Skill UI

This skill performs a comprehensive audit of Figma component libraries. It checks for naming inconsistencies, component coverage gaps, variant completeness, and overall design system health. The output provides a structured report with prioritized issues, actionable naming conventions, and a step-by-step fix plan, ensuring design consistency across products.

This skill automates a structured Product Management (PM) design review process. It assesses a Figma design against defined product requirements (PRD) and user flows, ensuring functional completeness and readiness for engineering handoff. It checks for requirements coverage, handles edge cases (empty/error/loading states), identifies functional concerns (not aesthetic), and provides a clear, actionable approval status.

This skill systematically defines all necessary variants, states, and properties for a Figma component before development. It ensures comprehensive coverage of all states (e.g., Hover, Disabled, Loading) and properties (e.g., Size, Type), generating a robust variant matrix that prevents critical design gaps and streamlines the entire component building process in Figma.

This skill transforms raw notes, bullet points, or unstructured memories into comprehensive, professional performance reviews. It structures the output for various review types (Self-assessment, Manager, Peer, 360 feedback), ensuring coverage of key achievements, core strengths, constructive areas for growth, and measurable development goals. Ideal for annual reviews, mid-year check-ins, and formal performance documentation.

This workflow is designed to rigorously stress-test any plan, design, or architectural concept before implementation. It acts as a demanding interviewer, asking deep, cascading questions one at a time to identify hidden dependencies, resolve ambiguities, and ensure comprehensive coverage. Use this when you need to validate a concept against all possible failure modes or explore every corner of a complex system design.

This tool evaluates how thoroughly a feature implementation meets product requirements (PRDs) or specifications. It scores acceptance criteria case-by-case, independently grading both the code implementation and the associated test coverage (unit and E2E). Ideal for benchmarking multiple development efforts, auditing adherence to specifications, or determining if a feature is 100% complete and compliant with the ground truth PRD.

A comprehensive skill for monitoring dark web forums, marketplaces, and leak sites to detect mentions of organizational assets, leaked credentials, and ongoing threat actor activities. Used for proactive threat intelligence gathering, incident response enrichment, and establishing continuous dark web OSINT coverage.

This advanced skill performs a full-sweep analysis, providing a unified assessment of code quality across multiple dimensions: code decay, architecture integrity, technical debt, and test coverage. It automatically applies safe fixes directly to the codebase, while requiring confirmation for risky changes. It is ideal for comprehensive pre-release code health audits and automated refactoring.

This skill provides a comprehensive test quality review, drawing on established principles from classic engineering texts like xUnit Test Patterns and How Google Tests Software. It diagnoses structural flaws—such as brittleness, decay, and poor coverage—in existing test suites, helping ensure that the codebase and its tests are robust, maintainable, and adhere to high industry standards.

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.

This skill provides comprehensive monitoring advisory services for data assets and AI agents. It helps users assess data coverage gaps, analyze use-case criticality, and automatically create data quality monitors (table, metric, validation). Furthermore, it enables monitoring of AI agent performance, latency, and observability, ensuring robust data governance across the entire ML lifecycle.

This skill generates comprehensive, production-grade unit and integration tests using the Python pytest framework. It covers advanced patterns such as fixtures (with scope and teardown), parametrization, custom markers, mocking external dependencies, and best practices for writing robust and maintainable test suites. Use this when implementing detailed test coverage for Python code.