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npx skills add https://github.com/proffesor-for-testing/agentic-qe --skill agentic-quality-engineeringيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
تحميل Zip جاري التحميل... المزيد من هذا المستودع 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.
المهن ذات الصلة SOC
استنادا إلى تصنيف SOC المهني
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
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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
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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 P roactiveAnalyze pre-merge, predict risk Set guardrails A utonomousExecute tests, fix flaky tests Review critical C ollaborativeMulti-agent coordination Provide context T argetedRisk-based prioritization Define risk areas S tructuredGovernance, 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
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
const tests = await qeTestGenerator.generate (prDiff);
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 '{...}' \
--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
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 '{...}' \
--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
Blackboard Events Event Trigger Subscribers test:generatedNew tests created executor, coverage coverage:gapGap detected test-generator quality:decisionGate evaluated fleet-commander security:findingVulnerability found quality-gate
Example: PR Quality Pipeline
const risks = await Task ("Analyze PR" , prDiff, "qe-regression-risk-analyzer" );
const tests = await Task ("Generate tests" , risks, "qe-test-generator" );
const results = await Task ("Run tests" , tests, "qe-test-executor" );
const coverage = await Task ("Check coverage" , results, "qe-coverage-analyzer" );
const decision = await Task ("Evaluate" , {results, coverage}, "qe-quality-gate" );
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
aqe agent spawn qe-test-generator
aqe agent metrics qe-test-generator
Limitations & Strengths
Agents Excel At
Volume : Scan thousands of logs in secondsPatterns : Find correlations humans missTireless : 24/7 testing and monitoringSpeed : 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
- risk-based-testing
- quality-metrics
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 - PACTS principles deep diverisk-based-testing - Prioritize agent focusquality-metrics - Measure agent effectivenessapi-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.
