Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
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Test quality validation through mutation testing, assessing test suite effectiveness by introducing code mutations and measuring kill rate. Use when evaluating test quality, identifying weak tests, or proving tests actually catch bugs.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
<default_to_action> When validating test quality or improving test effectiveness:
Quick Mutation Metrics:
Critical Success Factors:
| Score | Interpretation |
|---|---|
| 90%+ | Excellent test quality |
| 80-90% | Good, minor improvements |
| 60-80% | Needs attention |
| < 60% | Significant gaps |
| Category | Original | Mutant |
|---|---|---|
| Arithmetic | a + b | a - b |
| Relational | x >= 18 | x > 18 |
| Logical | a && b | a || b |
| Conditional | if (x) | if (true) |
| Statement | return x | (removed) |
// Original code
function isAdult(age) {
return age >= 18; // ← Mutant: change >= to >
}
// Strong test (catches mutation)
test('18 is adult', () => {
expect(isAdult(18)).toBe(true); // Kills mutant!
});
// Weak test (mutation survives)
test('19 is adult', () => {
expect(isAdult(19)).toBe(true); // Doesn't catch >= vs >
});
// Surviving mutant → Test needs boundary value
# Install
npm install --save-dev @stryker-mutator/core @stryker-mutator/jest-runner
# Initialize
npx stryker init
Configuration:
{
"packageManager": "npm",
"reporters": ["html", "clear-text", "progress"],
"testRunner": "jest",
"coverageAnalysis": "perTest",
"mutate": [
"src/**/*.ts",
"!src/**/*.spec.ts"
],
"thresholds": {
"high": 90,
"low": 70,
"break": 60
}
}
Run:
npx stryker run
Output:
Mutation Score: 87.3%
Killed: 124
Survived: 18
No Coverage: 3
Timeout: 1
// Surviving mutant: >= changed to >
function calculateDiscount(quantity) {
if (quantity >= 10) { // Mutant survives!
return 0.1;
}
return 0;
}
// Original weak test
test('large order gets discount', () => {
expect(calculateDiscount(15)).toBe(0.1); // Doesn't test boundary
});
// Fixed: Add boundary test
test('exactly 10 gets discount', () => {
expect(calculateDiscount(10)).toBe(0.1); // Kills mutant!
});
test('9 does not get discount', () => {
expect(calculateDiscount(9)).toBe(0); // Tests below boundary
});
// Analyze mutation score and generate fixes
await Task("Mutation Analysis", {
targetFile: 'src/payment.ts',
generateMissingTests: true,
minScore: 80
}, "qe-test-generator");
// Returns:
// {
// mutationScore: 0.65,
// survivedMutations: [
// { line: 45, operator: '>=', mutant: '>', killedBy: null }
// ],
// generatedTests: [
// 'test for boundary at line 45'
// ]
// }
// Coverage + mutation correlation
await Task("Coverage Quality Analysis", {
coverageData: coverageReport,
mutationData: mutationReport,
identifyWeakCoverage: true
}, "qe-coverage-analyzer");
aqe/mutation-testing/
├── mutation-results/* - Stryker reports
├── surviving/* - Surviving mutants
├── generated-tests/* - Tests to kill mutants
└── trends/* - Mutation score over time
const mutationFleet = await FleetManager.coordinate({
strategy: 'mutation-testing',
agents: [
'qe-test-generator', // Generate tests for survivors
'qe-coverage-analyzer', // Coverage correlation
'qe-quality-analyzer' // Quality assessment
],
topology: 'sequential'
});
High code coverage ≠ good tests. 100% coverage but weak assertions = useless. Mutation testing proves tests actually catch bugs.
Focus on critical paths first. Don't mutation test everything - prioritize payment, authentication, data integrity code.
With Agents: Agents run mutation analysis, identify surviving mutants, and generate missing test cases to kill them. Automated improvement of test quality.
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Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
中文简介正在准备中。当前显示原文说明。
WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
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