Skill UI

A comprehensive skill for automated UI testing. It records complex user interactions, navigates web pages, and validates functionality by producing a replayable RVF artifact. Use it to verify user flows, detect regressions against baseline sessions, and re-run complex tests in a real browser environment.

This skill monitors for regressions on deployed URLs following deployments, merges, or dependency upgrades. It performs comprehensive checks including HTTP status, console errors, network failures, performance metrics (LCP/CLS/INP), and crucial API endpoint health, ensuring service stability in production or staging environments. Supports quick checks, continuous monitoring, and differential comparison against baselines.

Code Health MCP provides structural maintainability feedback and design-level health scores, going beyond standard linting. It is crucial for AI-assisted coding workflows, allowing users to review code quality, verify score deltas after changes, and gate commits or pull requests to prevent codebase regression. Use it when refactoring, updating legacy modules, or ensuring the integrity of large diffs.

This tool detects cost regressions by comparing two cost-summary JSON snapshots (baseline vs. current). It is essential for PR-gate workflows, checking for unexpected spending increases using three orthogonal signals: total percentage change, absolute dollar increase, and disproportionate growth in specific token classes (e.g., cache writes).

This skill analyzes historical benchmark run data stored in JSON files. It surfaces key performance drift metrics over time, including win rate, average latency, p99 latency, and LLM-baseline cost. It is crucial for detecting subtle performance degradations or regressions that standard smoke tests might miss, ensuring stability across releases or corpus expansions.

A formal framework designed for evaluating AI-assisted code generation sessions. It implements Development Oriented Evals (EDD) principles, allowing users to define capability and regression criteria. The system supports multiple evaluation types—code-based, model-based, and human review—and tracks reliability using advanced metrics like pass@k, ensuring robust quality control for AI agents.

A comprehensive audit worker that integrates Open Infrastructure Architecture (OIA) manifest analysis, threat modeling, and MCP security scanning. It computes a composite worst-case severity score and persists time-stamped audit records in a memory namespace. Primarily designed for scheduled CI/CD pipelines to detect architectural drift and security regressions over time.

This tool orchestrates the complete workflow for addressing defects, ensuring a robust fix process. It guides the user through reproducing a bug as a failing regression test, applying the necessary fix until the test passes (goes green), and managing the subsequent code review and gated commit phases. Ideal for systems where existing functionality is broken, incorrect, or exhibiting regression.

Sign, verify, and track fix-marker regressions over time using a deterministic Ed25519 witness manifest. Works in any project — clone the toolkit, run init, register fixes, regen on each release.

A powerful post-deployment monitoring tool designed for continuous quality assurance in CI/CD pipelines. It captures a performance baseline before deployment and then continuously monitors live applications after deployment. It alerts on critical failures (e.g., 5xx errors), performance regressions, and unexpected changes, ensuring that new releases maintain production stability.

This diagnostic skill audits the entire local setup of Claude Code against several known, complex cache and cost bugs (CC#40524, B4, B5). It checks critical components including version compatibility, sentinel placeholders, session resuming logic, and attribution headers. Use this tool to ensure your development environment is optimized, stable, and minimizes unexpected operational costs and functional regressions.

This tool provides a preferred, end-to-end method for modifying code in real-world repositories. It simulates a professional development workflow: cloning the target repository, running changes on a dedicated task branch, and critically, verifying the implementation through an independent red $\to$ green $\to$ regression test loop. It is designed for fixing GitHub issues, implementing new features, or adding functions to existing codebases, ensuring the structural integrity and correctness of the changes.