Skill UI

Analyzes REST API specs for convention violations, detects breaking changes, and scores design quality using automated linting, change detection, and scorecards.

A comprehensive meta-skill for validating, testing, and scoring the quality of technical skills within an ecosystem. It performs deep structural checks (SKILL.md compliance), executes Python scripts for syntax, runtime, and output validation, and generates multi-dimensional quality scores using letter grades and tier recommendations. Ideal for new skill authorship, auditing existing components, or integrating rigorous QA gates into CI/CD pipelines.

This expert skill validates the deployment readiness of Vertex AI Agent Engines. It executes a weighted audit across five critical dimensions: security, monitoring, performance, compliance, and best practices. The output provides a comprehensive 0-100% composite score, a detailed pass/fail report for each check, and a prioritized, actionable remediation plan, ensuring high-quality, production-grade deployments.

Configure comprehensive CI/CD pipelines for integrating Grammarly's text analysis into your development workflow. This setup uses GitHub Actions to run unit and integration tests on pull requests, validating API connectivity, enforcing content quality gates (e.g., minimum grammar scores), and ensuring documentation quality before merging to the main branch.

This skill provides integration with Grammarly's Writing Score API, enabling applications to automatically score documents. It evaluates writing quality across four dimensions: engagement, correctness, clarity, and tone. Use this when building content quality assurance features, implementing quality gates, or providing detailed, multi-dimensional feedback on written content.

Use Mailtrap Email Sandbox to capture and inspect outbound emails without sending them to real recipients. This is crucial for development, staging, and CI/CD environments. It allows developers to test email content, headers, attachments, and spam scores using dedicated APIs, SMTP settings, or SDKs, ensuring mail integrity before deployment. Ideal for automation and rigorous quality checks.

Compare two recorded browser sessions step-by-step using screenshots and DOM snapshots. Identifies visual regressions, verifies replay accuracy, and compares A/B variants. Reports divergence scores and accessibility changes for quality assurance.

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.

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 tool provides a comprehensive, multi-faceted audit of a codebase's health. It scores the project across critical areas like security vulnerabilities, exposed secrets, dependency risks, and structural integrity. Ideal for senior engineers performing pre-commit or pre-release quality assessments to identify weak spots and generate a prioritized remediation plan.

This tool audits all existing skills within the current project. It performs a comprehensive quality check, validating frontmatter completeness, tool scoping, and content quality against established standards. It produces a detailed, scored report, including effort-level recommendations for each skill. Ideal for new project onboarding, pre-release quality assurance, or diagnosing skill triggering issues.

This utility analyzes code changes within a branch compared to the base branch. It calculates a complexity score, detects scope coherence issues (warning if changes are too large or unrelated), and generates a fully structured Pull Request (PR) draft. It automates the creation of titles, comprehensive descriptions (including checklists and technical details), and even suggests splitting the PR into smaller, manageable units, significantly improving code review quality and efficiency.