Skill UI

Guides an agent through C++17/20 testing with CMake, CTest, and GoogleTest/Mock, covering new or flaky tests, CI gates, coverage builds, and sanitizer diagnostics to keep regression safety in place.

Comprehensive guide for configuring Bring Your Own Key (BYOK) for major AI providers, including OpenAI, Anthropic, Google Gemini, Azure, and custom endpoints. This allows users to bypass default quotas, manage costs directly with the source provider, and utilize a wide range of specialized models within the Cursor IDE. It covers setup steps, security best practices, and cost monitoring strategies.

This tool optimizes the codebase indexing within Cursor. It processes your entire codebase, converting code chunks into vector embeddings stored in a vector database. This enables advanced @Codebase semantic search, allowing users to ask natural language questions (e.g., "Where is authentication handled?") and retrieve the most relevant code snippets, significantly improving AI context awareness across chat and coding tasks.

A comprehensive diagnostic and resolution guide for frequent issues encountered in the Cursor IDE. Covers advanced topics including authentication failures (OAuth loops), AI completion glitches, indexing problems, performance slowdowns, and extension conflicts, ensuring developers can quickly restore optimal workflow.

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.

Master Cursor's AI-powered Tab completion system, generating instant, context-aware 'ghost text' suggestions. This tool predicts the next code lines based on the current file, open tabs, and even comments, allowing developers to accept or modify suggestions with minimal effort. It is designed to significantly boost coding speed and maintain coding consistency.

This comprehensive dashboard tracks and analyzes usage metrics for AI coding assistants (Cursor) in enterprise environments. It monitors total requests, model consumption, user adoption rates, and associated costs. Managers can use it to optimize resource quotas, control AI spending, and calculate the Return on Investment (ROI) to justify AI tool adoption.

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.

A comprehensive debugging bundle designed to collect all necessary evidence for Customer.io support tickets. It performs API connectivity diagnostics (Track/App), validates user profiles, captures environment details (Node.js, SDK versions), and generates a structured report. Ideal for investigating delivery failures or complex integration issues.

This comprehensive guide identifies and explains the most common anti-patterns and mistakes when integrating with Customer.io. It covers crucial development topics, including proper API key usage, Unix timestamp formatting, ensuring correct event tracking order (Identify before Track), and optimizing API calls for performance. Use this as a definitive checklist for code review and developer onboarding.

An expert in designing, implementing, and optimizing scalable, reliable, and cost-effective data pipelines for both batch and streaming data. Covers advanced topics including ETL/ELT patterns, Kappa/Lambda architectures, workflow orchestration (Airflow/Prefect), transformation using dbt/Spark, and managing modern storage formats like Delta Lake and Iceberg. Focuses heavily on data quality, monitoring, and cost optimization strategies.

This skill models the expertise of a senior data scientist, covering the entire data science lifecycle. It provides advanced capabilities in statistical modeling, machine learning (deep learning, XGBoost), predictive analytics, and business intelligence. From raw data exploration (EDA) to deploying production-ready models and creating actionable dashboards, it covers technical depth across Python, R, SQL, and cloud platforms, while applying domain knowledge in finance, marketing, and operations.