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moai-workflow-testing

Orchestrates testing, debugging, refactoring, performance profiling, and TRUST 5 code review workflows.

Updated Jul 24, 2026
Search Tags:#testing#tdd#ci/cd#code review#debugging#Playwright#quality gates
One-click install
npx skills add https://github.com/jjjh7401/AI-Lighting_Console --skill moai-workflow-testing-jjjh7401
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: moai-workflow-testing
Source: https://github.com/jjjh7401/AI-Lighting_Console/tree/main/.claude/skills/moai-workflow-testing
Command: npx skills add https://github.com/jjjh7401/AI-Lighting_Console --skill moai-workflow-testing-jjjh7401

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? It unifies the fragmented development quality lifecycle—writing tests, debugging errors, refactoring legacy code, profiling performance, and reviewing code—into one consistent workflow with explicit quality gates, so teams stop skipping verification steps before merging. ## Core Features & Use Cases - DDD Testing: Characterization tests for legacy code (PRESERVE phase) plus specification tests for greenfield work, with behavior snapshots as regression safeguards. - AI Debugging & Refactoring: Error classification across 12 categories (syntax, network, concurrency, etc.) with ranked solution candidates, and technical-debt analysis with safe transformation plans. - TRUST 5 Code Review & PR Pipeline: Severity-weighted quality scoring across five dimensions, plus a multi-agent PR review flow (Haiku eligibility check, 5 parallel Sonnet reviewers, confidence filtering at 80+). - Use Case: Before merging a pull request, run the workflow to execute the test suite with coverage thresholds, score changed files against TRUST 5, detect N+1 queries or leaked secrets, and post prioritized findings via the gh CLI. ## Quick Start Ask the AI to run the full development workflow—tests, TRUST 5 review, and performance check—on your project's source directory before opening a pull request.

Frequently Asked Questions about moai-workflow-testing

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I write characterization tests for legacy code before refactoring?▼

Write characterization tests that document current behavior exactly as it is, not as it should be, and organize them by domain concepts. Verify all tests pass before any change, then refactor with continuous test execution and run TRUST 5 validation afterward.

What is the TRUST 5 framework for code review?▼

TRUST 5 scores code across five dimensions—Testability, Readability, Understandability, Security, and Transparency—using a weighted average with critical-dimension overrides. Security and testability scores cannot be masked by high scores elsewhere.

Which testing tools does the workflow support for Python, Go, and JavaScript?▼

Python uses pytest with coverage.py, ruff, and bandit; Go uses go test with staticcheck and gosec; JavaScript/TypeScript uses Jest or Vitest with ESLint and npm audit. The multi-language reference covers 16 languages including Rust, Java, Ruby, and PHP.

How does the multi-agent PR review pipeline filter false positives?▼

A Haiku agent first checks PR eligibility, then five parallel Sonnet agents review CLAUDE.md compliance, bugs, git blame, previous comments, and comment compliance. Each issue gets a 0-100 confidence score, and anything below 80 is dropped before posting via the gh CLI.

Does the workflow support Playwright end-to-end and visual regression testing?▼

Yes, it includes Playwright patterns for Page Object Model, cross-browser projects (Chromium, Firefox, WebKit, mobile), screenshot-based visual regression with pixel-diff analysis, Lighthouse performance audits, and axe accessibility checks.

What are the limitations of screenshot-based E2E evidence in CI?▼

Screenshot loops cost roughly 1.1-1.6K tokens per frame and are non-deterministic, so they are reserved for final visual evidence only. Filtered accessibility-tree text snapshots are the preferred CI-repeatable acceptance evidence across macOS, Windows, and Linux.

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