No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by tugkanboz · MCP Server · ★ 122
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-ai-testing safe to install? View the security audit →
Awesome AI Testing A curated list of AI-powered testing tools, frameworks, and resources for QA engineers. AI is reshaping software testing. This list collects tools, platforms, and resources that use AI or LLMs to generate tests, heal broken locators, triage failures, write assertions in natural language, and more. Both open source and commercial offerings are included, marked with badges so you can filter by what fits your stack. Contents Legend Test Generation MCP-Based Testing Self-Healing Test Frameworks AI-Powered E2E Platforms Mobile AI Testing Visual AI Testing Natural Language Test Authoring LLM-as-Judge Evaluation Test Analytics and Triage Code Coverage with AI AI Test Data Generation Mock and Service Virtualization Performance Testing with AI AI for Accessibility Testing [API Testing with AI]
| Stars | 122 |
| Forks | 45 |
| Category | MCP Server |
| License | CC0-1.0 |
| Quality Score | 55.9250454716905/100 |
| Open Issues | 27 |
| Last Updated | 2026-09-28 |
| Created | 2026-05-02 |
| Platforms | mcp |
| Est. Tokens | ~17k |
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awesome-ai-testing is A curated list of AI-powered testing tools, frameworks, and resources for QA engineers. From test generation to self-healing automation, MCP-based testing, LLM evaluation, and more.. It is categorized as a MCP Server with 122 GitHub stars.
You can find installation instructions and usage details in the awesome-ai-testing GitHub repository at github.com/tugkanboz/awesome-ai-testing. The project has 122 stars and 45 forks, indicating an active community.
awesome-ai-testing is released under the CC0-1.0 license, making it free to use and modify according to the license terms.
The top alternatives to awesome-ai-testing on Agent Skills Hub include awesome-qa-skills, qaskills, ai-api-test-skill. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: