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Use when orchestrating QE agents, understanding PACTS principles, configuring the AQE v3 fleet, or leveraging AI agents as force multipliers for quality work.
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インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/proffesor-for-testing/agentic-qe --skill agentic-quality-engineeringコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
Ruflo is a multi-agent orchestration platform for AI coding agents (Claude Code, Cursor, Codex, Copilot, Gemini, Amp, +12 more). Use this skill when the user wants to (1) install/init ruflo in a project, (2) run multi-agent swarms with hierarchical coordination, (3) use ruflo's 314+ MCP tools for memory, routing, hooks, sub-agents, or workflows, (4) check ruflo status/version/doctor health, or (5) discover which of ruflo's 30+ plugins fits their task.
Build dependency-aware execution plans for complex Agentic QE, Ruflo, integration, migration, or multi-stream engineering programs. Use when Codex must turn research or requirements into phased work, select a small AQE fleet, map critical paths and parallel streams, define acceptance gates, or sequence risky changes. Use aqe-plan-quality instead when the primary output is only a test or quality plan.
Conduct evidence-first technical research for Agentic QE, Ruflo, related ruvnet projects, or external tools. Use when Codex must investigate a repository, compare current upstream changes, trace dependencies and history, distinguish verified facts from inference, or synthesize findings into actionable engineering recommendations. Do not use for a simple known-answer lookup or an implementation-only task.
SOC 職業分類に基づく
SKILL.md を表示中
| 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"} |
<default_to_action> When implementing agentic QE or coordinating agents:
Task tool with agent typeaqe/learning/* namespaceQuick Agent Selection:
qe-test-generatorqe-coverage-analyzerqe-quality-gateqe-security-scannerqe-performance-testerqe-fleet-commanderCritical Success Factors:
| 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 |
| 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
✅ 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)
| 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.
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
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
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 '{...}' \
--json
2. Retrieve prior learnings before task:
// 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`);
}
3. Store coverage analysis results:
aqe memory store \
--key "aqe/coverage/auth-module" \
--namespace "aqe/coverage" \
--value '{...}' \
--json
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 '{...}' \
--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
| 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 |
# 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]
holistic-testing-pact - PACTS principles deep diverisk-based-testing - Prioritize agent focusquality-metrics - Measure agent effectivenessapi-testing-patterns, security-testing, performance-testing - Specialized testing.claude/agents/aqe agent --helpaqe fleet statusSuccess Metric: Deploy 10x more frequently with same or better quality through intelligent agent collaboration.