Skill UI

Expert AI-powered code review combining automated static analysis, intelligent pattern recognition, and modern DevOps practices. Leverages tools like GitHub Copilot, SonarQube, CodeQL, and Semgrep to identify bugs, vulnerabilities, and performance issues across 30+ languages, integrated with CI/CD pipelines for instant PR feedback.

Expert in Prisma ORM covering schema design, migrations, query optimization, relations modeling, and database operations for PostgreSQL, MySQL, and SQLite. Provides diagnostics, best practices, and prioritized fixes for common issues.

A specialized AI agent for product management in the Chinese internet market. It helps prioritize requirements, plan roadmaps, analyze growth and retention metrics, and handle operational issues like project delays. The agent uses silent reasoning to identify core blockages and dominant mechanisms before providing actionable next steps, stop lists, and switching conditions. It focuses on direct, executable advice without unnecessary theoretical explanations or historical references, suitable for product owners and managers facing complex decision-making scenarios.

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 skill provides a practical guide for comprehensive accessibility validation of web applications. It helps users test compatibility with various screen readers, validate ARIA implementations, and debug issues related to assistive technologies. Ideal for ensuring that web forms, dynamic content, and navigation are fully usable by people with disabilities.

This skill creates custom Semgrep rules for detecting security vulnerabilities, bug patterns, and code patterns. It guides users through a strict workflow including test-first development, AST analysis, and validation. Ideal for building static analysis detections, enforcing coding standards, and identifying data flow issues using taint mode.

A comprehensive automation toolkit for managing Sentry error tracking and monitoring operations. Users can programmatically investigate issues, configure project-level alert rules, manage release versions, and monitor organizational structure. Essential for DevOps and development teams needing deep integration and programmatic control over error data.

Audit-first SEO/AEO growth workflow for coding agents: discover the project, audit technical and content issues, implement fixes, research search intent, build foundational pages, set up measurement, deploy, verify, and optionally monitor weekly.

Analyzes multiple pages to identify keyword overlap and cannibalization issues, suggests differentiation strategies. Proactively use when reviewing similar content. Provides reports, resolution tactics, and prevention frameworks.

This skill scaffolds the necessary configurations for advanced engineering workflows. It guides the user through setting up the issue tracking system (GitHub, GitLab, or local markdown), defining the canonical triage label vocabulary, and establishing the domain documentation context (single or multi-context). It must be run once to initialize the repository for subsequent agent-driven development skills.

SkillCheck validates SKILL.md files against the agentskills specification and Anthropic best practices. It performs read-only checks on structure, semantics, naming, and quality to catch errors before publishing. The tool provides a score, grade, and detailed report with fix suggestions for critical issues to ensure high-quality skill authoring.

This tool audits the activation metadata of coding skills used by AI agents. It grades skills (A-F) based on description clarity, simulates which skill a prompt will trigger, and identifies metadata collisions where one skill might silently overshadow another. It is essential for ensuring robust, predictable, and reliable AI agent behavior during development and CI processes.