observability-testing-patterns
Observability and monitoring validation patterns for dashboards, alerting, log aggregation, APM traces, and SLA/SLO verification. Use when testing monitoring infrastructure, dashboard accuracy, alert rules, or metric pipelines.
pair-programming
Provides AI navigator for pair programming sessions with real-time code review, TDD guidance, and quality monitoring. Use when pair programming with AI assistance, practicing TDD with a navigator, debugging collaboratively, or refactoring with real-time verification.
pentest-validation
Use when validating security findings from SAST/DAST scans, proving exploitability of reported vulnerabilities, eliminating false positives, or running the 4-phase pentest pipeline (recon, analysis, validation, report).
performance-testing
Profiles application performance under load using k6, Artillery, or JMeter to measure latency, throughput, and error rates. Use when planning load tests, stress tests, soak tests, benchmarking APIs, or identifying performance bottlenecks.
pr-review
Use when reviewing a GitHub PR for quality, scope correctness, trust tier compliance, or generating user-friendly review feedback.
qcsd-cicd-swarm
Use when enforcing CI/CD quality gates before release, running regression analysis, detecting flaky tests, or assessing deployment readiness in the QCSD Verification phase.
qcsd-development-swarm
Use when monitoring in-sprint code quality with TDD adherence checks, complexity analysis, coverage gap detection, or defect prediction in the QCSD Development phase.
qcsd-ideation-swarm
Use when running Quality Criteria sessions during PI/Sprint planning with HTSM v6.3, Risk Storming, or Testability analysis in the QCSD Ideation phase.
qcsd-production-swarm
Use when assessing post-release production health with DORA metrics, root cause analysis, defect prediction, or cross-phase feedback loops in the QCSD Production phase.
qcsd-refinement-swarm
Use when running Sprint Refinement sessions with SFDIPOT product factors, generating BDD scenarios, or validating requirements in the QCSD Refinement phase.
qe-chaos-resilience
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.
qe-code-intelligence
Builds semantic code indexes, maps dependency graphs, and performs intelligent code search across large codebases. Use when understanding unfamiliar code, tracing call chains, analyzing import dependencies, or reducing context window usage through targeted retrieval.
qe-coverage-analysis
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.
qe-defect-intelligence
Predicts defect-prone code using change frequency, complexity metrics, and historical bug patterns. Use when predicting defects before they escape, analyzing root causes of test failures, learning from past defect patterns, or implementing proactive quality management.
qe-iterative-loop
Runs autonomous red-green-refactor loops to fix failing tests, reach coverage targets, and satisfy quality gates. Use when tests need to pass, coverage thresholds must be met, quality gates require compliance, or flaky tests need stabilization.
qe-github-multi-repo
Multi-repository coordination, synchronization, and architecture management with AI swarm orchestration
qe-learning-optimization
Optimizes QE agent performance through transfer learning, hyperparameter tuning, and pattern distillation across test domains. Use when improving agent accuracy, applying learned patterns to new projects, tuning quality thresholds, or implementing continuous improvement loops for AI-powered testing.
qe-quality-assessment
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.
qe-requirements-validation
Validates acceptance criteria for testability, traces requirements to test cases, and generates BDD scenarios from user stories. Use when validating acceptance criteria, building requirements traceability matrices, managing Gherkin scenarios, or ensuring complete requirements coverage before development.
qe-test-execution
Orchestrates test suite execution with parallel sharding, intelligent retry, and real-time reporting across Jest, Vitest, and Playwright. Use when running test suites, optimizing execution time, handling flaky tests, configuring CI test pipelines, or analyzing test run results.
qe-test-generation
Generates unit, integration, and e2e tests from code analysis including branch coverage, error paths, and edge cases. Use when creating tests for new or changed code, filling coverage gaps, or migrating test suites between Jest, Vitest, and Playwright.
qe-visual-accessibility
Captures and compares screenshots across viewports, runs axe-core accessibility scans, and detects visual regressions with pixel-diff analysis. Use when detecting UI regressions, validating responsive layouts, testing WCAG compliance, or ensuring visual consistency after CSS or component changes.
quality-metrics
Tracks quality metrics including defect density, test effectiveness ratio, DORA metrics, and mean time to detection. Use when establishing quality dashboards, defining KPIs, evaluating test suite effectiveness, or reporting quality trends to stakeholders.
