Skill UI

This skill provides a comprehensive framework for auditing and improving the efficiency of GitHub Codespaces environments. It helps users identify waste sources like oversized devcontainers, inefficient machine types, and poorly scoped prebuilds. By following a structured workflow, users can reduce monthly cloud spending, drastically cut startup times, and maintain optimal development throughput.

This skill converts complex, narrative technical documents, research excerpts, or natural language problem descriptions into a highly structured Mathematical Problem Specification (MPS). It systematically extracts variables, constraints, objectives, and uncertainty parameters. By adhering to a strict Zero-Inference Protocol, it ensures all extracted elements are strictly grounded in the source text, making it invaluable for formalizing real-world optimization, data science, and engineering problems.

A specialized skill for achieving asymptotic performance gains when classical algorithms are already near optimal. It leverages advanced mathematical techniques and probabilistic data structures (e.g., Bloom Filters, HyperLogLog, FFT, MinHash/LSH) to handle massive datasets (n ≥ 10⁶) by accepting bounded approximations or exploiting deep structural properties. Requires strict adherence to error parameter and complexity justification protocols.

A comprehensive reference guide detailing best practices, rules, and optimizations for building high-performance React Native and Expo applications. It covers critical areas such as list virtualization, complex animations, UI patterns, state management, and monorepo structure, helping developers avoid common pitfalls and maximize app speed and stability.

A comprehensive guide for planning, promoting, and optimizing virtual events and webinars. This skill treats the event as a full conversion funnel, assisting users in maximizing lead generation and pipeline acceleration. It covers end-to-end planning, data-driven performance diagnosis (scoring funnel breaks), and setting up evergreen lead engines.

SkillOpt-Sleep provides a mechanism for autonomous agent self-improvement. By simulating a 'sleep' or 'dream' cycle, it harvests past interaction sessions, mines recurring tasks, and replays them through the designated backend. It consolidates learned knowledge into persistent memory and skills, ensuring that only strictly validated improvements are adopted via a held-out gate, simulating long-term competence gain from short-term experience. Ideal for scheduled, background optimization.

This workflow systematically analyzes deployed AWS resources and Infrastructure-as-Code (IaC) definitions. It collects usage metrics from CloudWatch and Cost Explorer to identify over-provisioning, unused resources, and inefficient configurations (e.g., right-sizing EC2, S3 lifecycle policies). It generates actionable cost optimization recommendations and creates structured GitHub issues for tracking and implementing savings.

This skill provides comprehensive guides and references for writing, debugging, and systematically optimizing DAX formulas within semantic models. It covers advanced topics including systematic performance optimization workflows, engine internals (like xmSQL), and best practices for both local (Power BI Desktop) and remote (Fabric/XMLA) model environments, ensuring efficient data calculation and query performance.

This skill provides a comprehensive framework for evaluating Power BI reports. It assesses report quality, usage adoption rates, performance metrics, and structural integrity across different lifecycle stages (development, testing, active use). It analyzes usage data, audience reach, and performance bottlenecks, generating actionable feedback and optimization recommendations to ensure reports are effective and maintainable.

Design, build, refresh, and audit Power BI / Analysis Services tabular semantic models from the terminal: star schema, relationships, measures, calculation groups, RLS roles, incremental refresh, VertiPaq optimization, and Copilot readiness.

This skill autonomously optimizes artifacts like code or models using Hypothesis Tree Refinement. It runs iterative experiments with dev/test evaluators to prevent overfitting, coordinating subagents in isolated worktrees. Ideal for improving training recipes, agents, or pipelines without manual supervision.

A comprehensive guide detailing modern and idiomatic best practices for C# development. This reference covers advanced topics including LINQ optimization, null safety implementation, asynchronous programming patterns (Async/Await), and utilizing modern features like Records and Pattern Matching to write robust, high-performance code.