Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
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Quality Engineering iteration loops for autonomous test improvement, coverage achievement, and quality gate compliance. Use when tests need to pass, coverage targets must be met, quality gates require compliance, or flaky tests need stabilization. Integrates with AQE v3 fleet agents for coordinated quality iteration.
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QE Iterative Loop is a specialized adaptation of the Ralph Wiggum technique for Quality Engineering workflows. It enables autonomous, self-correcting quality cycles where AI agents iterate until quality objectives are achieved - tests pass, coverage targets met, quality gates satisfied, or flaky tests stabilized.
Quality Engineering has objective, measurable success criteria:
This makes QE ideal for iterative loops - we know exactly when we're done.
# Task: Fix all failing tests
/qe-loop "Run npm test and fix all failing tests.
Success: npm test exits with code 0
Output <promise>TESTS_GREEN</promise> when all tests pass."
# Task: Achieve 80% coverage
/qe-loop "Increase test coverage to 80%.
Success: Coverage report shows >= 80%
Output <promise>COVERAGE_MET</promise> when target achieved."
# Task: Pass all quality gates
/qe-loop "Pass all quality gates for deployment.
Gates:
- Unit tests: pass
- Integration tests: pass
- Coverage: >= 80%
- No critical vulnerabilities
- Performance < 200ms P95
Output <promise>QUALITY_GATES_PASSED</promise> when all pass."
Goal: All tests pass
## QE Test-Fix Loop
### Success Criteria
- `npm test` (or test command) returns exit code 0
- No skipped tests (unless explicitly allowed)
- No pending tests
### Iteration Steps
1. Run full test suite
2. Parse output for failures
3. Analyze first failure:
- Identify failing test file
- Understand assertion that failed
- Check if production code or test is wrong
4. Fix the issue
5. Re-run failed test file only (faster feedback)
6. If file passes, run full suite
7. If all pass -> output <promise>TESTS_GREEN</promise>
8. If failures remain -> continue to next failure
### Safety
- Max iterations: 30
- After 10 iterations: report remaining failures
- Stop if same test fails 5 times (possible design issue)
Goal: Achieve coverage target
## QE Coverage Loop
### Success Criteria
- Line coverage >= {target}%
- Branch coverage >= {target - 5}% (typically lower target)
- No critical paths uncovered
### Iteration Steps
1. Run tests with coverage: `npm test -- --coverage`
2. Parse coverage report
3. If target met -> output <promise>COVERAGE_MET</promise>
4. Identify uncovered files, sorted by:
- Critical business logic (highest priority)
- Lines uncovered (most impact)
- Complexity (McCabe score)
5. Generate test for highest-impact uncovered code
6. Run tests to verify new test passes
7. Check coverage improvement
8. Continue until target met
### Intelligence Integration
- Store successful test patterns in memory
- Learn from coverage achievements
- Predict best coverage strategies
### Commands
```bash
# Check coverage status (via AQE MCP)
aqe memory get --key "coverage-status" --namespace "coverage"
# Store coverage achievement pattern (via AQE MCP)
aqe memory store \
--key "coverage-pattern-auth" \
--value '{"approach": "mock external deps", "improvement": "12%"}' \
--namespace "coverage-patterns"
### Pattern 3: Quality Gate Compliance Loop
**Goal**: Pass all quality gates
```markdown
## QE Quality Gate Loop
### Gate Definitions
| Gate | Criteria | Priority |
|------|----------|----------|
| unit-tests | All pass | P0 |
| integration-tests | All pass | P0 |
| coverage | >= 80% | P1 |
| lint | No errors | P1 |
| typecheck | No errors | P1 |
| security | No critical/high CVEs | P0 |
| performance | P95 < 200ms | P2 |
### Iteration Strategy
1. Run all gate checks
2. Identify failing gates (sorted by priority)
3. Fix highest-priority failing gate
4. Re-run that gate to verify
5. When gate passes, move to next failing gate
6. When all pass -> output <promise>QUALITY_GATES_PASSED</promise>
### Gate Check Commands
```bash
# Check all gates
npm test && npm run lint && npm run typecheck && npm run coverage && npm audit
# Individual gate checks
npm test # unit-tests
npm run test:integration # integration-tests
npm run coverage # coverage
npm run lint # lint
npx tsc --noEmit # typecheck
npm audit --audit-level=high # security
npm run benchmark # performance
# Submit quality gate assessment task
aqe quality --runGate true
# Task orchestration for gate compliance
aqe task submit --task "Pass all quality gates" --strategy adaptive
### Pattern 4: Flaky Test Stabilization Loop
**Goal**: Eliminate test flakiness
```markdown
## QE Flaky Test Loop
### Flakiness Detection
1. Run test suite N times (e.g., 5 runs)
2. Identify tests that pass/fail inconsistently
3. Calculate flakiness score: (inconsistent runs / total runs)
### Iteration Steps
1. Run: `for i in {1..5}; do npm test; done`
2. Aggregate results per test
3. Identify flaky tests (passed some, failed some)
4. For each flaky test:
- Analyze failure modes
- Common causes:
- Timing issues (add retries/waits)
- Shared state (isolate test data)
- Network calls (mock external services)
- Random data (use deterministic seeds)
- Apply appropriate fix
- Re-run 5 times to verify stability
5. When all tests stable -> output <promise>TESTS_STABLE</promise>
### AQE v3 Flaky Detection
```bash
# Use qe-flaky-hunter agent
Task("Hunt flaky tests", "Detect and stabilize flaky tests", "qe-flaky-hunter")
# Or submit flaky detection task
aqe task submit --type "flaky-detection" --priority "p1"
### Pattern 5: Contract Validation Loop
**Goal**: API contracts aligned
```markdown
## QE Contract Loop
### Success Criteria
- Provider implements all consumer contracts
- No breaking changes detected
- Schema validation passes
### Iteration Steps
1. Run contract tests: `npm run test:contracts`
2. Parse contract violations
3. For each violation:
- Determine if provider or consumer needs update
- Update appropriate side
- Re-run contract tests
4. When all contracts valid -> output <promise>CONTRACTS_VALID</promise>
### AQE v3 Integration
```bash
# Validate contracts
aqe test contract --contractPath "./contracts"
# Or use specialized agent
Task("Validate API contracts", "Check consumer-provider alignment", "qe-contract-validator")
---
## AQE v3 Fleet Integration
### Spawning QE Iteration Agents
```bash
# Initialize AQE fleet for QE iteration
aqe fleet init --topology "hierarchical" --maxAgents 8
# Spawn specialized QE iterators using Task tool
Task("Fix failing tests", "Iterate until all tests pass", "qe-tdd-green", {run_in_background: true})
Task("Improve coverage", "Iterate until 80% coverage", "qe-coverage-analyzer", {run_in_background: true})
Task("Fix security issues", "Iterate until security scan passes", "qe-security-scanner", {run_in_background: true})
Task("Stabilize flaky tests", "Iterate until tests stable", "qe-flaky-hunter", {run_in_background: true})
# Store iteration patterns for learning (via AQE MCP)
aqe memory store \
--key "qe-iteration-test-fix" \
--value '{"approach": "mock external deps", "success_rate": 0.85}' \
--namespace "qe-patterns"
# Search for relevant QE patterns (via AQE MCP)
aqe memory search \
--pattern "test-fix-*" \
--namespace "qe-patterns"
# Record successful iteration completion (via AQE task tracking)
aqe task status --taskId "test-fix-iteration"
| QE Task | Recommended Agent | Iteration Goal |
|---|---|---|
| Test fixes | qe-tdd-green | All tests pass |
| Coverage gaps | qe-coverage-analyzer | Target coverage met |
| Quality gates | qe-quality-gate | All gates pass |
| Flaky tests | qe-flaky-hunter | Tests stable |
| Contract validation | qe-contract-validator | Contracts aligned |
| Security fixes | qe-security-scanner | No vulnerabilities |
| Performance | qe-performance-validator | Benchmarks pass |
# Test-related
<promise>TESTS_GREEN</promise> # All tests pass
<promise>TESTS_STABLE</promise> # Flaky tests fixed
<promise>TDD_COMPLETE</promise> # TDD cycle done
# Coverage-related
<promise>COVERAGE_MET</promise> # Target coverage achieved
<promise>GAPS_FILLED</promise> # Coverage gaps addressed
# Quality gates
<promise>QUALITY_GATES_PASSED</promise> # All gates pass
<promise>DEPLOYMENT_READY</promise> # Ready for deploy
# Contract/API
<promise>CONTRACTS_VALID</promise> # Contracts aligned
<promise>API_COMPLIANT</promise> # API matches spec
# Security
<promise>SECURITY_CLEARED</promise> # No vulnerabilities
<promise>COMPLIANCE_MET</promise> # Compliance requirements met
# Performance
<promise>PERF_TARGET_MET</promise> # Benchmarks satisfied
## Complete QE Iteration Task
### Objective
Achieve deployment readiness through iterative quality improvement
### Phase 1: Test Health (Priority)
1. Run `npm test`
2. Fix failing tests iteratively
3. Success: <promise>TESTS_GREEN</promise>
### Phase 2: Coverage (After Phase 1)
1. Run `npm test -- --coverage`
2. Write tests for uncovered critical paths
3. Success: Coverage >= 80% -> <promise>COVERAGE_MET</promise>
### Phase 3: Quality Gates (After Phase 2)
1. Run lint: `npm run lint`
2. Run typecheck: `npx tsc --noEmit`
3. Fix any violations
4. Success: <promise>LINT_PASS</promise> + <promise>TYPES_PASS</promise>
### Phase 4: Security (Parallel with Phase 3)
1. Run `npm audit`
2. Fix critical/high vulnerabilities
3. Success: <promise>SECURITY_CLEARED</promise>
### Phase 5: Integration
1. Run `npm run test:integration`
2. Fix any integration failures
3. Success: <promise>INTEGRATION_PASS</promise>
### Final Gate
When ALL phases complete -> <promise>DEPLOYMENT_READY</promise>
### Safety Limits
- Max iterations per phase: 15
- Total max iterations: 50
- Stuck detection: 5 iterations without progress triggers escalation
Cause: Likely a design issue, not implementation bug
Solution:
Cause: Remaining uncovered code is complex/conditional
Solution:
Cause: Deep timing or state issues
Solution:
Origin: Adapted from Ralph Wiggum plugin (anthropics/claude-code) Specialized for: Agentic QE v3 Fleet with 60 QE agents Domains: test-generation, test-execution, coverage-analysis, quality-assessment
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Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
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WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
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