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 environment provisioning, infrastructure as code for testing, Docker/Kubernetes for test environments, service virtualization, and cost optimization. Use when managing test infrastructure, ensuring environment parity, or optimizing testing costs.
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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 managing test environments:
Quick Environment Checklist:
Critical Success Factors:
| Type | Purpose | Lifetime |
|---|---|---|
| Local | Fast feedback | Developer session |
| CI | Automated tests | Per build (ephemeral) |
| Staging | Pre-prod validation | Persistent |
| Production | Canary/synthetic | Continuous |
| Item | Must Match |
|---|---|
| OS | Same version |
| Database | Same type + version |
| Dependencies | Same versions |
| Config | Same structure |
| Env vars | Same names |
# docker-compose.test.yml
version: '3.8'
services:
app:
build: .
ports:
- "3000:3000"
environment:
NODE_ENV: test
DATABASE_URL: postgres://postgres:password@db:5432/test
depends_on:
- db
- redis
db:
image: postgres:15
environment:
POSTGRES_DB: test
POSTGRES_PASSWORD: password
redis:
image: redis:7
Run tests in container:
docker-compose -f docker-compose.test.yml up -d
docker-compose -f docker-compose.test.yml exec app npm test
docker-compose -f docker-compose.test.yml down
# test-environment.tf
resource "aws_instance" "test_server" {
ami = "ami-0c55b159cbfafe1f0"
instance_type = "t3.medium"
tags = {
Name = "test-environment"
Environment = "test"
AutoShutdown = "20:00" # Cost optimization
}
}
resource "aws_rds_instance" "test_db" {
engine = "postgres"
engine_version = "15"
instance_class = "db.t3.micro"
backup_retention_period = 0 # No backups needed for test
skip_final_snapshot = true
}
// Mock external services with WireMock
import { WireMock } from 'wiremock-captain';
const wiremock = new WireMock('http://localhost:8080');
// Mock payment gateway
await wiremock.register({
request: {
method: 'POST',
url: '/charge'
},
response: {
status: 200,
jsonBody: { transactionId: '12345', status: 'approved' }
}
});
// Tests use mock instead of real gateway
# Auto-shutdown test environments after hours
0 20 * * * aws ec2 stop-instances --instance-ids $(aws ec2 describe-instances \
--filters "Name=tag:Environment,Values=test" \
--query "Reservations[].Instances[].InstanceId" --output text)
# Start before work hours
0 7 * * 1-5 aws ec2 start-instances --instance-ids $(aws ec2 describe-instances \
--filters "Name=tag:Environment,Values=test" \
--query "Reservations[].Instances[].InstanceId" --output text)
Use spot instances (70% savings):
resource "aws_instance" "test_runner" {
instance_type = "c5.2xlarge"
instance_market_options {
market_type = "spot"
spot_options {
max_price = "0.10"
}
}
}
// Provision test environment
await Task("Environment Provisioning", {
type: 'integration-testing',
services: ['app', 'db', 'redis', 'mocks'],
parity: 'production',
lifetime: '2h'
}, "qe-test-executor");
// Chaos testing in isolated environment
await Task("Chaos Test Environment", {
baseline: 'staging',
isolate: true,
injectFaults: ['network-delay', 'pod-failure']
}, "qe-chaos-engineer");
aqe/environment-management/
├── configs/* - Environment configurations
├── parity-checks/* - Dev/prod parity results
├── cost-reports/* - Infrastructure costs
└── service-mocks/* - Service virtualization configs
const envFleet = await FleetManager.coordinate({
strategy: 'environment-management',
agents: [
'qe-test-executor', // Provision environments
'qe-performance-tester', // Environment performance
'qe-chaos-engineer' // Resilience testing
],
topology: 'sequential'
});
Environment inconsistency = flaky tests. "Works on my machine" problems come from: different OS/versions, missing dependencies, configuration differences, data differences.
Infrastructure as Code ensures repeatability. Version control your environment configurations. Spin up identical environments on demand.
With Agents: Agents automatically provision test environments matching production, ensure parity, mock external services, and optimize costs with auto-scaling and auto-shutdown.
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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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