QE Skills

The skills-qe pack covers quality engineering across the delivery lifecycle. See the public package source for the current skill list.

Install

# Install the full QE pack
dz install @dzhechkov/skills-qe

# Or use the qe-engineer preset
dz init --target claude-code --preset qe-engineer

Skills

Core QE — Test Lifecycle

Skill	Description
qe-test-generation	Generates unit, integration, and E2E tests from code analysis including branch coverage, error paths, and edge cases. Supports Jest, Vitest, and Playwright.
qe-test-execution	Orchestrates test suite execution with parallel sharding, intelligent retry, and real-time reporting. Use to run suites, optimize time, and handle flaky tests.
qe-coverage-analysis	Analyzes test coverage data (Istanbul, c8, lcov) to identify uncovered lines, branches, and functions with risk-weighted gap detection.
qe-quality-assessment	Evaluates code quality through complexity analysis, lint results, code smell detection, and test health metrics for deployment readiness gates.
qe-defect-intelligence	Predicts defect-prone code using change frequency, complexity metrics, and historical bug patterns. Use for proactive quality management before release.
qe-requirements-validation	Validates acceptance criteria for testability, traces requirements to test cases, and generates BDD scenarios from user stories.
qe-visual-accessibility	Captures and compares screenshots across viewports, runs axe-core accessibility scans, and detects visual regressions with pixel-diff analysis.
qe-chaos-resilience	Injects controlled faults (network partition, latency, process kill, disk pressure) into distributed systems and validates recovery behavior.
qe-browser	Browser automation for QE agents using Vibium (WebDriver BiDi) with assertions, batch execution, visual diff, and prompt-injection scanning.
qe-code-intelligence	Builds semantic code indexes, maps dependency graphs, and performs intelligent code search across large codebases to reduce context window usage.
qe-iterative-loop	Runs autonomous red-green-refactor loops to fix failing tests, reach coverage targets, and satisfy quality gates without manual intervention.
qe-learning-optimization	Optimizes QE agent performance through transfer learning, hyperparameter tuning, and pattern distillation across test domains.

Review Techniques

Skill	Description
brutal-honesty-review	Delivers unvarnished technical criticism combining three personas: Linus Torvalds (technical precision), Gordon Ramsay (quality standards), and James Bach (BS detection).
six-thinking-hats	Applies Edward de Bono’s Six Thinking Hats methodology to software testing — facts, emotions, risks, benefits, creativity, and process perspectives.
sfdipot-product-factors	James Bach’s HTSM Product Factors (SFDIPOT) analysis for comprehensive test strategy generation across Structure, Function, Data, Interfaces, Platform, Operations, and Time.

QCSD Swarms

The QCSD (Quality Criteria Software Development) swarms integrate quality engineering into every phase of the Agile delivery lifecycle.

Skill	Description
qcsd-ideation-swarm	Quality Criteria sessions during PI/Sprint planning — HTSM v6.3, Risk Storming, and Testability analysis. Use at the start of planning to define quality criteria.
qcsd-refinement-swarm	Sprint Refinement sessions with SFDIPOT product factors, BDD scenario generation, and requirements validation. Use before a sprint begins.
qcsd-development-swarm	In-sprint code quality monitoring — TDD adherence checks, complexity analysis, coverage gap detection, and defect prediction. Use daily during development.
qcsd-cicd-swarm	CI/CD quality gates before release — regression analysis, flaky test detection, and deployment readiness assessment. Use in the verification phase.
qcsd-production-swarm	Post-release production health assessment — DORA metrics, root cause analysis, defect prediction, and cross-phase feedback loops. Use after deployment.