Skill UI

This framework provides comprehensive compliance and security auditing for Cursor IDE usage. It covers major regulations like SOC 2, GDPR, and HIPAA, guiding users through detailed checklists, evidence collection, and remediation steps. It is essential for enterprise teams requiring robust data governance and adherence to strict industry standards.

This is a hands-on walkthrough demonstrating the four core AI features of the Cursor IDE: Tab Completion, Chat, Inline Edit, and Composer. Users learn how to leverage AI for coding tasks, debugging, generating boilerplate code, and performing multi-file refactoring within a structured project environment. It's an essential guide for developers adopting AI-assisted coding workflows.

A comprehensive guide detailing how to optimize the entire software development lifecycle within the Cursor IDE. It walks users through an AI-augmented loop covering planning (Chat), scaffolding (Composer), implementing (Tab), refining (Inline Edit), testing (Chat/Terminal), committing, and reviewing code using integrated AI and Git features. Designed for maximizing developer efficiency and code quality.

A comprehensive guide on configuring and selecting the optimal AI model within the Cursor IDE. It details the strengths of various providers (OpenAI, Anthropic, Google) for specific coding tasks—from quick bug fixes to complex architecture planning—and guides users through model selection, API key setup (BYOK), and cost optimization strategies.

A comprehensive guide and set of scripts for optimizing Customer.io spending. It details strategies for auditing profile counts, suppressing/deleting inactive users, deduplicating high-volume events, and implementing event sampling to reduce billing costs and maintain data hygiene.

Automates D2 diagram creation guidance for visual content requests, offering best practice patterns, ready-to-use code/config snippets, and validation against standards whenever users mention the tool or need chart/diagram support.

A comprehensive, end-to-end workflow for building product features guided by rigorous data insights. This skill guides users through the entire lifecycle, encompassing exploratory data analysis, forming measurable business hypotheses, designing statistical A/B tests, architecting data pipelines, and implementing robust backend/frontend instrumentation. It ensures that every feature iteration is validated by data and measured for impact.

A specialized skill for optimizing database performance and scalability across PostgreSQL and MySQL. It guides users through analyzing slow queries, interpreting execution plans (EXPLAIN ANALYZE), designing optimal indexing strategies, tuning configuration parameters, and resolving bottlenecks like lock contention. Essential for DevOps engineers, database administrators, and backend developers.

A comprehensive guide to setting up a minimal working example on Databricks. This skill covers creating a development cluster, uploading and executing notebooks (Python SDK), running tasks as jobs, and setting up a Serverless SQL Warehouse. It is ideal for new users or initializing a basic data engineering project.

This skill provides comprehensive observability for the Databricks platform. It enables users to monitor job run histories, track cluster utilization and associated costs (DBU), detect slow SQL queries, and audit access logs. Use it for setting up robust monitoring dashboards, ensuring pipeline health, and optimizing resource consumption across the entire data stack.

This skill provides a structured methodology for tackling complex bugs, performance bottlenecks, and production incidents. It guides users through reproducing issues, forming hypotheses, performing controlled experiments, and narrowing down the scope using proven strategies and tools. Ideal for deep dive troubleshooting and root cause analysis in complex software systems.

A comprehensive, expert-level workflow for debugging complex software issues in production environments. This skill guides users through initial triage, advanced observability data collection (APM, distributed traces, logs), intelligent hypothesis generation, and full root cause analysis. Use it when dealing with intermittent failures, performance bottlenecks, or needing a complete fix and prevention plan.