3.96%
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