Agent-Skills.md

Agent Skills: Agentic Quality Engineering

AI agents as force multipliers for quality work. Core skill for all 19 QE agents using PACT principles.

UncategorizedID: proffesor-for-testing/agentic-qe/qe-agentic-quality-engineering

Repository

proffesor-for-testing/agentic-qe
proffesor-for-testingLicense: MIT
28258

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pnpm dlx add-skill https://github.com/proffesor-for-testing/agentic-qe/tree/HEAD/.kiro/skills/qe-agentic-quality-engineering

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.kiro/skills/qe-agentic-quality-engineering/SKILL.md

Skill Metadata

Name
qe-agentic-quality-engineering
Description
"AI agents as force multipliers for quality work. Core skill for all 19 QE agents using PACT principles."

Agentic Quality Engineering

<default_to_action> When implementing agentic QE or coordinating agents:

  1. SPAWN appropriate agent(s) for the task using Task tool with agent type
  2. CONFIGURE agent coordination (hierarchical/mesh/sequential)
  3. EXECUTE with PACT principles: Proactive analysis, Autonomous operation, Collaborative feedback, Targeted risk focus
  4. VALIDATE results through quality gates before deployment
  5. 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

PACT 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 |

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 |

Coordination Patterns

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

Success Criteria

✅ 10x deployment frequency with same/better quality ✅ Coverage gaps detected in real-time ✅ Bugs caught pre-production ❌ Agents acting without human oversight on critical decisions ❌ Deploying all 19 agents at once (start with 1-2)

Core Concepts

QE Evolution

| Stage | Approach | Limitation | |-------|----------|------------| | Traditional | Manual everything | Human bottleneck | | Automation | Scripts + fixed scenarios | Needs orchestration | | Agentic | AI agents + human judgment | Requires trust-building |

Core Premise: Agents amplify human expertise for 10x scale.

Key Capabilities

1. Intelligent Test Generation

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

2. Pattern Detection - Scan logs, find anomalies, correlate errors

3. Adaptive Strategy - Adjust test focus based on risk signals

4. Root Cause Analysis - Link failures to code changes, suggest fixes

Agent Coordination

Memory Namespaces

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

Memory Operations (MCP Tools)

CRITICAL: Always use aqe memory store with persist: true for learnings.

1. Store data to persistent memory:

# Store test plan decisions (persisted to .agentic-qe/memory.db)
aqe memory store \
  --key "aqe/test-plan/pr-123" \
  --namespace "aqe/test-plan" \
  --value '{"prNumber":123,"riskLevel":"medium","requiredCoverage":85,"testTypes":["unit","integration"]}' \
  --ttl 604800 \
  --json

2. Retrieve prior learnings before task:

# Query patterns before starting test generation
aqe memory search \
  --pattern "aqe/learning/patterns/test-generation/*" \
  --namespace "aqe/learning" \
  --json

3. Store coverage analysis results:

aqe memory store \
  --key "aqe/coverage/auth-module" \
  --namespace "aqe/coverage" \
  --value '{"moduleId":"auth-module","currentCoverage":78,"gaps":["error-handling","edge-cases"],"priority":"high"}' \
  --ttl 1209600 \
  --json

Three-Phase Memory Protocol

For coordinated multi-agent tasks, use the STATUS → PROGRESS → COMPLETE pattern:

# PHASE 1: STATUS - Task starting
aqe memory store \
  --key "aqe/coordination/task-123/status" \
  --namespace "aqe/coordination" \
  --value '{"status":"running","agent":"qe-test-generator"}' \
  --json

# PHASE 2: PROGRESS - Intermediate updates
aqe memory store \
  --key "aqe/coordination/task-123/progress" \
  --namespace "aqe/coordination" \
  --value '{"progress":50,"action":"generating-unit-tests","testsGenerated":25}' \
  --json

# PHASE 3: COMPLETE - Task finished
aqe memory store \
  --key "aqe/coordination/task-123/complete" \
  --namespace "aqe/coordination" \
  --value '{"status":"complete","result":"success","testsGenerated":47,"coverageAchieved":92.3}' \
  --json

Blackboard Events

| 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 |

Example: PR Quality Pipeline

// 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

Implementation Phases

| Phase | Duration | Goal | Agent(s) | |-------|----------|------|----------| | Experiment | Weeks 1-4 | Validate one use case | 1 agent | | Integrate | Months 2-3 | CI/CD pipeline | 3-4 agents | | Scale | Months 4-6 | Multiple use cases | 8+ agents | | Evolve | Ongoing | Continuous learning | Full fleet |

Phase 1 Example

# 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

Limitations & Strengths

Agents Excel At

  • Volume: Scan thousands of logs in seconds
  • Patterns: Find correlations humans miss
  • Tireless: 24/7 testing and monitoring
  • Speed: Instant code change analysis

Agents Need Humans For

  • Business context and priorities
  • Ethical judgment and trade-offs
  • Creative exploration ("what if" scenarios)
  • Domain expertise (healthcare, finance, legal)

Best Practices

| 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 |

Trust Progression

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

Agent Coordination Hints

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]

Related Skills

  • holistic-testing-pact - PACT principles deep dive
  • risk-based-testing - Prioritize agent focus
  • quality-metrics - Measure agent effectiveness
  • api-testing-patterns, security-testing, performance-testing - Specialized testing

Resources

  • Agent definitions: .claude/agents/
  • CLI: aqe agent --help
  • Fleet status: aqe fleet status

Success Metric: Deploy 10x more frequently with same or better quality through intelligent agent collaboration.