Skill UI

Monitor and evaluate Retrieval-Augmented Generation (RAG) systems using measurable metrics. This skill covers retrieval quality checks like Recall@k, hallucination detection, and groundedness scoring. It enables continuous regression testing and production monitoring for AI pipelines using Python libraries like RAGAS and OpenTelemetry.

This skill enables parallel read-only multi-agent review of git diffs or specific file scopes. It identifies behavioral regressions, security risks, performance issues, and coverage gaps using four specialized sub-agents. The main agent synthesizes findings to filter and summarize critical issues without modifying code. Ideal for ensuring code quality and safety before merging changes.

This specialized skill guides users through a structured, forensic investigation process when facing sudden, significant drops in organic traffic or search rankings. It is designed for incident scenarios—such as suspected penalties, Core Updates, major technical regressions, or unexplained traffic crashes—providing a systematic approach to triage, root-cause analysis, and developing a prioritized recovery plan.

This skill reviews code differences (diffs) to identify opportunities for improvement in clarity, reusability, efficiency, and overall quality. It employs multiple specialized sub-agents (e.g., Code Reuse, Efficiency, Quality) to audit changes. It can provide high-confidence, behavior-preserving fixes when requested, ensuring the code adheres to best practices without introducing regressions. Best used when refactoring, cleaning up, or auditing a specific code scope.

This skill generates SmartUI visual regression test configurations for screenshot comparison on TestMu AI cloud. It is framework-agnostic, supporting Playwright, Selenium, Cypress, and Puppeteer. Users can create baselines, compare screenshots, and approve changes via the LambdaTest SmartUI dashboard. Ideal for automated visual testing workflows.

This skill provides a systematic, multi-step approach to identify and resolve all failing unit tests within a codebase. It guides the user through smart error grouping (e.g., grouping by ImportError or AttributeError), prioritizing fixes from infrastructure to business logic, and ensuring thorough verification to prevent regressions. Ideal for use after major refactoring, debugging CI/CD failures, or when explicitly tasked with making the test suite pass.

Build end-to-end tests with TestDriver.ai, an AI code reviewer that runs every pull request in a real desktop sandbox, finds bugs, and creates regression tests for web, Electron, and native apps.

This skill validates Three.js graphics implementations using fixed-view visual contracts and diagnostics. It ensures reproducibility through seed sweeps, camera-scale tests, and temporal stability checks. It captures no-post baselines and GPU budgets to prevent regression in procedural scenes, evaluating the rendering mechanism rather than subjective screenshots.

Rigorous visual validation expert specializing in UI testing, design system compliance, and accessibility verification. Uses visual regression tools and systematic analysis to verify UI modifications, responsiveness, and cross-platform consistency.

This skill provides a standardized framework for evaluating autonomous AI agents. It tracks task completion rates, planning accuracy, tool invocation precision, and operational costs. Suitable for engineering teams managing distributed systems, it enables reproducible benchmarking, regression monitoring, and compliance tracking through structured data models like CSV, SQL, and JSON schemas.

Compares prompt changes against a fixed evaluation set, recording model settings, checks, missing results, judge limitations, and case-level regression evidence for a reviewable decision.

Govern cross-module regression in large repositories with a REGRESSION.md ledger recording each module's downstream consumers and executable acceptance commands, then run affected modules and use exit codes as regression evidence.