agentic-quality-engineering

Use when orchestrating QE agents, understanding PACTS principles, configuring the AQE v3 fleet, or leveraging AI agents as force multipliers for quality work.

仓库: proffesor-for-testing/agentic-qe
最近来源活动: 2026年6月20日 13:35
检测到的 SKILL.md 语言: 英语
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分支: 84

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name	agentic-quality-engineering
description	Use when orchestrating QE agents, understanding PACTS principles, configuring the AQE v3 fleet, or leveraging AI agents as force multipliers for quality work.
category	qe-core
priority	critical
tokenEstimate	1400
agents	["qe-test-generator","qe-test-executor","qe-coverage-analyzer","qe-quality-gate","qe-quality-analyzer","qe-performance-tester","qe-security-scanner","qe-requirements-validator","qe-production-intelligence","qe-fleet-commander","qe-deployment-readiness","qe-regression-risk-analyzer","qe-test-data-architect","qe-api-contract-validator","qe-flaky-test-hunter","qe-visual-tester","qe-chaos-engineer","qe-code-complexity","qx-partner"]
implementation_status	optimized
optimization_version	1
last_optimized	"2025-12-02T00:00:00.000Z"
dependencies	[]
quick_reference_card	true
tags	["pacts","agents","fleet","coordination","autonomous","structured","foundational"]
trust_tier	1
validation	{"schema_path":"schemas/output.json"}

Agentic Quality Engineering

<default_to_action> When implementing agentic QE or coordinating agents:

SPAWN appropriate agent(s) for the task using Task tool with agent type
CONFIGURE agent coordination (hierarchical/mesh/sequential)
EXECUTE with PACTS principles: Proactive analysis, Autonomous operation, Collaborative feedback, Targeted risk focus, Structured governance (observability and explainability of agent behavior)
VALIDATE results through quality gates before deployment
LEARN from outcomes - store patterns in aqe/learning/* namespace

Quick Agent Selection:

Test generation needed → qe-test-generator
Coverage gaps → qe-coverage-analyzer
Quality decision → qe-quality-gate
Security scan → qe-security-scanner
Performance test → qe-performance-tester
Full pipeline → qe-fleet-commander

Critical Success Factors:

Agents amplify human expertise, not replace it
Human-in-the-loop for critical decisions
Measure: bugs caught, time saved, coverage improved </default_to_action>

Quick Reference Card

When to Use

Designing autonomous testing systems
Scaling QE with intelligent agents
Implementing multi-agent coordination
Building CI/CD quality pipelines

PACTS Principles

Principle	Agent Behavior	Human Role
Proactive	Analyze pre-merge, predict risk	Set guardrails
Autonomous	Execute tests, fix flaky tests	Review critical
Collaborative	Multi-agent coordination	Provide context
Targeted	Risk-based prioritization	Define risk areas
Structured	Governance, observability, explainable decisions (measure confidence, not trust)	Audit behavior, set policy

19-Agent Fleet

Category	Agents	Primary Use
Core Testing (5)	test-generator, test-executor, coverage-analyzer, quality-gate, quality-analyzer	Daily testing
Performance/Security (2)	performance-tester, security-scanner	Non-functional
Strategic (3)	requirements-validator, production-intelligence, fleet-commander	Planning
Advanced (4)	regression-risk-analyzer, test-data-architect, api-contract-validator, flaky-test-hunter	Specialized
Visual/Chaos (2)	visual-tester, chaos-engineer	Edge cases
Deployment (1)	deployment-readiness	Release
Analysis (1)	code-complexity	Maintainability

Hierarchical: fleet-commander → [generators] → [executors] → quality-gate
Mesh: test-gen ↔ coverage ↔ quality (peer decisions)
Sequential: risk-analyzer → test-gen → executor → coverage → gate

// Agent analyzes code change, generates targeted tests
const tests = await qeTestGenerator.generate(prDiff);
// → Happy path, edge cases, error handling tests

aqe/test-plan/*     - Test planning decisions
aqe/coverage/*      - Coverage analysis results
aqe/quality/*       - Quality metrics and gates
aqe/learning/*      - Patterns and Q-values
aqe/coordination/*  - Cross-agent state

// Store test plan decisions (persisted to .agentic-qe/memory.db)
aqe memory store \
  --key "aqe/test-plan/pr-123" \
  --namespace "aqe/test-plan" \
  --value '{...}' \
  --json

// Query patterns before starting test generation
const priorData = await aqe memory get --key "aqe/learning/patterns/test-generation/*" --namespace "aqe/learning" --json

// Use patterns to guide current task
if (priorData.success) {
  console.log(`Loaded ${priorData.patterns.length} prior patterns`);
}

aqe memory store \
  --key "aqe/coverage/auth-module" \
  --namespace "aqe/coverage" \
  --value '{...}' \
  --json

// PHASE 1: STATUS - Task starting
aqe memory store \
  --key "aqe/coordination/task-123/status" \
  --namespace "aqe/coordination" \
  --value '{...}' \
  --json

// PHASE 2: PROGRESS - Intermediate updates
aqe memory store \
  --key "aqe/coordination/task-123/progress" \
  --namespace "aqe/coordination" \
  --value '{...}' \
  --json

// PHASE 3: COMPLETE - Task finished
aqe memory store \
  --key "aqe/coordination/task-123/complete" \
  --namespace "aqe/coordination" \
  --value '{...}' \
  --json

Event	Trigger	Subscribers
`test:generated`	New tests created	executor, coverage
`coverage:gap`	Gap detected	test-generator
`quality:decision`	Gate evaluated	fleet-commander
`security:finding`	Vulnerability found	quality-gate

// 1. Risk analysis
const risks = await Task("Analyze PR", prDiff, "qe-regression-risk-analyzer");

// 2. Generate tests for risks
const tests = await Task("Generate tests", risks, "qe-test-generator");

// 3. Execute + analyze
const results = await Task("Run tests", tests, "qe-test-executor");
const coverage = await Task("Check coverage", results, "qe-coverage-analyzer");

// 4. Quality decision
const decision = await Task("Evaluate", {results, coverage}, "qe-quality-gate");
// → GO/NO-GO with rationale

# Week 1: Deploy single agent
aqe agent spawn qe-test-generator

# Weeks 2-3: Generate tests for 10 PRs
# Track: bugs found, test quality, review time

# Week 4: Measure impact
aqe agent metrics qe-test-generator
# → Tests: 150, Bugs: 12, Time saved: 8h

Do	Don't
Start with one agent, one use case	Deploy all 18 at once
Build feedback loops early	Deploy and forget
Human reviews agent output	Auto-merge without review
Measure bugs caught, time saved	Track vanity metrics (test count)
Build trust gradually	Give full autonomy immediately

Month 1: Agent suggests → Human decides
Month 2: Agent acts → Human reviews after
Month 3: Agent autonomous on low-risk
Month 4: Agent handles critical with oversight

coordination:
  topology: hierarchical
  commander: qe-fleet-commander
  memory_namespace: aqe/coordination
  blackboard_topic: qe-fleet

preload_skills:
  - agentic-quality-engineering  # Always (this skill)
  - risk-based-testing           # For prioritization
  - quality-metrics              # For measurement

agent_assignments:
  qe-test-generator: [api-testing-patterns, tdd-london-chicago]
  qe-coverage-analyzer: [quality-metrics, risk-based-testing]
  qe-security-scanner: [security-testing, risk-based-testing]
  qe-performance-tester: [performance-testing]