Skill UI

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.

Use this skill to define and set up self-running, recurring marketing workflows (loops). Instead of one-off tasks, these loops operate on defined cadences (weekly, daily, on-trigger), acting as always-on systems for continuous monitoring and optimization. It orchestrates multiple marketing skills—like SEO scanning, ad fatigue checks, and churn detection—to maintain marketing momentum and efficiency.

This optimizer guides the acceleration of complex, multi-stage processes, such as CI/CD pipelines, large-scale refactoring, or data validation. It instructs users to decompose large tasks into independent execution lanes, build a dependency graph, batch read/check operations, and manage writes with strict isolation to maximize speed without sacrificing correctness. Ideal for optimizing development workflows and testing cycles.

This workflow performs a comprehensive review against the Azure Well-Architected Framework (WAF) using your existing Infrastructure as Code (IaC) and deployed resources. It systematically checks the five pillars—Reliability, Security, Cost Optimization, Operational Excellence, and Performance Efficiency—identifying architectural risks, drift, and compliance gaps. Findings are automatically logged and converted into actionable GitHub issues for streamlined remediation.

This methodology provides a structured approach to identifying the single binding constraint (bottleneck) that limits overall system throughput or increases latency. Rather than attempting local optimization everywhere, users must systematically apply the six steps (Exploit, Subordinate, Elevate, etc.) to focus efforts on the most critical choke point, ensuring efficient resource allocation across complex workflows.

This skill enables systematic improvement of existing AI agents by analyzing performance metrics, identifying failure modes, and applying prompt engineering techniques. It involves establishing baselines, optimizing prompts through chain-of-thought and few-shot examples, refining role definitions, and validating changes through structured testing and A/B comparisons to ensure reliability and efficiency.

This skill optimizes multi-agent systems through coordinated profiling, workload distribution, and cost-aware orchestration. It helps improve agent performance, throughput, and reliability by identifying bottlenecks, managing context windows, and reducing latency. Suitable for complex workflows requiring intelligent coordination and efficiency tracking across multiple AI agents.

This toolkit provides six production-ready AI engineering workflows including prompt evaluation, context budget planning, RAG pipeline design, agent security audits, evaluation harness building, and product sense coaching. It transforms AI coding assistants into senior engineering partners with structured methodologies, quantitative scoring, and checklists for consistent results in LLM system optimization and security.

This skill covers modern frontend development patterns for React and Next.js. It includes state management, performance optimization, and UI best practices. Use it for building performant user interfaces and following industry standards in web development workflows.

Use Clarvia AEO (Agent Experience Optimization) to score any MCP server, API, or CLI tool for agent-readiness. It evaluates 15,400+ AI tools across four dimensions—API accessibility, data structuring, agent compatibility, and trust signals—ensuring the components are robust enough for advanced AI agent workflows and dynamic selection processes.

A comprehensive workflow bundle covering the entire cloud-native development lifecycle. It guides users through critical phases, including cloud infrastructure setup (AWS, Azure, GCP, Terraform IaC), container orchestration with Kubernetes, robust CI/CD pipeline automation, observability implementation, security hardening, cost optimization, and disaster recovery planning. This ensures best practices across all stages of modern application deployment.