Orchestrate a fleet of 11 AI-powered QE agents to automate comprehensive quality engineering: generate unit/integration/E2E tests for Jest/Vitest/Playwright/Pytest, perform sublinear coverage analysis and gap prioritization, run chaos/resilience experiments on Docker/K8s, guide TDD workflows, benchmark performance, enforce git/CI quality gates, detect flakiness/security issues, and produce reports.
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Analyze test coverage, identify gaps, and optimize coverage strategy using sublinear algorithms
Run performance benchmarks and compare against baselines
Run chaos testing scenarios to validate system resilience and fault tolerance
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Execute test suites with parallel orchestration, retry logic, and real-time reporting
Chaos engineering specialist for controlled fault injection, resilience testing, and system weakness discovery
O(log n) sublinear coverage analysis with risk-weighted gap detection and HNSW vector indexing
Flaky test detection and remediation with pattern recognition and auto-stabilization
Fleet management with agent lifecycle, workload distribution, and cross-domain coordination at scale
Performance testing with load, stress, endurance testing and regression detection
Chaos engineering principles, controlled failure injection, resilience testing, and system recovery validation. Use when testing distributed systems, building confidence in fault tolerance, or validating disaster recovery.
Test quality validation through mutation testing, assessing test suite effectiveness by introducing code mutations and measuring kill rate. Use when evaluating test quality, identifying weak tests, or proving tests actually catch bugs.
Injects controlled faults (network partition, latency, process kill, disk pressure) into distributed systems and validates recovery behavior. Use when testing circuit breakers, failover paths, retry logic, or building confidence in system resilience through chaos engineering.
Analyzes test coverage data (Istanbul, c8, lcov) to identify uncovered lines, branches, and functions with risk-weighted gap detection. Use when analyzing coverage reports, identifying coverage gaps, comparing coverage between branches, or prioritizing which untested code to cover first.
Evaluates code quality through complexity analysis, lint results, code smell detection, and test health metrics. Use when assessing deployment readiness, configuring quality gates, scoring a codebase for release, or generating quality reports with pass/fail verdicts.
Release Notes | Changelog | Issues | Discussions
AI-powered quality engineering agents that generate tests, find coverage gaps, detect flaky tests, and learn your codebase patterns — across 11 coding agent platforms.
# Install
npm install -g agentic-qe
# Initialize your project (auto-detects tech stack, configures MCP)
cd your-project && aqe init --auto
# That's it — MCP tools are available immediately in Claude Code
# For other clients: aqe-mcp
After init, your coding agent can use AQE tools directly. For example in Claude Code:
"Generate tests for src/services/UserService.ts with 90% coverage target"
"Find coverage gaps in src/ and prioritize by risk"
"Run security scan on the authentication module"
"Analyze why tests in auth/ are flaky and suggest fixes"
If you only need a slim, scoped fleet inside Claude Code — without the full aqe init setup — install the agentic-qe-fleet plugin. It bundles 11 specialized QE agents, 9 slash commands, 9 skills, and auto-registers the MCP server.
git clone https://github.com/proffesor-for-testing/agentic-qe.git
claude --plugin-dir ./agentic-qe/plugins/agentic-qe-fleet
In any Claude Code session:
/plugin marketplace add proffesor-for-testing/agentic-qe
/plugin install agentic-qe-fleet
| Asset | Count | Notes |
|---|---|---|
| Agents (Task tool) | 11 | Model-routed: 6 on Opus (heavy reasoning), 5 on Sonnet (focused execution) |
| Slash commands | 9 | /aqe-analyze, /aqe-execute, /aqe-generate, /aqe-optimize, /aqe-chaos, /aqe-fleet-status, /aqe-report, /aqe-benchmark, /aqe-costs |
| Skills | 9 | All trust-tier 2 or 3 (validated/verified). Tier-1 untested skills excluded per policy. |
| MCP server | 1 | Auto-registers via npx -y agentic-qe@latest mcp — no separate claude mcp add |
Bundled agents: qe-test-architect, qe-coverage-specialist, qe-flaky-hunter, qe-chaos-engineer, qe-fleet-commander, qe-quality-gate, qe-security-scanner, qe-performance-tester, qe-regression-analyzer, qe-tdd-specialist, qe-requirements-validator.
Bundled skills: qe-test-generation, qe-coverage-analysis, qe-test-execution, qe-chaos-resilience, qe-quality-assessment, chaos-engineering-resilience, mutation-testing, risk-based-testing, tdd-london-chicago.
After loading the plugin, the slash commands and agents are available immediately:
/aqe-fleet-status # health and metrics
/aqe-generate src/services/Auth.ts
/aqe-analyze src/ # coverage gap analysis
Or invoke an agent through the Task tool:
"Use qe-test-architect to generate tests for src/services/PaymentService.ts"
"Use qe-flaky-hunter to find and stabilize flaky tests in tests/integration/"
"Use qe-chaos-engineer to inject network partitions into the order workflow"
aqe init — which to use?npx claudepluginhub proffesor-for-testing/agentic-qe --plugin agentic-qe-fleetAgentic Quality Engineering — AI-powered QE platform with 60 specialized agents, 75+ skills, sublinear coverage analysis, ReasoningBank pattern learning, and deep MCP integration for Claude Code and 11 coding agent platforms
Agentic Quality Engineering — AI-powered QE platform with 60 specialized agents, 75+ skills, sublinear coverage analysis, ReasoningBank pattern learning, and deep MCP integration for Claude Code and 11 coding agent platforms
Quality engineering: E2E, API, integration, performance, chaos, flaky tests, observability, mutation testing, coverage gap analysis. 21 reference sheets, 5 commands, 3 agents.
Agents specialized in quality assurance, testing strategies, and test architecture. Focuses on ensuring code quality and reliability.
Use this agent for analyzing test results, synthesizing test data, identifying trends, and generating quality metrics reports. This agent specializes in turning raw test data into actionable insights that drive quality improvements. Examples:\n\n<example>\nContext: Analyzing test suite results
Test execution, TDD workflow, testing strategies, and quality analysis
Test framework detection, convention-aware test generation, and changed-file test execution.