Skill UI

Master advanced techniques for scaling Supabase projects to handle high production load. This skill covers six critical layers: implementing read replicas for analytics, optimizing connectivity using connection pooling (Supavisor), upgrading compute resources, utilizing CDN caching for Storage, deploying Edge Functions regionally, and performing database table partitioning. Ideal for engineers preparing for traffic spikes or optimizing large-scale database architectures.

Automates time series decomposition work in data analytics by guiding through patterns and best practices, generating production-ready code/configuration, and validating outputs for SQL, visualization, and BI workflows.

This guide provides a comprehensive setup for Vercel observability. It covers enabling built-in analytics, collecting detailed runtime logs, implementing structured logging, configuring external log drains (e.g., Datadog, Sentry), and integrating OpenTelemetry tracing. Use this when monitoring production deployments and setting up robust error tracking.

Automated help for data analytics visualization best practices, offering step-by-step guidance, industry patterns, production-ready code, and validation for dashboards and reports.

This skill guides the development of custom extensions for the Windsurf environment, leveraging the full VS Code Extension API. You will learn to track various workspace events—such as file saves, editor changes, and terminal outputs—and integrate this data with external services by sending structured webhooks. This is essential for building complex workflow automation, implementing custom IDE features, or creating data analytics tools within the development lifecycle.

Automate Benchling lab data workflows via the Python SDK or REST API: manage registry entities (DNA, proteins), inventory, ELN entries, workflows, and Data Warehouse queries for syncing and analytics.

This comprehensive guide helps users build, audit, and debug robust analytics tracking systems using GA4 and Google Tag Manager. It focuses on establishing a consistent event taxonomy and capturing every meaningful user action—from initial visits to complex conversions—ensuring data integrity for accurate decision-making.

Pandas is a Python data analysis library that simplifies loading, cleaning, transforming, and analyzing structured datasets, making it ideal for data wrangling in analytics or engineering workflows.

A comprehensive guide to product analytics, covering essential concepts like event tracking (PostHog, Mixpanel), funnels, cohort analysis, retention metrics, North Star Metrics, and OKR setting. Use this skill to define data-driven product strategies, optimize user journeys, and build robust performance dashboards.

A comprehensive domain orchestrator designed to manage the entire project delivery lifecycle. It can route specific project management inquiries (e.g., Jira permissions, sprint velocity) to specialized sub-skills, or execute complex, goal-oriented workflows through a gated agentic loop. This process pulls live data from Jira/Atlassian, runs deterministic analytics, and verifies every step to ensure auditability and successful closure.

Live SEO data via DataForSEO MCP server: SERP analysis, keyword research, backlink profiles, on-page and competitor analysis, AI visibility tracking, and domain analytics across 79+ tools.

A comprehensive guide for defining, tracking, and interpreting product metrics across all stages—from discovery to maturity. This skill covers selecting metric frameworks (AARRR, HEART), defining stage-appropriate KPIs, designing multi-layered dashboards, and executing deep analyses like cohort and retention modeling. It helps teams transform raw data into actionable product strategies, moving beyond vanity metrics.