Skill UI

This skill generates a comprehensive RACI (Responsible, Accountable, Consulted, Informed) matrix for complex projects, product launches, or ongoing business processes. It clarifies ownership, defines decision rights, and maps roles across cross-functional teams (R, A, C, I), ensuring accountability and preventing bottlenecks. Ideal for governance documentation or onboarding new teams.

This skill generates a sophisticated prioritization matrix by combining quantitative RICE (Reach, Impact, Confidence, Effort) scoring with qualitative Strategic Alignment ratings. Use this when multiple features compete for resources, requiring a balanced decision between sheer user impact and adherence to core business OKRs. The output provides a scored priority matrix, quadrant placement (Now, Next, Later, Drop), and explicit sequencing recommendations.

This skill manages the end-to-end lifecycle of cloud test execution using HyperExecute for platforms like LambdaTest/TestMu AI. It provides comprehensive functionality to analyze projects, generate and validate complex YAML configurations, run CLI jobs, and debug failures. Ideal for integrating robust, scalable, and matrix-based testing into CI/CD pipelines.

A cinematic, data-rich dashboard for comprehensive multi-platform social media analysis. Designed for creators, this tool features an interactive KPI matrix, hover tooltips, range comparison charts, and a dedicated insights drawer to help users track and visualize performance across various channels.

This skill executes the final two phases of the SPARC methodology: Refinement and Completion. It systematically reviews the implemented feature through rigorous code reviews, expands test coverage (unit, integration, edge cases), validates performance, generates a traceability matrix, creates comprehensive documentation, and confirms deployment readiness. It ensures the feature is robust, tested, and production-ready.

This skill performs mean-variance portfolio optimization by solving the linear system Σ · x = μ using the Conjugate Gradient (CG) method. It is significantly faster (40-60x) and numerically robust compared to legacy methods like the Neumann series. It takes a covariance matrix (Σ) and expected return vector (μ) to determine optimal asset weights, providing full provenance tracking for auditability.