Dragan Spiridonov

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.

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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.

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Testing in production with feature flags, canary deployments, synthetic monitoring, and chaos engineering. Use when implementing production observability or progressive delivery.

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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).

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Use when reviewing which QE skills are being used, finding undertriggering skills, or analyzing skill effectiveness. Shows usage patterns and recommendations.

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SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) comprehensive development methodology with multi-agent orchestration

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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.

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Advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows

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Apply London (mock-based) and Chicago (state-based) TDD schools. Use when practicing test-driven development or choosing testing style for your context.

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Write clear, engaging technical content from real experience. Use when writing blog posts, documentation, tutorials, or technical articles.

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Design and implement effective test automation with proper pyramid, patterns, and CI/CD integration. Use when building automation frameworks or improving test efficiency.

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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.

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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.

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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.

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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.

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Use when running Quality Criteria sessions during PI/Sprint planning with HTSM v6.3, Risk Storming, or Testability analysis in the QCSD Ideation phase.

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Use when monitoring in-sprint code quality with TDD adherence checks, complexity analysis, coverage gap detection, or defect prediction in the QCSD Development phase.

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Use when enforcing CI/CD quality gates before release, running regression analysis, detecting flaky tests, or assessing deployment readiness in the QCSD Verification phase.

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Use when reviewing a GitHub PR for quality, scope correctness, trust tier compliance, or generating user-friendly review feedback.

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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.

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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).

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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.

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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.

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AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.

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