Skill UI

This framework provides a comprehensive methodology for defining, measuring, and implementing Service Level Indicators (SLIs), Service Level Objectives (SLOs), and managing error budgets. It helps engineering teams quantify service reliability (e.g., availability, latency) and establish clear, measurable targets (SLOs). By tracking error budgets, teams can strategically balance rapid feature development (innovation velocity) against maintaining high system stability and reliability.

This guide provides advanced patterns and best practices for optimizing Apache Spark jobs. It covers critical areas like effective partitioning, memory tuning, minimizing data skew, and optimizing different types of joins (broadcast, bucket). Use this when troubleshooting slow, large-scale data processing workflows or needing to scale Spark pipelines efficiently and reliably.

This skill provides systematic patterns and best practices for transforming slow database queries into high-performance operations. It covers techniques like proper indexing, query plan analysis (e.g., using EXPLAIN), schema design improvements, and resolving common issues like N+1 query problems. Use it to debug performance bottlenecks, reduce database load, and ensure scalable application backends.

A comprehensive skill for optimizing complex SQL queries, designing robust database schemas, and troubleshooting performance bottlenecks across various dialects (PostgreSQL, MySQL, etc.). Use this when queries are slow, complex set-based logic is needed (CTEs, Window Functions), or when structuring/migrating a database schema. It provides detailed query plan analysis, advanced indexing strategies, and industry best practices.

Provides SRE guidance for defining SLIs/SLOs, managing error budgets, automating toil, and configuring monitoring/alerting so teams can scale reliable services and respond to incidents effectively.

A comprehensive guide for deep PostgreSQL diagnostics, designed for advanced troubleshooting when standard methods fail. Covers using pg_stat_statements for identifying slow queries, analyzing lock contention with pg_locks, detecting connection leaks, debugging RLS policy conflicts, and profiling Edge Function cold starts. Essential for performance regression investigation and escalating issues to support.

Use this resource to implement comprehensive end-to-end monitoring and observability for Supabase projects. It guides users through analyzing API metrics, diagnosing slow database queries using pg_stat_statements, managing log drains for external aggregation, and setting up custom alerts, ensuring optimal project health and performance.

This comprehensive guide provides systematic methods to optimize Supabase query and database performance. Techniques cover PostgreSQL indexing (basic, composite, partial), using EXPLAIN ANALYZE for query plan analysis, optimizing client calls (column selection, pagination), and managing infrastructure resources like connection pooling. Use this when facing slow queries, high latency, or preparing a project for production-scale traffic.

Tracks and optimizes application response times using the response-time-tracker plugin, covering API endpoints, database queries, external calls, frontend rendering, and background jobs to highlight bottlenecks or SLO breaches.

Defines and monitors SLAs, SLIs, and SLOs so teams can set performance targets, track metrics like error budgets and availability, and react before issues impact customers.

Provides expert guidance on Unsloth to accelerate fine-tuning via LoRA/QLoRA, reduce memory usage, and streamline API usage, debugging, and best practices.

A comprehensive guide to optimizing Vercel deployment performance. This skill covers advanced techniques such as edge caching implementation (using Cache-Control headers), leveraging Incremental Static Regeneration (ISR), minimizing serverless function cold starts, and optimizing bundle size. Use this when facing slow page load times, needing to improve Core Web Vitals, or requiring robust, scalable deployment performance.