QASkills.sh QA Essentials — 10 curated skills that turn your agent into a QA engineer: Claude Code QA, E2E (Playwright/Cypress), API testing, pytest, Jest, k6 performance, and an autonomous QA agent loop.
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Automated accessibility testing with axe-core integrated into CI pipelines, including custom rule configuration, issue prioritization, and remediation guidance.
Fast Rust-based headless browser automation CLI with Node.js fallback for AI agents, featuring navigation, clicking, typing, snapshots, and structured commands optimized for agent workflows.
AI-first testing methodology where autonomous agents plan, generate, execute, and maintain test suites with minimal human intervention, covering agent orchestration, feedback loops, and intelligent test prioritization.
Comprehensive evaluation patterns for AI agents including multi-turn conversation testing, LLM-as-judge frameworks, benchmark suites, regression detection, and systematic eval pipelines for measuring agent quality and safety.
Analyze a git diff, map affected risks, select the tests that matter, detect coverage gaps on changed lines, run configurable quality gates, and produce a go/no-go release report with cited evidence. Recommends only; never merges or deploys.
Test LLM, RAG, MCP, and agentic systems end to end. Build golden datasets, run deterministic checks and LLM judges, score retrieval, probe prompt injection, verify tool use, and gate CI on thresholds. Orchestrates DeepEval, Ragas, promptfoo, and Langfuse.
Systematic patterns for prompting AI coding agents to generate high-quality tests including prompt engineering for test creation, coverage-driven generation, mutation-aware testing, and review checklists for AI-generated test code.
AI-powered test orchestration skill covering intelligent test selection, risk-based test prioritization, flaky test management, test impact analysis, parallel execution optimization, and predictive test failure detection using machine learning.
Configure and generate rich Allure test reports with test categorization, historical trends, environment details, and CI/CD integration for comprehensive test visibility.
Simulate aggressive user behavior patterns including rapid clicking, random navigation, form abuse, tab spamming, and unexpected interaction sequences to find UI resilience issues.
API endpoint fuzzing for discovering unexpected behaviors, crashes, and security vulnerabilities through malformed requests, boundary values, and protocol violations.
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: