Skill UI

This skill provides an autonomous, repository-wide pipeline for auditing and fixing complex logical inconsistencies. It systematically executes stages including health checks, deep review, locating divergences, generating fixes, and performing diff-verification until the codebase is deemed clean. Ideal for maintaining high code quality and resolving deep, systemic bugs across entire projects.

This skill integrates with Monte Carlo's observability platform to provide a comprehensive health report for data assets. It checks critical metrics including data freshness, active alerts, monitoring coverage, asset importance, and the health of upstream dependencies. Activates when users inquire about the status, reliability, or quality of a data table.

Provides championship-grade repository hygiene and health checks. It systematically audits critical areas, including dependency health, CI/CD pipeline alignment, documentation synchronization, and detecting stray artifacts. The tool generates a prioritized, actionable cleanup plan and offers auto-fix capabilities to keep codebases pristine and production-ready.

KubeEye is a comprehensive cluster inspection tool designed for Kubernetes environments, particularly within KubeSphere. It systematically detects issues across workloads, nodes, and configurations by integrating multiple policy engines, including OPA/Rego, PromQL, file integrity checks, kernel parameter validation, and systemd health checks. Users can define custom inspection rules (InspectRule), schedule checks (InspectPlan), and retrieve detailed reports (InspectResult) to ensure cluster compliance and stability.

This skill analyzes raw WordPress Site Health reports (text, screenshots, or debug info). It converts diagnostic data into a prioritized, risk-tiered fix plan, identifying critical issues and recommended improvements. It provides exact, safe commands (WP-CLI/PHP snippets) necessary to address the site and strictly enforces safety protocols like backups, lint checks, and dry-runs.

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 composite CI gate runs four critical cost monitoring checks—budget, burn, anomaly, and projection—in parallel. It surfaces a single, combined health status by taking the maximum exit code. This tool is an operational entry point for integrating comprehensive resource spending alerts directly into CI/CD pipelines, ensuring that resource usage remains within defined boundaries and predicts potential budget overruns.

This tool guides the creation of actionable monitoring dashboards for observability platforms like Grafana and SigNoz. Instead of merely displaying raw metrics, it focuses on structuring data to answer critical operational questions: Is the system healthy? Where are the bottlenecks? What changes occurred? And what action should be taken? It transforms raw metrics into decision-making tools.

Manage GitHub repositories effectively through issue triaging, PR reviews, and CI/CD workflow debugging. Automate release management and security monitoring using the GitHub CLI. Ideal for maintaining open-source project health and developer experience.

A comprehensive 7-section report providing a deep assessment of a repository's health and readiness level. It calculates key metrics including risk score, agent topology recommendations, MCP surface area, test confidence, and publish readiness. This tool is vital for pre-mint reviews, detecting architectural drift, and enforcing strict CI/CD quality gates.

This library provides comprehensive patterns for building robust Clinical Decision Support Systems (CDSS) integrated into Electronic Medical Records (EMR). It ensures patient safety by implementing critical checks such as drug interaction screening, weight/age/renal-adjusted dose validation, and standardized clinical scoring (e.g., NEWS2). The engine uses pure functions, guaranteeing high testability and reliability for critical medical insights.