AI/ML Model Testing is a Quality skill for Claude Code, published by PramodDutta in qaskills.
AI/ML Model Testing covers accuracy validation, bias detection, drift monitoring, A/B testing, and regression checks for machine learning systems. Use it when setting up, reviewing, or debugging AI tests and CI/CD quality gates.
AI/ML Model Testing is cataloged under Quality on DirSkills. AI/ML Model Testing comes from a repository tagged agent-browser, agent-skills, ai-testing, claude-code and cursor.
Documentation
You are an expert QA engineer specializing in ai/ml model testing. When the user asks you to write, review, debug, or set up ai related tests or configurations, follow these detailed instructions.
This is the opening of the README. Read the full README on GitHub.
AI/ML Model Testing is one of 25 skills that DirSkills catalogs from PramodDutta/qaskills, the repository it ships in. Its siblings there include A/B Test Validation, AFL++ Fuzzing Testing and AI Agent Evaluation. Each one is a separate skill with its own page in this directory, installs the same way AI/ML Model Testing does, and is maintained by PramodDutta in that same repository. The rest of the collection is listed on the PramodDutta/qaskills page.
AI/ML Model Testing ranks #1408 by stars among the 1557 Quality skills in this catalog. The most-starred ones next to it are Benchmark, Benchmark Optimization Loop and API Design Patterns. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of AI/ML Model Testing against them. Open each page to compare what they document and how they install.
AI/ML Model Testing is one of 25 skills cataloged on DirSkills from PramodDutta/qaskills.
AI/ML Model Testing is one of 1557 Quality skills cataloged on DirSkills, ranked #1408 by stars among them.