Move testing activities earlier in the development lifecycle to catch defects when they're cheapest to fix. Use when implementing TDD, CI/CD, or early quality practices.
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<default_to_action> When implementing early testing practices:
Quick Shift-Left Levels:
| Level | Practice | When |
|---|---|---|
| 1 | Unit tests in PR | Before merge |
| 2 | TDD | Before implementation |
| 3 | BDD/Example Mapping | During refinement |
| 4 | Risk Analysis | During design |
# CI pipeline - tests run on every commit
name: CI
on: [push, pull_request]
jobs:
test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- run: npm ci
- run: npm run test:unit
- run: npm run test:integration
- run: npm run lint
quality-gate:
needs: test
steps:
- name: Coverage Check
run: npx coverage-check --min 80
- name: No New Warnings
run: npm run lint -- --max-warnings 0
// Validate requirements testability
await Task("Requirements Validation", {
requirements: userStories,
check: ['INVEST-criteria', 'testability', 'ambiguity'],
generateBDD: true
}, "qe-requirements-validator");
// Generate tests from requirements
await Task("Generate Tests", {
source: 'requirements',
types: ['unit', 'integration', 'e2e'],
coverage: 'comprehensive'
}, "qe-test-generator");
// Smart test selection for changes
await Task("Select Regression Tests", {
changedFiles: prFiles,
algorithm: 'risk-based',
targetReduction: 0.7 // 70% time savings
}, "qe-regression-risk-analyzer");
aqe/shift-left/
├── requirements/* - Validated requirements
├── generated-tests/* - Auto-generated tests
├── coverage-targets/* - Coverage goals by component
└── pipeline-results/* - CI/CD test history
const shiftLeftFleet = await FleetManager.coordinate({
strategy: 'shift-left',
agents: [
'qe-requirements-validator', // Level 3-4
'qe-test-generator', // Level 2
'qe-regression-risk-analyzer' // Smart selection
],
topology: 'sequential'
});
With Agents: Agents validate requirements testability, generate tests from specs, and select optimal regression suites. Use agents to implement shift-left practices consistently.
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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.
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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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