ReasoningBank with AgentDB
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
ReasoningBank Intelligence
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.
refactoring-patterns
Apply safe refactoring patterns to improve code structure without changing behavior. Use when cleaning up code, reducing technical debt, or improving maintainability.
regression-testing
Strategic regression testing with test selection, impact analysis, and continuous regression management. Use when verifying fixes don't break existing functionality, planning regression suites, or optimizing test execution for faster feedback.
release
End-to-end npm release workflow with verification gates and hardcoded-version protection
risk-based-testing
Focus testing effort on highest-risk areas using risk assessment and prioritization. Use when planning test strategy, allocating testing resources, or making coverage decisions.
security-testing
Scans for security vulnerabilities including XSS, SQL injection, CSRF, and auth flaws using OWASP Top 10 methodology. Use when conducting SAST/DAST scans, auditing authentication flows, testing authorization rules, or implementing security test automation.
security-visual-testing
Security-first visual testing combining URL validation, PII detection, and visual regression with parallel viewport support. Use when testing web applications that handle sensitive data, need visual regression coverage, or require WCAG accessibility compliance.
security-watch
Use when working on security-sensitive code to catch secrets, eval(), innerHTML, and other dangerous patterns before they're written. Activate with /security-watch for real-time security scanning.
sfdipot-product-factors
James Bach's HTSM Product Factors (SFDIPOT) analysis for comprehensive test strategy generation. Use when analyzing requirements, epics, or user stories to generate prioritized test ideas across Structure, Function, Data, Interfaces, Platform, Operations, and Time dimensions.
sherlock-review
Evidence-based investigative code review using deductive reasoning to determine what actually happened versus what was claimed. Use when verifying implementation claims, investigating bugs, validating fixes, or conducting root cause analysis. Elementary approach to finding truth through systematic observation.
shift-left-testing
Move testing activities earlier in the development lifecycle to catch defects when they're cheapest to fix. Use when implementing TDD, CI/CD, or early quality practices.
shift-right-testing
Testing in production with feature flags, canary deployments, synthetic monitoring, and chaos engineering. Use when implementing production observability or progressive delivery.
six-thinking-hats
Apply Edward de Bono's Six Thinking Hats methodology to software testing for comprehensive quality analysis. Use when designing test strategies, conducting test retrospectives, analyzing test failures, evaluating testing approaches, or facilitating testing discussions. Each hat provides a distinct testing perspective: facts (White), risks (Black), benefits (Yellow), creativity (Green), emotions (Red), and process (Blue).
Skill Builder
Create new Claude Code Skills with proper YAML frontmatter, progressive disclosure structure, and complete directory organization. Use when you need to build custom skills for specific workflows, generate skill templates, or understand the Claude Skills specification.
skill-stats
Use when reviewing which QE skills are being used, finding undertriggering skills, or analyzing skill effectiveness. Shows usage patterns and recommendations.
sparc-methodology
SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) comprehensive development methodology with multi-agent orchestration
stream-chain
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
strict-tdd
Use when enforcing TDD discipline — blocks writing production code unless a failing test exists first. Activate with /strict-tdd to enable session-scoped Red-Green-Refactor guardrail.
swarm-advanced
Advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows
Swarm Orchestration
Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI systems.
tdd-london-chicago
Apply London (mock-based) and Chicago (state-based) TDD schools. Use when practicing test-driven development or choosing testing style for your context.
test-automation-strategy
Design and implement effective test automation with proper pyramid, patterns, and CI/CD integration. Use when building automation frameworks or improving test efficiency.
test-data-management
Strategic test data generation, management, and privacy compliance. Use when creating test data, handling PII, ensuring GDPR/CCPA compliance, or scaling data generation for realistic testing scenarios.
test-design-techniques
Systematic test design with boundary value analysis, equivalence partitioning, decision tables, state transition testing, and combinatorial testing. Use when designing comprehensive test cases, reducing redundant tests, or ensuring systematic coverage.
test-environment-management
Test environment provisioning, infrastructure as code for testing, Docker/Kubernetes for test environments, service virtualization, and cost optimization. Use when managing test infrastructure, ensuring environment parity, or optimizing testing costs.
test-failure-investigator
Use when a test is failing and you need to determine root cause: is it flaky, an environment issue, or a real regression? Traces failure from symptom to fix.
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